study of mirna polymorphisms and their potential...
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STUDY OF MIRNA POLYMORPHISMS AND
THEIR POTENTIAL ASSOCIATION WITH
BREAST CANCER RISK IN AUSTRALIAN
CAUCASIAN POPULATIONS.
Diego Fernando Chacon Cortes MBBS, MMedRes
Student No n6542042
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Biomedical Sciences
Institute of Health and Biomedical Innovation
Faculty of Health
Queensland University of Technology
2015
Keywords
Association study; breast cancer; DNA extraction; genomic DNA, genotyping;
MALDI-TOF MS analysis; microRNA (miRNA); microRNA SNPs (miR-SNPs);
single nucleotide polymorphisms (SNPs); haplotype
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. i
Abstract
Breast cancer is a malignant neoplasm that results from innate and/or acquired
genetic predisposition from somatic mutations in breast tissue and their interaction
with hormonal exposure and other risk factors. MicroRNAs might be important for
the pathogenesis of breast cancer since they have been described as important
epigenetic regulators of gene expression present in all the molecular pathways
involved in cancer biology. Previous research has shown that mutations present in
miRNA sequences could result in the changes in miRNA synthesis and function seen
in cancer biology and single nucleotide polymorphisms (SNPS) are the most
common type of variation present in miRNA genes. Only a small fraction of the total
of microRNA SNPs (miR-SNPs) identified to date, have been studied in relation to
breast cancer. Findings are still somewhat controversial and this is probably the
result of limitations in terms of the populations examined, experimental design and
methods used for analysis. To address this gap in the literature the present study aims
to achieve the following aims: i) establish a large sized and well characterised
prospective DNA biobank to be used as a replication cohort to the one previously
available in our facility, using the most appropriate DNA extraction protocol in a
time-efficient and cost-effective manner; and ii) identify miR-SNPs associated with
breast cancer risk in both Australian Caucasian populations using genotype and
haplotype association analysis.
Chapter One explores the scientific problem and aims of this study. Chapter
Two and Three provide a comprehensive literature review of the background in this
field of work and the methodology used to conduct our research. We aimed to
establish a prospective genomic DNA (gDNA) biobank extracted from whole blood
samples obtained from 929 breast cancer patients in Queensland recruited in
collaboration with the Cancer Council Queensland. In order to determine the best
possible method to perform DNA extraction from the ones available at our facility,
Chapter Four presents our in-house evaluation study conducted to determine the most
cost effective and time efficient method to perform genomic DNA (gDNA)
extraction from whole blood samples. We compared the modified salting-out
iiStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
protocol described by Nasiri et al, against a traditional salting out method and a
commercially available DNA extraction kit and we performed gDNA extraction from
whole blood samples. We evaluated the performance of each method in terms of the
quality and quantity of DNA obtained, as well as time requirements and costs for
each technique. Our results highlighted the modified salting out method to be the
most cost-effective and time-efficient technique to isolate genomic DNA from whole
blood samples. The modified salting out method has consistently proved to be an
appropriate technique based on the quality and quantity of DNA obtained from blood
samples extracted at our facility and these samples have also been shown to perform
well in downstream applications used for the genotyping studies included in this
thesis. Some of the limitations of this study included the small number of samples
used to test our chosen DNA extraction methods and that we also failed to include a
more comprehensive range of techniques that were unavailable at the time at our
facility.
We also decided to review current knowledge of genomic DNA extraction
from whole blood methods. Our study (Chapter Five) reviews the history of DNA
extraction techniques, the main categories of the methods most commonly used in
facilities around the world, as well as the available literature on evaluation studies
comparing different extraction techniques to highlight their strengths and
weaknesses. This review evidenced the limited scientific evidence available in
regards to the best choice for gDNA extraction and it also highlighted the need to
conduct our own validation study to determine the most appropriate technique to be
used in the establishment of our new case control cohort.
The following study (Chapter Six) validated previous findings in relation to the
miR-SNP rs2910164 present in MIR146A that has shown conflicting results in
previous studies because it had not been properly analysed in purely sporadic or
purely Caucasian breast cancer populations. We genotyped this variant using high
resolution melt (HRM) analysis and were able to determine that presence of the
mutant allele in this gene is significantly associated with breast cancer risk in both
our primary and replicate populations (p = 0.03 and p = 0.00013). However we
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. iii
acknowledge that we were limited by the small number of samples included in our
replication population (GU-CCQ BB population), as this was still under
development. To the best of our knowledge this is the first study to identify
association between this rs2910164 and breast cancer risk in a purely sporadic breast
cancer bearing Caucasian population.
Chapter Seven presents the results of a large scale genotyping study using both
of our previously established cohorts. In this study, we developed a selection
algorithm to choose miR-SNPs to be included in this and other studies, which
allowed the selection of a large panel of miR-SNPs (24 miR-SNPs in total present in
9 miRNA genes) to be analysed using multiplex PCR and chip-based matrix assisted
laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS).
Results from our study showed 6 of the miR-SNPs (rs73798217, rs112394324,
rs35301225, rs1547354, rs7050391 and rs3851812) not to be polymorphic in our
populations, providing initial validation of the rare presence of these miR-SNPs in
Caucasian populations. We were also most importantly, able to show significant
association to breast cancer risk for one of our selected variants present in MIR145.
We determined that miR-SNP rs353291 in the MIR145 gene had significant
differences in allele frequencies between cases and controls in both of our tested
populations (p = 0.041 and p = 0.023) and to the best of our knowledge this is the
first report of an association to breast cancer risk for this variant. Our study provided
initial validation of our findings using two independent populations, but further
validation of these results in larger cohorts or populations of both similar and
different ethnic background is required to strengthen their significance. If we take
into account multiple testing correction of our results, our findings are not
statistically significant, but since they were validated in two independent populations
they are still very interesting and worthy of further research.
Our last study (Chapter Eight) presents the results of our genotype and
haplotype analysis of 6 miR-SNPs present in the microRNA 17-92 cluster host gene
(MIR17HG) conducted in our two Australian Caucasian breast cancer cohorts. We
decided to study polymorphisms in MIR17HG because of its importance in cancer
development, which has been shown in previous studies. It was the first cluster host
ivStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
gene to be found to play a role in cancer biology and it has also been linked to the
pathogenesis of breast cancer targeting important genes in breast biology, like
NCOA3, CCDN1 and ESR1. To the best of our knowledge there are no previous
studies evaluating the role of miR-SNPs for this particular cluster host gene, making
it an ideal candidate for research. We selected 6 miR-SNPs located in the cluster
gene region or in close proximity to it providing good coverage for this miRNA
cluster gene, and they included a polymorphism located inside the mature sequence
for MIR92A1 (rs9589207). We genotyped these miR-SNPs using multiplex PCR and
chip-based MALDI-TOF MS analysis in our two independent Australian Caucasian
breast cancer case-control cohorts. Our results showed significant differences
between cases and control for miR-SNP rs4284505 in both of our tested populations
(p = 0.01 and p = 0.03) and for rs7336610 only in our initial population (p = 0.03) for
the genotypic analysis. Furthermore haplotypic analysis on the combined results
from both populations identified these two miR-SNPs (rs4284505/rs7336610) to be
in linkage disequilibrium and part of a haplotype block (D’ score = 0.98). Chi-square
analysis of the haplotypes contained in this block showed a lower frequency for
haplotype AC in cases compared to controls and our analysis showed it to be
significantly associated with breast cancer (p = 5x10-4). Odds ratio (OR) for this
haplotype suggests that individuals that carry the AC haplotype had a reduced risk of
developing breast cancer (about 25%) in comparison to other haplotype carriers (OR:
0.75; 95% CI: 0.60 to 0.94; p = 0.012). To the best of our knowledge this is the first
report of a significant association between these polymorphisms and breast cancer
risk.
Taken together, these data indicate that the presence of specific miR-SNPs may
play a role in breast cancer development and the results presented in Chapters Six to
Eight of this document are only the initial step required to determine their effect on
breast cancer pathogenesis. Further research that includes functional assays will be
required for these findings to be translated into strategies that impact breast cancer
diagnosis, treatment and prognosis. Further validation of our positive findings
through genotyping of larger populations or populations of different ethnic
backgrounds as well as functional validation using cell culture or animal models are
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. v
also required to proceed with translational studies and these would allow a better
understanding of the detailed mechanisms occurring in breast cancer biology.
viStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
List of publications and manuscripts
The following is a list of publications and manuscripts that have been prepared in
conjunction with this thesis
Chacon-Cortes D, Smith RA, Lea RA, Youl PH and Griffiths LR (2015).
Association of microRNA 17-92 cluster host gene (MIR17HG)
polymorphisms with breast cancer. Tumor Biology, 36(7), 5369-76
Chacon-Cortes D, and Griffiths LR (2014). Methods for extracting
genomic DNA from whole blood samples: current perspectives. Journal of
Biorepository Science for Applied Medicine, 2, 1-9.
Chacon-Cortes D, Haupt LM, Lea RA and Griffiths LR (2012).
Comparison of genomic DNA extraction techniques from whole blood
samples: a time, cost and quality evaluation study. Molecular Biology
Reports, 39(5), 5961-6.
Upadhyaya A, Smith RA, Chacon-Cortes D, Revêchon G, Haupt LM,
Chambers SK, Youl PH and Griffiths LR. Association of the microRNA-
single nucleotide polymorphism rs2910164 in miR146a with sporadic
breast cancer susceptibility: a case control study. Gene (Under revision)
Chacon-Cortes D, Smith RA, Lea RA, Youl PH and Griffiths LR. Genetic
association analysis of miRNA single nucleotide polymorphisms (miR-
SNPs) in Australian Caucasian breast cancer case control cohorts. BMC
Medical Genetics (Under revision)
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. vii
List of additional publications and manuscripts
The following is a list of publications involving collaborative research
undertaken as part of the PhD
Journal articles
Okolicsanyi RK, Faure M, Jacinto JM, Chacon-Cortes D, Chambers S,
Youl PH, et al. Association of the SNP rs2623047 in the HSPG
modification enzyme SULF1 with an Australian Caucasian Breast Cancer
Cohort. Gene. 2014; 547(1):50-4.
Okolicsanyi RK, Buffiere A, Jacinto JM, Chacon-Cortes D, Chambers S,
Youl PH, et al. Association of heparan sulfate proteoglycans SDC1 and
SDC4 polymorphisms with breast cancer in an Australian Caucasian
population. Tumor Biology. 2014:1-8
Conference papers
Chacon-Cortes D, Smith RA, Lea RA, Chambers S, Youl PH., Griffiths
LR. Disease association analysis of selected miRNA SNP genotypes for
two Australian Caucasian Breast Cancer Populations. IHBI Inspires
Postgraduate Conference. Gold Coast, November 20th-21st 2014.
viiiStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
Chacon-Cortes D, Haupt LM, Lea RA, Griffiths LR. DNA Extraction
techniques from whole blood samples: A time, cost and quality evaluation
study. GHU Gold Coast Health & Medical Research Conference. Gold
Coast, 1-2nd December 2011.
Chacon-Cortes D, Chambers S., Youl PH., Smith RA., Lea RA, Griffiths,
LR. A genetic approach to investigate clinical and psychological outcomes
for women with breast cancer – establishment of a Griffith Health Institute
Breast Cancer Biobank. Annual Human Genetics Society of Australasia
conference. Gold Coast International, Surfers Paradise. July 31st – August
2nd 2011.
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. ix
Table of Contents
Keywords .......................................................................................................................................... i
Abstract ............................................................................................................................................ ii
List of publications and manuscripts ................................................................................................. vii
List of additional publications and manuscripts................................................................................ viii
Table of Contents .............................................................................................................................. x
List of Tables ................................................................................................................................... xv
List of Figures ............................................................................................................................... xvii
List of Abbreviations .................................................................................................................... xviii
Statement of Original Authorship .................................................................................................... xxi
Acknowledgements ........................................................................................................................ xxii
CHAPTER 1: INTRODUCTION ................................................................................................ 24
1.1 A description of the scientific problem investigated .............................................................. 24
1.2 The overall objective of the study ......................................................................................... 25
1.3 The specific aims of the study .............................................................................................. 26
1.4 An account of scientific progress linking the scientific papers ............................................... 26
CHAPTER 2: LITERATURE REVIEW ..................................................................................... 29
2.1 Breast cancer epidemiology .................................................................................................. 29
2.2 Anatomy of the mamary gland.............................................................................................. 32
2.3 Breast cancer definition and aetiology .................................................................................. 35
2.4 Breast cancer classification................................................................................................... 38
2.4.1 Histological Classification ......................................................................................... 39
2.4.2 Bloom and Richardson grading system ...................................................................... 41
2.4.3 The American Joint Committee on Cancer (AJCC) staging system. ............................ 42
2.5 Genetics and breast cancer.................................................................................................... 43
2.5.1 Genes and breast cancer............................................................................................. 45
xStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
2.6 MicroRNAs and breast cancer .............................................................................................. 56
2.6.1 MicroRNA biogenesis and functions.......................................................................... 56
2.6.2 MicroRNAs in cancer ................................................................................................ 60
2.6.3 MicroRNAs and breast cancer ................................................................................... 66
2.6.4 MicroRNA SNPs (miR-SNPs) and breast cancer ........................................................ 73
2.7 Summary and Implications ................................................................................................... 78
CHAPTER 3: METHODOLOGY BACKGROUND .................................................................. 81
3.1 Study Populations ................................................................................................................ 81
3.1.1 Genomics Research Centre Breast Cancer Population (GRC-BC population) .............. 81
3.1.2 The Griffith University – Cancer Council Queensland Breast Cancer Biobank (GU-CCQ BB) .......................................................................................................... 81
3.2 Case - Control Association studies........................................................................................ 82
3.2.1 Genomic DNA Extraction ......................................................................................... 82
3.2.2 Genotyping Techniques ............................................................................................. 84
3.2.3 Statistical Analysis .................................................................................................... 88
CHAPTER 4: COMPARISON OF GENOMIC DNA EXTRACTION TECHNIQUES FROM WHOLE BLOOD SAMPLES: A TIME, COST AND QUALITY EVALUATION STUDY. ..... 91
4.1 Statement of contribution of co-authors for thesis by published paper ................................... 91
4.2 Abstract ............................................................................................................................... 93
4.3 Introduction ......................................................................................................................... 93
4.4 Materials and methods ......................................................................................................... 95
4.4.1 Samples .................................................................................................................... 95
4.4.2 Genomic DNA extraction methods ............................................................................ 95
4.4.3 Evaluation of extracted DNA ..................................................................................... 97
4.5 Results ................................................................................................................................. 98
4.6 Discussion ......................................................................................................................... 103
CHAPTER 5: METHODS FOR EXTRACTING GENOMIC DNA FROM WHOLE BLOOD SAMPLES: CURRENT PERSPECTIVES. ............................................................................... 105
5.1 Statement of contribution of co-authors for thesis by published paper ................................. 105
5.2 Abstract ............................................................................................................................. 107
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. xi
5.3 Introduction ....................................................................................................................... 107
5.4 Initial development of DNA extraction techniques .............................................................. 108
5.5 Main types of DNA extraction methods from human whole blood samples ......................... 110
5.5.1 Solution based DNA extraction methods .................................................................. 112
5.5.2 Solid phase DNA extraction methods ....................................................................... 114
5.6 Choosing the appropriate protocol ...................................................................................... 118
5.7 Conclusions ....................................................................................................................... 125
CHAPTER 6: ASSOCIATION OF THE MICRORNA-SNP RS2910164 IN MIR146A WITH SPORADIC BREAST CANCER SUSCEPTIBILITY: A CASE CONTROL STUDY. ............ 126
6.1 Statement of contribution of co-authors for thesis by published paper.................................. 126
6.2 Abstract ............................................................................................................................. 128
6.3 Background ....................................................................................................................... 128
6.4 Materials and methods........................................................................................................ 130
6.4.1 Study Cohort ........................................................................................................... 130
6.4.2 Genotyping ............................................................................................................. 131
6.4.3 Statistical Analysis .................................................................................................. 132
6.5 Results ............................................................................................................................... 132
6.6 Discussion ......................................................................................................................... 134
6.7 Conclusions ....................................................................................................................... 137
CHAPTER 7: GENETIC ASSOCIATION ANALYSIS OF MIRNA SNPS IMPLICATES MIR145 IN BREAST CANCER SUSCEPTIBILITY. ................................................................ 138
7.1 Statement of contribution of co-authors for thesis by published paper.................................. 138
7.2 Abstract ............................................................................................................................. 140
7.3 Background ....................................................................................................................... 140
7.4 Materials and methods........................................................................................................ 143
7.4.1 Study Populations .................................................................................................... 143
7.4.2 Genomic DNA sample preparation from whole human blood. .................................. 144
7.4.3 miRNA SNP selection ............................................................................................. 145
7.4.4 Primer design .......................................................................................................... 148
7.4.5 Primary Multiplex PCR ........................................................................................... 151
xiiStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
7.4.6 MALDI-TOF MS analysis and data analysis ............................................................ 151
7.4.7 Statistical analysis ................................................................................................... 151
7.5 Results and discussion ........................................................................................................ 152
7.6 Conclusions ....................................................................................................................... 158
CHAPTER 8: ASSOCIATION OF MICRORNA 17-92 CLUSTER HOST GENE (MIR17HG) POLYMORPHISMS WITH BREAST CANCER. .................................................................... 160
8.1 Statement of contribution of co-authors for thesis by published paper ................................. 160
8.2 Abstract ............................................................................................................................. 162
8.3 Introduction ....................................................................................................................... 162
8.4 Materials and methods ....................................................................................................... 166
8.4.1 Study populations .................................................................................................... 166
8.4.2 Genomic DNA sample preparation from whole human blood. .................................. 168
8.4.3 miRNA SNP selection ............................................................................................. 168
8.4.4 Primer design and multiplex PCR genotyping .......................................................... 169
8.4.5 MALDI-TOF MS analysis and data analysis ............................................................ 171
8.4.6 Statistical analysis ................................................................................................... 171
8.5 Results ............................................................................................................................... 172
8.5.1 Genotypic Analysis ................................................................................................. 172
8.5.2 Haplotypic Analysis ................................................................................................ 174
8.6 Discussion ......................................................................................................................... 176
CHAPTER 9: GENERAL DISCUSSION .................................................................................. 178
9.1 Introduction ....................................................................................................................... 178
9.2 Breast cancer and microRNAs ............................................................................................ 179
9.3 Population resources development ...................................................................................... 181
9.3.1 DNA extraction methods ......................................................................................... 182
9.3.2 Comparison of DNA Extraction methods ................................................................. 182
9.4 Association studies............................................................................................................. 184
9.4.1 MIR146A results ...................................................................................................... 184
9.4.2 miRNA selection and analysis ................................................................................. 184
9.4.3 MicroRNA 17-92 cluster host gene analysis ............................................................. 185 Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. xiii
9.4.4 Comparison to GWAS results and miRNA functional implications. .......................... 187
9.5 Limitations and future directions. ....................................................................................... 188
9.6 Conclusions ....................................................................................................................... 189
BIBLIOGRAPHY ....................................................................................................................... 190
APPENDICES............................................................................................................................. 216
Appendix A Modified salting out protocol for DNA extraction from whole blood samples. Adapted from Nasiri et al., 2005. ............................................................... 216
Appendix B Protocol for genotyping using multiplex PCR and matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) analysis. .................................................................................................................. 219
Appendix C Protocol for genotyping using automated Sanger sequencing ........................... 222
xivStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
List of Tables
Table 2-1 Cancer incidence and mortality in the world in 2007 and 2011 according to the International Agency for Research on Cancer (Ferlay et al., 2010; Ferlay et al., 2013). ............................................................................................................................. 30
Table 2-2 Incidence, mortality and 5 year prevalence of breast cancer in women in Australia and the world in 2011 according to the International Agency for Research on Cancer (Ferlay et al., 2013). ........................................................................................................ 30
Table 2-3 Histological Classification of Breast Cancer (Robbins et al., 2010). .................................. 40
Table 2-4 American Joint Committee on Cancer Staging System (Adapted from AJCC Staging Manual 2002) ................................................................................................................. 42
Table 2-5 Autosomal dominant inherited breast cancer syndromes. .................................................. 50
Table 2-6 Familial forms of breast cancer. ....................................................................................... 51
Table 2-7 Some of the miRNAs and their target genes most commonly found in relation to cancer. ............................................................................................................................ 63
Table 2-8 Summary of the most significantly associated microRNAs in breast cancer and their target genes .................................................................................................................... 69
Table 4-1 Spectrophotometry measures from whole blood samples (BC319-BC323)........................ 99
Table 4-2 Spectrophotometry measures from gDNA extracted from whole blood samples using 3 different extraction methods ....................................................................................... 100
Table 4-3 Time and Cost summary for different gDNA extraction methods ................................... 102
Table 5-1 DNA extraction methods commonly used for extraction from whole blood samples ....... 111
Table 5-2 Summary of comparative study of DNA extraction methods by Lahiri et al .................... 119
Table 5-3 Summary of results from comparative study on DNA extraction techniques by Lee et al .................................................................................................................................. 122
Table 5-4 Summary of features evaluated for deoxyribonucleic acid (DNA) extraction methods reviewed by Carpi et al.1 ............................................................................................... 123
Table 5-5 Summary of results from comparative study of DNA extraction methods by Chacon-Cortes et al ................................................................................................................... 124
Table 6-1 Genotype and allele frequencies for GRC-BC population .............................................. 132
Table 6-2 Genotype and allele frequencies for GU-CCQ BB population ........................................ 133
Table 6-3 Combined primary and secondary population genotype and allele frequencies and analysis results.............................................................................................................. 134
Table 6-4 Odds Ratios for combined population analysis results. ................................................... 134
Table 7-1 Selected features from the Human miRNA disease database (HMDD) included in present study ................................................................................................................ 145
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. xv
Table 7-2 List of the miRNA SNPs included in multiplex primer design using the MassARRAY® Assay Design Suite v1.0 software (SEQUENOM Inc., San Diego, CA, USA) ..................................................................................................................... 149
Table 7-3 Primer sequences for the miRNA SNPs included in genotyping study using multiplex PCR reaction and MALDI-TOF MS .............................................................. 150
Table 7-4 Allele and genotype frequencies for miRNA SNPs in MIR210 obtained from the GRC-BC population ..................................................................................................... 154
Table 7-5 Allele and genotype frequencies for miRNA SNPs in MIR221 and MIR222 from the GRC-BC Population ..................................................................................................... 154
Table 7-6 Allele and genotype frequencies for miRNA SNPs in MIR21 obtained from the GRC-BC population ..................................................................................................... 155
Table 7-7 Allele and genotype frequencies for miRNA SNPs in MIRLET7A1 obtained from the GRC-BC cohort ............................................................................................................ 155
Table 7-8 Allele and genotype frequencies for miRNA SNPs in MIRLET7A2 obtained from the GRC-BC population ..................................................................................................... 156
Table 7-9 Allele and genotype frequencies for rs55945735 located in MIR145 obtained from the GRC-BC population ................................................................................................ 156
Table 7-10 Allele and genotype frequencies for SNP rs353291 located in MIR145 obtained from the GRC-BC and GU-CCQ BB cohorts ................................................................. 157
Table 8-1 SNPs in the microRNA 17-92 cluster host gene selected for primer design using the MassARRAY® Assay Design Suite v1.0 software (SEQUENOM Inc., San Diego, CA, USA) ..................................................................................................................... 170
Table 8-2 Primer sequences for microRNA 17-92 cluster host gene SNPs included in genotyping study using multiplex PCR reaction and MALDI-TOF MS .......................... 170
Table 8-3 Chromosomal location and allele information for selected miRNA SNPs in the microRNA 17-92 cluster host gene ................................................................................ 173
Table 8-4 Allele frequencies of SNPs in the microRNA 17-92 cluster host gene obtained from the GRC-BC and GU-CCQ BB cohorts ......................................................................... 173
Table 8-5 Haplotype block association analysis for selected SNPS in the microRNA 17-92 cluster host gene in combined GRC-BC/GU-CCQ BB population .................................. 176
xviStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
List of Figures
Figure 2-1 Anatomical structure of mammary glands. Anterolateral disection and sagital section (Reproduced from , Netter, F. H. (2011). Atlas of human anatomy. Copyright © 2011 by permission of Elsevier, Inc.)........................................................................... 33
Figure 2-2 Lymphatic drainage of the breast ((Reprinted from , Netter, F. H. (2011). Atlas of human anatomy. Copyright © 2011 by permission of Elsevier, Inc.). ............................... 34
Figure 2-3 ERBB2 Receptor and intracellular signalling pathways. Reproduced from Johnston & Dowsett, 2003. Copyright © 2003, with permission from Nature Publishing Group. ............................................................................................................................ 48
Figure 2-4 Copyright © 2006. Canonical pathway of miRNA genesis and mRNA regulation. Reprinted by permission of Nature Publishing Group (Esquela-Kerscher & Slack, 2006). ............................................................................................................................. 59
Figure 3-1 Example of High Resolution Melt (HRM) curves showing three different miR-SNP genotypes. ...................................................................................................................... 85
Figure 4-1 Agarose gel showing PCR product amplification on DNA extracted by traditional salting out method(lanes 1-6) and modified salting out method(lanes 7-12) .................... 102
Figure 7-1 MicroRNA SNP (miR-SNP) selection algorithm using the Human miRNA Disease Database (HMDD). This flow chart shows workflow for selection of preliminary miR-SNPs included in genotyping study. ...................................................................... 147
Figure 8-1 Linkage Disequilibrium (LD) blocks for analysed microRNA polymorphisms in the microRNA 17-92 cluster host gene................................................................................ 175
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. xvii
List of Abbreviations
3’-UTR 3’ untranslated region
AJCC American joint committee on cancer
AT Ataxia telangiectasia
BCLL B-cell chronic lymphocytic leukaemia
CI Confidence interval
CLL Chronic lymphocytic leukaemia
CS Cowden syndrome
CsCl Cesium Chloride
DEAE diethylaminoethyl cellulose
DCIS Ductal carcinoma in situ
DGCR8 DiGeorge syndrome critical region gene 8
DLBCL Diffuse large B cell lymphoma
DNA Deoxyribonucleic Acid
dsRBD Double stranded RNA binding domain
EDTA Ethilenediaminetetraacetic acid
ERα Oestrogen receptor alpha
ERβ Oestrogen receptor beta
ER Oestrogen receptor
ERBB2 Human epidermal growth factor receptor 2 gene
FDA Food and drug administration
FGFR2 Fibroblast growth factor receptor 2
gDNA Genomic DNA
GRC-BC Genomics research centre breast cancer population
xviiiStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
GU-CCQ BB Griffith University-Cancer Council Queensland
Biobank
GWAS Genome-wide association studies
GTC Guanidine thiocyanate
HER2/neu Human epidermal growth factor receptor 2
HL Hodgkin’s lymphoma
HMDD Human miRNA disease database
HRM High resolution melting
HWE Hardy-Weinberg equilibrium
IARC International agency for research on cancer
ISH In situ hybridisation
Kb kilobases
LCIS Lobular carcinoma in situ
LD Linkage disequilibrium
µTAS Miniaturised total chemical analysis system
MAF Minor allele frequency
MALDI-TOF Matrix assisted laser desorption ionization time-of-
flight
MGA Microfluidic genetic analysis
MINDACT Microarray in node negative disease may avoid
chemotherapy trial
MIR17HG MicroRNA 17-92 cluster host gene
miRNA MicroRNA
miR-SNP MicroRNA-SNP
mRNA Messenger RNA
MS Mass Spectrometry
MTHFR Methylenetetrahydrofolate reductase gene
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. xix
NCBI National center for biotechnology information
NST No special type
OR Odds ratio
P-bodies Processing bodies
PCR Polymerase chain reaction
piRNA piwi-interacting RNA
PI3K phosphatidylinositol 3-kinase
PMBL primary mediastinal B cell lymphoma
PR Progesterone receptor
pre-miRNA MicroRNA precursor
pri-miRNA MicroRNA primary transcript
qPCR Quantitative real time polymerase chain reaction
RBP RNA binding protein
rcf Relative centrifugal force
RISC RNA-induced silencing complex
RNA Ribonucleic Acid
RNase Ribonuclease
SD Standard deviation
SDS Sodium dodecyl sulfate
siRNA Short interfering RNA
snoRNA Small nucleolar RNA
SNP Single Nucleotide Polymorphism
TS Tumour suppressor
XPO5 Exportin5
.
xxStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the
best of my knowledge and belief, the thesis contains no material previously
published or written by another person except where due reference is made.
Signature: QUT Verified Signature
Date: October 2015
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. xxi
Acknowledgements
I am pleased to thank the many colleagues, friends and family who have kindly
given me some advice, help, encouragement and support over the last four years.
I would like to thank my supervisor Prof Lyn Griffiths for welcoming me to the
Genomics Research Centre and for giving me the opportunity and support required to
develop my research project. I would like to also thank my associate supervisor Dr
Robert A. Smith because his invaluable support, guidance and advice were essential
to the successful completion of this degree.
I would like to thank staff at Queensland University of Technology, the
Institute of Health and Biomedical Innovation and/or the Genomics Research Centre
who contributed in any way to the completion of my work. I am also grateful to the
reviewers of this thesis for their time, valuable comments and suggestions throughout
the examination process of my thesis.
I would like to thank the Australian Government, Griffith University and
Queensland University of Technology for providing the financial support required to
undertake my studies, as well as all the financial sponsors and government bodies
supporting this research project.
I would also like to thank all my friends, both at Griffith University and
Queensland University of Technology, who provided me with advice and support
when I required it and for your friendship and company which made my studies in
much more enjoyable.
Finally, I would like to thank my wife for standing by my side during the ups
and downs of these past four years. You have been my rock and your love,
xxiiStudy of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations.
encouragement and support have been essential to this achievement. I would also like
to thank my family in particular both my parents, my brother and sister because even
in the distance their love, encouragement and support keeps me motivated and
because they are the reason behind everything I do.
Study of miRNA polymorphisms and their potential association with breast cancer risk in Australian Caucasian populations. xxiii
Chapter 1: Introduction
1.1 A DESCRIPTION OF THE SCIENTIFIC PROBLEM INVESTIGATED
Breast Cancer is the second most common type of cancer in the world and it is
the most common type of cancer diagnosed in women in Australia. It is a major
concern for Australian public health, because more and more cases are being
diagnosed every day, with Australia’s breast cancer incidence being the third highest
in the world. Furthermore, it is the second most common cause of death for women
in this country (AIHW, 2009, 2012; AIHW & AACR, 2010, 2012; Ferlay et al.,
2010; Ferlay et al., 2013; Robbins, Kumar, & Cotran, 2010).
Genetic information stored within the cells in breast tissue governs cellular fate
from cellular genesis to their death. Breast cancer is a complex disease, which arises
as a result of cumulative and permanent DNA damage to cells within tissues in the
breast and their interaction with many other risk factors (Robbins et al., 2010;
Strachan & Read, 2011; Weinberg, 2007). Therefore, genetics plays a key role in
understanding, preventing and managing this life threatening disease. Many different
genetic markers have been identified and are being used to refine early diagnosis and
prevention, to classify cases and to make more accurate treatment decisions and to
predict response to treatment and other outcomes in breast cancer patients. However,
none of the current genetic markers have proven to be a sufficient and highly
accurate way of addressing any of these important disease features (Ben Zhang,
Beeghly-Fadiel, Long, & Zheng, 2011), therefore more research is needed to identify
molecular markers of disease development and progression.
Study of MicroRNA genes is an emerging field of study in relation to cancer
and they could be useful to understand the complexity of the molecular and cellular
Chapter 1: Introduction 24
mechanisms involved in breast cancer carcinogenesis and progression. They have
been shown to be involved in many different types of cancer because they regulate
complex gene networks required for cell growth, differentiation and cell survival
(Farazi, Spitzer, Morozov, & Tuschl, 2011; Robbins et al., 2010; Westholm & Lai,
2011; Zuoren Yu et al., 2010). Different types of cancer have been seen to exhibit
up-regulation and down-regulation of specific miRNA genes. However, we still lack
knowledge of the mechanisms that result in changes in miRNA expression in cancer,
in particular for breast cancer. Polymorphisms present in the functional and
regulatory regions of specific miRNA genes could change the expression of the
miRNA genes and studying these would be an initial way to address the current gap
in knowledge for their role in the pathogenesis of breast cancer (Gong et al., 2012;
H.-C. Lee, Yang, Chen, & Au, 2011; Mishra, Banerjee, & Bertino, 2008). To
successfully characterise microRNA single nucleotide polymorphisms (miR-SNPs)
we are required to conduct genotyping studies in well-characterized, large case
control cohorts. Therefore in our study we also aimed to establish a larger population
of DNA samples from breast cancer patients and matched controls that could serve as
a replicate population to our previously existing cohort. We determined the most
appropriate method for DNA extraction from whole blood samples that would yield a
good quality and good quantity of genomic DNA to use in our miR-SNP genotyping
analysis. We identified microRNAs previously implicated in breast cancer
development and we selected polymorphisms within those genes to genotype in our
two Australian Caucasian breast cancer cohorts.
1.2 THE OVERALL OBJECTIVE OF THE STUDY
The overall objective of the work described in this thesis is to contribute to the
general knowledge of microRNA polymorphisms and their potential association with
breast cancer risk.
Chapter 1: Introduction 25
1.3 THE SPECIFIC AIMS OF THE STUDY
1. To create and establish a significant sized prospective DNA biobank from
blood samples collected from newly diagnosed breast cancer patients and
control volunteers in Queensland.
2. To determine the most cost-effective and time-efficient technique to
isolate DNA from whole blood samples that improves our genotyping
results.
3. To conduct association analysis of single nucleotide polymorphisms
present in previously identified microRNA genes using high resolution
melting (HRM) and multiplex PCR and matrix assisted laser desorption
ionization time-of-flight (MALDI-TOF) in both our previously
established and newly developed Australian Caucasian case-control
populations.
.
1.4 AN ACCOUNT OF SCIENTIFIC PROGRESS LINKING THE SCIENTIFIC PAPERS
The five papers presented as part of this thesis are directly linked to the topic of
genetic association analysis of miRNA polymorphisms and breast cancer risk in
Australian Caucasian populations.
Chapter Four presents an evaluation study that was performed to choose a
suitable technique to extract genomic DNA from whole blood samples for the GU-
CCQ BB population. It highlights the modified salting-out method developed by
Nasiri et al to be both a cost-effective and time-efficient technique. The work
presented in this chapter was published in the journal “Molecular Biology Reports”.
26 Chapter 1: Introduction
Chapter Five presents a review of most commonly used laboratory techniques
used to extract genomic DNA from whole blood samples. This literature review was
undertaken to try to identify current findings in terms of cost-effective and time
efficient methods for DNA extraction to determine the most suitable technique to use
for our large sized DNA biobank: The Griffith University-Cancer Council
Queensland Breast Cancer Biobank (GU-CCQ BB). The work presented in this
chapter was published in the “Journal of Biorepository Science for Applied
Medicine”.
Chapter Six describes our initial genotyping study for a single nucleotide
polymorphism (SNP) present in MIR146A in our Australian Caucasian breast cancer
case-control cohorts; the Genomics Research Centre Breast Cancer (GRC-BC)
population and the GU-CCQ Biobank. This study is the first to identify rs2910164 to
be associated with breast cancer risk in an Australian Caucasian population. The
work presented in this chapter has been submitted to the journal “Gene”.
Chapter Seven presents our large scale genetic association analysis of a
significant number of microRNA single nucleotide polymorphisms (miR-SNPs)
present in 10 miRNA genes in both of our Australian Caucasian breast cancer
populations. This work is the first to analyse the 24 selected miR-SNPs in 10 miRNA
genes in relation to breast cancer risk. It is also the first study to identify association
of rs353291 (MIR145) with risk of developing breast cancer in Australian Caucasian
females. The work presented in this chapter has been submitted the “BMC Medical
Genetics”.
Chapter Eight describes our large scale genotype and haplotype analysis of six
miR-SNPs present in the microRNA 17-92 cluster host gene (MIR17HG) conducted
in both Australian Caucasian breast cancer case-control populations. This is the first
association study for MIR17HG SNPs conducted in Australian Caucasian
individuals. It is also the first to identify association with breast cancer risk with both
rs4284505 and haplotype for rs4284505/rs7336610 in Australian Caucasian
Chapter 1: Introduction 27
populations. The work presented in this chapter has been accepted for publication in
the journal “Tumor Biology”.
Chapter Nine provides a comprehensive discussion of the main findings of this
study and highlights the outcome of the research.
28 Chapter 1: Introduction
Chapter 2: Literature Review
2.1 BREAST CANCER EPIDEMIOLOGY
Cancer represents a significant burden of disease worldwide. In 2007, cancer
accounted for 12.7 million new cases and 7.6 million deaths around the world
according to the International Agency for Research on Cancer (IARC). By 2011 it
had reached 14.1 million new cases and 8.2 million deaths which represented an
increase of about 11% in world incidence and about 8% in world mortality (See
Table 2-1) (Ferlay et al., 2010; Ferlay et al., 2013). This percentage increase in
cancer incidence is particularly remarkable since it is double the increase in world
population for the same period of time, which was only about 5.5% according to the
figures reported by the Population Reference Bureau (6,625 million people in 2007
and 6,987 million people in 2011) (PRB, 2007, 2011).
Cancer in Australia is also a major cause for concern, since incidence increased
about 13% from 2007 to 2011 with 108,368 and 122,031 new cancer cases diagnosed
in each year respectively. In 2011 52,361 cancer cases in Australia were diagnosed in
women, 43% of all diagnosed cancer cases. Similarly, cancer deaths in Australia are
rising with 39,884 people reported as dying from cancer in 2007 and 43,403 in 2011.
44% of all Australian cancer related deaths were women, with about 17,000 women
reported to die from cancer in 2007 and about 19,000 women in 2011 (AIHW &
AACR, 2010, 2012). The Australian population increased from 21,180,600 to
22485300 people in the 2007-2011 period according to the Australian Bureau of
Statistics, which corresponds to a 6.2% increment (ABS, 2007, 2011). Based on
these figures, we observe a similar pattern both in Australia and worldwide statistics
with growth of cancer incidence doubling the reported percent population increase.
Chapter 2: Literature Review 29
Table 2-1 Cancer incidence and mortality in the world in 2007 and 2011 according to the International
Agency for Research on Cancer (Ferlay et al., 2010; Ferlay et al., 2013).
World Mortality World Incidence Year 2007 2011 Increase 2007 2011 Increase All cancers 7,6000,000 8,200,000 8.2% 12,700,000 14,100,000 11% Breast cancer
458,000 522,000 14% 1,380,000 1,700,000 23%
Breast cancer is a particularly concerning type of cancer since 1.38 and 1.7
million new breast cancer cases were reported in 2007 and 2011, respectively,
showing an increase of about 23% in 4 years. In 2007 458,000 deaths in the world
were related to breast cancer, accounting for about 6% of all cancer related deaths,
and they increased to about 14% in 2011 with 522,000 deaths (See Table 2-1). Breast
malignancies are also the most common type of cancer in women in Australia and
worldwide, and it is estimated that they will be diagnosed in one in eight women
living to the age of 90 (Ferlay et al., 2010; Ferlay et al., 2013; Robbins et al., 2010).
Breast cancer has the highest rates for 5 year prevalence for any cancer for women
both in Australia and around the world and it is also the first cause of cancer related
deaths for women worldwide, while being the second cause of cancer death in
Australian women (see Table 2-2) (AIHW, 2009, 2012).
Table 2-2 Incidence, mortality and 5 year prevalence of breast cancer in women in Australia and the
world in 2011 according to the International Agency for Research on Cancer (Ferlay et al., 2013).
World Australia No Cases % Ranking No Cases % Ranking Incidence 1,676,633 25.2 1st 14,710 28.1 1st Mortality 521,817 14.7 1st 2,968 15.7 2nd 5 year prevalence 6,255,391 36.4 1st 59,584 37.5 1st
There are a number of epidemiological risk factors associated with breast cancer and
they include age; reproductive factors such as age at menarche, menopause and first
pregnancy, oral contraceptives and hormone replace therapy; lifestyle factors like
diet, weight, alcohol intake, smoking and ionizing radiation and chemical exposure.
30 Chapter 2: Literature Review
Breast cancer incidence is low in women under 25 years of age and it doubles about
every 10 years until menopause, when the rate of increase slows considerably.
However, this phenomenon seems to be dependent on geographical location, in some
countries like Japan and Colombia there is a flattening of the incidence curve after
menopause, while in US and Sweden risk keeps on increasing until 75 years of age
(Dumitrescu & Cotarla, 2005; McPherson, Steel, & Dixon, 2000).
Reproductive factors also seem to affect breast cancer incidence. Early age of
menarche and late menopause increase the risk of developing breast cancer by about
20%. On the other hand, women who undergo bilateral oophorectomy before 35
years of age have only 40% of the risk of breast cancer of women experiencing
natural menopause. Nulliparity and late age at first birth increase risk of breast
cancer. Furthermore, women giving their first birth after the age of 35 have a higher
risk than nulliparous women. Finally both use of oral contraceptives and hormonal
replacement therapy seem to be associated with a small increase in the risk of
developing breast cancer, but further studies are still required (relative risk (1.07)
(Dumitrescu & Cotarla, 2005; Hulka & Moorman, 2001; McPherson et al., 2000).
Although there seems to be a correlation between the incidence of breast cancer and
dietary fat intake, however epidemiological evidence available up to date is not
particularly consistent or strong. Obesity in postmenopausal women is associated
with a twofold increase in the risk of developing breast cancer. Conversely, obesity is
associated with a reduced incidence of breast cancer in premenopausal women.
Results from studies evaluating the role of other dietary components such as soy
protein, vitamins A, C, E and D have been inconclusive and further research is
required (Dumitrescu & Cotarla, 2005; Hulka & Moorman, 2001; McPherson et al.,
2000).
Alcohol consumption and smoking have been linked to many types of cancer and
there is evidence that suggests that alcohol consumption of one to two drinks on
average per day increases the risk of developing breast cancer (relative risk of 1.2 to
1.4). On the other hand, current evidence shows that smoking has a little overall
Chapter 2: Literature Review 31
effect on breast cancer risk (Dumitrescu & Cotarla, 2005; Hulka & Moorman, 2001;
McPherson et al., 2000).
Finally, studies on chemical compounds including the pesticide bis(4-chlorophenyl)-
1,1,1-trichloroethane (DDT) and its metabolite bis(4-chlorophenyl)-1,1-
dichloroethene (DDE) and polychlorinated biphenyls (PCBs) have shown no
association to breast cancer risk. Exposure to ionising radiation doubled the risk of
breast cancer for teenage girls exposed to radiation during the Second World War
(Hulka & Moorman, 2001; McPherson et al., 2000; Wolff, Collman, Barrett, & Huff,
1996).
2.2 ANATOMY OF THE MAMARY GLAND
Female mammary glands (or breasts) are skin-covered modified
sebaceous/sweat glands located in the superficial fascia on the ventral wall of the
thorax, anterior to the pectoral muscles. They stretch from the second rib to about the
seventh intercostal cartilage and from the lateral edge of the sternum to the
midaxillary line. Breasts are part of the female reproductive system and they become
functional after pregnancy finishes, producing milk to nurse the newborn baby. They
are markedly undeveloped during childhood and they start acquiring their round
conical adult shape after puberty.
On the exterior surface, mammary glands have a central ring of pigmented skin
known as the areola. It surrounds a protruding conical structure of about 12mm in
diameter called the nipple, commonly located at about the level of the fourth
intercostal space in young women. Lying underneath the skin, there are between 15
to 20 lobes that open through their individual excretory duct to the surface of the
nipple. Lobes are formed by smaller structures called lobules, which contain multiple
glandular alveoli (or acini) able to produce milk during lactation. Acini are connected
through a network of lactiferous ducts that merge into the lactiferous sinus, a dilated
structure leading to the excretory duct, where milk accumulates throughout nursing.
32 Chapter 2: Literature Review
Glandular tissue, lactiferous ducts and lactiferous sinuses are surrounded by fatty
connective tissue. These structures are connected to the skin and the muscular layer
underneath by fibrous bands of the superficial fascia, which form the suspensory
ligament of Cooper on the upper part of the breasts (Ellis, 2010; Love & Barsky,
2004; Marieb, 2010; Morehead, 1982) (See Figure 2-1).
Figure 2-1 Anatomical structure of mammary glands. Anterolateral disection and sagital section (Reproduced from , Netter, F. H. (2011). Atlas of human anatomy. Copyright © 2011 by permission of Elsevier, Inc.).
Blood supply to the mammary gland is provided by three main sources:
Internal thoracic artery, axillary artery and intercostal arteries. Internal thoracic
artery arises from the subclavian artery and it provides the largest arterial vessels
reaching the breasts. Axillary artery is the second major source of blood, which is
Chapter 2: Literature Review 33
supplied through 4 main branches: Superior thoracic, pectoral branch, lateral thoracic
and subscapular arteries. Venous drainage follows the pathway of the
aforementioned arteries and it ends in two major vessels: Axillary vein or the internal
thoracic vein.
There are two main sites for lymphatic drainage from mammary glands and
they are the axillary lymph nodes and the internal thoracic lymph nodes (Figure 2-2).
Axillary lymph nodes are divided into 5 groups and they receive approximately 75%
of all drainage, which explains why early metastases for most breast cancer patients
are located in the axilla. (Morehead, 1982; Netter, 2011).
Figure 2-2 Lymphatic drainage of the breast ((Reprinted from , Netter, F. H. (2011). Atlas of human anatomy. Copyright © 2011 by permission of Elsevier, Inc.).
34 Chapter 2: Literature Review
2.3 BREAST CANCER DEFINITION AND AETIOLOGY
Malignant neoplasm or cancer is the term to define a new growth that invades
local tissue and/or distant places within the human body. It is a complex disease in
which cells proliferate with no control and with no physiological purpose. Cancer is
the result of genetic alteration to a cell or a group of cells that modifies normal
growth pattern (Neighbors & Tannehill-Jones, 2006). Robert Weinberg defined it as
“a disease of chaos, a breakdown of existing biological order within the body”
(Weinberg, 2007), whereas Strachan and Read referred to it as “the natural end of a
multi cellular organism” (Strachan & Read, 2011). It is a medical condition that
requires at least six successive somatic mutations within a cell, or a number of
different cells in a tissue, to allow uncontrolled cellular division. Malignant
proliferation of cells can be accomplished through seven main cellular capabilities:
1. Autonomy from external growth signals.
2. Freedom from external anti-growth signals.
3. Ability to avoid apoptosis (programmed cell-death).
4. Faculty to replicate indefinitely.
5. Competence to trigger angiogenesis.
6. Ability to invade tissues and establish secondary tumours (metastasis).
7. Failure in DNA repair
Hanahan and Weinberg described these features as the hallmarks of cancer in
2000 and they have been a fundamental principle in our understanding of the
pathogenesis of cancer (Hanahan & Weinberg, 2000). These features are
accomplished in cancers by two basic means: Mutations that increase cell
proliferation and mutations that affect stability of the entire genome increasing
mutation rate (Robbins et al., 2010; Strachan & Read, 2011; Weinberg, 2007).
Research into the molecular mechanisms underlying neoplastic disease has also
identified two additional hallmarks: reprogramming of energy metabolism and
Chapter 2: Literature Review 35
evading immune destruction. These hallmarks are achieved through the activation
and/or silencing of complex intracellular pathways which are out of the scope of the
present review. However, neoplasms are not the result of isolated cancer cells.
Malignant tumours have also been recognised as organs because they require the
interplay of specialised cell types and the tumour microenvironment that they
develop during the progression of the disease. Cancer cells, cancer stem cells,
endothelial cells, pericytes, immune inflammatory cells, cancer-associated fibroblast
and stem and progenitor cells of the tumour stroma are key players of the tumour
microenvironment. Cancer progression is achieved through very complex signalling
networks that are established amongst all specialised cells previously mentioned. To
date, many of these networks remain to be fully identified and they are also the focus
of current research studies into targeted cancer therapies (Hanahan & Weinberg,
2011).
Although there is no definitive cause to the genetic alteration to most cancers,
one or many contributing risk factors have been identified, including race/ethnicity,
exposure to chemical carcinogens, hormones, radiation exposure, viruses, genetic
predisposition or familial history and other personal circumstances such as alcohol
use, nutritional habits and smoking (Martin & Weber, 2000; Neighbors & Tannehill-
Jones, 2006).
Breast cancer is a malignant neoplasm arising from the epithelial cells that line
the terminal duct lobular unit. Out of all the risk factors previously mentioned,
hormonal factors and genetic factors are the two mainly involved in the development
of carcinoma of the breast in women. As a result, pathologists usually divide breast
cancer into those related to hormonal exposure or sporadic cases and the ones
featuring germline mutations or hereditary cases (Copstead & Banasik, 2010;
Robbins et al., 2010; Sainsbury, Anderson, & Morgan, 2000).
Genes and germline mutations involved in the aetiology of breast cancer will
be further discussed in this chapter, however they account only for a small fraction of
all diagnosed breast cancer patients. Sporadic cases are primarily associated with
36 Chapter 2: Literature Review
hormone exposure and its main effects on women, mediated through factors such as
age at menarche and menopause, reproductive history, breastfeeding and use of
exogenous oestrogen (Martin & Weber, 2000). Oestrogen is a key hormone in the
development of normal breast epithelium, but it also seems to have an effect on
breast cancer development by increasing the number of malignant target cells
through breast growth stimulation. DNA damage may also occur through mutations
caused by hormonal metabolites or production of free radicals and due to their effect
on gene variants related to oestrogen synthesis and metabolism. It binds to two
specific receptors, oestrogen receptor alpha (ERα) and oestrogen receptor beta
(ERβ), which act as nuclear transcription factors. ERα is the receptor most
commonly associated with breast cancer since it is the one mainly expressed in breast
tissue (Abeloff, Armitage, & Niederhuber, 2008; Robbins et al., 2010).
Of the hormonal related pathways for malignant development, two of the most
clinically important ones are the ones related to oestrogen receptor (ER): ER-positive
and ER-negative pathways. The majority of breast cancers are ER-positive, about
70%, and they most probably develop initially in the epithelium of the glandular
ducts, mainly in the luminal cells expressing oestrogen receptor. ER-negative
carcinomas have no clear precursor cells but they might be derived from ER-negative
myoepithelial cells or from ER-positive cells that lose ER expression. ER- positive
carcinomas tend to have better differentiation and a slowly growing pattern. Almost
half of breast carcinomas in young women who develop breast cancer are ER-
negative or human epidermal growth factor receptor 2 (HER2/neu) positive, while
these markers are only positive in less than a third of women over 40 years of age,
contributing to a more aggressive phenotype seen in breast cancers arising earlier in
life. Most lobular carcinoma in situ (LCIS) are ER-negative, they usually tend to
over express HER2/neu and to lose expression of E-cadherin (Abeloff et al., 2008;
Copstead & Banasik, 2010; Robbins et al., 2010). HER2/neu will be discussed in
section 2.5.1 referring to genes and breast cancer further ahead in this chapter and
LCIS will be described as part of section 2.4.1, which deals with the classification of
breast cancer (Abeloff et al., 2008; Robbins et al., 2010).
Chapter 2: Literature Review 37
Progesterone receptor (PR) is an oestrogen regulated gene. There are two
isoforms of this receptor PR-A and PR-B, the last one being more specific to breast
cancer. Polymorphisms in the PR gene promoter seem to increase risk of endometrial
and breast cancer. The relationship of PR with ER seems to have an effect on tumour
biology. Tumours which are ER/PR-positive seem to be less aggressive, they usually
do not extend to surrounding lymph nodes and they have a better response to anti-
oestrogen therapy. On the other hand, ER-positive/PR-negative carcinomas are more
frequently found in women over 50 years of age and they associate with abnormal
chromosomal count and nodal involvement. Furthermore, they have a poor clinical
response to anti-oestrogen therapy (Abeloff et al., 2008; De Vivo et al., 2002).
Breast cancer is a complex disease and there is no robust association between a
single genetic marker and sporadic breast cancer development. However, there is
clear evidence about the role genetics play in the development of cancer. There are
many different molecular pathways described and they seem to interact with each
other creating very complex signalling networks. Therefore, it is more likely that
several genes play a role in carcinogenesis. These genes are the target for genetic
damage during carcinogenesis, a process aided by many other environmental factors
previously mentioned. Although inherited predisposition is very rare for most types
of breast cancer, recognition of genes contributing to such predisposition has had a
major impact in our understanding of carcinogenesis. Genes associated with cancers
with a strong hereditary component seem to also be involved in the sporadic forms.
Genetic predisposition to breast cancer is found within two categories: Autosomal
dominant inherited cancer syndromes and familial cancers, which are summarized in
Table 2-5, which will be discussed section 2.5.1 of this review, which covers
genetics and breast cancer due to their importance for both familial and sporadic
forms of the disease (Robbins et al., 2010; Weinberg, 2007; Ben Zhang et al., 2011).
2.4 BREAST CANCER CLASSIFICATION
Many different classifications of breast carcinomas have been created over the
years in an attempt to group tumours according to morphologic patterns and clinical
38 Chapter 2: Literature Review
behaviour (Bloom & Richardson, 1957; Meyer et al., 2005; Sainsbury et al., 2000).
We will focus on some of the most commonly used by pathologist and clinicians: the
histological classification, Bloom and Richardson grading system and the American
Joint Committee on Cancer (AJCC) staging system. However, the molecular
classification of breast cancer will be covered further ahead in section 2.5.1.2
discussing genetics and breast cancer because it relates to gene expression signatures
used in clinical settings to determine treatment and predict outcome.
2.4.1 Histological Classification
Adenocarcinomas are the main type of breast cancer and they can be further
grouped as in situ carcinomas and invasive carcinomas. A neoplastic tumour within
ducts and lobules limited by the basement membrane is called carcinoma in situ,
whereas the invasive form crosses the basement membrane and invades the stroma.
The latter form can potentially extend to vasculature and reach regional lymph nodes
and distant anatomical locations. Previously, ductal carcinoma was a general term
initially used to designate neoplastic proliferation within the ducts and lobular
carcinoma was the one used for malignant tumours within the lobules. However as
classification evolved, these terms are now used to designate differences in tumour
cell biology, based on particular patterns of growth and cellular morphology and
specific markers. Histological classification of breast cancer is summarised in Table
2-3 (Robbins et al., 2010; Sainsbury et al., 2000).
Chapter 2: Literature Review 39
Table 2-3 Histological Classification of Breast Cancer (Robbins et al., 2010).
CARCINOMA IN SITU Type Subtype
Ductal Carcinoma in Situ (DCIS)
Comedocarcinoma
Non-comedo DCIS
Solid Cribriform Papillary
Micropapillary Paget's Disease
Lobular Carcinoma in Situ (LCIS)
INVASIVE (INFILTRATING) CARCINOMA Type Subtype
Invasive Carcinoma No Special Type (NST; Invasive Ductal Carcinoma)
Invasive Lobular Carcinoma Special Type
Tubular Cribriform Mucinous Papillary
Medullary Classic Lobular
In general, in situ carcinomas have a better outcome overall compared to
invasive carcinomas but their overall incidence is lower, about 30% of total cases.
Ductal carcinoma in situ (DCIS) is the second most common type of carcinoma (12
to 24% of cases) and it is further classified into 5 architectural subtypes which are
present in the majority of DCIS tumours. It can have infiltrating edges and when it
becomes invasive it usually spreads to the surrounding normal breast tissue.
Following this it disseminates into the lymphatic system, initially to the lymph
system of the axilla and from here to other distant sites such as lung, liver and bone.
In its initial stages, it can be cured in about 95% of patients through mastectomy and
DCIS recurrences or deaths after surgery are rare. Most lobular carcinoma in situ
(LCIS) are found incidentally and they account for about 3 to 6% of total cases. They
are very hard to detect through mammography and they affect primarily young
women (up to 90% of cases are identified before menopause). Treatments for LCIS
tend to be more aggressive and they include bilateral prophylactic mastectomy
(Abeloff et al., 2008; Robbins et al., 2010).
40 Chapter 2: Literature Review
On the other hand, invasive carcinomas constitute the majority of breast cancer
cases (70 to 85% of all cases). Invasive carcinomas of no special type (NST), also
known as invasive ductal carcinomas, are the most common subtype and they are
about 55 to 67% of all cases. Molecular classification of invasive ductal carcinomas
is clinically important to determine prognosis and response to therapy and will be
discussed in detail in section 2.5.1.2. As a general rule, neoplasms classified as
special type carcinoma tend to have a better prognosis compared to the no special
type. Invasive lobular carcinoma of special type can be divided in 6 main subtypes:
Classic lobular carcinoma, cribriform, mucinous, papillary, tubular and medullary
carcinoma (Page, 2003). Classic lobular carcinoma account for about 7 to 8.5% of
total cases and they are hard to detect on physical examination and mammography.
Invasive lobular carcinoma is usually graded using grading scales and staging
systems, further described in section 2.4.2 and they have a different pattern of
metastasis, usually spreading to the abdominal and pelvic cavities (peritoneum,
retroperitoneum, gastrointestinal tract, ovaries and uterus). Medullary and mucinous
carcinomas together make up about 1.7% of all cases. Medullary carcinomas are very
common in the sixth decade and usually poorly differentiated. Mucinous carcinomas,
also known as colloid carcinomas, occur after the seventh decade and have a slow
growing rate. Both of these subtypes rarely involve lymph node metastases and have
a slightly better prognosis than no specific type carcinomas. Finally, tubular
carcinomas range from 4 to 5% of total cases and are commonly present in the fourth
decade. They are usually detected by mammography and overall they tend to have an
excellent clinical prognosis (Abeloff et al., 2008; Robbins et al., 2010).
2.4.2 Bloom and Richardson grading system
Bloom and Richardson grading, also known as Scarff, Bloom and Richardson
grading, is an histological grading scale developed to obtain prognostic information
especially for invasive carcinoma of no special type. Tumours are graded on three
main factors: Degree of glandular formation, nuclear pleomorphism and frequency of
mitoses. Each one of these factors is scored from 1 to 3 and these results combined
would allocate a tumour into one of three groups: Grade I (Scores 3 to 5), grade II
Chapter 2: Literature Review 41
(scores 6-7) and grade III (8-9). Lower scores for each factor, leading to lower
grades, would give a better prognosis for both disease free and overall survival, as it
has been shown that most deaths occur within the first 10 years after diagnosis for
grade III patients (Bloom & Richardson, 1957; Robbins et al., 2010; Sainsbury et al.,
2000).
2.4.3 The American Joint Committee on Cancer (AJCC) staging system.
The American Joint Committee on Cancer (AJCC) developed a staging system
in 2002, which relies on the pathological examination of the primary tumour as well
as the axillary lymph nodes to establish prognosis. This staging system is very
complex and it defines various different categories to classify tumours and lymph
nodes. The main features of the AJCC staging system, which divides patients into 5
different stages associated with different survival rates are summarized in Table 2-4.
Breast cancers classified in the lower grades show a better outcome compared to the
higher grades, starting with 92% survival for local, non-invasive carcinomas in grade
0 and lowering down to 13% for invasive carcinomas classified as grade IV. (F.
Greene, Balch, Haller, & Morrow, 2002; Robbins et al., 2010; Sainsbury et al.,
2000).
Table 2-4 American Joint Committee on Cancer Staging System (Adapted from AJCC Staging
Manual 2002)
Stage T: Primary Cancer Lymph Nodes (LNs) M: Distant Metastasis
5-Year Survival
(%) 0 DCIS or LCIS No metastases Absent 92 I Invasive Carcinoma ≤ 2 cm No metastases Absent 87 II Invasive Carcinoma > 2 cm No metastases Absent 75
Invasive Carcinoma < 5 cm 1-3 positive LNs Absent III Invasive Carcinoma > 5 cm 1-3 positive LNs Absent 46
Any size invasive carcinoma ≥ 4 positive LNs Absent Invasive carcinoma with skin or chest wall involvement or inflammatory carcinoma
0 to > 10 positive LNs
Absent
IV Any size invasive carcinoma Negative or positive LNs
Present 13
42 Chapter 2: Literature Review
Current available classifications for breast cancer, including molecular
profiling discussed in section 2.5.1.2, still fail to provide accurate patient prognosis,
response to treatment and prediction of disease relapse. Molecular diversity overlaps
with histopathology, therefore changes to these classifications are currently under
work. Reviews and studies are being conducted to include validated genetic profiling
in an effort to strengthen our understanding of breast cancer and to provide better
treatment rationale and disease outcome. (Singletary et al., 2003; Singletary &
Connolly, 2006).
2.5 GENETICS AND BREAST CANCER
Four main types of regulatory genes are involved in the development and
progression of breast cancer: growth promoting proto-oncogenes, growth-inhibiting
tumour suppressor genes, genes regulating apoptosis and genes participating in DNA
repair (Robbins et al., 2010; Strachan & Read, 2011; Weinberg, 2007).
Oncogenes are genes that promote self-sufficient cell growth in cancer cells.
They derive from proto-oncogenes and they are transcribed and translated to produce
oncoproteins, required for the continued, autonomous replication of cells even in the
absence of growth factors or other external signals. Proto-oncogenes, under normal
conditions, are involved in physiological processes like cellular growth and
proliferation and they transform into oncogenes when DNA damage occurs within
them. Activation or transformation of proto-oncogenes can be achieved through 3
main mechanisms: amplification, point mutation and chromosomal translocation.
Each of these results in overproduction of the product of the gene, or an altered
product with increased signalling capacity. Oncoproteins, the final product of
oncogenes, can be classified as one of four main categories: growth factors, growth
factor receptors, signal transduction proteins and nuclear-regulatory proteins
(Robbins et al., 2010; Strachan & Read, 2011; Weinberg, 2007).
Chapter 2: Literature Review 43
Tumour suppressor genes have a counteracting function to that of oncogenes.
They inhibit cell proliferation by restraining inadequate cell division, maintaining
DNA integrity or driving cells to apoptosis. They are able to achieve these functions
through different final protein products which include categories like transcription
factors, cell cycle inhibitors, signal transduction molecules, cell surface receptors and
regulators of cellular responses to DNA damage (Robbins et al., 2010; Strachan &
Read, 2011; Weinberg, 2007). Tumour suppressor genes rarely contribute directly to
cancer formation; instead it is primarily their inactivation or destruction, through
mutation or suppression of gene expression that allows the further progression of
early dysplasias and adenomas into malignancy.
Apoptosis is the process of programmed cell death. It is achieved through
highly regulated and controlled biochemical and molecular events that lead to
cellular changes and eventually death. Genes regulating apoptosis include those
involved in TNF, FAS and Caspase pathways. Changes in apoptosis-regulating genes
result in increased cell survival and this is generally achieved through mutations
leading to enhanced activity of genes suppressing apoptosis or reduced expression of
cell-death promoting genes (Robbins et al., 2010; Strachan & Read, 2011; Weinberg,
2007).
Finally, genes involved in various DNA repair mechanisms are also implicated
in carcinogenesis. Loss-of-function mutations in genes involved in DNA repair
impairs the cellular ability to identify and repair genetic damage, which leads to the
high rates of acquired mutations commonly seen in carcinogenesis. ATM, CHEK1,
CHEK2, BRCA1 and BRCA2 are examples of genes associated with inherited
disorders and familial cancer syndromes, which are usually the result of
chromosomal abnormalities like breaks, abnormal chromosome numbers and
widespread mutations. These genes have also been studied and associated with some
cases of sporadic cancer (Robbins et al., 2010; Strachan & Read, 2011; Weinberg,
2007).
44 Chapter 2: Literature Review
Due to the complexity of the cellular networks and the large number of genes
involved with potential roles in the development and progression of breast cancer, we
will focus on some of the most important genes identified to date in subsection 2.5.1.
We will also discuss microRNAs (miRNAs) and their role as epigenetic modifiers of
gene expression in relation to breast cancer as part section 2.6.3 of this review to give
a more comprehensive overview due to their importance for the topic of this study.
2.5.1 Genes and breast cancer
As mentioned before, approximately 5-10% of breast cancers are caused by the
inheritance of a susceptibility gene or a set of genes. These genes can be divided into
three different groups: High risk, high penetrance, breast cancer susceptibility genes,
moderate risk breast cancer susceptibility genes with moderate penetrance and low-
penetrance variants for some other identified genes (Oldenburg, Meijers-Heijboer,
Cornelisse, & Devilee, 2007; Ben Zhang et al., 2011). Families with multiple
affected first-degree relatives, especially with subjects affected before menopause
and/or medical history of other types of cancer increase the possibility of breast
cancer development within their members. Most common single gene mutations are
associated with increased susceptibility to breast cancer, autosomal dominant
syndromes and familial forms and they include BRCA1, BRCA2, p53, PTEN, ATM
and CHEK2 briefly outlined in Table 2-5 and Table 2-6 (Robbins et al., 2010;
Strachan & Read, 2011; Weinberg, 2007). Due to their apparent role in the
development of sporadic forms of breast cancer, the main features and current
findings in relation to these genes will be discussed in further detail.
BRCA1: BRCA1 is a tumour suppressor gene of about 100 kilobases (kb)
localised in chromosome 17q21, it contains 24 exons, 22 of which are protein
coding. (Martin & Weber, 2000). It was initially identified by linkage analysis in
1994 (Hall et al., 1990; Miki et al., 1994) and it is one of four high penetrance genes
described so far in relation with breast cancer. Prevalence of heterozygous carriers of
the high-risk mutation in the general Caucasian population is one in 1000 (0.10%)
and it has been shown to confer female carriers a cumulative lifetime risk of about 50
Chapter 2: Literature Review 45
to 85% for developing breast cancer. The BRCA1 protein, containing about 1863
amino acids, interacts with transcripts of some of the other genes described in this
section, including TP53, RB, ATM and BRCA2. It is involved in DNA-damage repair,
cell-cycle regulation, transcriptional regulation and chromatin remodelling (Carter,
2001; Martin & Weber, 2000; Oldenburg et al., 2007; Welcsh & King, 2001; Ben
Zhang et al., 2011). More than 500 sequence variations, mainly deletions or
insertions of nucleotides, have been described and associated with breast cancer,
including the founder mutations identified in the Ashkenazi Jewish population
(185delAG and 5382insC) (Carter, 2001; Martin & Weber, 2000). BRCA1
expressing tumours are commonly of the basal type, which will be discussed ahead
in more detail in the molecular classification of breast cancer (Ben Zhang et al.,
2011).
BRCA2: BRCA2 is a tumour suppressor gene, spanning about 70kb of genomic
DNA, localised in chromosome 13 (13q12.3) and it contains 27 exons, one of which
is not translated. Initially identified by linkage analysis in 1995, it is a high
penetrance gene involved in inherited forms of breast cancer. At least 250 mutations
in the gene have been identified and they include a founder mutation in the
Ashkenazi Jewish population (6174delT). Prevalence of heterozygous carriers of the
high risk BRCA2 mutations in the general Caucasian population is about 1 in 750
(0.13%) and mutation carriers have a lifetime breast cancer risk estimated in the
range of 60 to 85% (Martin & Weber, 2000; Oldenburg et al., 2007; Wooster et al.,
1995). BRCA2 encodes a protein of about 3418 amino acids, which has a role in
DNA recombination and repair pathways, interacting with other genes described in
this section, such as including CHEK2 and ATM, and it may possibly be involved in
cytokinesis (Martin & Weber, 2000; Ben Zhang et al., 2011).
p53: TP53 or p53, also known as “the guardian of the genome” is a commonly
altered gene in most human cancers. It is localised on chromosome 17p13.1 and it is
involved in inducing apoptosis or cell-cycle arrest in response to cellular stress,
through modulation of transcription of about a dozen genes. It is one of the high
penetrance risk genes identified in relation to breast cancer since inheritance of a p53
mutant allele causes Li-Fraumeni syndrome, which involves multiple early-onset
46 Chapter 2: Literature Review
cancers, including breast cancer in adulthood. 90% of all mutations for p53, which
account for over 2,000 in total, are located in exons 5 to 8 of the gene and more than
90% of mutation carriers are expected to develop cancer by age 70. Patients with
ductal and medullary tumours below 60 years of age typically carry mutations
located in these genomic regions of the p53 gene and they are about 30 to 50% of
sporadic breast cancer cases. It has also been studied as a marker for breast cancer-
specific mortality, showing an increase of 2.27-fold in overall relative risk. Germline
mutations of ATM and CHEK2 required for p53 activation also appear to increase
susceptibility to breast cancer (Martin & Weber, 2000; Oldenburg et al., 2007; Ben
Zhang et al., 2011).
ERBB2 (HER2): ERBB2, also known as HER2/neu, is a proto-oncogene
localised on chromosome 17q21.1. It encodes the human epidermal growth factor
receptor 2, which is a transmembrane tyrosine kinase receptor, a member of a family
of growth factor receptors that also includes EGFR (ERBB1), ERBB3 (HER3) and
ERBB4. HER2 is part of the RAS-MEK and AKT cellular pathway, it does not bind to
any particular ligand and therefore it requires interaction with other members of the
family, mainly HER3, to perform its cellular activity (See Figure 2-3). 20% of breast
cancers have HER2/neu amplification and/or overexpression of HER2 protein, which
correlates with these tumours being more aggressive and resulting in a reduced
patient’s survival time. It is well known and studied because it is the target of
specific therapy. Trastuzumab (Herceptin®) is a humanised monoclonal antibody
that binds to ERBB2 and it is used as an adjuvant therapeutic agent. It reduces
recurrence of tumours and prolongs survival when combined with chemotherapy in
patients who are HER2/neu positive (Abeloff et al., 2008).
Chapter 2: Literature Review 47
Figure 2-3 ERBB2 Receptor and intracellular signalling pathways. Reproduced from Johnston & Dowsett, 2003. Copyright © 2003, with permission from Nature
Publishing Group.
PTEN: PTEN, phosphatase and tensin homolog, is a tumour suppressor gene
localised on 10q23.3, which inactivates the signal of phosphatidylinositol 3-kinase
(PI3K). PI3K produces phosphatidylinositol-3,4,5-triphosphate, a second messenger
responsible of the activation of AKT and other pathways involved in cellular
proliferation and apoptosis inhibition. High penetrance germline mutations of PTEN
produce inherited cancer predisposition through Cowden syndrome (CS). CS is a
clinical syndrome featuring a high incidence of neoplasms which include breast
carcinoma. Female patients carrying a PTEN mutation have a 25 to 50% lifetime
breast cancer risk. This gene usually becomes inactive through point mutations and
methylation, which causes genetic instability. Although 11 to 41% of sporadic breast
cancers cases show loss of heterozygosity for PTEN, to date no somatic mutations in
48 Chapter 2: Literature Review
the PTEN gene have been identified in the remaining allele or in this gene in breast
cancer families without features of Cowden syndrome (Martin & Weber, 2000;
Oldenburg et al., 2007; Ben Zhang et al., 2011).
ATM: The ATM gene is located on chromosome 11q22-23. It produces a
protein that plays an important role in sensing and signalling DNA double-strand
breaks and genome instability. It becomes very active when cells are irradiated and it
autophosphorylates, regulating other tumour suppressor proteins, including tumour
suppressor proteins p53, BRCA1 and checkpoint kinase CHEK2. Homozygous or
heterozygous mutations in the ATM gene are associated with ataxia telangiectasia
(AT), an autosomal recessive disorder presenting with movement disorder (ataxia)
secondary to cerebellar degeneration, telangiectasia, immunodeficiency,
chromosomal instability and increased susceptibility to cancer. AT female patients
have an estimated relative risk for breast cancer of about 2.3. No germline mutations
in the ATM gene have been identified in studies of sporadic breast cancer cases;
haplotype analysis of common variants for the ATM gene suggested a slight
association with breast cancer risk but large population studies are required for
further validation of these results (Einarsdottir et al., 2006; Oldenburg et al., 2007;
Szabo et al., 2004).
CHEK2: The tumour suppressor gene CHEK2 is located on chromosome
22q12.1. It has been found to confer low to moderate risk for breast cancer, due to
moderate penetrance and it is involved in DNA repair of double strand-breaks. The
CHEK2 protein is a G2 check point kinase that requires phosphorylation by ATM,
usually after exposure to ionising radiation, and it also phosphorylates other cell
cycle proteins such as BRCA1 and p53. A 1100delC mutation of CHEK2 was found
in a patient with Li-Fraumeni syndrome with no mutation in TP53 (Li-Fraumeni
variant syndrome) and this variant was detected in about 1% of the healthy control
population and about 1.5 to 3 times more commonly in patients with breast cancer.
Furthermore, a two-fold increase in risk of breast cancer was found in
CHEK2*1100delC mutation carrier patients with no mutation in BRCA1/2 using
segregation analysis (Einarsdottir et al., 2006; Oldenburg et al., 2007; Ben Zhang et
al., 2011).
Chapter 2: Literature Review 49
Table 2-5 Autosomal dominant inherited breast cancer syndromes.
CATEGORY CLINICAL SYNDROME - GENE (Location) % OF
BREAST CANCERS
FUNCTION
BREAST CANCER RISK BY AGE 70
MECHANISM IN SPORADIC
BREAST CANCER
Autosomal Dominant Inherited Cancer Syndromes. Individuals have an increased risk of developing breast cancer after inheritance of a single autosomal dominant mutant gene. It involves usually a single point mutation in a single allele for a tumour suppressor gene.
Li-Fraumeni Syndrome - p53 (17p13.1) < 1% Tumour suppressor gene involved in cell cycle control, DNA replication, DNA repair and apoptosis.
> 90% Mutations in 20%. LOH in 30% to 42%. Most frequent in triple negative cancers.
Li-Fraumeni Variant Syndrome - CHEK2 (22q12.1) ~ 1% Cell cycle checkpoint kinase. Involved in recognition and repair of DNA damage. Activates BRCA1 and p53 by phosphorylation
10 - 20% Mutations are rare (< 5%). Loss of protein expression in 1/3 of cases.
Rare Syndromes: < 1% Cowden Syndrome – PTEN
Peutz-Jeghers Syndrome - LKBI/STK11 Ataxia telangiectasia – ATM
Chapter 2: Literature Review 50
Table 2-6 Familial forms of breast cancer.
CATEGORY CLINICAL SYNDROME - GENE (Location) % OF
BREAST CANCERS
FUNCTION
BREAST CANCER RISK BY AGE 70
MECHANISM IN SPORADIC
BREAST CANCER
Familial Breast Cancer. They occur at higher frequency within a family without a defined pattern of transmission. Usually early age at onset, tumours in two or more relatives and multiple or bilateral tumours. It is likely that familial susceptibility depends on multiple penetrating alleles.
BRCA1 (17q21) < 2% Tumour suppressor gene. Involved in transcriptional regulation and repair of double stranded DNA breaks.
40 - 90% Mutations are rare. Inactivated in 50% of some subtypes by methylation.
BRCA2 (13q12-13) < 1% Tumour suppressor. Involved in transcriptional regulation and repair of double stranded DNA breaks.
30 - 90% Mutations and loss of expression are rare.
Chapter 2: Literature Review 51
As previously stated, breast cancer is a complex disease. It originates from an
interaction between genetic and environmental factors and it involves different types
of tumours derived from tissues in the mammary glands. This might explain why all
of the single genes previously discussed have not shown strong association with the
sporadic forms of the disease. They have different cellular roles as tumour suppressor
genes or proto-oncogenes; hence they could be involved in the development and
progression of sporadic cases of breast cancer. However, in spite of the evidence
about their association to the inherited forms of breast cancer, they do not
sufficiently explain breast cancer aetiology and natural history of breast carcinoma
for all forms of the disease.
Genetic studies on breast cancer have been conducted in different ways to
address a diverse range of research questions. Some of these approaches include
studies on common genetic variants, like single nucleotide polymorphisms (SNPs),
to determine if they play a role in the biogenesis of breast cancer. Additional avenues
of research include gene expression studies aimed to develop molecular profiles that
could be used in the diagnosis or treatment of breast cancer. We will briefly discuss
these two approaches as part of this section of the literature review to present key
findings in the field of genetics in relation to breast cancer. Research focused on
miRNA genes and their role in breast cancer will be covered in section 2.6.3 of this
review to provide a more in-depth view of the subject due to its importance for the
general and specific aims of this study.
2.5.1.1 Single nucleotide polymorphisms and breast cancer
A single base mutation can be classified as a single nucleotide polymorphism
(SNP) when the frequency of the minor allele exceeds 1% in at least one population.
There are around 10 million SNPs in the human genome; about one SNP every 100
to 400 base pairs and about 56% of SNPs have been successfully typed. They form
haplotypes, which comprise genetic variants on the same chromosome that are
inherited together having little chance of recombination. They tend to be transmitted
as blocks within small chromosomal regions, that is, they tend to be in linkage with
Chapter 2: Literature Review 52
one another. Linkage disequilibrium is a term used to define the correlation between
SNPs at nearby sites descended from single, ancestral chromosomes. This allows the
prediction of the genotypes at a large number of SNP loci from identification of
representative SNPs called “tag SNPs” or “haplotype tag SNPs” (Brown, 2007; Dutt
& Beroukhim, 2007; LaFramboise, 2009; Reich et al., 2001).
It is very likely that more than a single gene variant is required to address
tumour heterogeneity in breast cancer. Therefore, genome-wide association studies
(GWAS) have been conducted to try to identify genes and genetic variants associated
with this particular disease. Results from 14 GWAS conducted between 2007 and
2010 show association between breast cancer incidence in different cohorts and 49
low penetrance genetic variants of different genes or loci. Seven single nucleotide
polymorphisms (SNPs) are the most common variants found from these GWAS and
they include: Two single nucleotide polymorphisms in intronic regions of fibroblast
growth factor receptor 2 (FGFR2) gene: rs1219648 and rs2981579 (Fletcher et al.,
2011; Hunter et al., 2007; Li et al., 2011; G. Thomas et al., 2009; Turnbull et al.,
2010) and; 5 SNPs located in intergenic regions. The five intergenic SNPs are one
SNP on chromosome 2q35 between TNP1 and DIRC3 (rs13387042), one SNP on
chromosome 3p24.1 near to the SLC4A7 gene (rs4973768), one SNP on chromosome
5q11.2 between the RPL26P19 and MAP3K1 genes (rs889312), one SNP located on
chromosome 8q24.21 between the SRRM1P1 and POU5F1B genes (rs1562430) and
one SNP located on chromosome 16q12.1-2 between the TOX3 and CHD9 genes
(rs3803662) (Easton et al., 2007; Fletcher et al., 2011; Li et al., 2011; Stacey et al.,
2007; G. Thomas et al., 2009; Turnbull et al., 2010). These variants are located in
relation to genes that are involved in molecular functions or cellular processes linked
to cancer development and their roles remain unclear because they are part of
complicated gene regulatory networks which are still under investigation.
In addition, due to the limitations of the platforms used to undertake GWAS
research, primarily microarray, the full array of human polymorphisms have not been
assessed for association with breast cancer. Many of the intergenic SNPs may have
been associated with breast cancer through linkage to functional SNPs in gene
sequences, while other areas of the genome may have had very little assessment due
Chapter 2: Literature Review 53
to the lack of a known common SNP in the region to be included on GWAS
microarrays. Regions previously thought to be intergenic and thus of less interest
during the development of GWAS microarrays may contain microRNA genes and
similar non-protein coding sequences nonetheless important for control of cellular
growth. Lack of assessment of previously unknown SNPs and microRNA containing
regions means that research examining the role of SNPs in breast cancer aetiology
should take these into consideration to improve our understanding of the disease.
Indeed, this is one of the main rationales for the development of this research.
2.5.1.2 Molecular classification of breast cancer
Molecular classification of breast carcinoma was developed to try to determine
suitable therapy and predict prognosis and response to therapy. As a result, 5
different patterns of gene expression have been identified using microarray
technologies (Blenkiron et al., 2007; Perou et al., 2000; Robbins et al., 2010; Schnitt,
2010; Sørlie et al., 2003):
a. Luminal A: They account for 40 to 55% of no special type (NST) cancers.
The category consists of breast cancers that are ER-positive and
HER2/neu-negative. They express genes under the control of ER and also
genes characteristic of luminal cells. Usually well differentiated, they
occur mainly in postmenopausal women. They are slow growing and
respond well to hormonal treatment.
b. Luminal B: They correspond to about 15 to 20% of NST cancers and they
are also known as triple-positive cancers. They are ER-positive, usually
highly proliferative and poorly differentiated (having high grading) and
overexpress HER2/neu. Luminal B tumours are likely to respond well to
chemotherapy and are often lymph node positive.
54 Chapter 2: Literature Review
c. Normal breast-like: They are about 6 to 10% of NST breast cancers. They
are well differentiated, ER-positive, HER2/neu-negative and they are very
similar in gene expression to normal breast tissue.
d. Basal-like: 13 to 25% of no special type breast cancer tumours fall within
this group. They are ER-negative, progesterone receptor (PR)-negative and
HER2/neu-negative and they have a similar gene expression pattern to
myoepithelial and putative stem cells. They can be grouped with other
types of breast carcinomas that are “triple-negative” carcinomas such as
medullary carcinomas and metaplastic carcinomas. Basal-like carcinomas
are poorly differentiated and have a high proliferation rate. They usually
metastasise to brain and viscera. Although they are associated with poor
prognosis, about 15 to 20% of cancers in this subgroup are sensitive to
chemotherapy and likely to be cured. They are frequently associated with
BRCA1 dysfunction, which occurs in about 80% of basal like tumours.
e. HER2 Positive: This subgroup comprises about 7 to 12% of ER- negative
no special type carcinomas, which also over express HER2/neu protein.
They are poorly differentiated, have a high proliferation rate and they
usually are associated with a high frequency of brain metastasis (Blenkiron
et al., 2007; Perou et al., 2000; Robbins et al., 2010; Schnitt, 2010; Sørlie
et al., 2003).
As a result of this molecular profiling classification, two main multigene
prediction score sets were designed and validated and they are pending approval
from the U.S. Food and Drug Administration (FDA). Both methods, the 21-gene
predictor set, commercially available under the name of Oncotype Dx® (Genomic
Health, Inc., Redwood City, CA), and the technology 70-gene predictor set,
commercially available as MammaPrint® (Agendia, Inc., Amsterdam, The
Netherlands) (Paik et al., 2004; Schnitt, 2010; van de Vijver et al., 2002) are based
on quantitative real time polymerase chain reaction (qPCR) and microarray
technologies, respectively. Oncotype Dx® produces a score for recurrence of breast
Chapter 2: Literature Review 55
carcinoma and it classifies patients as low, moderate or high risk based on testing for
16 target transcripts, including ER, PR and HER2. The TAILORx (Trial Assigning
Individualized Options for Treatment (Rx)) trial is currently in progress and it aims
to validate the “recurrence score”, which also appears to correspond to likelihood of
response to chemotherapy and endocrine therapy. On the other hand, the
MammaPrint® predictor set, currently under validation in the ongoing MINDACT
(Microarray In Node Negative Disease may Avoid Chemotherapy) trial, aims to
identify patients with good and poor prognostic signatures, likely to benefit from the
use of adjuvant therapy. Thus, molecular classification and profiling of patients using
the multigene predictor sets is currently being used to try to individualise therapy for
breast cancer patients to improve outcomes (Abeloff et al., 2008; Galanina, Bossuyt,
& Harris, 2011; Paik et al., 2004; Schnitt, 2010; van de Vijver et al., 2002).
2.6 MICRORNAS AND BREAST CANCER
2.6.1 MicroRNA biogenesis and functions.
MicroRNAs (miRNAs) are small non-coding single stranded RNA molecules
of about 21 to 25 nucleotides in length, involved in gene regulation at the
translational level. They are part of a family of small RNAs that also include small
nucleolar RNA (snoRNA), short interfering RNA (siRNA) and piwi-interacting RNA
(piRNA). miRNAs are estimated to account for about 1% of the human genome and
they were initially identified in Caenorhabditis elegans (R. C. Lee, Feinbaum, &
Ambros, 1993; Reinhart et al., 2000). They regulate important cellular processes like
cell growth, differentiation and cell survival and therefore they have been linked to
cancer and tumour development (Thalia A. Farazi et al., 2011; Robbins et al., 2010;
Westholm & Lai, 2011; Zuoren Yu et al., 2010).
52% of miRNA genes are located in different non-random positions within the
genome in regions of chromosomal instability or cancer-associated regions. The
majority of miRNA genes are found in intergenic regions, 1 kb away from known
56 Chapter 2: Literature Review
genes, and about one third of miRNA genes are found in the introns of protein-
coding genes. Many of these genes are transcribed independently and they have
promoter regions that allow gene expression in a cell-type specific manner. Many
others form genomic clusters, about 113 in total which can be up to 51 kb in length,
and they are probably transcribed as a single polycistronic transcript (Thalia A.
Farazi et al., 2011; Lynam-Lennon, Maher, & Reynolds, 2009). 1881 micro RNA
precursors and 2588 mature sequences have been identified for the human genome
according to miRBase (www.mirbase.org), a miRNA database, in their latest release:
miRBase release 21 (June 2014) (Kozomara & Griffiths-Jones, 2011, 2014).
miRNAs are transcribed from long microRNA primary transcripts (pri-
miRNAs) about 1kb in size that contain up to six miRNA precursors (pre-miRNA).
Pre-miRNAs are stem-loop molecules of about 55 to 70 nucleotides in length and
they are produced by canonical and non-canonical pathways. Non-canonical
pathways are also called “mirtron” pathways, which are well described in
invertebrates but they have not been experimentally demonstrated in mammalians or
humans (Ha & Kim, 2014; Westholm & Lai, 2011). In the canonical pathway, pre-
miRNAs are produced when pri-miRNA are cleaved by the complex Drosha –
DCGR8 (DiGeorge syndrome critical region gene 8, also known as pasha) within the
cellular nucleus (Yoontae Lee et al., 2003). Pre-miRNA are then transported to the
cytoplasm by XPO5 (exportin5) and the nuclear protein ran-GTP. In the cytoplasm
pre-miRNAs are processed by DICER1, a ribonuclease type III enzyme, and double-
stranded RNA binding domain (dsRBD) proteins TARBP2 and/or PKRA producing
a duplex molecule that contains both single-stranded mature miRNA strand and a
complementary strand (miRNA*) (Chendrimada et al., 2005; Yi, Qin, Macara, &
Cullen, 2003). The miRNA:miRNA* molecule is loaded into the RNA-induced
silencing complex (RISC), where the mature miRNA sequence is merged into the
Argonaute/EIFC2C (Ago) proteins and the miRNA* is released and degraded (Thalia
A. Farazi et al., 2011; Lynam-Lennon et al., 2009; Westholm & Lai, 2011).
Ago proteins bound to mature miRNA mediate target messenger RNA
(mRNA) recognition and interaction between these three components resulting in
gene regulation. The mRNA target is recognised by pairing of the miRNA seed
Chapter 2: Literature Review 57
region (nucleotides 2 to 8) located in the 5’-end of the miRNA and the
complementary sequence mainly located in the 3’ untranslated region (3’-UTR)
region of target mRNA but binding sites can also found in the coding region
(Khvorova, Reynolds, & Jayasena, 2003; Lai, 2002; Y. Lee, Jeon, Lee, Kim, & Kim,
2002). This process is mediated and regulated by Ago proteins and the mRNA
processing bodies (P-bodies), which are cytoplasmic structures that contain
regulatory proteins from the GW182/TNRC6 family of proteins. If the seed region of
the miRNA and the target region of mRNA are highly complementary, the RISC
complex will cleave the target mRNA, a process known as RNA interference. If there
is not enough complementarity between miRNA seed region and target mRNA
regulation occurs by repression of translation, a process that has not yet been fully
elucidated but that may occur before or after mRNA translation (see Figure 2-4)
(Thalia A. Farazi et al., 2011; Lynam-Lennon et al., 2009; Westholm & Lai, 2011).
58 Chapter 2: Literature Review
Figure 2-4 Copyright © 2006. Canonical pathway of miRNA genesis and mRNA regulation. Reprinted by permission of Nature Publishing Group (Esquela-Kerscher
& Slack, 2006).
Each miRNA may bind to up to 200 gene targets and each gene could have
multiple binding sites for different miRNAs. Therefore they have been reported to
regulate up to 60% of the genes within the human genome and targets for miRNAs
are still under current investigation and validation (Bartel, 2009; Rajewsky, 2006).
Chapter 2: Literature Review 59
2.6.2 MicroRNAs in cancer
It is very likely that miRNAs play an important role in cancer biology because
of the complexity of the molecular processes and mediators involved in miRNA
genesis and miRNA-mRNA regulation and because they are involved in cell
development, proliferation and death. Oncogenic miRNAs, or oncomirs, are known
to regulate cancer pathways and they can control neoplastic development by reducing
the expression of tumour suppressor genes or enhancing expression of certain
oncogenes (Baohong Zhang, Pan, Cobb, & Anderson, 2007).
Research studies have been conducted since microRNAs were initially
described and they have linked miRNAs to different types of cancer. Because of the
large number of miRNA genes currently identified and the large number of different
approaches used to study them, we will focus on some of the most important studies
published to date in relation to different types of cancers; those addressing breast
cancers will be further covered in subsection 2.6.3 of this review.
Initially, research studies focused on the expression patterns of different
microRNAs in relation to cancer. The first reports of miRNA involvement in cancer
date back about 12 years when Calin et al found MIR15 and MIR16 to be deleted or
downregulated in patients with chronic lymphocytic leukaemia (CLL), the most
common form of leukaemia in adults, as well as in prostate cancer cell lines in a
study published in 2002 (Calin et al., 2002). A t(8, 17) translocation in an aggressive
type of B cell leukaemia was also found to produce loss of expression of MIR142
resulting in overexpression of MYC (Gauwerky, Huebner, Isobe, Nowell, & Croce,
1989; Lagos-Quintana et al., 2002). In a review published in 2003, McManus et al
postulated that microRNAs could play a role in cancer as either tumour suppressor
(TS) genes or oncogenes (McManus, 2003). Following this, a study in colorectal
carcinoma identified reduced expression of MIR143 and MIR145 (Michael, O'
Connor, van Holst Pellekaan, Young, & James, 2003) and a different study showed
that reduced expression of MIRLET7A1 was associated with a poor prognosis in lung
cancer (Takamizawa et al., 2004). Downregulation of MIR145 is also a common
60 Chapter 2: Literature Review
finding previously reported in colorectal (Arndt et al., 2009; Michael et al., 2003),
bladder (Chiyomaru et al., 2010), lung (Cho, Chow, & Au, 2009, 2011) and
oesophageal (Kano et al., 2010) cancers and it has been associated with poor
prognosis for cancer patients.
On the other hand, two separate studies identified expression of MIR155 to be
increased in paediatric Burkitt lymphoma (Metzler, Wilda, Busch, Viehmann, &
Borkhardt, 2004) Hodgkin´s lymphoma (HL), primary mediastinal B cell lymphoma
(PMBL), diffuse large B cell lymphoma (DLBCL) and CLL (Eis et al., 2005; Kluiver
et al., 2005).Similarly, Chen et al found markedly elevated levels of MIR21 in
glioblastoma tumour tissue and glioblastoma cell lines (Chan, Krichevsky, & Kosik,
2005). Calin et al determined that microRNAs were frequently located in cancer
associated genomic regions as well as fragile chromosomal sites and they proposed
that microRNAs can act both as tumour suppressor or oncogenes due to the variety
of genes they regulate and the complexity of their networks (Calin, Sevignani, et al.,
2004).
The microRNA 17-92 cluster host gene (MIR17HG) located on chromosome
13q31.3 was the first miRNA cluster to be associated with cancer. The miRNA 17-92
cluster host gene transcript is about 800bp in length and contains six miRNAs:
MIR17, MIR18A, MIR19A, MIR20A, MIR19B1 and MIR92A1, which belong to 4
seed families (miRNA 17, miRNA 18, miRNA 19 and miRNA 92). Based on their
seed sequences, two related paralogues of this cluster have also been identified in the
human genome: miRNA 106b-25 cluster located on chromosome 7 (7q22.1) in the
13th intron of gene MCM7 and miRNA 106a-363 cluster located on chromosome X
(Xq26.2) (Ventura et al., 2008). In 2004 Ota et al observed amplification of
MIR17HG in DLBCL and lymphoma cell lines which was corroborated by another
study in 2005 (L. He et al., 2005; Ota et al., 2004). He et al demonstrated increased
expression of MIR17HG in B cell lymphomas in cell lines and tumour samples and
they also found this cluster gene promoted accelerated tumour development in a
murine model. Therefore they named MIR7HG as oncomir-1 (L. He et al., 2005). It
has also been implicated to play a role in different types of cancer including lung
Chapter 2: Literature Review 61
cancer, breast cancer, colon cancer gastric cancer and pancreatic cancer (Hayashita et
al., 2005; Mendell, 2008; Mogilyansky & Rigoutsos, 2013; Volinia et al., 2006).
Research studies have been conducted to identify specific miRNA expression
profiles for different types of cancer. For example, Lu et al analysed the expression
of 217 microRNAs using bead-based flow cytometry in 334 samples of leukaemia
and solid cancers and they were able to correlate miRNA expression with
developmental lineage and differentiation state (J. Lu et al., 2005). Using a
microarray platform, Volinia et al investigated the miRNA profile of 363 cancer
samples, including B-cell chronic lymphocytic leukaemia (BCLL), breast, colon,
lung, pancreas, prostate and stomach cancer to compare them to 177 normal tissue
samples. 228 miRNA genes were evaluated and they identified 36 microRNA genes
to be over expressed and 21 to be downregulated in cancer cells versus normal
tissues (Volinia et al., 2006). Many other profiling studies have been conducted in
different types of cancer including chronic lymphocytic leukaemia (CLL) (Calin,
Liu, et al., 2004), papillary thyroid carcinoma (H. He et al., 2005), breast cancer
(Iorio et al., 2005), colon cancer (Cummins et al., 2006), glioblastoma (Ciafre et al.,
2005), lung cancer (Yanaihara et al., 2006), hepatocellular carcinoma (Murakami et
al., 2006) and pancreatic tumours (Roldo et al., 2006). All these studies consistently
show that expression patterns for cancer tissues involve both overexpressed and
downregulated miRNA genes which may corroborate what was previously proposed
by McManus in 2003 (McManus, 2003). Some of the miRNAs that were most
commonly identified in these studies are shown in Table 2-7.
62 Chapter 2: Literature Review
Table 2-7 Some of the miRNAs and their target genes most commonly found in relation to cancer.
miRNA Upregulated Downregulated Predicted target genes References MIR17HG Lymphomas, lung cancer, breast
cancer, colon cancer, gastric cancer, pancreatic cancer
E2F1, BCL2L11, PTEN
He et al., 2005; Iorio et al., 2005; Mendell , 2008; Ota et al., 2004; Volinia et al., 2006
MIR21 Brain cancer, breast cancer, glioblastomas, acute myeloid leukaemia, colon cancer, pancreatic cancer, prostate cancer, liver cancer
BCL2, PTEN, PDCD4, TPM1, TIMP3
Ciafre et al., 2005; Chan et al., 2005; Iorio et al., 2005; Murakami et al., 2006; Volinia et al., 2006
MIR221 Brain Cancer, colon Cancer, pancreatic cancer, prostate cancer, papillary thyroid carcinoma
CDKN1B, CDK1C, PTEN, TIMP3
Ciafre et al., 2005; Chan et al., 2005; Cummins et al., 2006; He et al., 2005; Roldo et al., 2006; Volinia et al., 2006
MIR155 Lymphomas Breast Cancer MAF, AGTR1 Eis et al., 2005, Kluiver et al., 2005; Iorio et al., 2005
MIR15A Chronic lymphocytic leukaemia, prostate cancer, multiple myeloma
BCL2, WT1, RAB9B Calin et al., 2002; Calin et al., 2004
MIR16-1 Chronic lymphocytic leukaemia, prostate cancer, multiple myeloma
BCL2, WT1, RAB9B Calin et al., 2002; Calin et al., 2004
MIR34A Pancreatic cancer, colon cancer, breast cancer, liver cancer
CDK4, CDK6, CCNE2, E2F3, MET
Blenkiron et al, 2007; Cummins et al., 2006; Iorio et al., 2005; Farazi et al., 2011; Murakami et al., 2006; Roldo et al., 2006
MIR145 Breast Cancer, colorectal cancer ERG Cummins et al., 2006; Iorio et al., 2005; Michael et al., 2003
miRNA Let-7 family Lung cancer, breast cancer, haematopoietic malignancies
RAS family, MYC, HMGA2
Blenkiron et al., 2007; Yanaihara et al., 2006;
Chapter 2: Literature Review 63
MicroRNAs have been used in cancer diagnosis through the following
approaches: Profiling of tumours based on differential expression of miRNAs can be
used to identify tissue of origin in cancers of unknown primary site, to identify
tumour subtype and/or predict prognosis (Calin & Croce, 2006; J. Lu et al., 2005;
Paranjape, Slack, & Weidhaas, 2009; Baohong Zhang, Pan, et al., 2007). Profiling of
circulating blood or tumour derived exosomal miRNAs has also been studied for
their potential use as a non-invasive method for early detection of cancer (X. Chen et
al., 2008; Heneghan et al., 2010; Kosaka, Iguchi, & Ochiya, 2010; Mitchell et al.,
2008; Roth et al., 2010; W. Zhu, Qin, Atasoy, & Sauter, 2009).
It has been recognised that changes in miRNA synthesis and expression in
relation to cancer could occur through many different mechanisms and some of these
include: point mutations in miRNA and mRNA sequences, epigenetic changes like
mRNA gene methylation, loss or mutation in the promoter regions for specific
miRNA clusters and alterations in pathway related RNA binding protein (RBP)
(Thalia A. Farazi et al., 2011; H.-C. Lee et al., 2011; Lynam-Lennon et al., 2009;
Ryan, Robles, & Harris, 2010; G. Sun et al., 2009; Westholm & Lai, 2011).
Therefore, the study of single nucleotide polymorphisms (SNPs) in miRNA
sequences has emerged as an approach to investigate cancer predisposition, as well
as explain changes in miRNA biogenesis and function (Paranjape et al., 2009; G. Sun
et al., 2009).
1899 SNPs in 961 pre-miRNAs, 601 miR-SNPs in 470 miRNA mature
sequences and 203 miR-SNPs located in the seed region of 170 mature miRNAs
were identified in 2013 by Han et al (M. Han & Zheng, 2013) in a genome-wide
scan analysis of the SNPs contained in the NCBI dbSNP database (build 137 for
humans) (Smigielski, Sirotkin, Ward, & Sherry, 2000) and the genomic locations and
sequences of pre-miRNAs and mature miRNAs from the miRBase (release 18.0,
November 2011) (Kozomara & Griffiths-Jones, 2011). This highlights miRNA SNPs
(miR-SNPs) as the most common type of genetic variation in miRNA sequences and
neighbouring regions and they have been determined to alter miRNA biogenesis and
processing (R. Duan, Pak, & Jin, 2007; Gottwein, Cai, & Cullen, 2006; Ryan et al.,
2010; G. Sun et al., 2009), which could have an effect on numerous targets and
Chapter 2: Literature Review 64
diverse functional consequences. Therefore miR-SNPs may be ideal candidate
biomarkers to use in early diagnosis of cancer, to determine prognosis and to predict
outcome in cancer patients, and this hypothesis has obtained significant support from
a number of studies in different types of cancer. Some of the most relevant miR-SNP
studies in various types of cancer will be the focus of subsection 2.6.2 since similar
studies in relation to breast cancer will be discussed further in subsection 2.6.3 of this
review.
In 2008, a miR-SNP in MIR196A2 (rs11614913) which is present in the mature
sequence was found to increase processing of the mature miRNA and be associated
with significantly decreased survival in patients with non-small cell lung cancer (Z.
Hu et al., 2008). rs2910164 present in the precursor sequence of MIR146A was found
to reduce the amount of both pre- and mature MIR146A and it was also shown to
affect the Drosha/DGCR8 processing step (Z. Hu et al., 2009; Jazdzewski et al.,
2008; Shen et al., 2008). Hu et al also showed that rs11614913 (MIR196A2),
rs2910164 (MIR146A), rs2292832 (MIR149) and rs3746444 (MIR499A) were
associated with increased breast cancer risk in Chinese women (Z. Hu et al., 2009).
Similarly, two separate studies showed rs2910164 to be associated with papillary
thyroid carcinoma (Jazdzewski et al., 2008) and hepatocellular carcinoma (T. Xu et
al., 2008). Ye et al showed that rs6505162 located in the precursor of MIR423 to be
significantly associated with oesophageal cancer risk (Ye et al., 2008). All of the
previously mentioned studies show that the effects of miR-SNPs can be modulated in
a cell type-specific manner, implying that not all functional miR-SNPs affect cancer
risk globally. However, there are still a few issues to overcome in regards to miR-
SNP studies in relation to cancer and they include the following: Minor allele
frequencies of many identified miR-SNPs are still unknown, pointing to the need to
conduct population studies to determine whether these are polymorphic in specific
populations; most studies focus on the analysis of pre and mature regions of miRNAs
because further clarification of the extent of the pri region is still required for many
miRNAs to determine the true extent of miRNA-related genetic variation;
identification of tag SNPs for miRNA-related SNPs will be useful to identify miR-
SNPs which are in linkage disequilibrium and therefore inherited together; and
Chapter 2: Literature Review 65
finally the inclusion of more miR-SNPs in genome wide association studies (GWAS)
to identify low penetrance susceptibility mutations.
2.6.3 MicroRNAs and breast cancer
In an effort to understand the role of miRNAs in breast cancer, they have been
studied using microRNA expression evaluation in different breast cancer cell lines
and normal and breast cancer tissue samples. This approach is potentially valuable as
miRNAs could be used as a diagnostic tool for prediction of breast cancer
development and tumour profiling, as therapeutic targets for cancer treatment and as
prognostic markers to predict metastasis and to evaluate treatment response and
patient survival (Paranjape et al., 2009).
In 2005, Iorio et al examined expression of 246 miRNA using 76 breast tumour
samples and they identified 29 miRNAs that showed significantly different levels of
expression in cancer samples versus normal breast tissue (Iorio et al., 2005). They
identified 5 miRNAs to be the most consistently deregulated across all tumour
samples: MIR10B, MIR125B1 and MIR145 were significantly down-regulated and
MIR21 and MIR155 were significantly up-regulated, suggesting their role as tumour
suppressor genes or oncogenes, respectively. However they failed to identify
miRNAs that were differentially expressed between tumour subtypes. A following
study conducted by Mattie et al in 2006 was able to distinguish HER2+/ER- and
HER-/ER+ breast cancer using profiling of 204 miRNAs in 20 tumour samples. This
study also identified a subset of miRNAs specific to HER2+ tumours (MIRLET7F1,
MIRLET7G, MIR107, MIR10B, MIR126, MIR154 and MIR195) and to ER+/PR+
tumours (MIR142 5p, MIR200A, MIR205 and MIR25) (Mattie et al., 2006).
A further study conducted by Blenkiron et al using a bead-based flow
cytometric platform showed that miRNAs are differentially expressed between
molecular subtypes of breast cancer (Luminal A, Luminal B, Basal-like and HER2
positive), previously described in section 2.5.1.2 on molecular classification of breast
66 Chapter 2: Literature Review
cancer (Blenkiron et al., 2007). Blenkiron et al also evidenced changes in miRNA
biosynthesis in 93 frozen breast cancer tumour samples due to changes in gene
expression of post-transcriptional mediators. Basal-like, HER2 positive and luminal
B type tumours showed down regulation of DICER1 expression and a higher
expression of AGO2. Oestrogen receptor (ER) status was also found to be associated
with gene expression changes and ER negative tumours showed AGO2 and DROSHA
to be up regulated and DICER1 to have lower levels of gene expression (Blenkiron et
al., 2007).
Sempere et al found that altered microRNA expression corresponded to
particular epithelial cell subpopulation using microarray analysis, northern blot and
in-situ hybridization (ISH) with archived formalin-fixed paraffin-embedded breast
cancer tissue samples breast cancer. They found MIR145 and MIR205 to be restricted
to the myoepithelial/basal cell compartment of normal ducts and lobules and their
expression was significantly down-regulated in tumour breast samples. On the other
hand MIR21 was found to be present in luminal epithelial cells in normal breast
tissue and its expression significantly increased in breast cancer samples compared to
normal tissue (Sempere et al., 2007).
Lowery et al validated three different miRNA cluster signatures to be
predictive of oestrogen receptor (ER) (MIR342, MIR299, MIR217, MIR190,
MIR135B and MIR218), progesterone receptor (PR) (MIR520G, MIR377,
MIR518A1, MIR520F and MIR520C) and HER2/neu receptor (MIR520D, MIR181C,
MIR302C, MIR376B and MIR30E) status in breast cancer using artificial neural
network algorithms, miRNA microarray and quantitative PCR (qPCR) (Lowery et
al., 2009). There is room for debate in regards to some of the findings for some of
the miRNA signatures previously mentioned. For instance, Blenkiron et al showed
MIR342 to be downregulated in Basal-like and HER2 positive type tumours
(Blenkiron et al., 2007). There is also some contradicting evidence in relation to
HER2 positive tumours; since the cluster microRNA signature developed and
validated by Lowery et al found MIR342 expression to be higher in ER positive,
HER2/neu positive and luminal B type tumours and lowest in Basal-like tumours
(Lowery et al., 2009). Finally, Buffa et al (Buffa et al., 2011) found that high levels
Chapter 2: Literature Review 67
of expression of MIR342 were associated with good prognosis in ER negative
tumours. Target genes for MIR342 include RRM2, PGM2L1 and TLE1, which could
be potential therapeutic targets for breast cancer. Some of the differences in the
results of these studies could be the result of differences in methods of collection,
preservation and storage of primary tumours used, resulting in RNA degradation and
distortion of qPCR results. There are also differences in the experimental methods
used and statistical analysis of results which could lead to the inconsistencies in
findings.
We will discuss some of the miRNAs most commonly identified to have a role
in breast cancer development before we move on to discuss findings in relation to
miR-SNPs and breast cancer. These miRNAs are summarised in Table 2-8 along
with some of their gene targets.
As mentioned before, MicroRNA-10b (MIR10B) has been described as an
oncogene and it was initially identified in the work conducted by Iorio et al (Iorio et
al., 2005). They found MIR10B to be down-regulated across tumour samples but
further studies showed different findings. Studies by Mattie et al (Mattie et al., 2006)
and Liu et al (Y. Liu et al., 2012) showed MIR10B to be significantly up-regulated
particularly in metastatic breast cancer. Differences in the results found could be the
result of the primary sample used and methodological approach. Studies conducted
by Iorio et al and Mattie et al both used cryopreserved tumour samples, while Liu et
al used freshly isolated tumour tissue. Moreover, there were significant differences in
their methods because not every study considered important features such as tumour
size, ERBB2 status, ER/PR positivity, tumour staging and grading and lymph node
metastasis in their analysis. Target genes for MIR10B include FLT1, BDNF,
HOXD10, CDH1 and TGFB1 (X. Han et al., 2014; Y. Liu et al., 2012; Mattie et al.,
2006).
68 Chapter 2: Literature Review
Table 2-8 Summary of the most significantly associated microRNAs in breast cancer and their target
genes
MiRNA PREDICTED TARGET GENES AUTHOR MIR10B FLT1
BDNF HOXD10
CDH1 TGFB1
Iorio et al., 2005 Volinia et al., 2006
Blenkiron et al., 2007 Ma et al., 2007 Liu et al., 2012
MIR17HG NCOA3 E2F1 MYC RB1
CCND1
O’Donnell et al., 2005 Hossain et al., 2006
Yu et al., 2008 Leivonen et al., 2009
Mogilyansky et al., 2013 MIR125A - MIR125B ERBB2
ERBB3 YES1
Iorio et al., 2005) Volinia et al., 2006
ETS1 AKT3
FGFR2 MAP3K10 MAP3K11 MAPK14
MIR145 MYCN Iorio et al., 2005 Volinia et al., 2006
Blenkiron et al., 2007 Sempere et al., 2007
FOS CCND2
MAP3K3 MAP4K4
MIR155 SOCS1 TP53INP1
VHL APC
WEE1 RAB6A RAB6C
Iorio et al., 2005 Volinia et al., 2006
Blenkiron et al., 2007 Jiang et al., 2010 Zhang et al., 2013 Kong et al., 2014
MIR21 Caspase protease family Iorio et al., 2005 Volinia et al., 2006
Si et al., 2007 Zhu et al., 2007
TPM1 PDCD4 PTEN BCL2
TGFB1 RAB6A RAB6C
Chapter 2: Literature Review 69
As was mentioned in subsection 2.6.2 of this review the microRNA 17-92
cluster host gene (MIR17HG) has been associated with different types of cancer
including lymphomas, lung cancer, colon cancer, gastric cancer and pancreatic
cancer. MIR17HG was the first miRNA cluster to be identified in relation to cancer
and subsequently named oncomir-1 (L. He et al., 2005; Ota et al., 2004). It has been
found to inhibit tumour growth in breast cancer cell lines and it targets cell cycle
regulators including NCOA3, E2F1, MYC, RB1 and CCND1(Hossain, Kuo, &
Saunders, 2006; Leivonen et al., 2009; Z. Yu et al., 2008) .
He et al demonstrated increased expression of MIR17HG in B cell lymphomas
in cell lines and tumour samples they also found this cluster gene to promote
accelerated tumour development in a murine model. (L. He et al., 2005) and it has
also been implicated to play a role in different types of cancer including lung cancer,
breast cancer, colon cancer gastric cancer and pancreatic cancer (Hayashita et al.,
2005; Mendell, 2008; Mogilyansky & Rigoutsos, 2013; Volinia et al., 2006)
Mattie et al found microRNA-125a (MIR125A) and miRNA-125b (MIR125B)
to be downregulated in in HER2+ breast cancer. Both miRNAs are considered to be
tumour suppressor genes and they target important genes including ERBB2 (HER2),
ERBB3 (HER3) and FGFR2 (Scott et al., 2007).Overexpression of ERBB2 is also
associated with poor prognosis for breast cancer patients (Ross & Fletcher, 1998).
MIR145 has been previously reported to play a role in cancer biogenesis and
progression for other types of cancer (Arndt et al., 2009; Chiyomaru et al., 2010; Cho
et al., 2009, 2011; Kano et al., 2010; Michael et al., 2003). As previously mentioned,
it has also been found to have a tumour suppressor role in breast tissue particularly in
the myoepithelial/basal cell compartment and it is found to be downregulated in
breast cancer tissue samples (Iorio et al., 2005; Sempere et al., 2007; Volinia et al.,
2006). Research using breast cancer cell lines showed it regulates some important
genes involved in modulation of apoptosis, shown in Table 2-8 (Spizzo et al., 2010;
S. Wang et al., 2009). In vitro studies by Michael et al using MCF-7 and T47-D cell
lines showed down regulation of MIR145 (Michael et al., 2003). Finally studies
70 Chapter 2: Literature Review
conducted by Goette et al (Goette et al., 2010) and Radojicic et al (Radojicic et al.,
2011) showed down-regulation of MIR145 to be associated with a more aggressive
behaviour of breast cancer based on results performed in both breast cancer cell line
and tissue samples.
miRNA-155 (MIR155) has been determined to be upregulated in breast cancer
according to studies previously mentioned (Iorio et al., 2005; Volinia et al., 2006). It
is considered to be an oncogene because it has been shown to target tumour
suppressor genes including SOCS1, TP53INP1 and VHL according to studies
performed both in breast cancer cell lines and tumour tissue samples. It has also been
associated with poor prognosis in patients with triple negative breast cancer (Czyzyk-
Krzeska & Zhang, 2014; Jiang et al., 2010; Kong et al., 2014; C.-M. Zhang, Zhao, &
Deng, 2013)
miRNA-21 (MIR21) has been found to be consistently up-regulated in different
types of cancer (Chan et al., 2005; Y. Hu et al., 2011; Tetzlaff et al., 2007; Volinia et
al., 2006). In 2005 Iorio et al identified MIR21 to be up-regulated in breast cancer
tumour samples as well (Iorio et al., 2005). As previously mentioned, Sempere et al
found it to be expressed in luminal epithelial cells and to be further increased in
malignant tissue when compared to normal samples (Sempere et al., 2007) and
Radojicic et al found it to be overexpressed in triple negative tumours (Radojicic et
al., 2011). Moreover, overexpression of MIR21 has been associated with metastasis
and poor prognosis for breast cancer sufferers (Yan et al., 2008). Using cell culture
and a xenograft carcinoma mouse model, Si et al identified that knock-down of
MIR21 resulted in increased apoptosis and reduced tumour proliferation (Si et al.,
2007). This group established that the oncogenic role of MIR21 was the result of the
inhibition of a tumour suppressor gene (TPM1) (S. Zhu, Si, Wu, & Mo, 2007). Other
studies have identified additional targets of MIR21 in breast cancer including
PDCD4, Caspase protease family, BCL2 and PTEN amongst others (Thalia A. Farazi
et al., 2011; Frankel et al., 2008; Robbins et al., 2010; Z. X. Wang, Lu, Wang,
Cheng, & Yin, 2011; Wen et al., 2007).
Chapter 2: Literature Review 71
Other miRNA findings in relation to breast cancer include the following:
MIR222 host gene (MIR222HG) targets CDK inhibitor to regulate cell cycle both in
breast cancer MCF-7 cell line and HER2/neu positive breast tumours and it is also
associated with Tamoxifen resistance (Thalia A. Farazi et al., 2011; Negrini & Calin,
2008; Robbins et al., 2010; Zuoren Yu et al., 2010); epigenetic modification such as
inactivation of expression by methylation of MIR9-1, MIR124-2HG, MIR148,
MIR152 and MIR663 has been described in breast cancer (Lehmann et al., 2008) and
copy number alteration of miRNA genes has also been evidenced in breast cancer
using array-based comparative genomic hybridization (L. Zhang et al., 2006). Other
miRNAs have been associated with metastasis including the microRNA let-7 family,
MIR10B, MIR9-1 and MIR31. On the other hand MIR335, MIR206 and MIR126 have
been implicated to have a role as breast cancer metastasis suppressor miRNAs
(Thalia A. Farazi et al., 2011; Negrini & Calin, 2008; Robbins et al., 2010; Zuoren
Yu et al., 2010).
MicroRNAs mentioned in this literature review have been evaluated in
different studies using breast cancer cell lines and breast cancer tissue samples,
however recent studies have been conducted to try to measure microRNA expression
in blood products. Evaluation of circulating microRNAs in breast cancer patients
could be used for early diagnosis, follow up and to characterise novel miRNAs.
MicroRNAs are remarkably stable molecules and therefore it was hypothesised that
they should be present in the circulation of cancer patients. Heneghan et al
investigated whether cancer specific miRNAs (MIR10B, MIR16, MIR21, MIR145,
MIR155, MIR195 and MIRLET7A1) were detectable and expression was altered in
serum, plasma or whole blood obtained from cancer patients in comparison to age-
matched controls. They identified whole blood as the best source of circulating
miRNAs and using quantitative PCR (qPCR) they also showed changes in miRNA
expression for MIR195 and MIRLET7A1 when comparing cancer patients to controls
and also when miRNA expression was measured before and after surgical treatment
(Heneghan et al., 2010). However, Roth et al (Roth et al., 2010) showed contrasting
results when they evaluated levels of MIR10B, miR34A, MIR141 and MIR155 in
blood serum of post-operative breast cancer patients and in breast cancer cell lines.
Using blood serum, they found MIR155 in breast cancer patients with no metastasis
72 Chapter 2: Literature Review
and MIR-10B, MIR34A and MIR155 in patients with metastatic breast cancer were
significantly higher than healthy controls. Therefore differences in results obtained
for both studies using similar miRNA panels, need to be addressed, primarily by
evaluating these circulating microRNAs using bigger and more thoroughly
characterised cohorts.
Using a different approach, Wu et al (Q. Wu et al., 2011) performed next-
generation sequencing of microRNAs, comparing breast tissue samples and normal
breast tissue and validated the top five miRNAs found to be altered more than
fivefold using RT- PCR in serum samples from breast cancer patients and controls.
This study found significantly elevated levels of MIR21 and MIR29A in breast cancer
patients compared to controls, which could be used as early non-invasive markers for
the detection of breast cancer, a finding that requires further validation in a bigger
sample population and correlation with clinical features if it is to translate to clinical
practice (Budhu, Ji, & Wang, 2010; Heneghan et al., 2010; Roth et al., 2010; Q. Wu
et al., 2011).
2.6.4 MicroRNA SNPs (miR-SNPs) and breast cancer
Scientific literature addressing SNPs in microRNA genes in relation to breast
cancer is scarce but it seems to be increasing as it has been recognised that point
mutations in miRNA sequences can affect their synthesis and expression (Gong et
al., 2012; H.-C. Lee et al., 2011; Mishra et al., 2008).
A SNP located in the terminal loop of the precursor of MIR27A, rs895819, has
been associated with gastric cancer and colorectal cancer. However findings for this
polymorphism in relation to breast cancer remain controversial. Initially, rs895819
was found to reduce risk of developing breast cancer in German families with a
history of non-BRCA related breast cancer (1217 patients versus 1422 controls)
(Rongxi Yang et al., 2010), as well as in individuals from Jewish families with
mutant BRCA2 (58 patients vs 48 control) (Kontorovich, Levy, Korostishevsky, Nir,
Chapter 2: Literature Review 73
& Friedman, 2010). However two studies by Zhang et al, conducted on Chinese
women (252 patients versus 248 controls) (M. Zhang et al., 2012); and Catucci et al,
conducted on Italian women with non-BRCA related familial breast cancer (1205
cases versus 1593 controls) (Catucci et al., 2012), failed to replicate these findings.
It was argued that the genotyping results using the TaqMan Assay obtained by
Catucci et al (Catucci et al., 2012) were affected and biased by a nearby SNP
(rs11671784) (R. Yang & Burwinkel, 2012). To make matters worse, a recent study
by Zhang et al conducted in Chinese women (264 patients versus 255 controls)
identified an association between rs895819 and reduced sporadic breast cancer risk
(N. Zhang et al., 2013) contradicting the result previously found in a similar
population (M. Zhang et al., 2012). Most recently, Zhong et al conducted a meta-
analysis of the published studies for rs895819 in an effort to clarify the conflicting
results in relation to cancer risk. As a result, they were able to identify the G allele in
rs895819 to be associated with decreased risk in breast cancer cases and in the
Caucasian population (Zhong, Chen, Xu, Li, & Zhao, 2013).
In 2009 Kontorovich et al identified the AA genotype of rs560516, present in
MIR423 to reduce the risk of developing both ovarian and breast cancer in
individuals from Jewish families with mutant BRCA2 (Kontorovich et al., 2010). On
the other hand, a study conducted by Smith et al in 2012 showed that the presence of
the CC genotype for the same miR-SNP was significantly associated with reduced
risk of sporadic breast cancer development in Australian Caucasian individuals (R.
A. Smith et al., 2012). It is argued that this contrasting result may be due to the
difference in ethnicities for the population examined as well as the type of cancer
(familial versus sporadic). It may also be due to the effect this miR-SNP has on
expression or processing of the mature sequence of the microRNA or how it interacts
with BRCA2 mutations.
The microRNA SNP rs2910164 present in MIR146A constitutes another
example of conflicting results in breast cancer susceptibility studies for miR-SNPs.
Shen et al initially reported that the presence of this polymorphism was associated
with a younger age of diagnosis in 2008. This study, conducted in the United States,
included patients with familial breast cancer with different ethnic backgrounds (35
74 Chapter 2: Literature Review
Caucasian, 4 African American and 3 unknown) and they determined the C allele to
significantly lower the age of diagnosis in breast cancer patients, as well as increase
the levels of expression for the mature MIR146A (Shen et al., 2008). However a
similar study that year conducted on Chinese individuals (1009 cases versus 1093
controls) failed to find association with age of diagnosis or risk of breast cancer
development (Z. Hu et al., 2009). In 2010 Pastrello et al conducted a similar study in
Italian patients diagnosed with non-BRCA related familial breast cancer (348 cases
versus 148 controls) and they found the presence of the C allele to be associated with
a younger age of diagnosis (Pastrello, Polesel, Della Puppa, Viel, & Maestro, 2010).
These results confirmed those found by Shen et al (Shen et al., 2008) and
contradicted the results of Hu et al (Z. Hu et al., 2009) and this may be explained by
the difference in the ethnicity of the populations examined, attributable to genetic or
environmental factors differing between them. On the other hand, Catucci et al
conducted a similar study for rs2910164 in German and Italian individuals with non-
BRCA related familial breast cancer (1894 versus 2760) and they found no
association to breast cancer risk or age of onset (Catucci et al., 2010). Also on the
previous year, a study by Hoffman et al conducted in the United States failed to
identify association between rs2910164 and sporadic breast cancer. It is worth noting
that this study included patients with different ethnic backgrounds (about 9% of
patients were African American or other mixed ethnicities) and also about 20% of
individuals in both cases and controls had familial history of breast cancer (1st degree
relative), which may have introduced bias to the study results (Hoffman et al., 2009).
Garcia et al conducted a genotyping study on French individuals with familial
history of breast cancer and either BRCA1 or BRCA2 mutations both in cases and
controls (1130 individuals and 596 individuals respectively). Results from this study
did not show an association between breast cancer risk and rs2910164 (Amandine I.
Garcia et al., 2011). A meta-analysis was conducted in 2011 by Gao et al to try to
clarify the conflicting results obtained for rs2910164 to date in relation to breast
cancer. Results of this meta-analysis showed no association between this miR-SNP
and breast cancer risk, however it is to be noted that the studies included in the
analysis contained a mixture of different ethnic backgrounds (Caucasians, African
Americans, unknown ethnicities and Chinese) and this was not considered as part of
the analysis, raising potential questions about its utility (Gao et al., 2011). Three
studies were conducted in 2012 in relation to rs2910164 and breast cancer and they
Chapter 2: Literature Review 75
included two case control genotype association analyses and one meta-analysis.
Alshatwi et al performed a genotyping analysis in Saudi-Arabian patients with
sporadic breast cancer (100 cases vs 100 controls) and they showed no association
between the presence of mutation to breast cancer risk (Alshatwi et al., 2012).
Similarly, Esteban Cardeñosa et al showed no association between rs2910164 and
breast cancer risk, for both sporadic and familial types, or any association with age of
onset in their study conducted on a Spanish Caucasian population (538 cases versus
189 controls) (Esteban Cardenosa et al., 2012). However, a meta-analysis by Lian et
al published that year showed significant association between the CC genotype of
rs2910164 and breast cancer risk only in European Caucasians (Lian, Wang, &
Zhang, 2012). Interestingly, a very similar meta-analysis by Wang et al, which
included most of the studies analysed by Lian et al (Lian et al., 2012), reported no
association between rs2910164 and breast cancer risk (P. Y. Wang et al., 2013).
Finally, a recent study by Bansai et al conducted in 121 patients with sporadic breast
cancer and 164 controls from a North-Indian population identified that the presence
of the C allele significantly decreased the risk of developing breast cancer (Bansal et
al., 2014).
Conflicting results obtained for rs2910164 may be due to the variations in the
studies in terms of ethnicity of participants and type of cancer that were evaluated, as
well as the statistical approach to analyse the results. It is also worth noting that only
one study evaluated risk of familial breast cancer in a Caucasian population (Catucci
et al., 2010) and there is no replication of these results, since the following studies
for sporadic breast cancer were not performed in a purely Caucasian populations. To
the best of our knowledge there is no evidence in the literature about the association
of this miR-SNP with risk of sporadic breast cancer in a purely Caucasian
population. Therefore, more research for this miR-SNP is required using well-
characterised cohorts to determine whether there is an association with risk of
developing breast cancer.
Another example of conflicting evidence in terms of association analysis with
breast cancer risk is seen in the results of the studies conducted for rs11614913
present in MIR196A2. A study previously mentioned, conducted by Hu et al on
76 Chapter 2: Literature Review
Chinese women identified significant association between presence of mutation in
rs11614913 and a higher risk of developing breast cancer (Z. Hu et al., 2009). On the
other hand, Hoffman et al showed that the presence of mutation in rs11614913
significantly decreased risk of breast cancer, a fact that was corroborated by both
their methylation assay of a CpG island upstream from the precursor of MIR196A2
and their functional validation in vitro using the MCF-7 breast cancer cell line
(Hoffman et al., 2009). However in 2010, Catucci et al failed to identify an
association between rs11614913 and breast cancer risk or age of onset (Catucci et al.,
2010). A meta-analysis conducted by Gao et al showed the CC genotype of
rs11614913 to be associated with an increased breast cancer risk (Gao et al., 2011),
which contradicted the results obtained by Hoffman et al (Hoffman et al., 2009). In
211, a study performed by Jedlinski et al in an Australian Caucasian sporadic breast
cancer cohort (193 cases versus 190 controls) showed no association to breast cancer
risk (Jedlinski, Gabrovska, Weinstein, Smith, & Griffiths, 2011). Linhares et al
showed that the presence of the CC allele significantly decreased the risk of breast
cancer in their study conducted in Brazilian women (388 cases and 388 controls)
(Linhares et al., 2012). Zhang et al found a marginally significant association
between the presence of mutation in rs11614913 and decreased risk of breast cancer
in their study in a Chinese cohort (252 cases versus 248 controls) (M. Zhang et al.,
2012). A previously mentioned meta-analysis conducted by Wang et al showed
significant association between CC genotype of rs11614913 and increased breast
cancer risk (P. Y. Wang et al., 2013). A study by Bansal et al, previously mentioned,
conducted in a North Indian population showed no association between mutation at
this loci and breast cancer risk (Bansal et al., 2014). Looking at patient outcomes
instead of risk, a recent study conducted in a Korean population by Lee et al
identified the presence of mutation in rs11614913 to be significantly associated with
poor prognosis for patients with sporadic breast cancer, which implicates some role
for the polymorphism in breast cancer aetiology (S. J. Lee et al., 2014). Despite the
conflicting evidence presented in here, it is more likely that rs11619913 is not
associated with sporadic breast cancer risk in Caucasian individuals, since it is a
common finding for the only two studies that evaluated this miR-SNP in purely
Caucasian breast cancer cohorts (Catucci et al., 2010; Jedlinski et al., 2011).
However further studies in larger cohorts may be required to ascertain this finding
Chapter 2: Literature Review 77
and determine whether the role in patient outcome identified by Lee et al can be
replicated.
Finally rs3746444 present in MIR499 has been associated with increased breast
cancer risk in Asian populations according to the results obtained in the studies
conducted by Hu et al (Z. Hu et al., 2009) and Chen et al (P. Chen, Zhang, & Zhou,
2012). However studies performed in populations of other ethnicities have failed to
replicate this finding (Bansal et al., 2014; Catucci et al., 2010; P. Y. Wang et al.,
2013), which may suggest that the effects of this miR-SNP are specific to certain
ethnicities or affected by environmental factors.
2.7 SUMMARY AND IMPLICATIONS
Breast cancer represents a significant burden of disease, in particular to the
Australian population. Both incidence and mortality rates are increasing despite our
current knowledge on the disease (AIHW, 2009, 2012).
It is a complex disease caused by the interplay between our genetic
susceptibility and a number other risk factors (Martin & Weber, 2000; Robbins et al.,
2010). Single gene mutations associated with the most commonly found types of
familial breast cancer have already been identified, but they only account to about
10% of all cases and even in familial cancer, not all mutations have been identified
(Robbins et al., 2010; Strachan & Read, 2011; Weinberg, 2007). We still lack
knowledge about the genetics mechanisms involved in development of the sporadic
forms of the disease. Complicated gene networks interacting with the environment
may be at play in the aetiology of the disease and we need to identify mediators with
an important role in the pathophysiology of breast cancer. MicroRNA genes have
been identified to regulate a large number of genes involved in molecular pathways
important for cancer development. Therefore, a deeper understanding of these genes
is required to develop diagnostic or therapeutic strategies that will allow earlier
detection or will have a big impact for breast cancer sufferers in their prognosis.
78 Chapter 2: Literature Review
Deregulation of microRNA genes has been identified as a feature present in
most types of cancer, in particular breast cancer. However, we still lack detailed
knowledge of the mechanisms that result in the miRNA expression patterns seen in
relation to breast cancer. It has been proposed that changes in the sequence of
miRNA genes might have an impact on their biogenesis and function in relation to
cancer and functional sequence variants have been identified. Single nucleotide
polymorphisms (SNPs) are the most common type of variation identified in miRNA
sequences and they are very likely to be key players in the development of cancer. A
large number of miR-SNPs have been identified to date and only a few have been
associated with breast cancer. Moreover, there is conflicting evidence for some of the
miR-SNPs that have been previously studied in relation to breast cancer. Further
studies to validate those in larger cohorts are needed, as well as studies to address a
large number of miR-SNPs not previously analysed, in order to successfully identify
those variants significantly associated with breast cancer.
One of the many problems that research studies face today in relation to
validating polymorphisms in miRNA genes; is the availability of large and well
characterized cohorts and the resources to recruit them. Additionally, genomic DNA
of good quality is essential to perform genotyping association studies that will
successfully identify miRNA variants. Therefore, it is important to use a DNA
extraction method that is cost-effective and time efficient and that will result in
optimal DNA yield. It is even more important when we have to deal with a large
number of samples in order to be quick and effective in the establishment of the
study population.
Careful selection of the miRNA variants to investigate in relation to breast
cancer is another key step. It requires careful assessment of the validity of previous
reports for miRNA genes and their selected variants. It is also important to consider
their prevalence and the potential impact they may have in the target study
population. Selection of an efficient and reliable genotyping technique is also
fundamental to obtain valid results.
Chapter 2: Literature Review 79
Therefore our study aimed to establish a new prospective large cohort of DNA
samples obtained from Cancer patients recruited in Queensland in collaboration with
the Queensland Cancer Council that would serve as a replication population to our
previously established case control population. To validate any positive association
result it is important to test for association in a second independent case-control
population. Hence the establishment of a large second independent cohort was a
primary aim of this research. We also developed a selection process for miR-SNPs to
validate in both our Australian Caucasian case control cohorts to identify variants
that could be further analysed in order to determine their role in breast cancer
development.
80 Chapter 2: Literature Review
Chapter 3: Methodology background
3.1 STUDY POPULATIONS
3.1.1 Genomics Research Centre Breast Cancer Population (GRC-BC population)
The Genomics Research Centre Breast Cancer (GRC-BC) population was
obtained from two facilities: 244 Caucasian Australian breast cancer patients residing
in the South East Queensland Region recruited from the Gold Coast Hospital,
Southport and 187 female individuals matched for age (± 5 years) and ethnicity with
no history of personal or familial breast cancer collected at the Genomics Research
Centre Clinic, Southport. All participants supplied informed written consent and
research was approved by Griffith University’s Human Ethics Committee (Approval:
MSC/07/08/HREC and PSY/01/11/HREC) and the Queensland University of
Technology Human Research Ethics Committee (Approval: 1400000104). The mean
age for cases and controls in the GRC-BC population was 57.52 years and 57 years,
respectively.
3.1.2 The Griffith University – Cancer Council Queensland Breast Cancer Biobank (GU-CCQ BB)
The Griffith University – Cancer Council Queensland Breast Cancer Biobank
(GU-CCQ BB) was established as a collaboration between the Genomics Research
Centre and the Cancer Council of Queensland. All women aged 20 to 79 years
resident in Queensland with a histologically confirmed diagnosis of invasive breast
cancer between have been notified to the Queensland Cancer Registry since January
2010 and recruitment will extend throughout the following 5 years. 929 Patients were
recruited at the Cancer Council Queensland as part of this 5-year population-based
longitudinal study of women newly diagnosed with breast cancer. Exclusion criteria
Chapter 3: Methodology background 81
for this study include women unable to speak or understand English and women with
cognitive impairment.
All patients provided informed written consent to participate and clinical and
demographic information was obtained from through the database at the Queensland
Cancer Registry, computer assisted telephone interview (CATI), self-administered
questionnaires (SAQ) and the Genomics Research Centre General Questionnaire.
Data was collected over two main time points: 4 to 6 months post diagnosis (Time 1)
and 18 months post diagnosis [approximately 12 months after initial interview]
(Time 2). Patients were also requested to provide a blood sample for molecular and
genetic marker analysis at Time 1 (4 to 6 months post diagnosis).
Control population comprised 102 healthy female volunteers with no personal
or familial history of breast cancer matched by age (± 5 years) and ethnicity and it
was recruited at the Genomics Research Centre Clinic, Southport. Individuals from
the control population provided written consent to participate and research was
approved by Griffith University’s Human Ethics Committee (Approval:
MSC/07/08/HREC and PSY/01/11/HREC) and the Queensland University of
Technology Human Research Ethics Committee (Approval: 1400000104). Mean
ages for cases and controls was 60.17 years and 60.20 years respectively.
3.2 CASE - CONTROL ASSOCIATION STUDIES
3.2.1 Genomic DNA Extraction
There are a number of DNA extraction techniques available to perform nucleic
acid extraction from the blood samples obtained for our case and control population.
DNA extraction can be performed using laboratory protocols that involve organic
and non-organic reagents, centrifugation methods and there is also a wide range of
commercially available kits (J. J. Greene & Rao, 1998; Grimberg et al., 1989; Miller,
82 Chapter 3: Methodology background
Dykes, & Polesky, 1988). We conducted a review of the evidence supporting DNA
extraction techniques most commonly used at research facilities to determine the
most suitable protocol for the establishment of the GU-CCQ BB population. Results
from our literature review are shown in Chapter Five. As it is further described in
detail in Chapter Four, we tested 3 different protocols including a commercially
available kit to determine the most suitable one in terms of cost-effectiveness to use
for DNA extraction of the samples obtained for the DNA biobank (Chacon-Cortes,
Haupt, Lea, & Griffiths, 2012). Our study showed the modified salting-out DNA
extraction method, developed by Nasiri et al (Nasiri, Forouzandeh, Rasaee, &
Rahbarizadeh, 2005), to have the best performance in terms of cost-effectiveness and
time-efficiency. This method is very similar to traditional salting out extraction
protocols, but it replaces proteinase K with laundry detergent instead, as a reagent for
the lysis and removal of contaminants in DNA isolation, reducing time (no overnight
incubation) and general costs overall. However, it maintains similar results, in terms
of quality and quantity of final DNA yield obtained, as the ones observed with
traditional salting out method and commercially available DNA extraction kit
(Chacon-Cortes et al., 2012). Therefore, DNA isolation from blood samples from the
GU-CCQ BB population were performed using the modified salting-out protocol
initially developed by Nasiri et al (Nasiri et al., 2005), which is further described in
detail in Appendix A.
Following DNA extraction from blood, samples were assessed for integrity
(260/280 nm ratio and absorbance spectrum) and concentration (obtained in ng/µl)
using the Thermo Scientific NanoDropTM 8000 UV-Vis Spectrophotometer
(Thermo Fisher Scientific Inc., Wilmington, DE. USA). To ensure consistency of
DNA sample evaluation, samples were measured in duplicate to check for
consistency and the average result was kept on record for our sample population
database.
Chapter 3: Methodology background 83
3.2.2 Genotyping Techniques
As previously mentioned, this project aimed to investigate genetic variants
(miR-SNPs) in microRNA genes identified to play a role in breast cancer biology.
Therefore, we chose the following genotyping techniques to conduct the case-control
association studies presented in Chapters Six to Eight: Genotyping using High-
Resolution Melting (HRM) analysis and Multiplex PCR and matrix-assisted laser
desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) analysis
3.2.2.1 Genotyping by High Resolution Melting Analysis (HRM)
High resolution melting (HRM) is a genotyping method that involves melting
curve analysis of real- time PCR by continuously monitoring each cycle. It relies on
the same principle of real-time PCR where a fluorescent dye, which produces a
signal only when it is attached to double stranded DNA (dsDNA), is monitored to
produce a graph that will be specific for homozygotes and heterozygotes from a
chosen SNP. High level of fluorescence is observed at low temperatures when the
DNA is still in double stranded form and fluorescence decreases when the
temperature rises denaturing DNA into single strands.
The highest rate of fluorescence decrease is generally at the melting
temperature of the DNA sample (Tm). The Tm is defined as the temperature at which
50% of the DNA sample is double-stranded and 50% is single-stranded. The Tm is
typically higher for DNA fragments that are longer and/or have a high GC content.
SNPs or sequence variations are classified into four different classes (I to IV)
according to the expected shift in Tm. SNPs in class I correspond to base changes C/T
and G/A, which occur in approximately 64% of the human genome. Class II includes
C/A and G/T base changes and they occur in about 20% of the human genome. SNPs
in class I and II generally produce large changes in Tm (> 0.5°C). Class III includes
SNPs with C/G base changes, which usually produce a small change in Tm (0.2 to
0.5°C) and they are found in approximately 9% of the human genome. Finally, SNPs
in class IV are found in 7% of the human genome and they correspond to A/T base
84 Chapter 3: Methodology background
changes producing a very small change in Tm (< 0.2°C). Changes in the binding
strength of the fluorescent dye to the amplicon are plotted showing the level of
fluorescence vs. the temperature, generating a melting curve. Melt curves generated
after High Resolution Melting analysis are normally plotted with fluorescence on the
Y axis and temperature on the X axis. These are similar to real-time PCR
amplification plots but with the substitution of temperature for cycle number (Liew
et al., 2004; Liew, Wittwer, & Voelkerding, 2010; Reed, Kent, & Wittwer, 2007;
Wittwer, Reed, Gundry, Vandersteen, & Pryor, 2003).
Figure 3-1 Example of High Resolution Melt (HRM) curves showing three different miR-SNP genotypes.
HRM curve seen from genotyping experiments for miR-SNP rs2910164 showing similar shape for both homozygous genotypes and a distinct curve for heterozygous
genotype.
A temperature shift along the x-axis in a HRM melt curve is characteristic of
homozygous allelic variants. In contrast, a change in the shape is indicative of
heterozygotes and these changes provide clear distinctions between all three potential
genotypes allowing both automatic and visual calling of genotypes (See Figure 3-1).
Chapter 3: Methodology background 85
It is a reliable and fast technique, but it requires careful optimization to be able to
produce distinctive melting curves shapes for proper identification of the different
genotypes from the chosen amplicon (Liew et al., 2004; Liew et al., 2010; Reed et
al., 2007; Wittwer et al., 2003). Specific details of the protocol used for HRM
genotyping are detailed in the methods section of Chapter 6.
3.2.2.2 Multiplex PCR and matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) analysis.
Matrix-assisted laser desorption ionisation time-of flight (MALDI-TOF) was
developed both by Tanaka et al and Karas and Hillenkamp in 1988 and it was
initially used to study proteins and peptides (Karas & Hillenkamp, 1988; Tanaka et
al., 1988). MALDI-TOF has also been developed since then to be used in other
applications including DNA methylation analysis, expression profiling, mutation
detection and SNP analysis (Buetow et al., 2001; Griffin & Smith, 2000; Gut, 2004;
Werner et al., 2002). To date MALDI-TOF mass spectrometry (MS) analysis has
been used successfully in different SNP genotyping studies (Adkins, Strom,
Jacobson, Seemann, & et al., 2002; Falzoi, Mossa, Congeddu, Saba, & Pani, 2010;
Hung et al., 2005; Little, Braun, Darnhofer-Demar, & Koster, 1997; Little, Braun,
O'Donnell, & Koster, 1997; Nakai et al., 2002; Shi, Xiang, Li, & Shen, 2011;
Stepanov & Trifonova, 2013; Syrmis et al., 2013).
Multiplex PCR and Chip-based SNP genotyping using MALDI-TOF MS
analysis allows high-throughput genotyping in single tube reactions based on primer
extension. This technique consists of an initial locus-specific PCR to amplify the
target region where the SNP is located, followed by an extension reaction where an
extension primers anneals immediately upstream to the target site. This extension
reaction contains mass modified dideoxynucleotide terminators with differences
large enough to be detected by the mass spectrometry detection system. Products are
then loaded onto a chip coated with a matrix that allows crystallization of the PCR
product. A laser is fired at the crystals to ionise the molecules and they travel through
a vacuum tube with an ion detector located at the end. The software in the detection
86 Chapter 3: Methodology background
system calculates the mass of the fragments based on the time it takes fragments to
reach the detector and the mass determines the specific genotype of the fragments
present in each reaction (Gabriel, Ziaugra, & Tabbaa, 2001).
Details of the protocol used for genotyping by multiplex PCR and MALDI-
TOF MS analysis used in our miR-SNP association studies are presented in
Appendix C.
3.2.2.3 Sanger Sequencing
Sanger sequencing has been widely used since it was initially developed by
Frederick Sanger and colleagues in 1974 (Sanger, Nicklen, & Coulson, 1977). It was
used in the human genome project (Lander et al., 2001) and it has been highly
developed over the past decades. The principle behind it is to generate all possible
single stranded DNA molecules differing in one nucleotide in length that are
complementary to a template. It basically starts by annealing an oligonucleotide of
about 20 bases specific to a sequence of a target gene in the 5’ end of a denatured
template. Complementary strand is catalysed by DNA polymerase in presence of
four deoxynucleotide triphosphates (dNTPs), creating molecules that differ in length
by one nucleotide randomly terminated by the incorporation of a random
dideoxynucleotide triphosphate (ddNTP). DNA is denatured again and fragments are
separated, a process that was initially done manually on polyacrylamide gel
electrophoresis and which is nowadays performed automatically using different
techniques and equipment including fluorescently tagged chain-terminators, scanning
of laser induced fluorescence, reading using thermostable polymerases or capillary
electrophoresis (Gibson & Muse, 2009; Pettersson, Lundeberg, & Ahmadian, 2009;
L. M. Smith et al., 1986).
Disadvantages of this technique include high costs and limitations to detect
rare mutations due to analogue nature of the sequence output. New sequencing
technologies are being developed to address these issues, which involve sequencing-
by-synthesis principle in a parallel format, making them able to detect rare mutations
Chapter 3: Methodology background 87
in a more affordable manner, including pyrosequencing (Gibson & Muse, 2009;
Morozova & Marra, 2008; Pettersson et al., 2009; Strausberg, Levy, & Rogers,
2008).
Automated DNA Sanger sequencing was used to validate results from
genotyping techniques or on samples that were not adequately genotyped by any
other technique. Details of the Sanger sequencing protocol used in our genotyping
studies are described in Appendix C.
3.2.3 Statistical Analysis
We examined the power of our available 2 populations (GRC-BC and CCQ-
GU BB) for breast cancer association analysis. Power analysis indicated that even if
any of the analysed polymorphism had a relatively small effect size (w ≥ 0.2) in the
risk of developing breast cancer, both our populations (GRC-BC and CCQ-GU BB)
are of sufficient size to have ~80% or greater power to detect an allelic association at
the 0.05 level.
We determined genotype and allele frequencies for each miRNA SNP in our
case and control populations. Hardy-Weinberg equilibrium (HWE) was used to
evaluate deviation between observed and ideal Hardy-Weinberg frequencies. (Guo
& Thompson, 1992; Hardy, 1908). HWE states that the frequencies of alleles and
genotypes in a population always remain constant due to the presence of random
mating in absence of evolutionary influences. It is represented by the following
equation: p2 + 2pq + q2 = 1, where p represents proportion of individuals carrying the
dominant allele (homozygous) and q represents the proportion of individuals
carrying the recessive allele. We compared our genotyping results to an ideal Hardy-
Weinberg population to identify significant deviation which indicates selection bias,
population stratification, inbreeding or genotyping error (Guo & Thompson, 1992;
Hardy, 1908; Wigginton, Cutler, & Abecasis, 2005).
88 Chapter 3: Methodology background
Following this, we evaluated differences in genotype and allele frequencies
between cases and controls using Chi-square analysis for each independent
population to determine association with breast cancer. Chi-square analysis (χ2) is a
non-parametric test used to examine the association between two variables at the
categorical level. It compares observed population values against values for an
expected population, using the differences between them to determine a p-value. The
equation of the chi-squared is χ2 = Σ [(O-E)2 / E] where the observed frequency in
this study was the cancer affected population while the expected frequency was the
control population (Fisher & Yates, 1963). The α for p-values was set at 0.05 to
determine statistical significance indicating that the differences observed would not
happen by chance 95% of the time. Some limitations for Chi-square analysis include
a large enough sample size so that the minimum expected frequency is five in each
category and normal distribution of the data. We also calculated odds ratio (OR) and
a confidence interval (CI) of 95% to assess disease risk (Fisher & Yates, 1963;
Rosner, 2006).
We used the G*Power statistical package version 3.1.9.2 (Heinrich-Heine-
Universitat Dusseldorf) to determine the power of our study (Faul, Erdfelder,
Buchner, & Lang, 2009; Faul, Erdfelder, Lang, & Buchner, 2007). All other
statistical analyses for genotypes and alleles were performed using Plink software
version 1.07 (http://pngu.mgh.harvard.edu/purcell/plink/) (Purcell et al., 2007).
Genetic linkage is an association between genetic polymorphisms which occurs
when there is non-random association of their alleles as a result of their proximity on
the same chromosome. Haplotypes comprise of SNPs or genetic variants on the same
chromosome that are inherited together which have little chance of recombination
and Linkage disequilibrium (LD) refers to the correlation between neighbouring
haplotypes descended from single, ancestral chromosomes (Hong & Park, 2012;
NAP, 2012; Reich et al., 2001). Therefore, where appropriate, we performed linkage
disequilibrium (LD) and haplotype block association analysis on the combined
genotyping data using Haploview software version 4.2 (Daly Lab, Broad Institute,
Chapter 3: Methodology background 89
Cambridge, MA, USA) (Barrett, Fry, Maller, & Daly, 2005) to determine association
between haplotypes and disease risk.
90 Chapter 3: Methodology background
Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study.
4.1 STATEMENT OF CONTRIBUTION OF CO-AUTHORS FOR THESIS BY PUBLISHED PAPER
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the
conception, execution, or interpretation, of at least that part of the publication in their
field of expertise;
2. they take public responsibility for their part of the publication, except for the
responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, and (b)
the editor or publisher of Molecular Biology Reports, and;
5. they agree to the use of the publication in the student’s thesis and its publication
on the QUT ePrints database consistent with any limitations set by publisher
requirements.
In the case of this chapter:
Chacon-Cortes D, Haupt LM, Rod LA and Griffiths LR (2012). Comparison of
genomic DNA extraction techniques from whole blood samples: a time, cost and
quality evaluation study. Molecular Biology Reports, 39, 5961–5966.
Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 91
Contributor Statement of Contribution
Diego Chacon-Cortes
(Candidate)
Conducted all experimental work, contributed to
experimental design, research plan and data analysis
and prepared and approved final version of the
manuscript.
Larisa M Haupt Assisted in experimental design, provided advice on
manuscript editing, critically reviewed and approved
final version of the manuscript.
Rod A Lea Assisted in statistical analysis, critically reviewed and
approved final version of the manuscript.
Lyn R. Griffiths Critically reviewed and approved the final version of
the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all co-authors confirming
their certifying authorship.
92Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study
ABSTRACT
Genomic DNA obtained from patient whole blood samples is a key element for
genomic research. Advantages and disadvantages, in terms of time-efficiency, cost-
effectiveness and laboratory requirements, of procedures available to isolate nucleic
acids need to be considered before choosing any particular method. These
characteristics have not been fully evaluated for some laboratory techniques, such as
the salting out method for DNA extraction, which has been excluded from
comparison in different studies published to date. We compared three different
protocols (a traditional salting out method, a modified salting out method and a
commercially available kit method) to determine the most cost-effective and time-
efficient method to extract DNA. We extracted genomic DNA from whole blood
samples obtained from breast cancer patient volunteers and compared the results of
the product obtained in terms of quantity (concentration of DNA extracted and DNA
obtained per ml of blood used) and quality (260/280 ratio and Polymerase Chain
Reaction product amplification) of the obtained yield. On average, all three methods
showed no statistically significant differences between the final result, but when we
accounted for time and cost derived for each method, they showed very significant
differences. The modified salting out method resulted in a 7 and 2 fold reduction in
cost compared to the commercial kit and traditional salting out method, respectively
and reduced time from 3 days to one hour compared to the traditional salting out
method. This highlights a modified salting out method as a suitable choice to be used
in laboratories and research centres, particularly when dealing with a large number of
samples.
4.2 INTRODUCTION
Genomic research studies benefit from the use of large populations. Biobanks
are becoming more popular and they are being recognized as a useful tool for
research in the field (Cambon-Thomsen, 2003; Cambon-Thomsen, Ducournau,
Gourraud, & Pontille, 2003). Whole blood is one of the available sources to obtain
genomic DNA (gDNA) required for experimental purposes in human science Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 93
research. After blood samples are collected, DNA extraction is the initial step of
laboratory procedures required to perform any chosen molecular application.
Therefore, it is essential to choose the appropriate DNA extraction method to obtain
high-yield and high-quality genomic DNA from any selected population of samples.
Technical requirements, time-efficiency and cost-effectiveness of the chosen method
are among the issues to consider when choosing a particular technique, especially if
you are dealing with a large number of samples.
DNA extraction techniques have come a long way since they were initially
described and most of them follow the same basic steps. They include the use of
organic and non-organic reagents, centrifugation methods (J. J. Greene & Rao, 1998)
and a variety of commercially available kits. One of these methods is DNA
extraction by salting out. It was initially described by Miller et al in 1988 (Miller et
al., 1988) and it is one of the techniques used for DNA isolation in many science
research facilities. In order to purify genomic DNA, laboratory protocols may require
the use of organic solvents such as phenol, chloroform and isoamyl alcohol or
enzymes such as proteinase K (Grimberg et al., 1989). As a result such protocols can
be expensive and time-consuming and they require the use of toxic materials. In an
effort to modify the methods used to isolate DNA, variations to the initially describe
techniques have been tested. In 2005, Nasiri et al described a different approach to
the traditional salting-out method, in which they did not require the use of toxic
reagents or long digestions with proteinase K to purify DNA. Instead of those, they
used a common, easily available laundry detergent in the process and the protocol
was optimized to obtain high-quality and high-yield genomic DNA (Nasiri et al.,
2005). Their research study showed the modified protocol to be a fast, safe and
efficient technique. It has been used as a protocol for DNA extraction in different
research facilities around the world (Garcia-Sepulveda, Carrillo-Acuña, Guerra-
Palomares, & Barriga-Moreno, 2010; Iri-Sofla, Rahbarizadeh, Ahmadvand, &
Rasaee, 2011; B. Liu et al., 2010; Trejo-de la O et al., 2008; Y. Wu et al., 2011; Y.
Zhang et al., 2009) and it has shown to be a suitable method. However, there is
limited evidence attesting to its advantages in important characteristics like cost-
effectiveness, time efficiency and technical requirements when compared to other
available methods. There are a number of studies which have evaluated different
94Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study
DNA extraction techniques using whole blood samples (Abd El-Aal, Abd Elghany,
Mohamadin, & El- Badry, 2010; Di Pietro, Ortenzi, Tilio, Concetti, & Napolioni,
2011), but they excluded the salting out method and modified salting out from their
comparisons. In order to assess the modified salting-out protocol described by Nasiri
et al (Nasiri et al., 2005), we compared it against a traditional salting out method and
against an established kit method to determine which of these available procedures
was the best choice to obtain high-quality and high-quantity DNA.
4.3 MATERIALS AND METHODS
4.3.1 Samples
Eleven whole blood samples obtained from breast cancer patients residing in
the South East Queensland Region, recruited from the Gold Coast Hospital,
Southport were randomly selected for this study. Collection of samples was approved
by Griffith University’s Ethics Committee and it was performed in 2003-2004.
Samples were collected in EDTA tubes and stored at - 80oC prior to DNA extraction
about 8 years before gDNA extraction. It was noted that a number of the blood
samples showed some marked clotting, which may have been the result of incorrect
mixing prior to freezing and long term storage prior to their use.
4.3.2 Genomic DNA extraction methods
Three genomic DNA extraction methods were evaluated on the previously
selected samples, as follows:
Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 95
5.4.2.1 Traditional salting out method
5 ml of whole blood were thawed and poured into a 50 ml falcon tube. 10 ml of
NKM buffer (3 mM MgCl2, 140 mM NaCl, 30 mM KCl) was added to the samples
and tubes were shaken vigorously. After centrifugation at 4600 rcf for 25 minutes at
4ºC, supernatant was discarded and 5 ml of RSB buffer (40mM Tris-HCl pH 7.5, 3
mM MgCl2, 40mM NaCl) was added to the falcon tube to break up the pellet. Tubes
were shaken vigorously again and RSB buffer was added to bring the volume up to
15 ml in total. Mixture was centrifuged at 3800 rcf for 15 minutes at 4°C, supernatant
was discarded and pellets were resuspended by adding 0.5 ml of RSB buffer. 2 ml of
lympholysis buffer (50 mM EDTA pH 8.0, 50 mM Tris-HCl pH 7.5, 150 mM NaCl,
10% SDS) and 125 µl of proteinase K solution (2 mg/ml proteinase K, 1 mM CaCl2
– 1 mM Tris pH 7.5) were added to the samples and falcon tubes were placed
overnight in a 37°C shaking water bath. On the following day 2ml of sterile saturated
NaCl (6M) solution was added and samples were mixed by inversion. Tubes were
centrifuged at 2400 rcf for 15 minutes at 4°C. Supernatant was carefully transferred
to a 10ml tube and they were centrifuged again at 2400 rcf for 15 minutes at 4°C.
Supernatant was transferred into a new 50ml falcon tube and two volumes of -20°C
chilled absolute (100%) ethanol were added. DNA strand precipitate was transferred
using an inoculating loop into a labelled 2ml tube containing 1ml of 1X TE buffer
(10mM Tris pH 8.0, 1mM EDTA). Extracted DNA was allowed to dissolve by
incubating at 37°C overnight and samples were stored at 4°C after quantification and
testing.
5.4.2.2 Modified salting out method
5 ml of whole blood were thawed and transferred into a 15 ml falcon tube. 8 ml
of lysis buffer (0.3 M sucrose, 0.01 M TrisHCl, pH 7.5, 5 mM MgCl2, 1% Triton
X100) were added to each sample and they were mixed by inversion. Tubes were
centrifuged at 2500 rcf for 30 minutes at 4°C. Supernatant was discarded without
disturbing pellet on the bottom. 300 µl of 10 mM TrisHCl, pH 8.0 were added to the
falcon tube and the pellet was broken by pipetting. Samples were transferred to fresh
96Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study
microfuge tubes and they were resuspended by vortexing. After centrifugation at
2500 rcf for 20 minutes at 4oC, supernatant was discarded. 330 µl of 10 mM
TrisHCl, pH 8.0; 330 µl of commercial laundry powder (Biozett Attack© Front
Loader, Kao Marketing Pty. Ltd., Australia) solution (30 mg/ml) and a glass bead
were added to the pellet. Samples were vortexed for 1 minute and 250 µl of 6M NaCl
were added and samples were vortexed again for 20 seconds. Mixture was
transferred to fresh 2 ml microfuge tubes and they were centrifuged at 15000 rcf for
10 minutes. Supernatant was transferred to fresh microfuge tubes and DNA was
precipitated by addition of 1 to 2 volumes of -20°C chilled absolute (100%) ethanol.
Using an inoculating loop, washed twice in 70% ethanol, DNA strands were placed
into one labelled 2 ml tube containing 1 ml of 1X TE buffer (10 mM Tris pH 8.0, 1
mM EDTA). DNA was allowed to dissolve by incubating at 70°C for 5 minutes.
Samples were finally stored at 4°C after quantification and testing.
5.4.2.3 QIAGEN© QIAamp® DNA Blood Maxi Kits (QIAGEN Pty Ltd, PO Box 25, Clifton Hill, Victoria 3068, Australia)
5ml of whole blood were thawed and the protocol was carried out according to
manufacturer’s instructions in their handbook. Obtained gDNA samples were stored
at 4°C after quantification and testing.
4.3.3 Evaluation of extracted DNA
Genomic DNA extracted from blood samples was evaluated using the
following procedures:
5.4.3.1 gDNA yield concentration and quality evaluation by spectrophotometry
To assess integrity and quantity of genomic DNA extracted from blood
samples using each method, we performed spectrophotometry measurements using
Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 97
The Thermo Scientific NanoDrop™ 1000 Spectrophotometer (Thermo Fisher
Scientific Inc, Wilmington, DE. USA.). Samples were measured in duplicate and the
average result was used for comparisons. Results for DNA quality (260/280 nm ratio
and absorbance spectrum) and concentration (measured in ng/µl) were compared for
each individual sample and using the average of all samples from each method.
Statistical analysis of the results was performed using Office Excel Spreadsheet 2007
(Microsoft®) and IBM® SPSS® software version 19 (IBM Company ©).
5.4.3.2 Polymerase Chain Reaction
Extracted samples were also tested using standard polymerase chain reaction
(PCR) to amplify the methylenetetrahydrofolate reductase (MTHFR) gene producing
a fragment of about 190 base pairs using the following primer sequence: 5’-
TGAAGGAGAAGGTGTCTGCGTTA. Reaction mixture contained 0.2 µl of
GoTaq® Flexi DNA polymerase (Promega, Madison, WI, USA) 100 nM of each
primer, 1X PCR Buffer, 1.75 nM MgCl2, 200 nM of deoxynucleotides (dNTPs) and
40 ng of gDNA in a final reaction volume of 20µl. Reactions were performed using
Applied Biosystems Veriti™ Thermal Cycler (Applied Biosystems, Foster City,
California, USA) with cycling conditions as follows: Initial cycle of denaturation at
94oC for 10 minutes, followed by 30 cycles of denaturation at 94oC for 10 seconds,
and annealing at 58oC for 45 seconds and extension at 72oC for 45 seconds followed
by additional 7 minutes at 72oC and a final hold at 4oC for 5 minutes. Each DNA
sample was run in duplicate and against a negative control. Product amplification
was determined by electrophoresis on 2% agarose gel stained with ethidium bromide,
visualized under U.V. light using Gel Doc XR System (Bio-Rad) and Quantity One®
software.
4.4 RESULTS
In an initial experiment, 10 ml of whole blood from 5 different patient samples
were split to be extracted using both the traditional salting out and the modified
98Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study
salting out method according to the protocols outlined above. The average values of
measures obtained by spectrophotometry from initially extracted gDNA samples are
shown in Table 4-1.
Table 4-1 Spectrophotometry measures from whole blood samples (BC319-BC323)
TRADITIONAL SALTING OUT
METHOD MODIFIED SALTING OUT
METHOD Sample ID Final
gDNA Yield (µg)
Obtained gDNA Yield (µg) per ml
of blood
260/280 nm
Ratio
Final gDNA Yield (µg)
Obtained gDNA Yield (µg) per ml
of blood
260/280 nm
Ratio
BC319 6.54 1.30 1.63 250.99 50.20 1.73 BC320 17.34 3.47 1.99 168.55 33.71 1.68 BC321 13.07 2.61 1.80 33.72 6.74 1.82 BC322 43.27 8.65 1.90 19.68 3.94 1.72 BC323 13.99 2.80 1.84 14.16 2.83 1.44
Average 18.84 3.77 1.83 97.42 19.48 1.68
On average final gDNA yield and obtained gDNA yield per ml of blood was
significantly higher when using the modified salting out method but 260/280 nm
ratio was slightly lower to the results obtained using the traditional salting out
method. However, when performing statistical analyses (independent t-test and
ANOVA) on the results obtained, there were no statistically significant differences
for final gDNA yield and obtained gDNA yield per ml of blood (P = 0.176 for both
measures) and for 260/280 nm ratio (P = 0.116). Because both methods proved to be
comparable in terms of effectiveness, we decided to compare them against a
commercially available DNA extraction kit, believed to be more effective and
efficient than the ones previously mentioned.
Six different new 15 ml whole blood patient samples were split to extract
gDNA, using all previously selected methods: traditional salting out, modified
salting out and QIAGEN© QIAamp® DNA Blood Maxi Kit. gDNA extracted from
each sample using each method was evaluated by spectrophotometry and the results
obtained are presented in Table 4-2.
Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 99
Table 4-2 Spectrophotometry measures from gDNA extracted from whole blood samples using 3 different extraction methods
TRADITIONAL SALTING OUT
METHOD MODIFIED SALTING OUT METHOD QIAGEN© QIAamp® DNA Blood Maxi Kit
Sample ID Final gDNA Yield (µg)
Obtained gDNA Yield (µg) per ml
of blood
260/280 nm Ratio
Final gDNA Yield (µg)
Obtained gDNA Yield (µg) per ml
of blood
260/280 nm Ratio
Final gDNA Yield (µg)
Obtained gDNA Yield (µg) per ml
of blood
260/280 nm Ratio
BC316 50.55 10.11 1.86 67.48 13.50 1.65 112.00 22.40 2.12 BC317 26.22 5.24 1.59 30.55 6.11 1.76 84.55 16.91 1.78 BC324 10.46 2.09 1.66 27.21 5.44 1.82 47.26 9.45 2.59 BC325 31.00 6.20 1.77 44.68 8.94 1.71 60.98 12.20 1.67 BC326 50.01 10.00 1.83 38.46 7.69 1.76 11.99 2.40 2.06 BC327 27.64 5.53 1.80 34.46 6.89 1.82 54.41 10.88 1.91
Average 32.65 6.53 1.75 40.47 8.09 1.75 61.86 12.37 2.02
Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 100
As shown in Table 4-2, on average, QIAGEN© QIAamp® DNA Blood Maxi
Kit provided higher final gDNA yield, obtained higher gDNA yield per ml of blood
and a better 260/280 nm ratio than traditional and modified salting out methods. On
the other hand, the modified salting out method resulted, on average, in higher final
gDNA yield and obtained higher gDNA yield per ml of blood than traditional salting
out method. However, they both had the same average result for 260/280 nm ratio
measure. It appeared as if QIAGEN © QIAamp® DNA Blood Maxi Kit provided a
more highly concentrated and better quality gDNA than the other two methods.
Although, statistical analysis using ANOVA showed that the results from all 3
methods were not significantly different (p value= 0.110 for gDNA yield) and a
marginally significant difference (p value = 0.05) was obtained for the 260/280 nm
ratio. Furthermore, using an independent t-test to compare modified salting out
method and QIAGEN © QIAamp® DNA Blood Maxi Kit, there were no statistically
significant differences between these results (P value = 0.187 for gDNA yield and P
value= 0.076 for 260/280 nm ratio).
Once quality and quantity of gDNA obtained was evaluated, a polymerase
chain reaction (PCR) to investigate amplification of the methylenetetrahydrofolate
reductase (MTHFR) gene was performed on all samples obtained from traditional
and modified salting out method. PCR was run using the experimental conditions
previously exposed in our methods section and the results from this experiment are
shown in Figure 4-1.
PCR product amplification for the MTHFR gene was equally achieved using
gDNA samples extracted from both traditional and modified salting out. gDNA
quality was shown to be similar and both techniques were able to result in suitable
products for future use in molecular applications (Figure 4-1).
Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 101
Figure 4-1 Agarose gel showing PCR product amplification on DNA extracted by traditional salting out method(lanes 1-6) and modified salting out method(lanes 7-12)
When analysing the costs derived from the use of either one of the previously
exposed methods, based on prices from our local providers, we were able to define
the information summarized in the following table (Table 4-3).
Table 4-3 Time and Cost summary for different gDNA extraction methods
gDNA Extraction Method
Cost per gDNA extraction from
5ml of whole blood sample
Cost of gDNA extraction from
100 Samples Overnight steps
Traditional salting out method
AUD 3.65 AUD 365.00 Incubation 37oC
Modified salting out method
AUD 1.90 AUD 190.00 None
QIAGEN© QIAamp® DNA Blood Maxi Kit
AUD 12.3 AUD 1,230.00 None
As shown in Table 4-3, QIAGEN © QIAamp® DNA Blood Maxi Kit is the
most expensive out of all the techniques chosen for extraction comparison. It is
almost seven times more expensive than the modified salting out method. The
102Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study.
modified salting out method and QIAGEN © QIAamp® DNA Blood Maxi Kit both
require about the same time to be performed, taking on average about 1 hour per
sample extracted. On the other hand, the traditional salting out method requires the
most time to be performed because it involves an overnight incubation at 37°C to
allow proteinase K digestion and it almost doubles the cost of the modified salting
out protocol. As a result the modified salting out method proved to be the most cost-
effective technique, reducing expenses about seven times less per sample than the
commercial kit and approximately half the costs of traditional salting out method.
4.5 DISCUSSION
Genomic DNA (gDNA) is a key component in order to perform molecular
applications involving genomic studies. Whole blood is the primary source for DNA
isolation and there are a number of different methods to choose to prepare this
material. They go all the way from old-school basic laboratory protocols to
commercially available tested kits and they have been tested and improved over the
years. Nonetheless, there are a number of advantages and disadvantages to each one
of the available choices and they must be considered before choosing a particular
technique. Issues like the time, direct costs and indirectly generated costs and
efficiency from each method should be carefully evaluated, especially when dealing
with large populations and particular baseline conditions of the samples to extract.
Because of technological advances, some protocols have been disregarded
despite their potential advantages, particularly in terms of costs. Although it is being
widely used nowadays, the salting out method has been excluded from studies where
comparisons have been made between available techniques for DNA isolation.
Therefore, we decided to compare a modified version of the salting out method to the
traditional protocol and also to one of the commercially available choices to
determine their advantages. Our results show that despite some differences in total
gDNA obtained from one individual to the next, all of these techniques do not differ
significantly in the quality and quantity of DNA yield obtained from whole blood
samples, previously frozen. They all result in gDNA that can be used safely for Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study. 103
molecular genomic studies. However, when you take time and money into
consideration some important differences were observed. The modified salting out
and QIAGEN © QIAamp® DNA Blood Maxi Kit method significantly reduce the
amount of time required for DNA extraction, from up to 3 days to about 1 hour.
When compared to the traditional salting out method, the modified salting out
method provides an additional advantage, in spite of their similarities in time
performance to QIAGEN © QIAamp® DNA Blood Maxi Kit. With our findings
showing almost a 7-fold cost reduction when using modified salting out method
instead of the commercially available kit and almost half a reduction in costs to the
traditional salting out technique.
In conclusion, any of these three techniques perform similarly in terms of
quantity and quality of final product, but our results showed the modified salting out
method to be the most cost-effective and time-efficient technique to isolate gDNA
from whole blood samples.
104Chapter 4: Comparison of genomic DNA extraction techniques from whole blood samples: a time, cost and quality evaluation study.
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
5.1 STATEMENT OF CONTRIBUTION OF CO-AUTHORS FOR THESIS BY PUBLISHED PAPER
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the
conception, execution, or interpretation, of at least that part of the publication in their
field of expertise;
2. they take public responsibility for their part of the publication, except for the
responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, and (b)
the editor or publisher of Journal of Biorepository Science for Applied Medicine,
and;
5. they agree to the use of the publication in the student’s thesis and its publication
on the QUT ePrints database consistent with any limitations set by publisher
requirements.
In the case of this chapter:
Chacon-Cortes D, and Griffiths LR (2014). Methods for extracting genomic DNA
from whole blood samples: current perspectives. Journal of Biorepository Science
for Applied Medicine, 2, 1-9.
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 105
Contributor Statement of Contribution
Diego Chacon-Cortes
(Candidate)
Conducted literature research, prepared and approved
final version of the manuscript.
Lyn R. Griffiths Critically reviewed and approved the final version of
the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all co-authors confirming
their certifying authorship.
106 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
5.2 ABSTRACT
DNA extraction has considerably evolved since it was initially performed back
in 1869. It is the first step required for many of the available downstream
applications used in the field of molecular biology. Whole blood samples are one of
the main sources used to obtain DNA and there are many different protocols
available to perform nucleic acid extraction on such samples. These methods vary
from very basic manual protocols to more sophisticated methods included in
automated DNA extraction protocols. Based on the wide range of available options,
it would be ideal to determine the ones that perform best in terms of cost-
effectiveness and time-efficiency.
We have reviewed DNA extraction history and the most commonly used
methods for DNA extraction from whole blood samples, highlighting their individual
advantages and disadvantages. We also searched current scientific literature to find
studies comparing different nucleic acid extractions methods to determine the best
available choice. Based on our research we have determined that there is not enough
scientific evidence to support one particular DNA extraction method from whole
blood samples. Choosing a suitable method is still a process that requires
consideration of many different factors and more research is needed to validate
choices made at facilities around the world.
5.3 INTRODUCTION
Human health studies in the field of molecular biology require the use of
deoxyribonucleic acid (DNA), ribonucleic acid (RNA) and protein samples.
Successful use of available downstream applications will benefit from the use of high
quality and high quantity DNA. Therefore, nucleic acid extraction is a key step in
laboratory procedures required to perform further molecular research applications. It
is essential to choose a suitable extraction method and there are a few considerations
to be made when evaluating the available options. These may include technical
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 107
requirements, time-efficiency, cost-effectiveness, as well as biological specimens to
be used and their collection and storage requirements (Carpi, Di Pietro, Vincenzetti,
Mignini, & Napolioni, 2011).
Whole blood is one of many different available sources to obtain genomic
DNA (gDNA) and it has been widely used in facilities around the world. Therefore,
we will focus on DNA extraction protocols using whole blood samples. Issues
regarding collection, storage and manual handling of human whole blood specimens
escape the scope of this publication and will not be covered. However, they are
important and they should be considered as they could potentially impact on the
performance and success of any DNA extraction technique chosen.
5.4 INITIAL DEVELOPMENT OF DNA EXTRACTION TECHNIQUES
Friedrich Miescher was the first scientist to isolate DNA, while studying the
chemical composition of cells. In 1869, he used leucocytes that he collected from the
samples on fresh surgical bandages and he conducted experiments to purify and
classify proteins contained in these cells. During his experiments, he identified a
novel substance in the nuclei, which he called “nuclein” (Dahm, 2005). He then
developed two protocols to separate cells’ nuclei from cytoplasm and to isolate this
novel compound, nowadays known as DNA, which differed from proteins and other
cellular substances. This scientific finding, along with the isolation protocols used,
was published in 1871 in collaboration with his mentor Felix Hoppe-Seyler (Dahm,
2005; Holmes, 2001). However, it was only in 1958, that Meselson and Stahl
developed a routine laboratory procedure for DNA extraction. They performed DNA
extraction from bacterial samples of E.Coli using a salt density gradient
centrifugation protocol (Holmes, 2001; Meselson & Stahl, 1958). Since then, DNA
extraction techniques have been adapted to perform extractions on many different
types of biological sources.
108 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
DNA extraction methods follow some common procedures aimed to achieve
effective disruption of cells, denaturation of nucleoprotein complexes, inactivation of
nucleases and other enzymes and removal of biological and chemical contaminants
and finally DNA precipitation (Tan & Yiap, 2009). Most of them follow similar
basic steps and they include the use of organic and non-organic reagents and
centrifugation methods. Finally they have developed into a variety of automated
procedures and commercially available kits (Carpi et al., 2011; J. J. Greene & Rao,
1998; Price, Leslie, & Landers, 2009; Tan & Yiap, 2009).
Initially we will discuss protocols and steps aimed to achieve cell lysis,
inactivation of cellular enzymes and denaturation of cellular complexes and DNA
precipitation, which require similar procedures and/or reagents during DNA
extraction from whole blood samples. Key differences in steps aiming to remove
biological and chemical contaminants will be highlighted further ahead when we
discuss each protocol in detail.
As previously mentioned, lysis of cells is a common step to most DNA
extraction protocols and it is commonly achieved through the use of detergents and
enzymes. SDS and Triton X-100 are examples of popular detergents used to
solubilize cell membranes. Enzymes are also combined with detergents to target cell
surface or cytosolic components. Proteinase K is a commonly used enzyme used in
various protocols in order to cleave glycoproteins and inactivate RNases and
DNases. Other denaturants such as urea, guanidium salts and chemical chaotropes
have also been used to disrupt cells and inactivate cellular enzymes, but these can
impact on quality and nucleic acid yield (Boom et al., 1990; Carpi et al., 2011;
Herzer, 2001; Price et al., 2009).
DNA precipitation is achieved by adding high concentrations of salt to DNA-
containing solutions, since cations from salts such as ammonium acetate counteract
repulsion caused by negative charge of phosphate backbone. A mixture of DNA and
salts in the presence of solvents like ethanol, used in final concentrations from 70%
to 80%, or isopropanol (final concentrations of 40-50%) causes nucleic acids to
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 109
precipitate. Some protocols include washing steps with 70% ethanol to remove
excess salt from DNA. Finally nucleic acids are resuspended in water or TE buffer
(10mM Tris, 1mM ethilenediaminetetraacetic acid (EDTA)) (Boom et al., 1990;
Herzer, 2001; Price et al., 2009). TE buffer is commonly used for long term DNA
storage because it prevents it from being damaged by nucleases, inadequate pH,
heavy metals and oxidation by free radicals. Tris provides safe a safe pH of 7 to 8
and EDTA chelates divalent ions used in nuclease activity and counteracts oxidative
damage from heavy metals (Herzer, 2001).
5.5 MAIN TYPES OF DNA EXTRACTION METHODS FROM HUMAN WHOLE BLOOD SAMPLES
Table 5-1 shows the main categories and sub-categories of DNA extraction
methods from whole blood samples that are generally used in research facilities
worldwide. Laboratory reagents commonly used for each stage of the nucleic acid
extraction protocol are included in this table in order to highlight similarities and
differences between them.
DNA extraction techniques included in Table 5-1 will be discussed in more
detail in the following sections, along with a brief summary of the technique history
and background. In recent years, some of these protocols have been adapted to
microdevices that would develop miniaturized total chemical analysis system
(μTAS) or microfluidic genetic analysis (MGA) microchips (Duarte et al., 2010;
Price et al., 2009). However, we will limit the scope of our review to those
techniques that are available for macroscale nucleic acid extraction.
110 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
Table 5-1 DNA extraction methods commonly used for extraction from whole blood samples
DNA Extraction
Method (Main
category)
DNA Extraction Method
(Sub-category)
DNA Extraction Protocol Stage Cell Lysis Denaturation of
Nucleoproteins/ Inactivation of
cellular enzymes
Removal of Contaminants DNA precipitation
Solution Based DNA Extraction Methods
Salting Out Methods • SDS • SDS/
Proteinase k • Triton X-100
• Proteinase K • Laundry
powder
• Potassium acetate • Sodium acetate • Sodium chloride
• Ethanol • Isopropanol
Organic Solvent/Chaotropes Methods
• SDS • SDS/
Proteinase k
• Guanidine thiocyanate (GTC)
• Phenol
• Phenol • Phenol: chloroform • Phenol: chloroform
isoamyl alcohol
• Sodium acetate/ethanol • Sodium
acetate/isopropanol
Solid Phase DNA
Extraction Methods
Glass Milk/ Silica Resin Methods
• SDS • Triton X-100
• Guanidine thiocyanate
• Glass Milk (Silica in chaotropic buffer)
• Silica matrix • Diatomaceous earth
• Ethanol • Isopropanol
Anion Exchange Methods
• Heat • Chelex • Chelex/Prote
inase K
• Chelex • N/A
Magnetic Beads Methods
• SDS • N/A • Sodium Chloride/ Polyethylene glycol
• Magnetic beads
Abbreviations: DNA, deoxyribonucleic acid; N/A, not applicable; SDS, sodium dodecyl sulfate
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 111
5.5.1 Solution based DNA extraction methods
As previously mentioned, solution based protocols have two main approaches:
I) Solutions based methods using organic solvents and II) those based on a salting
out technique. Further description of both methods follows.
Solution based DNA extraction methods using organic solvents
DNA extraction protocols using organic solvents derived originally from a
series of related RNA extraction methods. Some of the main steps used in these
methods are: I) Cell lysis undertaken by adding a detergent/chaotropic-containing
solution, including sodium dodecyl sulfate (SDS) or N-Lauroyl sarcosine. II)
Inactivation of DNases and RNases usually through the use of organic solvents, III)
purification of DNA and removal of RNA, lipids and proteins and finally IV)
resuspension of extracted nucleic acids (Carpi et al., 2011; Sambrook & Russell,
2001; Tan & Yiap, 2009).
This method was initially developed in 1977 when an RNA extraction
technique using guanidium isothyocyanate was used by Ulrich et al to isolate
plasmid DNA (Ullrich et al., 1977). This technique was later modified by Chirgwin
et al in 1979 and it required the use of guanidium thyocyanate and long hours of
ultracentrifugation through a cesium chloride (CsCl) cushion (Chirgwin, Przybyla,
MacDonald, & Rutter, 1979). In an effort to improve this method, Chomczynski et al
in 1987 developed a protocol for RNA extraction using guanidium thiocyanate-
phenol-chloroform and much shorter centrifugation (Chomczynski & Sacchi, 1987,
2006). This last RNA extraction protocol was able to isolate RNA, DNA and
proteins, but in order to be used as a DNA extraction technique, guanidium
thiocyanate-phenol-chloroform was later replaced by a mixture of phenol:
chloroform: isoamyl alcohol since the former solvent did not completely inhibit
RNase activity (Sambrook & Russell, 2001). Phenol is a carbolic acid that denatures
proteins quickly, but it is highly corrosive, toxic and flammable. This organic solvent
is usually added to the sample and then using centrifugal force a biphasic emulsion is
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 112
obtained. The top hydrophilic layer contains diluted DNA and the bottom
hydrophobic layer is composed of organic solvents, cellular debris, proteins and
other hydrophobic compounds. DNA is then precipitated after centrifugation by
adding high concentrations of salt, such as sodium acetate and ethanol or isopropanol
in 2:1 or 1:1 ratios. Excess salt can be removed by adding 70% ethanol and the
sample is then centrifuged to collect the DNA pellet which can be resuspended in
sterile distilled water or TE buffer (10mM Tris; 1mM EDTA pH 8.0) (Carpi et al.,
2011; Tan & Yiap, 2009).
Because these techniques involve the use of toxic and corrosive organic
solvents, safety is a main concern. Personal protective equipment, safety measures
involving the use of a biohazard hood and training are required. Phenol/chloroform
needs to be equilibrated to an adequate pH and protocol conditions should be
optimized (Price et al., 2009). In an effort to improve safety and ease of use of these
protocols, certain modifications have been introduced to avoid physical contact with
solvents. These include incorporating a silica gel polymer (S. Thomas, Tilzer, &
Moreno) or replacing solvents with other substances like benzyl alcohol (Ness.,
Cimler., Rich B. Meyer, & Vermeulen.).
Solution based DNA extraction methods using salting out
Some nucleic acid extraction techniques that avoid the use of organic solvents
have also been developed over the years (Carpi et al., 2011; J. J. Greene & Rao,
1998; Sambrook & Russell, 2001). In 1988, Miller et al published a protocol that
achieved DNA purification through protein precipitation at high salt concentration
(Miller et al., 1988). The traditional protocol involves initial cell disruption and
digestion with SDS-Proteinase K, followed by addition of high concentrations of
salts, usually 6M NaCl. The mixture is then centrifuged to allow proteins to
precipitate to the bottom, with the supernatant containing DNA then transferred to a
new vial. DNA is then precipitated using ethanol or isopropanol in the same manner
as described for organic solvent methods (Carpi et al., 2011; F. Greene et al., 2002;
Grimberg et al., 1989; Miller et al., 1988).
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 113
However, the use of proteinase K can be time consuming and expensive when
compared to other reagents used in different solution-based approaches, so there
have been a few attempts to find alternative reagents for deproteinisation of DNA
(Bahl & Pfenninger, 1996; Drabek & Petrek, 2002; Gaaib, Nassief, & Al-Assi, 2011;
Debomoy K Lahiri & Nurnberger Jr, 1991). In 1991, Lahiri et al developed a DNA
extraction protocol from blood samples that eliminated the use of organic solvents
and prolonged incubation with Proteinase K. Their protocol used Nonidet P-40 (NP-
40, Sigma) to lyse blood cells and high salt buffers and 10% SDS to inactivate and
remove contaminants. Another protocol is the modified salting out method published
in 2005 by Nasiri et al in 2005 (Nasiri et al., 2005), which replaced Proteinase K
digestion with the use of laundry powder. This modified technique has been
successfully used as a DNA extraction protocol in many facilities around the world
(Arévalo-Herrera et al., 2011; Garcia-Sepulveda et al., 2010; Iri-Sofla et al., 2011; B.
Liu et al., 2010; H. Liu et al., 2013; Trejo-de la O et al., 2008; Y. Wu et al., 2011; Y.
Zhang et al., 2008; Y. Zhang et al., 2009).
5.5.2 Solid phase DNA extraction methods
Purification of DNA using the liquid/solid phase approach can be traced back
to 1979 when Vogelstein et al used silica in a glass powder form in their protocol to
purify DNA fragments previously separated by agarose gel electrophoresis
(Vogelstein & Gillespie, 1979). Solid phase extraction methods for DNA extraction
from blood samples were initially described in 1989 by McCormick et al. They
published a technique using siliceous-based insoluble particles, chemically similar to
phenol, that interact with proteins to allow DNA purification (McCormick, 1989). A
number of different procedures using the liquid/solid DNA extraction approach have
been developed since then and they are used in the majority of commercially
available extraction kits.
These techniques will absorb DNA under particular pH and salt content
conditions through any of the following principles: I) hydrogen-binding in the
114 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
presence of a chaotropic agent to a hydrophilic matrix, II) ionic exchange using an
anion exchanger under aqueous conditions and III) affinity and size exclusion
mechanisms (Price et al., 2009; Tan & Yiap, 2009). Most of these methods follow a
series of similar steps to achieve cell disruption, DNA adsorption, nucleic acid
washing and final elution. Most solid phase techniques use a spin column to bind
nucleic acid under centrifugal force. Spin columns are made of silica matrices, glass
particles or powder, diatomaceous earth or anion exchange carriers and these
compounds generally need to be conditioned using buffer solutions at specific pH to
turn them into the required chemical form. Blood cells previously degraded using
particular lysis buffers are applied to the columns and centrifuged and the DNA
binds to column aided by pH and salt concentration conditions provided by binding
solutions. Some proteins and other biochemical compounds may also bind to the
column and they are later removed using washing buffers containing competitive
agents during a series of washing steps. DNA is finally eluted in sterile distilled
water or TE buffer (Carpi et al., 2011; Price et al., 2009; Tan & Yiap, 2009).
4.5.2.1 DNA extraction methods using silica and silica matrices
Silica matrices have unique properties for DNA binding. They are positively
charged and have high affinity towards the negative charge of the DNA backbone.
High salt conditions and pH are achieved using sodium cations, which bind tightly to
the negatively charged oxygen in the phosphate backbone of DNA. Contaminants are
removed with a series of washing steps, followed by DNA elution under low ionic
strength (pH ≥ 7) using TE buffer or sterile distilled water. Commercially available
kits using silica based approach are manufactured by Clontech (NucleoSpin™), Mo
Bio Laboratories (Ultraclean™ BloodSpin™), QIAGEN (QiaAmp®), Promega
(Wizard™), Epoch Biolabs (EconoSpin™), Sigma Aldrich (GenElute™) amongst
others. In these protocols blood samples are incubated for a few minutes with a lysis
buffer and most protocols take about 40 minutes to one hour to complete producing
high yields of DNA with minimum contamination (Carpi et al., 2011; Price et al.,
2009; Tan & Yiap, 2009).
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 115
A substance that contains high amounts of silica (up to 94%), known as
kieselguhr, diatomite or diatomaceous earth has also been used for DNA purification.
It was initially described by Boom et al in 1990 (Boom et al., 1990). It binds DNA
in the presence of chaotropic agents, followed by washing with a buffer containing
alcohol and finally DNA is eluted in a low salt buffer or sterile distilled water.
BioRad (Quantum Prep®) is an example of a DNA extraction product developed
using diatomaceous earth (Price et al., 2009).
DNA extraction kits have also evolved and they are incorporated into semi-
and fully automated equipment able to perform protocols from sample lysis to some
downstream applications like polymerase chain reaction (PCR), such as BioRobot
EZ1® Advanced (QIAGEN), Biomek® 4000 Laboratory Automation Workstation
(Beckman Coulter, Inc., Brea, CA, USA), amongst others. Pipetting error, reduced
number of sample transfer and less protocol time are among the advantages of these
devices, however they should be carefully considered given the high cost of some of
the available choices of equipment. They have also been incorporated into
miniaturized total chemical analysis systems, which are silicon microchips where
DNA purification separation and detection is achieved (Price et al., 2009).
4.5.2.2 DNA extraction using anion exchange resins
Positively charged chemical substances able to bind to negatively charged
nucleic acids or contaminants or enzymes, such as nucleases, are called anion
exchange resins and they have also been used as part of DNA extraction protocols
from blood samples (Herzer, 2001).
Chelex® 100 resin is made of styrene divinylbenzene copolymers that contain
paired imminodiacetate ions. It is used in DNA extraction protocols as a chelating
ion exchange resin that binds polyvalent metal ions, such as nucleases, commonly
used in DNA extraction from forensic samples. The initial laboratory protocol, using
blood as a biological source, was described by Wash et al in 1991 (Walsh, Metzger,
& Higushi, 2013). Based on this initial approach, other protocols have been
116 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
developed to perform nucleic acid extraction from whole blood samples. They
require small sample volumes (under 1 ml of blood) and are usually performed in a
single tube reaction with different steps and reagents involved. Blood samples could
be lysed using proteinase K and/or incubation at high temperature, and removal of
contaminants is achieved by adding Chelex® 100 resin which precipitates them.
Single stranded DNA is obtained and it remains suspended in the supernatant, which
can be immediately used in downstream application or can be transferred to a new
vial for long-term storage (Butler, 2009; NFSTC, 2006; Walsh et al., 2013).
Selingson et al (Seligson & Shrawder) used anion exchange materials as part of
their invention to isolate nucleic acid samples from a variety of sources, including
whole blood samples. Selingson’s protocol uses a column containing a resin with
positively charged diethylaminoethyl cellulose (DEAE) groups on its surface to bind
negatively charged phosphates of the backbone of DNA. The strength of DNA
binding to column, as well as RNA and other impurities, can be altered through salt
concentrations and pH conditions of buffers used in this nucleic acid isolation
protocol. Contaminants such as protein and RNA can be washed from the DNA-
containing column using medium salt buffers (Seligson & Shrawder; Tan & Yiap,
2009).
4.5.2.3 DNA extraction methods using magnetic beads
Nucleic acid extraction techniques using magnetic separation have been
emerging since the early 90’s. They were originally used to extract plasmid DNA
from bacterial cell lysates by Hawkins et al in 1994 (Hawkins, O'Connor-Morin,
Roy, & Santillan, 1994) and by 2006 Saiyed et al (Saiyed, Bochiwal, Gorasia,
Telang, & Ramchand, 2006; Saiyed, Ramchand, & Telang, 2008) developed and
validated a protocol using naked magnetic nanoparticles for genomic DNA
extraction from whole blood samples.
Magnetic particles are made of one or several magnetic cores, such as
magnetite (Fe3O4) or maghemite (gamma Fe2O3), coated with a matrix of polymers,
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 117
silica or hydroylapatite with terminal functionalized groups. In the protocol
developed by Saiyed et al, 30 µl of whole blood are mixed with an equal volume of
1% (w/v) SDS solution. The tube is mixed by inversion two or three times and
incubated at room temperature one minute. 10 µl of magnetic nanoparticles are
added to this mixture, followed by addition of 75µl of binding buffer (1.25 M sodium
chloride and 10% polyethylene glycol 6000). The solution is mixed by inversion and
allowed to rest for 3 minutes at room temperature and the magnetic pellet is
immobilized using an external magnet to discard the supernatant. The magnetic
pellet is washed with 70% ethanol and dried. The magnetic pellet is resuspended in
50 µl of TE buffer and magnetic particles bound to DNA are eluted by incubation at
65ºC in continuous agitation (Saiyed et al., 2006; Saiyed et al., 2008).
5.6 CHOOSING THE APPROPRIATE PROTOCOL
The ideal extraction method should fit the following criteria: It should be
sensitive, consistent, quick and easy to use and depending on the country in which it
is used it may be important to minimize specialized equipment or biochemical
knowledge. It should also pose minimum risk to users, as well as avoid possible
cross-contamination of samples. Finally and most importantly, the DNA extraction
technique chosen should be able to deliver pure DNA samples ready to be used in
downstream molecular applications (Boom et al., 1990; Debomoy K. Lahiri, Bye,
Nurnberger Jr, Hodes, & Crisp, 1992; Price et al., 2009).
The quality and quantity of genomic DNA extracted from blood samples is a
key feature most facilities consider when choosing a protocol. Measuring UV light
absorbance using spectrophotometry at different wavelengths (230, 240, 260 and
280nm) is an initial quick and efficient way of determining purity and concentration
of nucleic acid samples. Concentration is usually calculated from DNA absorbance
reading at 260 nm using Beer-Lambert Law. Purity of nucleic acid samples is
assessed on 260/280 absorbance ratio and values in the range of 1.8 to 2.0 are
generally considered acceptable. 260/230 absorbance ratios between 2.0 and 2.2 are
also considered to be adequate as a secondary measure of purity for DNA
118 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
(Huberman, 1995; Sahota, Brooks, & Tischfield, 2007; Sambrook & Russell, 2001;
Troutman et al., 2002).
Lahiri et al (Debomoy K. Lahiri et al., 1992) published a study in 1992 where
they compared ten solution-based extraction methods for DNA extraction using
whole blood as source. They compared a protocol previously developed by their
group (method 10a and 10b) (Debomoy K Lahiri & Nurnberger Jr, 1991), which
required no use of organic solvents or enzyme digestion against nine other methods
previously published and used for DNA extraction from blood. On their study, Lahiri
et al (Debomoy K. Lahiri et al., 1992) extracted whole blood samples from 5
individuals in triplicate using the aforementioned methods. They determined DNA
concentration from samples using spectrophotometry absorbance reading at 260nm
and assessed quality through 260/280 absorbance ratio and electrophoresis on
agarose gel, as well as restriction enzyme digestion and southern blot. A summary of
some of the protocol features, as well as findings, is presented in Table 5-2.
Table 5-2 Summary of comparative study of DNA extraction methods by Lahiri et al
Method Enzyme Digestion
Hazardous Reagents
RNase Treatment
Time (Hours)
260/280 Ratio
DNA Yield (μg)
10a No No No 1 1.81 210 10b No No No 1 1.8 175 6 Yes Yes No 6 1.94 174 9 No Yes No 3 1.7 170 7 Yes Yes No 6 1.81 147 3 Yes Yes Yes 3 1.81 135 2 Yes No Yes 5 1.7 95 8 Yes Yes No Overnight 1.74 130 1 Yes No No Overnight 1.75 116 5 Yes Yes No Overnight 1.72 75 4 Yes Yes No Overnight 1.72 55
Note: Reprinted from J Biochem Biophys Methods 1992;25(4). Lahiri DK, Bye S, Nurnberger Ji Jr,
Hodes Me, Crisp M. A non-organic and non-enzymatic extraction method gives higher yields of
genomic DNA from whole-blood samples than do nine other methods tested. 193–205. Copyright ©
1992, with permission from elsevier. Abbreviations: DNA, deoxyribonucleic acid; RNase, ribonucleic
acid.
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 119
All protocols tested were able to isolate DNA with relatively good purity
(260/280 ratios from 1.7 to 1.94), but DNA obtained with methods 2, 5 and 6 showed
different amounts of degradation evidenced in gel electrophoresis. Seven of the
protocols tested, methods 3 to 9, required use of organic solvents and/or hazardous
substances such as phenol: chloroform or chloroform. Methods 1 and 2 did not use
organic compounds, but method 1 was the most time consuming. It required
overnight incubation with proteinase K, a problem solved in protocol 2 by incubating
samples for 30 minutes with both proteinase K and RNase A reducing DNA
extraction time to 5 hours. Method 10 (version a and b) was the quickest of all DNA
isolation methods (1 hour) and it removed enzyme digestion and the use of organic
solvents/hazardous substances. Both versions of this protocol were able to recover
similar or higher DNA yields than the other tested protocols, with about comparable
260/280 ratios. Based on their study findings, Lahiri et al were able to conclude that
the DNA extraction method they developed was the quickest and safest of the
solution-based method tested, recovering DNA of comparable quality and quantity
(Debomoy K. Lahiri et al., 1992).
Abd El-Aal et al compared a combination of manual and automated extraction
methods for DNA extraction from whole blood samples. Their study included six
techniques: phenol-chloroform purification, DNA extraction using microwave
thermal shock, DNA extraction with Wizard Genomic DNA Purification Kit
(Promega Corporation, Madison, WI, USA), magnetic separation (LC MagNA Pure
Compact Instrument – Roche Diagnostics) both manually and partially automated
using the Precision™ XS Microplate Sample processor from Biotek Instruments
(Vermont, USA) and finally they modified the Wizard SV 96 Genomic DNA
Purification Kit (Promega) combining it with magnetic separation using the MagNA
Pure purification method. They extracted 96 blood samples and used 100 μl as the
initial volume. However, they failed to mention how many samples were extracted
using each method and the number of experimental replicates performed. Their
results showed DNA extracted for each protocol with final concentrations ranging
from 0.50 to 0.98 μg/μl. While phenol-chloroform, manual magnetic separation and
the combined Promega-MagNA pure method isolated DNA showed relatively similar
DNA concentrations (0.72 to 0.79 μg/μl); magnetic separation and microwaving
120 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
technique achieved the highest and lowest DNA concentrations, 0.98 μg/μl and 0.50
μg/μl respectively. In their comparisons, they established five categories for
simplicity of extraction: extremely simple; simple; less simple; more simple and
difficult; but their system can be confusing since they failed to present criteria used
for each category. However, they categorized phenol/chloroform as difficult and
automated MagNA Pure as extremely simple using their previously mentioned
category system. They also have five categories for cost of each protocol, with
automated MagNA Pure technique as the most expensive and microwaving as the
cheapest method. Their costing categories were also not defined and there is no
actual mention of specific costs for each method in their study. Based on previously
mentioned findings, they concluded that magnetic separation using an automated
protocol performed best in terms of simplicity of extraction, purity of extracted DNA
and speed, even though it is the one with the highest cost. They also concluded that it
was essential to optimize any method chosen and they recommended the use of
magnetic separation, because it required minimal starting material and it was both
cost-effective and user-friendly. However, as previously stated there is no mention of
how cost-effectiveness was determined (Abd El-Aal et al., 2010).
Lee et al (J.-H. Lee, Park, Choi, Lee, & Kim, 2010) extracted DNA from 22
whole blood samples using three automated extraction systems. All three protocols
compared were based on solid phase extraction techniques: QIAamp® Blood Mini
Kit (Qiagen, Hilden, Germany) with QIAcube, which uses a silica membranes and
resins within a spin column to bind DNA and two other protocols that are based on
magnetic based DNA isolation techniques MagNA Pure LC Nucleic Acid Isolation
Kit I with MagNA Pure LC (Roche Diagnostics, GmbH, Manheim, Germany) and
Magtration-Magnazorb DNA common kit-200N with Magtration System 12GC
(Precision System Science (PSS) Co. Ltd., China, Japan)). DNA concentration was
measured by spectrophotometry and purity was assessed by 260/280 ratio, DNA
electrophoresis on agarose gel and PCR. Statistical analysis was performed to
validate study results and they are summarized in Table 5-3.
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 121
Table 5-3 Summary of results from comparative study on DNA extraction techniques by Lee et al
DNA Extraction Method
Mean Corrected
DNA Concentration (ng/µl) ± SD
DNA Concentration
range (min-max)
Mean 260/280
Ratio ± SD
260/280 Range (min-max)
QIAamp® Blood Mini Kit (Qiagen, Hilden, Germany) with QIAcube
25.42 ± 8.82
13.49 - 52.85
1.84 ± 0.09
1.59 - 2.04
MagNA Pure LC Nucleic Acid Isolation Kit I with MagNA Pure LC
22.66 ± 7.24
9.59 - 46.70
1.88 ± 0.08
1.6 - 1.97
Magtration-Magnazorb DNA common kit-200N with Magtration System 12GC
22.35 ± 6.47
12.57 - 35.08
1.70 ± 0.08
1.56 -1.90
Note: Reproduced from Lee J-H, Park Y, Choi JR, Lee eK, Kim H-S. Comparisons of three automated
systems for genomic DNA extraction in a clinical diagnostic laboratory. Yonsei Med J.
2010;51(1):104–110.49
Abbreviations: DNA, deoxyribonucleic acid; min, minimum; max, maximum; SD, standard deviation.
Results showed no statistical difference between DNA concentrations obtained
among the three commercial methods, but DNA purity was slightly lower for
Magtration-Magnazorb DNA common kit-200N when compared to the other two
methods. DNA extracted was of similar quality based on results from PCR and
electrophoresis on agarose gel. Therefore, they concluded that effectiveness for all
systems was equivalent and they all produced acceptable nucleic acid isolation (J.-H.
Lee et al., 2010).
Table 5-4 was adapted from a review published by Carpi et al in 2011 (Carpi
et al., 2011), where they reviewed DNA extraction methods used in a wide range of
biological sources, including 6 methods used on whole blood samples. Summary of
the three evaluated features for nucleic acid isolation methods, such as use of toxic
compounds, cost per sample and time required.
122 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
Table 5-4 Summary of features evaluated for deoxyribonucleic acid (DNA) extraction methods
reviewed by Carpi et al.1
DNA Extraction Method
Toxic Compounds
Cost estimate per sample ($)
Time Required (hours)
Phenol-Chloroform method
Phenol, Chloroform
<5
>3
Silica gel method Phenol, Chloroform
<5
>3
Benzyl alcohol method
Benzyl alcohol <5 >3
Salting out method None <5 >3 Magnetic bead-based
method None >5
>0.5
Note: Copyright © 2011. Bentham Science Publishers. Carpi FM, Di Pietro F, vincenzetti S,
Mignini F, Napolioni v. Human DNA extraction methods: patents and applications. Recent Pat DNA
Gene Seq. 2011;5(1):1–7. Reprinted by permission of eureka Science Ltd.1
Based on the methods included in Table 5-4, it can be noted that magnetic
bead-based method is the quickest DNA extraction protocol requiring over 30
minutes to be performed, whereas all the other protocols require more than 3 hours.
Also, the magnetic bead-based method is the most expensive one costing more than
$5 per sample extracted. However, there is no mention in this review of the
differences when comparing quality and quantity of nucleic acid isolated for each
method (Carpi et al., 2011).
Chacon-Cortes et al evaluated cost effectiveness and time efficiency of three
available DNA extraction techniques from whole blood samples: a traditional salting
out method, a modified salting out method and a commercially available kit based on
solid-phase DNA extraction method (QIAGEN© QIAamp® DNA Blood Maxi Kits
(QIAGEN Pty Ltd, Victoria, Australia) (Chacon-Cortes et al., 2012). Modified
salting out protocol (Nasiri et al., 2005) replaced the sample overnight incubation
step from the traditional salting out method (Miller et al., 1988), required for
contaminant removal using proteinase K, with the use of laundry detergent to reduce
time of extraction to about one hour. 5 ml of whole blood from 6 breast cancer
patients were manually extracted using each protocol and techniques were compared
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 123
in terms of quality and quantity of DNA extracted, as well as required cost and time.
DNA quantity was measured using spectrophotometry and DNA quality was
assessed by both 260/280 ratio and agarose gel electrophoresis of PCR product. A
summary of findings is presented in Table 5-5 (Chacon-Cortes et al., 2012).
Table 5-5 Summary of results from comparative study of DNA extraction methods by Chacon-Cortes
et al
DNA Extraction Method
Average Final gDNA Yield
(μg)
Average 260/280 Ratio
Cost estimate per sample
(AUD)
Time Required (hours)
Traditional Salting Out Method
32.65 1.75 3.65 Overnight
Modified Salting Out Method
40.47 1.75 1.9 1
QIAGEN© QIAamp® DNA Blood Maxi Kit
61.86 2.02 12.3 1
Note: Copyright © 2012. Springer. Mol Biol Rep. 2012;39:5961–5966. Comparison of genomic DNA
extraction techniques from whole blood samples: a time, cost and quality evaluation study. Chacon-
Cortes D, Haupt L, Lea R, Griffiths L. Reproduced with kind permission from Springer Science and
Business Media.50
Abbreviations: AUD, Australian dollars; gDNA, genomic deoxyribonucleic acid.
As shown in Table 5-5 QIAGEN© QIAamp® DNA Blood Maxi Kits produced
the highest yield and 260/280 ratio from all methods evaluated with average measure
of 61.86 µg and 2.02 respectively, but traditional and modified salting out protocols
both produced similar results in terms of DNA yield and 260/280 ratios. However,
statistical analysis using ANOVA showed that DNA yield and 260/280 ratio results
were not significantly different for all 3 methods (P-value of 0.110 and 0.05
respectively). On the other hand, the traditional salting out method required an
overnight incubation step and the QIAGEN© QIAamp® DNA Blood Maxi Kit was
the most expensive of all the methods included costing about 12.30 Australian
dollars, almost 7 times than the modified salting out protocol. Therefore, based on
these findings the modified salting out protocol developed by Nasiri et al (Nasiri et
al., 2005) proved to be to be the most cost-effective and time-efficient technique to
isolate gDNA from whole blood samples (Chacon-Cortes et al., 2012).
124 Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives.
Unfortunately, no other solid phase extraction methods for DNA extraction were
included in this study.
5.7 CONCLUSIONS
DNA extraction has evolved for the past 145 years and it has developed into a
diversity of laboratory techniques. This review highlights currently available
methods for DNA extraction from whole blood samples and it summarizes
comparison studies using different nucleic acid extraction approaches published to
date. DNA extraction has evolved from solution and solid-phase manual techniques
initially performed manually into incorporating these into automated methods. There
is no consensus on a gold standard method for DNA extraction from whole blood
samples and they all differ in many different aspects. Studies comparing extraction
techniques and highlighting their strengths and weaknesses are limited and to our
knowledge there is no publication that evaluates all approaches in terms of all
possible features. Therefore, it is very difficult to determine the best choice available.
Facilities around the world usually choose a method based on the availability of
equipment, sample and reagents, as well as considering speed, extraction efficiency
and quality, technical requirements and cost, but based on our review findings there
is not enough scientific evidence to support these choices
Chapter 5: Methods for extracting genomic DNA from whole blood samples: current perspectives. 125
Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study.
6.1 STATEMENT OF CONTRIBUTION OF CO-AUTHORS FOR THESIS BY PUBLISHED PAPER
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the
conception, execution, or interpretation, of at least that part of the publication in their
field of expertise;
2. they take public responsibility for their part of the publication, except for the
responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, and;
5. they agree to the use of the publication in the student’s thesis and its publication
on the QUT ePrints database consistent with any limitations set by publisher
requirements.
In the case of this chapter:
Upadhyaya A, Smith RA, Chacon-Cortes D, Revêchon G, Haupt LM, Chambers SK,
Youl PH and Griffiths LR. Association of the microRNA-single nucleotide
polymorphism rs2910164 in miR146a with sporadic breast cancer susceptibility: a
case control study.
Gene (Under revision).
Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study. 126
DOI:10 .1016/j.gene.2015.10.019
Contributor Statement of Contribution
Akanksha Upadhyaya Performed experimental work on first breast cancer
cohort, prepared and approved final version of the
manuscript
Robert A. Smith Assisted in assay design and optimisation and
critically reviewed and approved final version of the
manuscript.
Diego Chacon-Cortes
(Candidate)
Performed genotyping on replication population and
assisted in sequencing, critically reviewed and
approved final version of the manuscript.
Gwladys Revêchon Participated in experimental work and assisted with
DNA extraction for populations.
Larisa M. Haupt Assisted in assay design, provided advice on
manuscript editing, critically reviewed and approved
final version of the manuscript.
Suzanne K. Chambers Administered recruitment of the replication population
and approved final version of the manuscript.
Philippa H. Youl Assisted in recruitment of the replication population
and provided organisation for replicate data entry for
control matching and approved final version of the
manuscript
Lyn R. Griffiths Provided advice on study design, laboratory oversight,
recruited the primary population, critically reviewed
and approved the final draft of the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all co-authors confirming
their certifying authorship.
Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study. 127
6.2 ABSTRACT
Background: Breast Cancer (BC) is primarily considered a genetic disorder
with a complex interplay of factors including age, gender, ethnicity, family history,
personal history and lifestyle with associated hormonal and non-hormonal risk
factors. The SNP rs2910164 in MIR146A (a G to C polymorphism) was previously
associated with increased risk of BC in cases with at least a single copy of the C
allele in breast cancer, though results in other cancers and populations have shown
significant variation. Methods: In this study, we examined this SNP in an Australian
sporadic breast cancer population of 160 cases and matched controls, with a replicate
population of 403 breast cancer cases using High Resolution Melting. Results: Our
analysis indicated that the rs2910164 polymorphism is associated with breast cancer
risk in both a primary and replicate population (p= 0.03 and 0.0013, respectively). In
contrast to the results of familial breast cancer studies, however, we found that the
presence of the G allele of rs2910164 is associated with increased cancer risk, with
an OR of 1.774 (95% CI 1.402-2.237). Conclusions: The microRNA MIR146A has a
potential role in the development of breast cancer and the effects of its SNPs require
further inquiry to determine the nature of their influence on breast tissue and cancer.
6.3 BACKGROUND
Breast cancer (BC) is the leading cause of death amongst Australian women
with risk increasing with age and with an average age of diagnosis of 60 years
(Australia, 2011). BRCA1 and BRCA2 are the two most commonly associated genes
in familial BC. With familial BC cases comprising only 5-10% of all BC cases,
sporadic BC cases have a much higher incidence rate (Anderson, 1991). Causes of
sporadic BC are not yet clearly understood and it is regarded as the more complex
form of the disease. miRNAs are a highly conserved class of small (~22 nucleotides),
endogenous, non-protein coding RNAs recently found to play a role not only in BC
but also in various other cancers. These miRNAs bind to target mRNA regions where
they can both inhibit transcription or initiate mRNA cleavage (Bartel, 2004; Lynam-
Lennon et al., 2009). Depending on target interaction, miRNAs can be classified as
128Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study.
either cancer causing (oncogenic) or tumour-suppressing (non-oncogenic).
Polymorphisms in miRNAs potentially alter these interactions by interfering,
generating or removing miRNA-binding sites as well as affecting expression of
miRNAs. Thus, these polymorphisms have the potential to significantly alter how the
miRNA interacts with its target/s and its effect on cellular biology. Consequently, the
identification of these SNPs and their association with disease susceptibility could be
used for improved diagnostic and therapeutic strategies.
MIR146A is a miRNA located on human chromosome 5 at locus 5q34 and has
been linked with BRCA1/BRCA2 activity, as these are among its potential targets.
The SNP rs2910164 is located in the middle of the stem hairpin in the miRNA and
leads to a G:U pair mismatch (instead of C:U) in the structure of MIR146A. In a
study by Shen et al (Shen et al., 2008), this polymorphism was linked with age of
diagnosis in breast and ovarian cancer patients and was the first research to show
association of MIR146A with breast cancer. The study reported that the presence of
at least one C allele lowered the age of diagnosis in BC patients with the level of
expression of mature MIR146A 60% higher in C allele carriers (n= 124 white
Caucasian women, 42 diagnosed with familial BC, 82 diagnosed with ovarian
cancer). In addition the authors identified an association of MIR146A with
BRCA1/BRCA2 by confirming that MIR146A could directly bind to the 3’UTR of
both genes and therefore play a role in regulating their expression. Interestingly, a
similar study conducted by Hu et al in 2009 on Han Chinese familial BC cases
(n=1009), produced contradictory results. In their study, although a much larger
cohort was examined, no association of the MIR146A SNP with age of BC diagnosis
was demonstrated; suggesting the effect of genotype or alleles for the SNP
rs2910164 may be population specific or modulated by environmental factors (Z. Hu
et al., 2009). In 2010, Pastrello et al conducted a similar study on Italian patients
diagnosed with familial BC and negative for BRCA1/BRCA2 mutations (n=101) to
ascertain if rs2910164 had any association with age of diagnosis (Pastrello et al.,
2010). Their results confirmed those by Shen et al and contradict those found by Hu
et al (Z. Hu et al., 2009; Shen et al., 2008). In addition, a study by Alshatwi et al in
Saudi-Arabian breast cancer patients indicated that while miR146a expression was
significantly altered in breast cancer patients compared to controls, rs2910164 did
Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study. 129
not appear to be associated with breast cancer risk, though their population was only
100 individuals and the ages of some of their patients indicated a potential familial
aspect in these cases (Alshatwi et al., 2012). Due to the variations in these studies
and the lack of research in a purely sporadic population of breast cancer patients in
Caucasians, we examined this SNP in a case-control Australian Caucasian sporadic
breast cancer population.
6.4 MATERIALS AND METHODS
6.4.1 Study Cohort
The study population (GRC-BC population) consisted of 160 Caucasian
Australian females, who were recruited on a voluntary basis with the criteria of being
diagnosed with sporadic breast cancer, showing no family history of the disease.
These women were matched for age (±5 years), Caucasian ethnicity and sex to a
control cohort of healthy individuals likewise showing no family history of breast or
related cancers. The case and control populations had average ages of 58±12 and
57.5±11.6 years, respectively. Case samples were recruited from the Gold Coast
Hospital, Southport, Australia, inviting all women with breast cancer attending to
participate. Control samples were recruited by the Genomics Research Centre Clinic,
Southport, Australia by invitation for healthy women from the community to
volunteer for the research.
The secondary replication population consisted of 403 sporadic breast cancer
patient samples obtained from the Griffith University-Cancer Council Queensland
Breast Cancer Biobank (GU-CCQ BB). These individuals were all those currently
available, recruited on a prevalence basis, but with confirmed diagnosis on the
Queensland Cancer Registry. The secondary population consisted of an additional 89
healthy controls, representing the currently available healthy women volunteers,
recruited and matched on a first-available-match basis to one of the cases as for the
130Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study.
primary population (age (±5 years), ethnicity and sex). Biobank samples and controls
had average ages of 61.09±25.09 and 60.2±28.2 years, respectively.
The project was carried out with the approval of the Griffith University Human
Research Ethics Committee, approval numbers: MSC/07/08/HREC and
PSY/01/11/HREC. All participants supplied informed written consent.
6.4.2 Genotyping
The candidate SNP in this study was identified using dbSNP
(http://www.ncbi.nlm.nih.gov/projects/SNP). Primers were designed using Primer3
(http://frodo.wi.mit.edu/) and checked for specificity through BLAST
(http://blast.ncbi.nlm.nih.gov). The SNPs were genotyped using high resolution melt
(HRM) analysis on a RotorGene-Q (Qiagen, Australia). The primer sequences used
were: forward 5’-TTACAGGGCTGGGACAG-3', reverse-5' -
CCTCAAGCCCACGATGA-3'. PCR amplification and HRM analyses were carried
out in a 12µL reaction containing: 2µL of 20ng/µL genomic DNA, 1µL of 10µM
each forward and reverse primers, 2.3µL of 25mM MgCl2, 0.8µL of 5µM dNTPs,
2.4µL of 5X GoTaq Buffer, 0.1µL of 5Units/µL GoTaq and 1.75µL dH2O. PCR
conditions were: 95oC for 1 minute, followed by 40 cycles of 95oC for 15 seconds,
62oC for 15 seconds and 75oC for 30 seconds, followed by 7 minutes at 72oC.
Melting analysis was carried out between 78oC and 86oC with data collected every
0.1oC. Two negative controls and two positive controls (Homozygous G and
Heterozygous CG) were included in each run to ensure a maintained high quality of
genotyping. Each sample was performed in duplicate. Sanger Sequencing (DNS) was
used on a random selection of four of each genotype in each population for
validation of genotypes identified by HRM.
Following HRM, the number of samples that were able to be accurately
genotyped was 143 cases and 157 controls for the GRC-BC population and the full
403 cases and 89 controls for the GU-CCQ BB population. Samples which were
Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study. 131
unable to provide an unambiguous genotype result or would not amplify in PCR for
direct sequencing were discarded.
6.4.3 Statistical Analysis
Analysis was undertaken using chi-square analysis in the SPSS statistics
package. Analysis was undertaken using the Chi-square test of independence and
Odds Ratios and 95% confidence intervals were calculated from the combined results
of both populations. All populations also underwent testing for Hardy Weinberg
equilibrium. The α was set at 0.05.
6.5 RESULTS
Following detection by HRM and sequencing validation, we obtained and
compared the observed genotypic and allelic frequencies for rs2910164 between the
case and control populations. There were some shifts in genotype frequencies
between the case and control populations, with the C allele approximately 10% rarer
in the case population, reflected in decreases in the CC and GC genotypes in cases
compared to controls, as seen in Table 6-1. Hardy-Weinberg analysis indicated that
both populations were in Hardy-Weinberg equilibrium. Chi square analysis indicated
that the genotype differences were not significant, but that allelic frequencies
between the populations were significantly altered (p= 0.11 and 0.03 respectively).
Table 6-1 Genotype and allele frequencies for GRC-BC population
Genotypes Alleles GG GC CC G C Cases 80 49 14 209 77 (n=143) (55.9%) (34.3%) (9.8%) (73.1%) (26.9%) Controls 70 63 24 203 111 (n=157) (44.6%) (40.1%) (15.3%) (64.6%) (35.4%) Allele Analysis x2 = 4.95 P = 0.03
132Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study.
Because the primary breast cancer population (GRC-BC) showed a significant
allelic association between rs2910164 alleles and breast cancer susceptibility, we
undertook genotyping in our secondary population (GU-CCQ BB). The results of
this genotyping showed a similar trend, with the C allele being approximately 10%
rarer in the case population compared to the controls, with both CC and GC
genotypes rarer in the case population compared to controls (Table 6-2). As for the
initial population, both case and control populations for the secondary analysis were
in Hardy-Weinberg equilibrium. Chi square analysis on the secondary association
population indicated that the differences between cases and controls were significant
for both genotype and allelic frequencies, at p= 0.00088 and 0.0013, respectively.
Table 6-2 Genotype and allele frequencies for GU-CCQ BB population
Genotypes Alleles GG GC CC G C Cases 245 144 14 634 172 (n=403) (60.8%) (35.7%) (3.5%) (78.6%) (21.4%) Controls 42 36 11 120 58 (n=89) (47.2%) (40.4%) (12.4%) (67.4%) (32.6%) Allele Analysis x2 = 10.29 P = 0.00134
Since both populations showed significant association with breast cancer
susceptibility, we undertook odds ratio calculations to determine the magnitude of
the effect. All three models tested (G vs. C for additive, GG vs. GC+CC for recessive
and GG+GC vs. CC for dominant inheritance models) showed increases in breast
cancer risk associated with a higher dose of the G allele, with the G vs. C model
showing the most precise odds estimate, with an OR of 1.774 (95% CI 1.402 -
2.237). A summary of combined statistical analysis for both populations can be
found in Table 6-3 and the complete Odds Ratios can be found in Table 6-4.
Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study. 133
Table 6-3 Combined primary and secondary population genotype and allele frequencies and analysis
results.
Genotypes Alleles GG GC CC G C Cases 325 193 28 843 249 (n=546) (59.5%) (35.3%) (5.2%) (77.2%) (22.8%) Controls 112 99 35 323 169 (n=246) (45%) (40.2%) (14.2%) (65.7%) (34.3%) Genotype Analysis x2 = 24.78 P = 0.000004 Allele
Analysis x2 = 23.28 P = 0.000001
Table 6-4 Odds Ratios for combined population analysis results.
Odds Ratios OR 95% CI G vs. C model 1.774 1.402 - 2.237 GG vs. GC and CC model 1.8428 1.3584 – 2.4999 GG and GC vs. CC model 3.0687 1.8206 - 5.1725
6.6 DISCUSSION
Our results indicate that the presence of the G allele for rs2910164 increases
the risk for sporadic breast cancer in a Caucasian population, confirmed through
analysis in two independently collected populations. These results show some
deviation from the expected frequency of the C allele in the general population
compared to reported frequencies for the rs2910164 SNP in Caucasian populations.
The reported allelic frequency as per the 1000 Genomes Project in Europeans is G-
78.2% and C-21.8%, while HapMap gives frequencies of G-76.5% and C-23.5%.
Data from other populations in both the 1000 Genomes Project and HapMap,
however, show that rs2910164 varies significantly in different ethnic groups, and in
Asians, the most common allele is C, with frequencies of C-60% and G-40%. More
importantly for this research, a subset breakdown within the European population
shows that Italians have an increased frequency of the C allele compared to other
European groups, at C-27% and G-73%. It thus seems possible that our elevated C
allele count is the result of a contribution from Italian and related Caucasian
subgroups, especially given that sequencing of HRM analysed samples showed
confirmation of the genotype calls in both cancer and control populations.
134Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study.
The results also show some variation form the implications of previous
research on rs2910164 in breast cancer, though those studies have also shown
conflicting results. In 2008, Shen et al identified an increased risk of breast cancer
amongst patients who had a single copy of the C allele and that the binding capacity
of MIR146A to BRCA1 was significantly higher in vitro when cells were transfected
with C allele mimics as compared to the G allele (Shen et al., 2008). These results
led them to draw the conclusion that the CC or CG variant of the rs291016
polymorphism increases susceptibility to familial BC. A similar study conducted by
Hu et al in 2009 found contrasting results (Z. Hu et al., 2009). The Hu et al study
involved 1009 familial breast cancer patients and 1093 healthy controls from a Han
Chinese population. In this study, no association of miR146a with increased risk of
familial breast cancer was observed. More recently, a study conducted by Catucci et
al in 2010 on 1894 familial breast cancer patients and 2760 healthy controls from
German and Italian populations also demonstrated no association with increased risk
of familial breast cancer and miR146a (Catucci et al., 2010). The discrepancy
between the results obtained from previous studies, including the current study may
be due to the varied ethnic backgrounds investigated, however, the observed
differences are more likely a result of fundamental differences in the type of breast
cancer addressed in the population cohorts examined.
This difference is that each of the previous breast cancer association studies
have been carried out in populations of individuals with familial cancer, most of
whom show symptoms of BRCA pathway malfunction, but lack BRCA1 or BRCA2
mutations. The suggested association of MIR146A with increased risk of breast
cancer by Shen et al in 2008 was a result of a study conducted on patients with
BRCA1/BRCA2 mutations, indicating familial breast cancer where these genes are
known to play an essential role (Shen et al., 2008). In addition, the study by Pastrello
et al where BRCA-negative patients were examined supports the data observed by
Shen et al for the effects of the C allele on age of onset (Pastrello et al., 2010; Shen
et al., 2008). Similar to the study by Shen et al and Alshatwi et al, the Pastrello et al
study cohort was a much smaller population when compared to the studies conducted
by Hu et al and Catucci et al (Catucci et al., 2010; Shen et al., 2008). Finally, a study Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study. 135
on 1166 BRCA1 mutation carriers and 560 BRCA2 mutation carriers from France
and America (no ethnicity specified) by Garcia et al failed to find an association
between rs2910164 and cancer risk in these patients. MIR146A has thus been
variably associated with BRCA1/BRCA2 familial BC risk, age of onset and age of
diagnosis in these studies. Meta-analyses on the BC studies to date indicate that both
ethnicity and gender is a factor in the effect of rs2910164 on cancer susceptibility
(Amandine I. Garcia et al., 2011; A. Wang et al., 2012). This would make sense in
the context of recent results from Omrani et al. who tested the rs2910164 SNP in an
Iranian breast cancer population and failed to find any association with the alleles
and breast cancer (Omrani et al., 2014).
The conflicting results of previous studies may arise through differential effects
of the SNP in the background of a familial impairment of BRCA1 and 2 associated
pathways compared to non-impairment of these pathways in sporadic breast cancer
patients. If BRCA associated pathways are poorly functional in familial breast cancer
patients, then the effect of the increased BRCA1/BRCA2 binding for the C allele of
rs2910164 may result in greater loss of cell growth regulation. By comparison, in
sporadic breast cancer the increased binding of the C allele may be compensated for
by upregulation of the precise BRCA related mechanisms that are impaired in
familial patients. This would lead to a situation where the control populations used to
determine association for the familial populations are subject to allelic pressure in the
same direction, partially masking the effect of the SNP on risk in these patients.
There is also support for differential biological effects for the SNP from data
gathered in other cancer types. In head and neck cancers, colorectal cancer, acute
lymphoblastic leukaemia and H. pylori infection related dysplasia, the C allele of
rs2910164 has been associated with increased risk of development (Chae et al., 2013;
Hasani et al., 2014; Lian et al., 2012; Ma et al., 2013; Orsós et al., 2013; Song et al.,
2013). By contrast, the G allele has been associated with increased risk of
hepatocellular carcinoma and prostate cancer, while melanomas showed a more
complex relationship where heterozygotes had increased risk of cancer development,
but cells carrying the G allele had increased invasive and proliferative characteristics
(B. Xu et al., 2010; Yeqiong Xu et al., 2013; Yumin Xu et al., 2013; Yamashita et
136Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study.
al., 2013). These differences add weight to the implications from the breast cancer
studies suggesting that the effect of the polymorphism is modulated by genetic and
environmental factors, probably due to the relative expression of its target mRNAs
and factors affecting its own expression. In addition, there may be significant effects
on the effect of miR146a through the presence or absence of BRCA1 or BRCA2
mutations, epistatic effects from other SNPs, or specific environmental triggers
common to closely related individuals compared to more sporadic population
cohorts. These in turn may influence the overall impact of the rs2910164 SNP on
breast cancer susceptibility, despite its observed functional effects.
6.7 CONCLUSIONS
Our analysis indicates that the rs2910164 SNP in MIR146A has a significant
effect on breast cancer susceptibility. Given the interaction with BRCA pathways, the
potential role of other polymorphisms (inferred from the role of ethnicity) and the
variable results of research on rs2910164 in breast cancer, further analyses on this
polymorphism should take into account the potential for familial versus sporadic
breast cancer effects, and should carefully choose controls to establish the effects of
the SNP more precisely in the target populations. This latter point may be a
limitation on our study, as ethnic variation of the SNP differs strongly and may have
affected our frequency data and obscured the true strength of the polymorphism in
breast cancer risk. Leading on from this, our relatively low population size may also
be affecting overall accuracy of the study, especially in the lack of controls available
for the secondary population. As a result, research using sporadic breast cancer
populations in larger cohorts of Caucasians and other ethnicities should be
undertaken to confirm the role of rs2910164 in cancer risk in the general population.
Chapter 6: Association of the microRNA-SNP rs2910164 in MIR146A with sporadic breast cancer susceptibility: a case control study. 137
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility.
7.1 STATEMENT OF CONTRIBUTION OF CO-AUTHORS FOR THESIS BY PUBLISHED PAPER
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the
conception, execution, or interpretation, of at least that part of the publication in their
field of expertise;
2. they take public responsibility for their part of the publication, except for the
responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, and;
5. they agree to the use of the publication in the student’s thesis and its publication
on the QUT ePrints database consistent with any limitations set by publisher
requirements.
In the case of this chapter:
Chacon-Cortes D, Smith RA, Rod LA, Youl PH and Griffiths LR. Genetic
association analysis of miRNA single nucleotide polymorphisms (miR-SNPs) in
Australian Caucasian breast cancer case control cohorts.
BMC Medical Genetics. (Under revision)
Contributor Statement of Contribution
Diego Chacon-Cortes
(Candidate)
Performed experimental work, contributed to
experimental design, research plan and data analysis,
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 138
Contributor Statement of Contribution
prepared and approved final version of the manuscript.
Robert A. Smith Assisted in assay design and optimisation and
critically reviewed and approved final version of the
manuscript.
Rodney A. Lea Assisted in statistical analysis, critically reviewed and
approved final version of the manuscript.
Philippa H. Youl Assisted in recruitment of the replication population
and provided organisation for replicate data entry for
control matching and approved final version of the
manuscript
Lyn R. Griffiths Provided advice on study design, laboratory oversight,
recruited the primary population, critically reviewed
and approved the final draft of the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all co-authors confirming
their certifying authorship.
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 139
7.2 ABSTRACT
MicroRNAs (miRNAs) are important small non-coding RNA molecules that
regulate gene expression in many important cellular processes related to the
pathogenesis of cancer. Genetic variation in miRNA genes could impact their
synthesis and cellular effects and single nucleotide polymorphisms (SNPs) are one
example of genetic variants previously studied in relation to breast cancer. Studies
aimed at identifying miRNA SNPs (miR-SNPs) associated with breast malignancies
could be the initial step towards further understanding of the disease and in
developing clinical applications for early diagnosis and treatment. In this study we
genotyped a panel of 24 miR-SNPs using multiplex PCR and chip-based matrix
assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry
(MS) analysis in our Australian Caucasian breast cancer case control populations.
Statistical analysis of our genotyping results showed six of these miR-SNPs to be
non-polymorphic and twelve of our selected miR-SNPs to have no association with
breast cancer risk. However we were able to show association between rs353291
(located in MIR145) and the risk of developing breast cancer in two independent case
control cohorts (p = 0.041 and p=0.023). Our study is the first to report an
association between a miR-SNP present in MIR145 in individuals of Caucasian
background and breast cancer risk. This finding requires further validation through
genotyping of larger cohorts or in individuals of different ethnicities to determine the
potential significance of this finding as well as studies to determine functional
significance.
7.3 BACKGROUND
MicroRNAs (miRNAs) are small non-coding single stranded RNA molecules
of about 21 to 25 nucleotides in length involved in negative regulation of gene
expression. They account for about 1% of the human genome and they were initially
identified in Caenorhabditis elegans (R. C. Lee et al., 1993; Reinhart et al., 2000).
They are transcribed from long microRNA primary transcripts (pri-miRNAs) that
contain miRNA precursors (pre-miRNA), which are stem-loop molecules of about 55
140 Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility.
to 70 nucleotides in length. Pre-miRNA are usually generated via the canonical
pathway, however a small group are produced through splicing/debranching
machinery in the “miRtron pathway”, or non-canonical pathway (Westholm & Lai,
2011). In the canonical pathway, pri-miRNA are cleaved by the complex formed by
the nuclear ribonuclease (RNase) III DROSHA and the RNA-binding protein
DCGR8 (DiGeorge syndrome critical region gene 8) (Yoontae Lee et al., 2003). Pre-
miRNAs are transported to the cytoplasm by XPO5 (exportin5) and the nuclear
protein ran-GTP and they are processed by DICER1, a RNase type III enzyme, and
double-stranded RNA binding domain (dsRBD) proteins TARBP2 and/or PKRA
(Chendrimada et al., 2005; Yi et al., 2003). This process results in the production of
a duplex molecule that contains both single-stranded mature miRNA sequence and a
complementary strand named miRNA*. The miRNA* is released and degraded while
the mature miRNA is loaded into the RNA-induced silencing complex (RISC) ,
where it is merged into the Argonaute/EIFC2C (Ago) proteins to mediate target
messenger RNA (mRNA) recognition and interaction. The mRNA target is
recognised by base pairing of the second to eighth nucleotides in the 5’-end of the
miRNA (Seed region) and the complementary sequence mainly located in the 3’
untranslated region (3’-UTR) region of the mRNA, but binding sites may also be
found in the coding region (Khvorova et al., 2003; Y. Lee et al., 2002). Each
miRNA may bind to up to 200 gene targets and each gene could have multiple
binding sites for different miRNAs, making a highly complex web of interactions
that may have extensive effects on a variety of biological pathways (Bartel, 2009;
Rajewsky, 2006).
miRNAs are very likely to play an important role in cancer biology because
they are involved in regulation of important cellular processes like cell growth,
differentiation and cell survival (Bartel, 2004; Thalia A. Farazi et al., 2011; Robbins
et al., 2010; Zuoren Yu et al., 2010; Baohong Zhang, Pan, et al., 2007; Baohong
Zhang, Wang, & Pan, 2007). Some of the mechanisms that can result in changes in
miRNA synthesis and expression in relation to cancer include: point mutations in
miRNA and mRNA sequences, loss or mutation in the promoter regions for specific
miRNA clusters, epigenetic changes and alterations in pathway related to dsRBD
proteins (Ha & Kim, 2014; Kim, 2005; Lynam-Lennon et al., 2009). Single
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 141
nucleotide polymorphisms (SNPs) in miRNA genes (miR-SNPs) are one example of
point mutation (albeit one occurring in the past) that could affect miRNA function in
one of three possible ways: altering transcription of the primary miRNA transcript;
altering processing of the pri-miRNA and pre-miRNA and; through their effects on
modulation of miRNA-mRNA interactions(S. Duan, Mi, Zhang, & Dolan, 2009; H.-
C. Lee et al., 2011; Ryan et al., 2010; G. Sun et al., 2009). As a result miR-SNPs
have been associated with different types of cancer, including chronic lymphocytic
leukaemia, gastric, lung and thyroid carcinoma (Calin & Croce, 2006; Z. Hu et al.,
2008; Jazdzewski et al., 2008; Q. Sun et al., 2010).
Breast cancer is the second most commonly diagnosed cancer around the world
(1.67 million new cases, 25%) and the most common type of cancer for women in
developed countries (793,684 new cases, 28%) according to the International Agency
for Research on Cancer (IARC) (Ferlay et al., 2013). Breast cancer is also the most
common cause of cancer-related deaths for women in the world (521,817, 14.7%)
and it ranks second for women in developed countries amongst all cancers
deaths(Ferlay et al., 2013). There is evidence of the role that miR-SNPs play in
relation to breast cancer. Hu et al. showed that the presence of mutant alleles of
MIR196A2 rs11614913 and MIR499A rs3746444 significantly increased breast
cancer susceptibility in Chinese women (Z. Hu et al., 2009). However in the genetic
association analysis of Caucasian populations and functional studies in breast cancer
cell lines performed by Hoffman et al. the presence of mutation in MIR196A2
rs11614913 was significantly associated with reduced risk of breast cancer, as well
as less efficient processing of MIR196A2 and reduced capacity to regulate target
genes, indicating additional factors may be at work in this SNP’s effect on breast
cancer (Hoffman et al., 2009). Kontorovich et al. found 2 SNPs, rs6505162 and
rs895819 located in MIR423 and MIR27A precursors respectively, to be significantly
associated with decreased risk of breast cancer in BRCA2 mutation carriers from a
Jewish population (Kontorovich et al., 2010). rs895819 was also significantly
associated with reduced risk of developing breast cancer in families with a history of
non-BRCA related breast cancer in a later study performed by Yang et al (Rongxi
Yang et al., 2010). Finally rs2910164, a miR-SNP located in the 3p strand of
142 Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility.
MIR146A, was found to be associated with a younger age of diagnosis in familial
breast cancer for BRCA1 mutation carriers (Shen et al., 2008).
This study investigated a panel of miR-SNPs present in miRNA genes
previously identified to be involved in the pathophysiological mechanisms of breast
cancer. Our initial selection included 24 variants located in 9 miRNA genes or in
close proximity to them and these were genotyped using multiplex PCR and matrix-
assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF
MS) analysis. Genotyping results of the 19 miR-SNPS successfully genotyped and
included in this study identified six previously reported microRNA SNPs as non-
polymorphic. From the remaining polymorphisms twelve miR-SNPS showed no
significant differences between cases and controls and one variant in MIR145 was
identified to be associated with breast cancer susceptibility in both of our Australian
Caucasian breast cancer case-control populations.
7.4 MATERIALS AND METHODS
7.4.1 Study Populations
Two independent Australian Caucasian case control populations were available
for our study: The Genomics Research Centre Breast Cancer (GRC-BC) population
and part of the Griffith University-Cancer Council Queensland Breast Cancer
Biobank (GU-CCQ BB). We conducted single nucleotide polymorphism genotyping
in the GRC-BC population initially. It consisted of DNA samples from 173 breast
cancer patients from South East Queensland and DNA samples from 187 healthy age
and sex matched females with no history of personal or familial cancer collected at
the Genomics Research Centre Clinic, Southport, with research approved by Griffith
University’s Human Ethics Committee (Approval: MSC/07/08/HREC and
PSY/01/11/HREC) and the Queensland University of Technology Human Research
Ethics Committee (Approval: 1400000104). All participants supplied informed
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 143
written consent. Average age of test population was 57.52 years and 57 years for
cases and controls respectively.
Further validation of genotyping results was performed on a subset of the GU-
CCQ BB population. 679 DNA samples from breast cancer patients residing in
Queensland with a diagnosis of invasive breast cancer confirmed histologically were
used to validate genotyping of miR-SNPs. Patient samples had been collected by the
Genomics Research Centre in collaboration with the Cancer Council of Queensland
as part of a 5-year population-based longitudinal study commenced in January 2010.
Patients included in this study were between 33 and 80 years of age, with an average
age of 60.16 years. Control population for the GU-CCQ BB was established from 2
sources: 107 healthy age and ethnicity matched females with no personal or familial
history of cancer, recruited since January 2000 at the Genomics Research Centre at
the Institute of Health and Biomedical Innovation, Queensland University of
Technology and genotyping result data taken from 201 age, ethnicity and sex
matched individuals belonging to the phase 1 European population from the
1000Genomes project (Abecasis et al., 2012).
7.4.2 Genomic DNA sample preparation from whole human blood.
Genomic DNA was extracted from whole blood samples using a modified
salting out method described previously (Chacon-Cortes et al., 2012; Nasiri et al.,
2005). DNA samples were evaluated by spectrophotometry using the Thermo
Scientific NanoDropTM 8000 UV-Vis Spectrophotometer (Thermo Fisher Scientific
Inc., Wilmington, DE. USA) to determine DNA yield and 260/280 ratios (Chacon-
Cortes & Griffiths, 2014; Huberman, 1995; Sahota, Brooks, Tischfield, & King,
2007). Samples with a reading below 1.7 for their 260/280 ratio were purified using
an ethanol precipitation protocol to guarantee DNA sample purity (Sambrook &
Russell, 2001).
144 Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility.
7.4.3 miRNA SNP selection
Figure 7-1 shows the selection process we followed to determine miRNA SNPs
(miR-SNPs) that could be included in our study. Two datasets, “The whole miRNA-
disease association data” and “The miRNA function set data” from the human
miRNA disease database (HMMDD) created by Lu et al (M. Lu et al., 2008) and
updated in January 2012, were used to select 8 diseases and/or pathological
characteristics and 24 biological and/or cellular functions related to breast cancer
(See Table 7-1).
Table 7-1 Selected features from the Human miRNA disease database (HMDD) included in present
study
Selected diseases and/or pathological features related to breast cancer
miRNA-disease
association dataset
Human miRNA genes in dataset
1. Adenocarcinoma 2. Breast Neoplasms 3. Carcinoma 4. Ductal breast carcinoma
5. Squamous Cell Carcinoma 6. Neoplasms 7. Germ cell and embryonal neoplasm 8. Squamous cell neoplasms
503 Human Diseases in
dataset 396
Selected biological and/or cellular function in relation to Cancer
miRNA function dataset
Human miRNA genes in dataset
1. Activation of caspases cascade 2. AKT pathway 3. Angiogenesis 4. Anti-cell proliferation 5. Apoptosis 6. Cell cycle related 7. Cell death 8. Cell differentiation 9. Cell division 10. Cell fate determination 11. Cell motility 12. Cell proliferation
13. Chemosensitivity of tumour cells 14. Chemotaxis 15. Chromatin remodelling 16. DNA Repair 17. Epithelial-mesenchymal transition 18. Human embryonic stem cell (hESC) regulation 19. Immune response 20. Immune system 21. Inflammation 22. miRNA tumour suppressors 23. Onco-miRNAs
503
Biological and cellular functions in
dataset
43
As shown in Figure 7-1, we picked the 50 miRNA genes from each dataset that
were present in the majority of selected features for inclusion in the following steps.
This list was narrowed down to the 25 miRNA genes on each dataset with the
strongest evidence in order to maximise the potential for identification of
biologically relevant molecules using two main criteria: miRNAs involved in the
largest number of selected features from each group followed by a literature search to
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 145
confirm the number of publications showing significant relationships to cancer
biology or the possession of known functional effects of polymorphisms within the
miRNA itself. Following this, we chose 10 miRNA genes from the 25 genes on both
lists, again prioritising by number of functions and publications, and conducted a
search to identify SNPs using both dbSNP database from The National Center for
Biotechnology Information (NCBI) (Sherry et al., 2001) and 1000 Genomes project
browser (Consortium, 2012). Final selection of SNPs was done using this algorithm:
All microRNA-SNPs located inside the pre-miRNA gene were automatically
included in the SNP selection. However, SNPs located outside of the pre-miRNA
gene were assessed using the following criteria: miR-SNPs located up to 500bp
upstream or downstream from pre-miRNA were automatically included in the SNP
selection. On the other hand, SNPs located more than 500bp from the 3’ or 5’ end
were chosen only if they had a previously reported minor allele frequency higher
than 5% in Caucasian populations. As a result 56 microRNA SNPs were identified in
this preliminary selection (Data not shown) (See Figure 7-1).
146 Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility.
Figure 7-1 MicroRNA SNP (miR-SNP) selection algorithm using the Human miRNA Disease Database (HMDD). This flow chart shows workflow for selection of preliminary miR-SNPs included in genotyping study.
Abbreviations: dbSNP, single nucleotide polymorphism database; MAF, minor allele frequency; miRNA, microRNA; NCBI National Center for Biotechnology Information; SNP, Single nucleotide polymorphisms
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 147
7.4.4 Primer design
Using the MassARRAY® Assay Design Suite v1.0 software (SEQUENOM
Inc., San Diego, CA, USA) we were able to create a single multiplex PCR
genotyping assay containing 24 miR-SNPs from our preliminary selection (See Table
7-2).
We designed forward and reverse PCR primers and one iPLEX® (extension)
primer and verified that the mass of extension primers differed by at least 30 Da
among different SNPs and by 5 Da between alternative alleles of the same marker to
achieve successful marker and allele identification by mass spectrometry analysis.
Primers were manufactured by Integrated DNA Technologies (IDT®) Pte. Ltd.
(Baulkham Hills, NSW 2153, Australia) and primer information is shown in Table
7-3.
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 148
Table 7-2 List of the miRNA SNPs included in multiplex primer design using the MassARRAY® Assay Design Suite v1.0 software (SEQUENOM Inc., San Diego, CA,
USA)
miRNA Locus Location SNPs in gene region MAF Location SNPs near
3' end MAF Location SNPs near 5' end MAF Location
MIR210 11p15.5 568,089 - 568,198 rs1062099 0.187 567,627 rs7395206 0.500 568,211 rs1090217
3 0.483 570,072
MIR34A 1p36.22 9,211,727 - 9,211,836 rs35301225 NA 9,211,802 rs77585961 0.024 9,211,468 MIR155 21q21.3 26,946,292 - 26,946,356 rs2829801 0.470 26,944,305 rs1547354 0.081 26,946,709 MIR221 Xp11.3 45,605,585 - 45,605,694 rs7050391 0.013 45,605,273 rs2858061 0.435 45,606,132
rs2858060 0.499 45,607,008 rs2858059 0.478 45,607,190
MIR222 Xp11.3 45,606,421 - 45,606,530 rs2858061 0.435 45,606,132 rs2858060 0.499 45,607,008 rs2858059 0.478 45,607,190
MIR21 17q23.1 57,918,627 - 57,918,698 rs112394324 0.003 57,918,251 rs1292037 0.244 57918908 rs3851812 0.037 57,918,370 rs13137 0.244 57919031
MIRLET7A1 9q22.32 96,938,239 - 96,938,318 rs10761322 0.458 96,937,478 rs10739971 0.260 96,937,680
MIRLET7A2 11q24.1 122,017,230 - 122,017,301
rs629367 0.145 122,017,014 rs1143770 0.486 122,017,598 rs562052 0.326 122,018,550 rs693120 0.326 122,019,011
MIR145 5q32 148,810,209 - 148,810,296
rs55945735 0.201 148,809,158 rs353291 0.332 148,810,746 rs73798217 0.054 148,809,918
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 149
Table 7-3 Primer sequences for the miRNA SNPs included in genotyping study using multiplex PCR reaction and MALDI-TOF MS
SNP Forward primer sequence Reverse primer sequence iPLEX® (Extension) primer sequence rs2829801 ACGTTGGATGTTCCCACTCAGGCATATAGC ACGTTGGATGTGGAGTCTATGTCCACTTCC TGGGCTAAGCAACCA rs7395206 ACGTTGGATGTCGGACGCCCAAGTTGGAG ACGTTGGATGTCACACGCACAGTGGGTCT GGTGGGCGGGCGGAG rs73798217 ACGTTGGATGACTGGAGGTTATCAGAGAGG ACGTTGGATGATGTGTATTCCCCAGTCTCC ACCATTCAGTTCCTAGC rs353291 ACGTTGGATGGTAGAGATGCCACAAGAGGG ACGTTGGATGAAACCTTAAGTCTTCGTTCG GGTTGTTCTCTGGCTGC rs55945735 ACGTTGGATGGGTCTCAAACTCCTGACTTC ACGTTGGATGCTGCATCTGAGCACTTCAAG TGATGCCTGGCGGGGCG rs10902173 ACGTTGGATGTCACAGGCACCTTTTCTCAG ACGTTGGATGGAAGCCTGGGTATTAGGATG ACCTTTTCTCAGCATCTG rs112394324 ACGTTGGATGACTGGAGAGAGAAATTACCC ACGTTGGATGGTTGAAACCAGAGTACATGC CAGATACGACAGAGTGTG rs1292037 ACGTTGGATGTACAGCTAGAAAAGTCCCTG ACGTTGGATGGGAGGGAGGATTTTATGGAG AACCTTTTCAAAACCCACA rs562052 ACGTTGGATGCTCCAGGCTAGTGGAATAAC ACGTTGGATGCTACTGCACTGATCGTGTTC CAGATTCAAATGCCATAGCA rs2858059 ACGTTGGATGCAGTAAGTATTTCTGGGGTG ACGTTGGATGCTCCCATGATACAATGAAGG TGGGGTGGATAAATGAATAG rs1062099 ACGTTGGATGGACCCGGTCCTGATTTTAAC ACGTTGGATGTGTGTTTCTGCCGCTTCAGT TTTAACAGTAGACTTGAGAAG rs10739971 ACGTTGGATGCCTAATAAGACCACTTAGTGT ACGTTGGATGATGCACTAACATACAACGAG CCACCTACTCATTTATCCCATG rs2858060 ACGTTGGATGACTGTATTATCCTCAGTTC ACGTTGGATGACTTGGGTAATCTAGCAATG GTATTATCCTCAGTTCGTAACA rs2858061 ACGTTGGATGGCTTTCAATACTACAAGGG ACGTTGGATGAATGATACCTTTCATAGGGG GTAAAACAAAAACAGGTAAGAG rs35301225 ACGTTGGATGGGCAGTATACTTGCTGATTG ACGTTGGATGGCTGTGAGTGTTTCTTTGGC GGATTATTGCTCACAACAACCAG rs1547354 ACGTTGGATGTTGCAGGTTTTGGCTTGTTC ACGTTGGATGGGAGGTTAGTAGTCCTTCTA TTTGATTCAACTGTTAGAAATGTG rs13137 ACGTTGGATGAGGTGAAAGAGATGAACCAC ACGTTGGATGAAAGCATTCCCAAAATGCTC TCCCAAAATGCTCTATTTTAGATAG rs10761322 ACGTTGGATGGCTTTTGGTTACTAAATCAC ACGTTGGATGCTTCATATTTAGGAGGTAGC GGCATATTTAGGAGGTAGCTACTAC rs693120 ACGTTGGATGGTAGATGGCACATATAGAAA ACGTTGGATGCATCCCTTAACTGTAAGTTC GATTGTCAAATGAAAAGAAGAATAT rs1143770 ACGTTGGATGCTGAACAATTTAATGCCTTC ACGTTGGATGTTCAGTTTTACCAGAGGAAC CCCCCATGCCTTCTGATATCTGTTGA rs77585961 ACGTTGGATGGTTTCCTTCTCTGCAAGACG ACGTTGGATGCACTTACTATGCAGGAAGGC CCTACATGATGTAATACACTTACAATA rs7050391 ACGTTGGATGGTAAGGCAGTATGATTAGGC ACGTTGGATGGCCTCAACTGTCAAAGATTG TGTTCATAATTATTATCAGAAGGCATA rs3851812 ACGTTGGATGTTTTCCTCCCAAGCAAAAC ACGTTGGATGTTCTTGCCGTTCTGTAAGTG GGTGGAAGTGTTTTATTCTTAGTGTGA rs629367 ACGTTGGATGTATGCAGCATTTTTGTGAC ACGTTGGATGATTCTGTTTCCTCGGGTTAG GGGGTCAGCATTTTTGTGACAATGGACA
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 150
7.4.5 Primary Multiplex PCR
Genotyping was undertaken following the iPLEX™ GOLD genotyping
protocol using the iPLEX® Gold Reagent Kit (SEQUENOM Inc., San Diego, CA,
USA). Primer extension reactions were performed according to the instructions for
the SEQUENOM linear adjustment method included in the iPLEX™ GOLD
genotyping protocol (SEQUENOM Inc., San Diego, CA, USA). All reactions were
performed using Applied Biosystems® MicroAmp® EnduraPlate™ Optical 96-Well
Clear Reaction Plates with Barcode (Life Technologies Australia Pty Ltd.,
Mulgrave, VIC, Australia) and an Applied Biosystems® Veriti® 96-Well Thermal
Cycler (Life Technologies Australia Pty Ltd., Mulgrave, VIC, Australia).
7.4.6 MALDI-TOF MS analysis and data analysis
A total of 12-16 nl of each iPLEX® reaction product were transferred onto a
SpectroCHIP® II G96 (SEQUENOM Inc., San Diego, CA, USA) using
SEQUENOM® MassARRAY® Nanodispenser (SEQUENOM Inc., San Diego, CA,
USA). SpectroCHIP® analysis was carried out by SEQUENOM® MassArray®
Analyzer 4 and the SpectroAcquire software Version 4.0 (SEQUENOM Inc., San
Diego, CA, USA). Finally data analysis for genotype determination was done using
the MassARRAY® Typer software version 4.0 (SEQUENOM Inc., San Diego, CA,
USA).
7.4.7 Statistical analysis
Statistical analysis of genotypes and alleles was conducted using Plink
software version 1.07 (http://pngu.mgh.harvard.edu/purcell/plink/) (Purcell et al.,
2007). The α for p-values was set at 0.05 to determine statistically significant
association with breast cancer. Genotype and allele frequencies for each miRNA
SNP in our case and control populations were established and we used Hardy-
Weinberg equilibrium (HWE) to evaluate deviation between observed and expected
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 151
frequencies for identification of unexpected population or genotyping biases (Guo &
Thompson, 1992; Hardy, 1908). We performed Chi square analysis to evaluate
differences in genotype and allele frequencies between cases and controls for each
independent population (Fisher & Yates, 1963). Finally we calculated odds ratio
(OR) and obtained 95% confidence interval (CI) 95% to assess disease risk.
7.5 RESULTS AND DISCUSSION
MicroRNAs are some of the small non-coding RNA molecules responsible for
gene regulation at the translational level. They require a very complex series of
nuclear and cytoplasmic processes for their synthesis and to achieve their functional
effects on genes involved in key cellular functions like replication and cell
differentiation (Bartel, 2009; Y. Lee et al., 2002; Baohong Zhang, Wang, et al.,
2007). As a result they are likely to play a role in the development and progression of
cancer due to the important biological mechanisms they regulate (Thalia A. Farazi et
al., 2011; Robbins et al., 2010; Baohong Zhang, Pan, et al., 2007). They have been
shown to have different roles in various types of cancer including breast cancer
(Calin & Croce, 2006; Lynam-Lennon et al., 2009). Breast cancer is a cause for
concern since recent reports by the IARC show it has very high incidence and
mortality rates around the world (Ferlay et al., 2013). Analysis of single nucleotide
polymorphisms in well-defined case control cohorts has provided information on
miR-SNPs involved in the pathophysiology of different types of cancer including
breast cancer (Hoffman et al., 2009; Z. Hu et al., 2008; Z. Hu et al., 2009;
Jazdzewski et al., 2008; Kontorovich et al., 2010; Shen et al., 2008; Q. Sun et al.,
2010; Rongxi Yang et al., 2010). Therefore we selected a panel of 24 miR-SNPs
related to 9 miRNA genes previously identified to play a role in breast cancer to
genotype in our Australian Caucasian breast cancer case control populations.
Genotyping of our selected miRNA variants in the GRC-BC cohort showed six of
them to be non-polymorphic (rs73798217, rs112394324, rs35301225, rs1547354,
rs7050391 and rs3851812) and another five of the chosen miR-SNPs failed to
successfully deliver genotypes (rs2829801, rs7395206, rs2858059, rs1143770 and
rs77585961).
152 Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility.
On the other hand, genotype and allele frequencies of the remaining 13 miR-
SNPs in the GRC-BC cases and controls showed these to be closely similar to those
found in Hapmap for Caucasian populations and they were also in Hardy Weinberg
Equilibrium (HWE) (p > 0.05). Ultimately, however, chi-square analysis of
genotyping results of 12 SNPs located in relation to seven miRNAs (MIR210,
MIR221, MIR222, MIR21, MIRLET7A1, MIRLET7A2 and MIR145) showed no
significant differences for genotype and allele frequencies between cases and
controls in our GRC-BC population (See Tables 6-4 to 6-9).
In contrast, we were able to determine significant differences at the allelic level
for rs353291 after chi-square analysis in our GRC-BC cohort (p=0.041) although no
significant difference in genotype frequencies (p=0.09) (See Table 7-10). We then
proceeded to genotype this SNP in the GU-CCQ BB population and we also found
that genotype and allele frequencies closely matched Hapmap frequencies for
Caucasian populations and that cases and controls were in HWE as well. Statistical
analysis of genotyping in our replication population showed similar findings to those
obtained in the GRB-BC cohort shown in Table 7-10. We were able to find
significant differences in allele frequencies between cases and controls in the GU-
CCQ BB population (p=0.023) and also no statistically significant differences at the
genotype level (p=0.07). Finally we calculated odds ratios for alleles in the GRC-BC
and GU-CCQ BB populations to be 1.37(CI 95%: 1.01-1.84) and 1.25 (CI 95%:
1.03-1.52) respectively, which suggests that the presence of the C allele at this locus
increases the risk of developing breast cancer. However it should be noted that if we
consider statistical correction for multiple testing, our finding for the analysis of
allele frequencies for both populations for this variant is not significant if we
determine Bonferroni correction of the α for p-value to be 2.08 x 10-3. However these
results are still potentially interesting, particularly considering the similarity of allelic
significance in both independent case-control cohorts and point to the need for
further genotyping in extended populations.
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 153
Table 7-4 Allele and genotype frequencies for miRNA SNPs in MIR210 obtained from the GRC-BC population
rs1062099 rs10902173 Allele Genotype Allele Genotype G
(%) C
(%) p-value GG
(%) CG (%)
CC (%)
p-value C (%)
T (%)
p-value CC (%)
TC (%)
TT (%)
p-value
Control 300 (83.3)
60 (16.7)
0.36 126 (70.0)
48 (26.7)
6 (3.3)
0.61 216 (59.3)
148 (40.7)
0.93 63 (36.6)
90 (49.5)
29 (15.9)
0.64
Cases 263 (80.7)
63 (19.3)
106 (65.0)
51 (31.3)
6 (3.7)
203 (59.0)
141 (41.0)
63 (34.6)
77 (44.8)
32 (18.6)
Hapmap (%) 81.7 18.3 66.8 29.8 3.4 60.4 39.6 34.8 51.2 14.0
Table 7-5 Allele and genotype frequencies for miRNA SNPs in MIR221 and MIR222 from the GRC-BC Population
rs2858061 rs2858060 Allele Genotype Allele Genotype G
(%) C
(%) p-value GG
(%) CG (%)
CC (%)
p-value C (%)
G (%)
p-value CC (%)
GC (%)
GG (%)
p-value
Control 323 (87.8)
45 (12.2)
0.43 142 (77.2)
39 (21.2)
3 (1.6)
0.38 219 (69.3)
97 (30.7)
0.75 81 (51.3)
57 (36.1)
20 (12.7)
0.35
Cases 295 (85.8)
49 (14.2)
130 (75.6)
35 (20.3)
7 (4.1)
218 (68.1)
102 (31.9)
74 (46.3)
70 (43.8)
16 (10.0)
Hapmap (%) 86.2 13.8 80.2 12.4 7.4 72.8 27.2 62.3 20.6 17.2
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 154
Table 7-6 Allele and genotype frequencies for miRNA SNPs in MIR21 obtained from the GRC-BC population
rs1292037 rs13137 Allele Genotype Allele Genotype A
(%) G
(%) p-
value AA (%)
GA (%)
GG (%)
p-value
T (%)
A (%)
p-value
TT (%) AT (%) AA (%)
p-value
Control 300 (80.6)
72 (19.4)
0.86 122 (65.6%)
56 (30.1)
8 (4.3)
0.95 299 (80.8)
71 (19.2)
0.85 122 (65.9)
55 (29.7)
8 (4.3)
0.94
Cases 274 (80.1)
68 (19.9)
110 (64.3) 54 (31.6)
7 (4.1)
276 (80.2)
68 (19.8)
111 (64.5)
54 (31.4)
7 (4.1)
Hapmap (%)
81.0 19.0 65.4 31.1 3.4 81.0 19.0 65.4 31.1 3.4
Table 7-7 Allele and genotype frequencies for miRNA SNPs in MIRLET7A1 obtained from the GRC-BC cohort
rs10761322 rs10739971 Allele Genotype Allele Genotype
T (%)
C (%)
p-value TT (%) TC (%)
CC (%)
p-value G (%)
A (%)
p-value GG (%)
AG (%)
AA (%)
p-value
Control 225 (61.8)
139 (38.2)
0.73 76 (41.8)
73 (40.1)
33 (18.1)
0.08 243 (67.5)
117 (32.5)
0.39 89 (49.4)
65 (36.1)
26 (14.4)
0.36
Cases 207 (60.5)
135 (39.5)
59 (34.5)
89 (52.0)
23 (13.5)
223 (64.5)
123 (35.5)
74 (42.8)
75 (43.4)
24 (13.9)
Hapmap (%) 61.9 38.1 40.9 42.0 17.2 68.1 31.9 46.4 43.3 10.3
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 155
Table 7-8 Allele and genotype frequencies for miRNA SNPs in MIRLET7A2 obtained from the GRC-BC population
rs629367 rs562052 rs693120
Allele Genotype Allele Genotype Allele Genotype
A
(%) C
(%) p-
value AA (%)
CA (%)
CC (%)
p-value G (%) A (%) p-
value GG (%)
AG (%)
AA (%)
p-value
G (%)
A (%)
p-value
GG (%)
AG (%)
AA (%)
p-value
Control 302 (82.5)
64 (17.5) 0.72
125 (68.3)
52 (28.4)
6 (3.3) 0.88
242 (65.4)
128 (34.6) 0.76
81 (43.8)
80 (43.2)
24 (13.0) 0.57
230 (67.3)
112 (32.7) 0.77
82 (48.0)
66 (38.6)
23 (13.5) 0.21
Cases 289 (83.5)
57 (16.5)
122 (70.5)
45 (26.0)
6 (3.5)
230 (66.5)
116 (33.5)
74 (42.8)
82 (47.4)
17 (9.8)
225 (66.2)
115 (33.8)
72 (42.4)
81 (47.6)
17 (10.0)
Hapmap (%) 84.6 15.4 75.4 18.5 6.2 68.1 31.9 46.2 43.8 10.0 68.1 31.9 46.2 43.8 10.0
Table 7-9 Allele and genotype frequencies for rs55945735 located in MIR145 obtained from the GRC-BC population
rs55945735 Allele Genotype
A (%) G (%) p-value AA (%) GA (%) GG (%) p-value Control 193 (59.9) 129 (40.1) 0.29 61 (37.9) 71 (44.1) 29 (18.0) 0.58 Cases 182 (64.1) 102 (35.9) 60 (42.3) 62 (43.7) 20 (14.1)
Hapmap (%) 62.8 37.2 38.3 49.1 12.7
156 Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility.
Table 7-10 Allele and genotype frequencies for SNP rs353291 located in MIR145 obtained from the GRC-BC and GU-CCQ BB cohorts
GRC-BC Population GU-CCQ BB Population Allele Genotype Allele Genotype
T (%)
C (%)
p-value TT (%)
CT (%)
CC (%)
p-value T (%)
C (%)
p-value TT (%)
CT (%)
CC (%)
p-value
Control 211 (58.6)
149 (41.4)
0.041 61 (33.9)
89 (49.4)
30 (16.7)
0.09 386 (62.7)
230 (37.3)
0.023 128 (41.6)
130 (42.2)
50 (16.2)
0.07
Cases 171 (50.9)
165 (49.1)
40 (23.8)
91 (54.2)
37 (22.0)
769 (57.2)
575 (42.8)
229 (34.1)
311 (46.3)
132 (19.6)
Hapmap (%) 62.7 37.3 40.6 44.1 15.3 62.7 37.3 40.6 44.1 15.3
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 157
miR-SNP rs353291 is located 450 bp upstream from the MIR145 gene, inside the
MIRNA143HG transcript, in the long arm of chromosome 5 region 32 at position
148,810,746. To the best of our knowledge there are no previous association studies
in relation to this miR-SNP and breast cancer risk in Caucasians or any populations
of other ethnicities. However, MIR145 has been previously reported to play a role in
cancer biogenesis and progression. Downregulation of MIR145 is a common finding
previously reported in colorectal (Arndt et al., 2009; Michael et al., 2003), bladder
(Chiyomaru et al., 2010), lung (Cho et al., 2009, 2011) and oesophageal (Kano et al.,
2010) cancers possibly leading to poor prognosis. Similarly, it has also been found to
have a tumour suppressor role in breast tissue particularly in the myoepithelial/basal
cell compartment and it is found to be downregulated in breast cancer tissue samples
(Iorio et al., 2005; Sempere et al., 2007; Volinia et al., 2006). Research using breast
cancer cell lines showed it regulates genes involved in modulation of apoptosis
(Spizzo et al., 2010; S. Wang et al., 2009). Finally downregulation of MIR145 has
been associated with a more aggressive behaviour of breast malignancies based on
results performed in both breast cancer cell line and tissue samples (Goette et al.,
2010; Radojicic et al., 2011). However the molecular mechanisms leading to
decreased expression of MIR145 still remain unknown and our finding could
potentially help to provide further knowledge on these mechanisms through further
functional validation on breast cancer cell culture and/or animal/models. It is also
possible that rs353291 is linked to a different nearby SNP not considered in this
research that may have direct functional effects on MIR145, so additional
sequencing/genotyping studies may need to be performed prior to functional
assessment of the link between rs353291 and breast cancer.
7.6 CONCLUSIONS
This study suggests a role for a mutation in miR-SNP rs353291 in breast
cancer. To the best of our knowledge, this is the first report on breast cancer risk
association for this variant. This finding could potentially explain the previously
described role that MIR145 plays in breast cancer, previously documented in the
literature, but it requires confirmation via functional studies using cell culture or
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 158
animal models. It also requires further validation on larger Caucasian population as
well as in cohorts of individuals with different ethnic backgrounds before it can be
translated into clinical applications used in breast cancer diagnosis or treatment.
Chapter 7: Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility. 159
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer.
8.1 STATEMENT OF CONTRIBUTION OF CO-AUTHORS FOR THESIS BY PUBLISHED PAPER
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the
conception, execution, or interpretation, of at least that part of the publication in their
field of expertise;
2. they take public responsibility for their part of the publication, except for the
responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, and;
5. they agree to the use of the publication in the student’s thesis and its publication
on the QUT ePrints database consistent with any limitations set by publisher
requirements.
In the case of this chapter:
Chacon-Cortes D, Smith RA, Lea RA, Youl PH and Griffiths LR (2015). Association
of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast
cancer. Tumor Biology, 36(7), 5369-76
Contributor Statement of Contribution
Diego Chacon-Cortes
(Candidate)
Performed experimental work, contributed to
experimental design, research plan and data analysis,
prepared and approved final version of the manuscript.
Robert A. Smith Assisted in assay design and optimisation and
critically reviewed and approved final version of the
160 Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer.
Contributor Statement of Contribution
manuscript.
Rodney A. Lea Assisted in statistical analysis, critically reviewed and
approved final version of the manuscript.
Philippa H. Youl Assisted in recruitment of the replication population
and provided organisation for replicate data entry for
control matching and approved final version of the
manuscript
Lyn R. Griffiths Provided advice on study design, laboratory oversight,
recruited the primary population, critically reviewed
and approved the final draft of the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all co-authors confirming
their certifying authorship.
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 161
8.2 ABSTRACT
Breast cancer incidence and mortality rates are increasing despite our current
knowledge of the disease. 90-95% of breast cancer cases correspond to sporadic
forms of the disease and are believed to involve an interaction between
environmental and genetic determinants. The microRNA 17-92 cluster host gene
(MIR17HG) has been shown to regulate expression of genes involved in breast
cancer development and progression. Study of single nucleotide polymorphisms
(SNPs) located in this cluster gene could help provide a further understanding of its
role in breast cancer. Therefore, this study investigated 6 SNPs in the MIR17HG
using two independent Australian Caucasian case-control populations (GRC-BC and
GU-CCQ BB populations) to investigate association to breast cancer susceptibility.
Genotyping was undertaken using chip-based matrix assisted laser desorption
ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). We found
significant association between rs4284505 and breast cancer at the allelic level in
both study cohorts (GRC-BC p = 0.01 and GU-CCQ BB p = 0.03). Furthermore
haplotypic analysis of results from our combined population determined a significant
association between rs4284505/rs7336610 and breast cancer susceptibility (p = 5x10-
4). Our study is the first to show that the A allele of rs4284505 and the AC
haplotype of rs4284505/rs7336610 are associated with risk of breast cancer
development. However, definitive validation of this finding requires larger cohorts or
populations in different ethnic backgrounds. Finally, functional studies of these SNPs
could provide a deeper understanding of the role that MIR17HG plays in the
pathophysiology of breast cancer.
8.3 INTRODUCTION
In 2012, 14.09 million people in the world were newly diagnosed with cancer
and about 21% of them corresponded to breast cancer (1,676,633 cases in total)
according to the International Agency for Research on Cancer (IARC). It is the most
common type of cancer in women worldwide and is estimated to be developed by
one in eight women living to the age of 90. Breast cancer is also the fifth cause of
162 Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer.
cancer-related deaths in the world (6.4%) and accounted for 197,528 of all cancer
related deaths in developed countries. It has become a cause for concern since both
incidence and mortality rates have increased by more than 20% and 14% respectively
from figures reported in 2008 (Ferlay et al., 2013; Robbins et al., 2010; Stewart,
Wild, International Agency for Research on, & World Health, 2014). Sporadic forms
of breast cancer account for about 90-95% of all cases and they are most likely the
result of interaction between genetic and environmental factors (Anderson, 1991;
Claus, Schildkraut, Thompson, & Risch, 1996; Martin & Weber, 2000; Robbins et
al., 2010).
MicroRNAs (miRNAs) are small non-coding single stranded RNA molecules
of about 21 to 25 nucleotides in length involved in gene regulation at the
translational level. They account to about 1% of the human genome and are very
likely to be important in cancer biology due to their role in regulating important
cellular processes like cell growth, differentiation and cell survival (Calin & Croce,
2006; Thalia A. Farazi et al., 2011; Lynam-Lennon et al., 2009; Robbins et al., 2010;
Zuoren Yu et al., 2010). miRNAs are usually generated following the canonical
pathway where they are transcribed from long microRNA primary transcripts (pri-
miRNAs) about 1kb in size that contain up to six miRNA precursors (pre-miRNA).
Pre-miRNAs are stem-loop molecules of about 55 to 70 nucleotides in length and
they are produced when pri-miRNA are cleaved by the complex Drosha – DCGR8
(DiGeorge syndrome critical region gene 8, also known as pasha) within the cellular
nucleus. Pre-miRNAs are then transported to the cytoplasm by XPO5 (exportin5)
and the nuclear protein Ran-GTP. In the cytoplasm pre-miRNAs are processed by
DICER1, a ribonuclease type III enzyme, and double-stranded RNA binding proteins
(RBP) TARBP2 and/or PKRA producing a duplex molecule that contains both the
single-stranded mature miRNA sequence and its complementary strand named
miRNA*. The miRNA:miRNA* molecule is loaded into the RNA-induced silencing
complex (RISC), where the mature miRNA sequence is merged into the
Argonaute/EIFC2C (Ago) proteins and the miRNA* is released and degraded. Ago
proteins bound to mature miRNA mediate target messenger RNA (mRNA)
recognition and interaction between these three components results in gene
regulation. The mRNA target is recognised by pairing of the miRNA seed region
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 163
(nucleotides 2 to 8) located in the 5’-end of the miRNA and the complementary
sequence mainly located in the 3’-UTR region of the mRNA(Chendrimada et al.,
2005; Yoontae Lee et al., 2003; Y. Lee et al., 2002; Yi et al., 2003). Each miRNA
may bind to up to 200 gene targets and each gene could have multiple binding sites
for different miRNAs (Bartel, 2009; Rajewsky, 2006). Location of promoter regions
of microRNA genes, mechanisms that regulate their biogenesis and molecular
mediators of their functional effects within cells and tissues are still being studied
and remain unclear for most miRNAs identified to date (Ha & Kim, 2014; Kim,
2005).
Around 52% of all miRNA genes are located in different non-random positions
within the genome, generally in regions of chromosomal instability or cancer-
associated regions (Calin, Sevignani, et al., 2004). Changes in miRNA synthesis,
expression and effect on target genes in relation to cancer could occur through many
different mechanisms and some of them include: point mutations in miRNA genes,
mRNA sequences and surrounding regions, epigenetic changes such as mRNA gene
methylation, loss or mutation in the promoter regions for specific miRNA clusters
and/or alterations in pathway related RBP (Ha & Kim, 2014; Lynam-Lennon et al.,
2009).
Approximately one-third of all miRNAs form genomic clusters, about 113 in
total, which may measure up to 51 kb in length, and they are usually transcribed as a
single polycistronic transcript (Thalia A. Farazi et al., 2011; Lynam-Lennon et al.,
2009). One such cluster is the microRNA 17-92 cluster host gene (MIR17HG) cluster
located on chromosome 13q31.3 in the third intron of the c13orf25 gene. It was
initially identified in relation to B cell lymphoma both in cell lines and tissue sample
from patients by Ota et al in 2004 (Ota et al., 2004). The miRNA 17-92 cluster host
gene transcript is about 800bp in length and contains six miRNAs: MIR17, MIR18A,
MIR19A, MIR20A, MIR19B1 and MIR92A1, which belong to 4 seed families
(miRNA 17, miRNA 18, miRNA 19 and miRNA 92). Based on their seed sequences,
two related paralogues of this cluster have also been identified in the human genome:
miRNA 106b-25 cluster located on chromosome 7 (7q22.1) in the 13th intron of gene
164 Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer.
MCM7 and miRNA 106a-363 cluster located on chromosome X (Xq26.2) (Ventura
et al., 2008).
The MIR17HG was named oncomiR-1 by He et al who found it to promote
oncogenesis in B-cell lymphoma using a murine model (L. He et al., 2005). Further
studies have confirmed roles both in cell proliferation and cell death, a feature that
seems to be dependent on the cell types and tissues in which it is found. Research has
shown that MIR17HG inhibits tumour growth by targeting important cell cycle
regulator genes including E2F1, MYC, RB1 and CCND1. In 2005, O’Donnell et al
observed that MIR17HG down-regulated expression of both E2F1 and MYC genes
using Burkitt lymphoma cells (P493-6) as a model (O'Donnell, Wentzel, Zeller,
Dang, & Mendell, 2005). On the other hand, later studies have shown this cluster
gene region seems to have an oncogenic effect in other types of tumour.
Experimental models of human B-cell lymphomas overexpressing MIR17HG
showed evasion of apoptosis due to enhanced activity of MYC. Moreover, studies of
small-cell lung carcinomas showed negative regulation of the pro-apoptotic gene
BCL2L11 resulting from overexpression of MIR17HG (Hayashita et al., 2005). This
finding may suggest that fine regulation of MIR17HG expression is important.
Research studies indicate that specific processing of miRNAs included in this cluster
might be the result of largely unknown intricate regulatory mechanisms. There are 34
transcription factors that regulate MIR17HG transcription identified to date and they
include MYC, MYCN, MXI, E2F3 and TP53 amongst others, that are specific to
different cell types and biological contexts (Mogilyansky & Rigoutsos, 2013; Olive,
Jiang, & He, 2010).
In relation to breast malignancies, a number of studies on the effects of
MIR17HG in breast cancer cell lines have also been published. In 2006 MIR17 5p,
one of the miRNAs included in the cluster, was shown to inhibit oestrogen receptor α
(ESR1) co-activator NCOA3 in different breast cancer cell lines (Hossain et al.,
2006). Following this finding, Yu et al demonstrated a tumour suppressor role for
MIR17HG inducing cell cycle arrest and decreasing cell proliferation by directly
inhibiting CCND1 in the MCF-7 breast cancer cell line in 2008 (Z. Yu et al., 2008).
Similarly in a later study, Leivonen et al showed that MIR17HG reduced cell growth
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 165
and progression of cell cycle in breast cancer cell lines MCF-7 and BT-474 using
two mechanisms: translational repression of ESR1 and/or downregulation of ESR1
responding genes. Additionally, they were able to confirm their in-vitro findings
through expression studies on breast cancer tumour samples showing significant
association between high expression levels of miR-18a and ESR1-negative breast
cancer tumours (Leivonen et al., 2009).
According to the evidence previously presented, MIR17HG seems to play an
important role in the development of different carcinomas including breast cancer.
However, there is a lack of knowledge on the molecular mechanisms leading to
altered expression or regulation of this particular cluster as well as each of the mature
miRNAs included in it. Genetic variants such as single nucleotide polymorphisms
(SNPs) could potentially impact biological processes involved in the production or
functional effects of the microRNA 17-92 cluster host gene and the study of such
variants in relation to cancer could provide insight into its aetiological mechanisms.
In this study, we genotyped 6 SNPs located in the cluster gene region or in
close proximity to it. Genotyping of these variants was performed using multiplex
PCR and Matrix-assisted laser desorption/ionization time-of-flight mass
spectrometry (MALDI-TOF MS) analysis in two independent Australian Caucasian
breast cancer cohorts. Results from our allele and haplotype association analysis
showed statistical significance for two of the selected SNPs and the risk of breast
cancer in these Caucasian populations.
8.4 MATERIALS AND METHODS
8.4.1 Study populations
Genotyping was carried out in two independent cohorts of females of
Caucasian (Northern European) origin: The Genomics Research Centre Breast
166 Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer.
Cancer (GRC-BC) population and a subset of the Griffith University-Cancer Council
Queensland Breast Cancer Biobank (GU-CCQ BB). GRC-BC population was
recruited from the Gold Coast Hospital, Southport and included 244 breast cancer
patient samples from patients residing in the South East Queensland region. Samples
from 187 healthy females with no history of personal or familial cancer were used as
controls and they were also obtained via the Genomics Research Centre Clinic,
Southport, with the research approved by Griffith University’s Human Ethics
Committee (Approval: MSC/07/08/HREC and PSY/01/11/HREC) and the
Queensland University of Technology Human Research Ethics Committee
(Approval: 1400000104). Mean age of the test populations was 57.52 years and 57
years, for cases and controls, respectively.
Replication was performed using 679 samples from the GU-CCQ BB
population. Patient samples were collected by the Genomics Research Centre in
collaboration with the Cancer Council of Queensland as part of a 5-year population-
based longitudinal study of women newly diagnosed with breast cancer. 929 women
resident in Queensland with a diagnosis of invasive breast cancer confirmed
histologically have been recruited by this biobank since January 2010. Age range of
patient samples included in this study varied from 33 to 80 years, with an average
age of 60.16. Clinical and demographic information was obtained through the
Queensland Cancer Registry, Genomics Research Centre General Questionnaire,
computer assisted telephone interview (CATI) and self-administered questionnaires
(SAQ) over two main time points: 4 to 6 months post diagnosis and 18 months post
diagnosis [approximately 12 months after initial interview]. Additionally, using the
Queensland Cancer Registry and the National Death Index, the study cohort will be
followed-up for five years post diagnosis to record cancer recurrence and mortality
(Youl et al., 2011). 308 ethnicity and sex matched control samples for the GU-CCQ
BB population were obtained from 2 sources: 107 females with no personal or
familial history of cancer, recruited from January 2000 through the Genomics
Research Centre at the Institute of Health and Biomedical Innovation Institute,
Queensland University of Technology and also genotyping data obtained from 201
individuals belonging to the phase 1 European population from the 1000Genomes
project (Abecasis et al., 2012).
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 167
8.4.2 Genomic DNA sample preparation from whole human blood.
Genomic DNA was obtained from whole blood samples using a modified
salting out method (Chacon-Cortes et al., 2012; Nasiri et al., 2005). DNA samples
were checked for quantity and purity using spectrophotometry measurements using
the Thermo Scientific NanoDropTM 8000 UV-Vis Spectrophotometer (Thermo Fisher
Scientific Inc., Wilmington, DE. USA) to determine concentration and 260/280
ratios (Chacon-Cortes & Griffiths, 2014; Huberman, 1995; Sahota, Brooks,
Tischfield, et al., 2007). To guarantee DNA sample purity, those with a value below
1.7 for their 260/280 ratio were further purified using an ethanol precipitation
protocol (Sambrook & Russell, 2001).
8.4.3 miRNA SNP selection
We used the human miRNA disease database (HMDD), developed by Lu et al
(M. Lu et al., 2008) and updated in January 2012, to identify microRNAs involved in
development and progression of breast cancer. We selected 24 biological/cellular
functions and 8 diseases and/or pathological features related to breast cancer from
two datasets included in the HMDD: “The whole miRNA-disease association data”
and “The miRNA function set data”. The microRNA 17-92 cluster host gene
(MIR17HG) was found to be present in most of the features on each dataset and
therefore we conducted a preliminary literature search to determine its role in cancer
development and progression and to investigate any SNP genotyping studies for this
cluster gene. Following this, we searched for previously identified SNPs in the
MIR17HG using both the dbSNP database from The National Center for
Biotechnology Information (NCBI) (Sherry et al., 2001) and 1000 Genomes project
browser (Abecasis et al., 2012). MicroRNA SNPs (miR-SNPs) that were located
inside the pre-miRNA gene were automatically included in a preliminary selection
and those located outside of the pre-miRNA gene were selected using either one of
the following criteria: SNPs located within 500bp to the 3’ or 5’ end of the pre-
miRNA gene region were automatically included in the preliminary selection.
168 Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer.
Finally, miR-SNPs located at a distance higher than 500bp from either end of the
pre-miRNA gene were chosen only if they had a minor allele frequency (MAF)
higher than 0.05 in Caucasian populations. Our preliminary selection identified 13
miR-SNPs in the MIR17HG cluster or located up to 1.6 kb downstream and two
variants (rs72631821 and rs9589207) located inside one of the mature miRNA
sequences (MIR92A1) (See Table 8-1).
8.4.4 Primer design and multiplex PCR genotyping
We were able to design a multiplex PCR assay to include six miR-SNPs: one
of the variants located inside the MIR92A1 gene (rs9589207) and five miR-SNPs
located throughout the MIR17HG region using the MassARRAY® Assay Design
Suite v1.0 software (SEQUENOM Inc., San Diego, CA, USA). Two PCR primers
(forward and reverse) and one iPLEX® (extension) primer were designed for each
miR-SNP to achieve successful marker and allele identification by mass
spectrometry (See Table 8-2). We verified that masses of extension primers differed
by at least 30 Da among different SNPs and by 5 Da between alternative alleles of
the same marker and primers were synthesized by Integrated DNA Technologies
(IDT®) Pte. Ltd. (Baulkham Hills, NSW 2153, Australia). Multiplex PCR reactions
were performed according to the manufacturer’s protocol for the iPLEX™ GOLD
genotyping application using iPLEX® GOLD reaction kit reagents (SEQUENOM
Inc., San Diego, CA, USA). Primer extension reactions were carried out using the
SEQUENOM linear adjustment method as per manufacturer’s iPLEX® GOLD
genotyping protocol. All iPLEX® GOLD genotyping reactions were performed using
Applied Biosystems® MicroAmp® EnduraPlate™ Optical 96-Well Clear Reaction
Plates with Barcode (Life Technologies Australia Pty Ltd., Mulgrave, VIC,
Australia) and a Applied Biosystems® Veriti® 96-Well Thermal Cycler (Life
Technologies Australia Pty Ltd., Mulgrave, VIC, Australia).
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 169
Table 8-1 SNPs in the microRNA 17-92 cluster host gene selected for primer design using the MassARRAY® Assay Design Suite v1.0 software (SEQUENOM Inc., San
Diego, CA, USA)
miRNA Locus Location miRNA SNPs in gene region
MAF Location SNPs near 3' end
MAF Location Downstream distance (bp)
MIR92A1 13q31.3 92,003,568 - 92,003,645 rs72631821 NA 92,003,588
rs9589207 0.0211 92,003,589 MicroRNA 17-92 cluster host gene
13q31.3 92,000,074 - 92,006,829 rs4284505 0.4867 92,001,472 rs138514639 0.0517 91,998,497 1577 rs72640333 0.0939 92,004,927 rs113242099 0.0504 92,005,118 rs1888138 0.0916 91,999,496 578 rs111371822 0.1484 92,005,134 rs7336610 0.4524 92,005,137 rs7318578 0.3594 92,005,469 rs2351704 0.3539 91,999,716 358 rs17735387 0.0829 92,006,054 rs1428 0.463 92,006,770
Table 8-2 Primer sequences for microRNA 17-92 cluster host gene SNPs included in genotyping study using multiplex PCR reaction and MALDI-TOF MS
SNP Forward primer sequence Reverse primer sequence iPLEX® (Extension) primer sequence rs1888138 ACGTTGGATGGTTTATTACTTTACCGGCCC ACGTTGGATGTACTTCTCTGGTTCCGGTTG TGGGTTTCCGAGGTA rs7336610 ACGTTGGATGAAAAAGTTCCGGCTGGACAC ACGTTGGATGACAGCGTTTCACCATGTCGG GACTGACCTCAGGTAATCC rs9589207 ACGTTGGATGACTCAAACCCCTTTCTACAC ACGTTGGATGGGACAAGTGCAATACCATAC AAGGAACACAGCATTGCAAC rs17735387 ACGTTGGATGAGCCTTAACTATTTGGAGGG ACGTTGGATGGCTTTCTTTCCAAATATAGGC GGGGAGAAAGTTGTACATGCAAA rs4284505 ACGTTGGATGCTTTGCAGTCTCGGGTGTTC ACGTTGGATGTGATATTGCAACGACGAGCC TGATCCTGCCTTTTTCAGTTCCTT rs1428 ACGTTGGATGTCAATATTCTCGTTCTGGAC ACGTTGGATGACAGTTTGGTCTGGCTGTTT GCATTTAATGTTAATAAATAAAATACTG
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 170
8.4.5 MALDI-TOF MS analysis and data analysis
We dispensed a total of 12-16 nl of each iPLEX® reaction product onto a
SpectroCHIP® II G96 (SEQUENOM Inc., San Diego, CA, USA) using
SEQUENOM® MassARRAY® Nanodispenser (SEQUENOM Inc., San Diego, CA,
USA) and SpectroCHIP® analysis was performed by SEQUENOM® MassArray®
Analyzer 4 (SEQUENOM Inc., San Diego, CA, USA). We acquired automated
spectra after laser desorption/ionisation using the SpectroAcquire software version
4.0 (SEQUENOM Inc., San Diego, CA, USA) and genotype data analysis was
performed using the MassARRAY® Typer software version 4.0 (SEQUENOM Inc.,
San Diego, CA, USA).
8.4.6 Statistical analysis
We determined genotype and allele frequencies for each miRNA SNP in our
case and control populations. Hardy-Weinberg equilibrium (HWE)(Guo &
Thompson, 1992; Hardy, 1908) was used to evaluate deviation between observed and
ideal Hardy-Weinberg frequencies. Differences in genotype and allele frequencies
between cases and controls were evaluated using Chi-square analysis (Fisher &
Yates, 1963) for each independent population to determine association with breast
cancer. The α for p-values was set at 0.05 to determine statistical significance.
Statistical analysis of genotypes and alleles was conducted using Plink software
version 1.07 (http://pngu.mgh.harvard.edu/purcell/plink/) (Purcell et al., 2007). We
also calculated odds ratio (OR) and a confidence interval (CI) of 95% to assess
disease risk. Finally, we merged results from both populations and we performed
linkage disequilibrium (LD) and haplotype block association analysis on the
combined genotyping data using Haploview software version 4.2 (Daly Lab, Broad
Institute, Cambridge, MA, USA) (Barrett et al., 2005).
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 171
8.5 RESULTS
8.5.1 Genotypic Analysis
Table 8-3 shows the 6 SNPs genotyped in our study provided good coverage of
the MIR17HG cluster. They also included rs9589207 located inside a mature miRNA
sequence (MIR92A1) and rs1888138 located 578 bp downstream of the cluster gene.
All SNPs had a MAF greater than 0.05, with the exception of rs9589207 which had a
MAF of 0.02. We proceeded to include all these SNPs in our multiplex PCR, in
particular rs9589207 because of their genetic location. However, rs9589207 was
finally excluded from our association analysis because we were unable to find the
mutant allele in our initial genotyping of the GRC-BC cohort.
Hence genotyping results of the 5 remaining SNPs obtained from both
populations are shown in Table 8-4. All control populations were in HWE (p > 0.05)
for all tested SNPs and allele and genotype frequencies closely matched those found
in Hapmap for Caucasian populations. As shown in Table 8-4, we were able to
validate a significant difference in allele frequencies between cases and controls for
rs4284505 in both the GRC-BC and GU-CCQ BB populations using chi-square
analysis (p = 0.01 and 0.03 respectively). Statistical analysis of differences between
allele frequencies in cases and controls in the GRC-BC population for rs7336610
also showed significance (p=0.03), but we were unable to obtain a similar finding in
our independent replication population (p=0.10) and all the other remaining SNPs
showed no significant differences between cases and controls in both populations.
We observed lower frequencies (38.3% and 35.9% for GRC-BC and GU-CCQ
cohorts respectively) of the A allele for rs4284505 in cases for both independent
populations (See Table 8-4) and we calculated an odds ratio of 0.70 (CI 95%: 0.52 –
0.93) for GRC-BC population and of 0.79 (CI 95%: 0.65 – 0.97) for GU-CCQ
population. These results indicate that the presence of the A allele in this locus seems
to have a protective effect on susceptibility to breast cancer.
172 Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer.
Table 8-3 Chromosomal location and allele information for selected miRNA SNPs in the microRNA 17-92 cluster host gene
SNP Locus Wild Type allele Mutant allele Chromosomal position MAF rs1888138 13q31.3 A T chr13: 91999496 0.0916 rs4284505 13q31.3 G A chr13: 92001472 0.4867 rs9589207 13q31.3 G A chr13: 92003589 0.0211 rs7336610 13q31.3 T C chr13: 92005137 0.4524 rs17735387 13q31.3 G A chr13: 92006054 0.0829 rs1428 13q31.3 T G chr13: 92006770 0.4625
Table 8-4 Allele frequencies of SNPs in the microRNA 17-92 cluster host gene obtained from the GRC-BC and GU-CCQ BB cohorts
rs1888138 rs4284505 rs7336610 rs17735387 rs1428 Alleles Population A
(%) T
(%) p-
value G
(%) A
(%) p-
value T
(%) C
(%) p-
value G
(%) A
(%) p-
value T
(%) G
(%) p-
value
GRC-BC Population
Control 361 (96.5)
13 (3.5)
0.86 189 (53.1)
167 (46.9)
0.01 185 (52.6)
167 (47.4)
0.03 360 (96.3)
14 (3.7)
0.68 182 (56.2)
142 (43.8)
0.67
Cases 470 (96.3)
18 (3.7)
263 (61.7)
163 (38.3)
263 (60.0)
175 (40.0)
449 (96.8)
15 (3.2)
278 (57.7)
204 (42.3)
GU-CCQ BB
Population
Control 590 (95.8)
26 (4.2)
0.87 362 (58.8)
254 (41.2)
0.03 346 (56.2)
270 (43.8)
0.10 590 (95.8)
26 (4.2)
0.66 383 (62.8)
227 (37.2)
0.36
Cases 1295 (95.9)
55 (4.1)
767 (64.1)
429 (35.9)
712 (60.1)
472 (39.9)
1287 (96.2)
51 (3.8)
655 (60.5)
427 (39.5)
Hapmap (%) 95.9 4.1 57.9 42.1 55.7 44.3 98.2 1.8 65.6 34.4
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 173
Finally, chi square analysis of genotypes for all selected MIR17HG SNPs
between cases and controls showed significant differences for rs4284505 and
rs7336610 (p = 0.01 for both SNPs) in the GRC-BC population. Interestingly, we
were only able to obtain statistical significance in the analysis at the genotype level
for rs7336610 in our replication cohort (p = 0.04).
8.5.2 Haplotypic Analysis
Following the basic genotyping association analysis, we combined genotyping
results from both GRC-BC and GU-CCQ populations to conduct linkage
disequilibrium (LD) and haplotype block association analysis. As shown in Figure
8-1, two of our selected SNPs (rs4284505 and rs7336610) were part of a 3kb LD
block (Block 1) with a D’ score of 0.98.
Table 8-5 Haplotype block association analysis for selected SNPS in the
microRNA 17-92 cluster host gene in combined GRC-BC/GU-CCQ BB population
shows frequencies for the different haplotypes in block 1 found in cases and controls,
as well as results of haplotype association analysis to breast cancer using chi-square
analysis. According to our findings, cases had a lower frequency of haplotype AC
(36.4%) than controls (43.3%) and statistical analysis proved this haplotype to be
significantly associated with breast cancer risk (p = 5x10-4). Finally, we calculated an
odds ratio for haplotype AC of 0.75 with a 95% confidence interval of 0.60 to 0.94 (p
= 0.012). Results from our haplotype analysis validate our finding for the genotyping
analysis at the allele level in rs7336610. These results also suggest that for the
individuals that carry the AC haplotype, it appears to confer a protective effect on
risk of breast cancer in Caucasians reducing the risk of being affected by breast
cancer to about 25% in comparison to other haplotype carriers.
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 174
Figure 8-1 Linkage Disequilibrium (LD) blocks for analysed microRNA polymorphisms in the microRNA 17-92 cluster host gene.
Figure 8-1 shows the haplotype block formed by rs4284505 and rs7336610 found by linkage disequilibrium analysis using Haploview software version 4.2 (Daly Lab,
Broad Institute, Cambridge, MA, USA) for the SNPs in the microRNA 17-92 cluster host gene.
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 175
Table 8-5 Haplotype block association analysis for selected SNPS in the microRNA 17-92 cluster host
gene in combined GRC-BC/GU-CCQ BB population
GT (%) AC (%) GC (%) Control 269 (54.3) 214 (43.3) 12 (2.4) Cases 544 (58.9) 336 (36.4) 43 (4.7)
x2 5.05 12.21 6.58 p-value 0.02 5.0E-04 0.01
Global Statistics x2 = 9.21 p-value = 0.01
8.6 DISCUSSION
Breast cancer affects a significant number of women and mortality rates seem
to be increasing, despite substantial research and our current knowledge of the
disease (Ferlay et al., 2013; Stewart et al., 2014). MicroRNAs are involved in
molecular pathways leading to cell growth, differentiation and survival and there is
enough evidence to show they play a significant role in the mechanisms that lead to
development and progression of different types of cancer. Changes in expression of
the microRNA 17-92 cluster host gene, also known as oncomiR-1, were initially
described in haematopoietic and lung cancers where it was shown to play a role in
tumour growth and apoptosis (Hayashita et al., 2005; L. He et al., 2005; O'Donnell et
al., 2005; Ota et al., 2004; Ventura et al., 2008). Research has also highlighted
MIR17HG as an important regulator of genes involved in breast cancer pathways.
Studies by Hossain et al and Leivonen et al showed it plays a role in regulation of
oestrogen receptor α (ESR1) in different breast cancer cell lines and tumour tissues
through transcriptional downregulation of this receptor and genes that interact or co-
activate ESR1 (Hossain et al., 2006; Leivonen et al., 2009). Yu et al found the
microRNA 17-92 cluster host gene to have a tumour suppressor role through CCND1
inhibition in the MCF-7 cell line (Z. Yu et al., 2008). However, we currently lack
understanding of the specific features that result in altered biogenesis and functional
effects of this particular miRNA cluster gene in breast malignancies. Therefore the
study of single nucleotide polymorphisms located inside the MIR17HG cluster gene
using well defined breast cancer cohorts may help to assess whether they have a role
on the molecular events leading to the development of this disease.
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 176
Selection of 6 miRNA SNPs found 0.6 kb downstream and inside the
microRNA 17-92 cluster host gene region provided a thorough coverage of the gene
and we were able to successfully genotype all of these variants in the two available
cohorts, even rs9589207 located inside the MIR92A1 transcript. Unfortunately this
particular SNP was not included in our association analysis because we could not
identify the presence of the mutant allele in any of our populations. However two of
the five remaining SNPs, rs4248505 located about 1.4 kb downstream from MIR17A
and rs7336610 found about 1.4 kb upstream from MIR92A1, showed some
interesting results on our association analysis of breast cancer risk. Moreover, there
does not appear to be any previous genotyping studies on these variants in relation to
breast cancer or other diseases in Caucasian or any other ethnic populations.
Statistical analysis of our results for rs4284505 showed a significantly higher
presence of the A allele in healthy individuals for both GRC-BC and GU-CCQ BB
populations (p = 0.01 and 0.03 respectively) and a reduction in breast cancer risk of
up to 30%. We also found higher frequencies for the C allele of rs7336610 in
controls in our GRC-BC cohort but failed to establish a similar finding in our
replication population. Analysis of the haplotypes present in the LD block
rs4284505/rs7336610 showed results consistent to our previous finding in the
individual SNP analysis, with the presence of the AC haplotype found to have
significantly higher frequencies in controls (p = 5x10-4) decreasing the risk of
developing breast cancer by about 25%.
In conclusion, via analysis of SNPs from MIR17HG, we were able to identify
significant association between rs4248505 at the allele level and
rs4248505/rs7336610 at the haplotype level with susceptibility to breast cancer. To
the best of our knowledge this is the first study investigating SNPs in this cluster
gene in breast cancer populations and the first to determine that the presence of the
AC haplotype significantly affects the risk of developing breast cancer. However our
findings require further validation in larger populations and/or population of different
ethnicities, as well as functional studies to further define the role of this haplotype in
miRNA expression or molecular pathways leading to the development of breast
cancer.
Chapter 8: Association of microRNA 17-92 cluster host gene (MIR17HG) polymorphisms with breast cancer. 177
Chapter 9: General Discussion
9.1 INTRODUCTION
Cancer affects a large number of individuals worldwide. Increase in incidence
and mortality rates is a concerning fact and it is a trend also observed in Australian
population (AIHW & AACR, 2010, 2012; Ferlay et al., 2010; Ferlay et al., 2013).
Breast cancer is the most common type of cancer in women and it ranks first as a
cancer-related cause of death worldwide and second in Australia (AIHW, 2009,
2012).
Breast cancer is a malignant neoplasm characterised by uncontrolled cellular
growth within breast tissue with the potential to invade distant tissues and organ
within the human body. It is a complex disease that results from innate and/or
acquired genetic predisposition from somatic mutations in breast tissues and their
interaction with hormonal exposure and other risk factors (Robbins et al., 2010;
Strachan & Read, 2011; Weinberg, 2007). Hereditary or familial forms of the disease
that can be primarily attributed to germline mutation are only a small fraction of the
total of cases currently diagnosed accounting for about 10% of all reported cases
(Anderson, 1991). On the other hand sporadic breast cancer requires very complex
signalling networks created by the interaction of many molecular pathways that have
been identified up to date. Therefore several genes and mediators may play a role in
the development of the sporadic disease, including input from the environment
(Robbins et al., 2010; Weinberg, 2007; Ben Zhang et al., 2011). Genes involved in
the inherited or familial types of the disease have helped our understanding of the
molecular mechanisms and have shown the molecular pathways involved in the
pathogenesis but we still lack knowledge on the detailed molecular mechanisms
involved in those pathways. Better understanding of the mediators and molecular
178 Chapter 9: General Discussion
regulator involved in those pathways will give us a better understanding of the
pathophysiology of the disease, as well as help in the development of diagnostic or
treatment approaches that would have a substantial impact for breast cancer patients.
9.2 BREAST CANCER AND MICRORNAS
MicroRNAs have been described as important epigenetic regulators of gene
expression present in all the molecular pathways involved in cell growth,
differentiation and survival. Therefore, they might be important for the pathogenesis
of breast cancer because they are known to regulate about 60% of the genes in the
human genome (Thalia A. Farazi et al., 2011; Robbins et al., 2010; Zuoren Yu et al.,
2010). Research studies aimed at a better understanding of the molecular
mechanisms and mediators involved in miRNA biogenesis and function in relation to
cancer are fundamental. Scientific research to elucidate the role of microRNAs in
cancer biology is increasing and differential patterns of expression for miRNAs have
been identified in many types of cancer including breast cancer (Calin & Croce,
2006; Iorio et al., 2005; J. Lu et al., 2005; Volinia et al., 2006). The molecular
mechanisms leading to miRNA expression deregulation that results in cancer
development are still not fully understood. However it has been recognised that
mutations present in the miRNA sequences could result in the changes in miRNA
synthesis and function seen in cancer biology.
Single nucleotide polymorphisms (SNPS) are the most common type of
variation present in miRNA genes (T. A. Farazi et al., 2011; H.-C. Lee et al., 2011;
Ryan et al., 2010). Therefore the study of miRNA SNPs (miR-SNPs) has the
potential to explain the mechanisms behind miRNA changes in biogenesis and
function. They can also be used as markers to identify cancer predisposition, predict
cancer behaviour and prognosis and as targets for cancer therapy (Budhu et al., 2010;
Heneghan et al., 2010; Roth et al., 2010; Q. Wu et al., 2011). There is not currently a
lot of evidence for involvement of miR-SNPs in cancer but it is a field of research
that seems to be growing (Gong et al., 2012; H.-C. Lee et al., 2011; Mishra &
Bertino, 2009). The lack of data arises primarily because only a small fraction of the
Chapter 9: General Discussion 179
total of miR-SNPs identified to date have been studied in relation to cancer and
breast cancer more specifically (Z. Hu et al., 2008; Z. Hu et al., 2009; Jazdzewski et
al., 2009; Jazdzewski et al., 2008; B. Xu et al., 2010; T. Xu et al., 2008; Yeqiong Xu
et al., 2013; Yamashita et al., 2013; Ye et al., 2008).
miR-SNPs located in MIR27A, MIR423, MIR146A, MIR196A2 and MIR499
have been associated with breast cancer, but findings are still somewhat controversial
(Alshatwi et al., 2012; Bansal et al., 2014; Catucci et al., 2012; Catucci et al., 2010;
Esteban Cardenosa et al., 2012; Gao et al., 2011; Amandine I. Garcia et al., 2011;
Hoffman et al., 2009; Jedlinski et al., 2011; Kontorovich et al., 2010; S. J. Lee et al.,
2014; Lian et al., 2012; Linhares et al., 2012; Pastrello et al., 2010; Shen et al., 2008;
R. A. Smith et al., 2012; P. Y. Wang et al., 2013; R. Yang & Burwinkel, 2012;
Rongxi Yang et al., 2010; M. Zhang et al., 2012; N. Zhang et al., 2013; Zhong et al.,
2013). Conflicting results in regards to miR-SNPs and breast cancer are probably the
result of limitations in terms of the populations examined, experimental design and
methods used for analysis. Therefore all of these need to be carefully considered and
addressed in the study of the remaining miR-SNPs that need to be analysed in
relation to breast cancer or to validate previous findings. More research is required to
address a larger number or miR-SNPs, in particular those related to genes previously
identified to be involved in breast cancer development and progression as this would
allow better understanding of the mechanisms involved and it might result in the
development of new and/or improved diagnostic and therapeutic approaches.
This study aimed to contribute to the understanding of microRNA
polymorphisms and their potential association with breast cancer risk as an initial
step in the identification of those miR-SNPs that may have a substantial role in the
biology of cancer. In order to successfully identify those miRNA variants that are
significantly associated with breast cancer we established some specific aims: i) the
establishment of a large sized and well characterised prospective DNA biobank to be
used as a replication cohort to the one previously available in our facility. ii) to
determine the most appropriate protocol to use in the establishment of our new breast
cancer case-control cohort in a time-efficient and cost-effective manner. iii) to
180 Chapter 9: General Discussion
identify miR-SNPs associated with breast cancer risk in both Australian Caucasian
populations using genotype association analysis.
9.3 POPULATION RESOURCES DEVELOPMENT
We aimed to establish a significant sized and well-characterised prospective
genomic DNA (gDNA) biobank extracted from whole blood samples obtained from
breast cancer patients in Queensland recruited in collaboration with the Cancer
Council Queensland. A well-characterised biobank would contain comprehensive
information from volunteers regarding ethnic origin, age, sex, biometric data and
complete clinical information to allow selection of candidates with very specific
phenotypes to be included in genetic studies in relation to a condition, which would
minimise bias from experimental findings. This goal was achieved through the very
thorough process of recruitment and follow-up described in Section 3.1.2, resulting
in a very comprehensive database containing the information required for appropriate
characterisation of volunteers. However, for this study, we were limited in our access
to some of the information, due to the fact that both the biobank and the database are
still under development and data is still being collated and finalised. This limited
information in turn prevented us from including important features like type of breast
cancer, stage at diagnosis and characteristics of the tumours into our analysis and
results. This could have an impact on our findings that is yet to be determined.
Therefore, once such information is available it could be included as part of the
future work needed to further validate and interpret our findings.
About 3000 samples were expected to be collected during the 5 year period of
patient recruitment and 929 breast cancer patients had been recruited by this biobank
since January 2010. We were able to successfully perform DNA extraction of all the
929 samples collected to date with the resources available to the study. This was the
result of careful planning and assessment of the DNA extraction technique that we
used which allowed us to progress the construction of the population most
efficiently. As previously mentioned in Section 3.2.3, we were also able to establish
significant sized populations because both GRC-BC and CCQ-GU BB populations
Chapter 9: General Discussion 181
has a sufficient size to achieve ~80% or greater power to detect an allelic association
at the 0.05 level.
9.3.1 DNA extraction methods
In order to determine the best possible method to perform DNA extraction
from the ones available at our facility, we decided to review current knowledge of
genomic DNA extraction from whole blood methods to determine the most suitable
one to successfully achieve the establishment of our biobank population. Chapter
Five reviews the history of DNA extraction techniques and the main categories of the
methods used in DNA extraction from whole blood samples most commonly used in
facilities around the world. Finally, we reviewed available literature on evaluation
studies previously published comparing different extraction techniques to highlight
their strengths and weaknesses. This review evidenced the limited scientific
evidence available in regards to the best choice for gDNA extraction, since to the
best of our knowledge there is no publication that comprehensively evaluates all
approaches in terms of all possible features. It also highlighted the need to conduct
our own validation study to determine the most appropriate technique to be used
from the ones currently available at our facility to determine the one to be used in the
establishment of our new case control cohort.
9.3.2 Comparison of DNA Extraction methods
Chapter Four presents our in-house evaluation study conducted to determine
the most appropriate method in terms of cost effectiveness and time efficiency to
perform genomic DNA (gDNA) extraction from whole blood samples. We
conducted a study that compared the modified salting-out protocol described by
Nasiri et al (Nasiri et al., 2005), against a traditional salting out method (Miller et al.,
1988) and a commercially available DNA extraction kit. We performed gDNA
extraction in eleven whole blood samples obtained from breast cancer patients and
evaluated the performance of each method in terms of the quality and quantity of
182 Chapter 9: General Discussion
DNA obtained, as well as time requirements and costs for each technique. Our
results showed that any of these three techniques perform similarly in terms of
quantity and quality of final DNA product, but it highlighted the modified salting out
method to be the most cost-effective and time-efficient technique to isolate gDNA
from whole blood samples achieving similar results to the ones obtained with the
commercial DNA extraction kit. Unfortunately, costs associated with the collection,
handling and storage of the sample were not taken into account. However they are
important because this could potentially impact on the integrity of the initial sample,
as well as the performance and success of any DNA extraction technique chosen.
They are also a significant factor to consider, since it is very important to carefully
obtain and preserve initial samples, especially for future extraction of DNA samples
from those which are exhausted from the biobank.
Some of the limitations of this study included the small number of samples
used to test our chosen DNA extraction methods and we also failed to include a more
comprehensive range of techniques like magnetic bead-based extraction methods or
automated method unavailable at our facility. Spectrophotometry can potentially
overestimate the amount of double stranded DNA resulting from the chosen
extraction method up to 35% compared to other methods such as PicoGreen, because
there are different amounts of double and single stranded DNA and RNA remaining
as a result of differences in chemistry between different methods. Moreover, using
qPCR to evidence differences in quantification cycle (Cq) would potentially improve
our data in regards to the quality of DNA yield.
Nonetheless, the modified salting out method has consistently proved to be an
appropriate technique based on the quality and quantity of DNA obtained from blood
samples extracted for the GU-CCQ biobank population. DNA samples extracted with
this technique have also showed to perform well in downstream application, as
evidenced by the genotyping results obtained in the genotyping analysis included in
this study, as well as other studies performed at our facility not included here because
they escaped the scope of this manuscript.
Chapter 9: General Discussion 183
9.4 ASSOCIATION STUDIES
9.4.1 MIR146A results
Chapter Six was our pilot genotyping study to determine whether our newly
established population extracted with the modified salting out technique performed
well in downstream applications. It also provided us with the opportunity to validate
previous findings in relation to a miR-SNP that has shown conflicting results in
studies previously conducted by other research groups. We selected the miR-SNP
rs2910164 present in MIR146A because it had not been properly analysed in purely
sporadic or purely Caucasian breast cancer populations to validate previous findings.
We genotyped this variant using high resolution melt (HRM) analysis and were able
to determine that presence of the mutant allele in this locus to be significantly
associated with breast cancer risk in both our primary and replicate populations. One
limitation of this study was the small number of samples included in our replication
population (GU-CCQ BB population), as this was still under development. However
we were able to successfully conduct our assay and to achieve statistically significant
results. Our study showed that the presence of the mutant allele in rs2910164 was
significantly associated with sporadic breast cancer risk in both of our cohorts (p =
0.03 and p = 0.00013). To the best of our knowledge this is the first study to identify
an association between this variant and breast cancer risk in a purely sporadic breast
cancer bearing Caucasian population.
9.4.2 miRNA selection and analysis
Chapter Seven presents the results of a large scale genotyping study using both
of our previously established cohorts. In this study, we developed a selection
algorithm to choose miR-SNPs to be included shown in Figure 7-1 of this chapter.
This allowed the selection of a large panel of miR-SNPs (24 miR-SNPs in total
present in 9 miRNA genes) to be evaluated using multiplex PCR and chip-based
matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) mass
184 Chapter 9: General Discussion
spectrometry (MS) analysis. Results from our study showed 6 of the miR-SNPs
(rs73798217, rs112394324, rs35301225, rs1547354, rs7050391 and rs3851812) not
to be polymorphic in our populations. As mentioned in Section 2.5.1.1, there are
around 10 million SNPs in the human genome and only about 56% of SNPs have
been successfully typed. Many of these SNPs have been tested using small
populations and therefore it is very likely that a large number of those will turn out to
be non-polymorphic, or have altered minor allele frequencies when genotyping larger
cohorts, or cohorts from different ethnicities. Therefore, it is not surprising that up to
21% of our selected miR-SNPs were shown not to be polymorphic. Our results
provide initial validation of the rarity of these variants in Caucasian populations.
From the remaining polymorphisms analysed, we were able to show significant
association to breast cancer risk for one of our selected variants. We determined that
miR-SNP rs353291, occurring in relation to MIR145 had significant differences in
allele frequencies between cases and controls in both populations (p = 0.041 and p =
0.023). To the best of our knowledge this is the first report of an association to breast
cancer risk for this variant in Caucasian populations. Although we provided initial
validation of our findings using two independent populations, further validation of
this results in larger cohorts or populations of both similar and different ethnic
background is required to strengthen their significance. If we take into account
multiple testing correction of our results, however, our findings are no longer
significant, yet still interesting and worthy of further research based on the similarity
of significance found in both populations. Functional studies to help refine the likely
mechanism by which the miR-SNP may interact with breast cancer influencing
pathways may also help in properly identifying populations who may be affected by
it.
9.4.3 MicroRNA 17-92 cluster host gene analysis
Chapter Eight presents the results of our genotype and haplotype analysis of 6
miR-SNPs present in the microRNA 17-92 cluster host gene (MIR17HG) conducted
in our two Australian Caucasian breast cancer cohorts. We decided to study
Chapter 9: General Discussion 185
polymorphisms in MIR17HG because it has been previously shown in the literature
to be associated with cancer development. It was the first cluster host gene to be
found to play a role in cancer biology for hematopoietic malignancies and other types
of cancer (Hayashita et al., 2005; L. He et al., 2005; Mogilyansky & Rigoutsos,
2013; O'Donnell et al., 2005; Olive et al., 2010). It has also been linked to the
pathogenesis of breast cancer. It has been shown to target important genes in breast
biology, such as NCOA3, CCDN1 and ESR1 (Hossain et al., 2006; Leivonen et al.,
2009; Z. Yu et al., 2008). The molecular mechanisms that lead to deregulation of
MIR17HG may be influenced by SNPs and to the best of our knowledge there are no
previous studies evaluating the role of miR-SNPs for this particular cluster host gene,
making it an ideal candidate for study. We selected 6 miR-SNPs located in the
cluster gene region or in close proximity to it providing good coverage for this
miRNA cluster, and they included a polymorphism located inside the mature
sequence for MIR92A1 (rs9589207), a variant almost certain to be functional in some
respect. We genotyped these SNPs using multiplex PCR and chip-based MALDI-
TOF MS analysis in our two independent Australian Caucasian breast cancer case-
control cohorts. Genotypic analysis of our results showed significant differences
between cases and control for miR-SNP rs4284505 in both of our populations (p =
0.01 and p = 0.03) and for rs7336610 only in our initial population (p = 0.03). We
then performed haplotypic analysis on the combined results from both populations
and we identified these two miR-SNPs (rs4284505/rs7336610) to be in linkage
disequilibrium and part of a haplotype block (D’ score = 0.98). Chi-square analysis
of the haplotypes contained in that block showed a lower frequency for haplotype
AC in cases compared to controls and our analysis showed it to be significantly
associated with breast cancer (p = 5x10-4). Odds ratio for this haplotype was
calculated to be 0.75 with a 95% confidence interval of 0.60 to 0.94 (p = 0.012)
suggesting that individuals that carry the AC haplotype had a reduced risk of
developing breast cancer (about 25%) in comparison to other haplotype carriers. To
the best of our knowledge this is the first report of a significant association between
these polymorphisms and breast cancer risk in Caucasian populations.
186 Chapter 9: General Discussion
9.4.4 Comparison to GWAS results and miRNA functional implications.
As mentioned before and to the best of our knowledge the genotype association
studies included in this thesis are the first to report an association between the risk of
sporadic breast cancer in purely Caucasian populations and the presence of mutation
in these loci (MIR146A (rs2910164), MIR145 (rs353291) and MIR17HG
(rs4284505)). These miR-SNPs are located in intergenic regions at a large distance
from any implicated genomic regions previously identified in GWAS for breast
cancer (Easton et al., 2007; Fletcher et al., 2011; Hunter et al., 2007; Khan et al.,
2014; Li et al., 2011; Michailidou et al., 2013; Stacey et al., 2007; G. Thomas et al.,
2009; Turnbull et al., 2010) with none of these GWAS implicating regions near these
miRNA genes.
Our findings are supported by the functional implications of these miRNA
genes for breast cancer development and progression previously identified by other
studies. For instance, MIR146A has been found to downregulate expression of
BRCA1 and BRCA2 genes (A. I. Garcia et al., 2011; Shen et al., 2008) and to inhibit
breast cancer cell invasion and metastasis (Bhaumik et al., 2008; Hurst et al., 2009).
Moreover, some of the predicted targets for MIR146A include IRAK1, CARD10,
MMP16, MNAT1, GTF2H4 and TRAF6 which are found in diverse cancer related
pathways (Z. Hu et al., 2009). MIR145 has been found to play a tumour suppressor
role in breast cancer through activation of TP53 and reduced expression of ESR1
which reduces proliferation and induces apoptosis of breast cancer cells (Spizzo et
al., 2010). Furthermore, it has been found to reduce expression of RTKN which
confers breast cancer cell resistance to apoptosis (S. Wang et al., 2009). Finally
MIR17HG has been also described to have a tumour suppressor role through
regulation of expression of important genes for breast cancer development including
ESR1 and CCND1 (Hossain et al., 2006; Leivonen et al., 2009; Z. Yu et al., 2008).
Chapter 9: General Discussion 187
9.5 LIMITATIONS AND FUTURE DIRECTIONS.
There are some limitations to the studies presented in this thesis and they
include: Size and ethnicity of the study populations, failing to include other variables
like age and type of breast cancer in the analysis and lack of studies to establish the
functional effects of the miR-SNPs positively associated to breast cancer risk.
We acknowledge that the size of the populations used in some of our studies
may limit their power to detect association for the rare variants that were included.
We may also be able to strengthen the significance of our findings by testing the
miR-SNPs found to be positively associated with breast cancer risk in larger
populations. This would be particularly important for those association studies were
multiple testing was performed. Another alternative to increase the power for our
populations would be to increase the number of individuals present in the control
populations to obtain a case-control ratio of 1:4 (Hong & Park, 2012; Schork, 2002).
However, some of these populations are still recruiting volunteers for both cases and
controls which could address this issue of population size for future studies.
Our populations only included women of Caucasian (Northern European)
origin due to the importance of using well-characterised population to conduct
genetic association studies but this also limits the strength of our findings. Therefore,
it would be important to test our findings using population of other ethnic
backgrounds as this changes the distribution of genotypes and alleles for miR-SNPs.
Findings in regard to other ethnicities may also indicate the role these miR-SNPs
play in the development of the disease, therefore they should be the focus of future
genotyping studies.
The studies included in this thesis focused on the association between the
presence of mutation in miRNAs and the risk of developing breast cancer. However
it would be also important to determine their association to some other important
characteristics like type of breast cancer, age of onset, response to treatment options
188 Chapter 9: General Discussion
and recurrence of cancer. Unfortunately, information on these features was
unavailable to us because some of the cohorts were still under development but they
could be the focus of future studies as they could provide information on the role
they may play in the development and progression of breast cancer.
Finally it is important to conduct functional studies using cell culture and/or
animal models to establish the effects of these SNPs in the synthesis and expression
of the miRNA genes where they are present, as well as their effect on the genes these
miRNAs target and the molecular pathways where they are present. This is a
fundamental step towards the translation of these findings into diagnostic or
therapeutic approaches that could impact prognosis of breast cancer sufferers.
9.6 CONCLUSIONS
This study has implicated a number of miR-SNPs in breast cancer. More
specifically, we were able to identify significant association with the presence of
mutation and breast cancer risk for three miR-SNPs and one haplotype (rs2910164 in
MIR146A, rs353291 in MIR145, rs4284505 in MIR17HG and rs4284505/rs7336610
in MIR17HG) in both of our tested populations.
Results presented in Chapters Six to Eight of this document are only the initial
step required to determine whether the studied microRNA SNPs have an effect in
breast cancer development. Further research that includes functional assays will be
required for these findings to be able to translate into strategies that impact breast
cancer diagnosis, treatment and prognosis. The next immediate step would be
validation of our positive findings through genotyping of larger populations or
populations of different ethnic backgrounds to ascertain our findings. Following this,
functional validation using cell culture or animal models would allow a better
understanding of the detailed mechanisms behind their involvement in breast cancer
biology, which is also fundamental to proceed with future translational studies.
Chapter 9: General Discussion 189
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Appendices
Appendix A Modified salting out protocol for DNA extraction from whole blood samples.
Adapted from Nasiri et al., 2005.
1. Thaw blood by placing in 4°C fridge for a few hours or quick-thawing at
room temperature (RT) on bench.
2. Transfer contents of blood vial by pipetting blood carefully into a 15ml
falcon tube.
3. Add 8ml of lysis buffer (0.3 M sucrose, 0.01 M TrisHCl, pH 7.5, 5mM
MgCl2, 1% Triton X100) to each sample and mix by inversion.
4. Centrifuge the tubes at 2500rcf for 30mins at 4°C. Prepare a beaker with
diluted (5-10%) Decon90® for blood waste disposal.
5. Discard the supernatant by pouring carefully into beaker containing
Decon90® without disturbing pellet on the bottom. Alternatively, remove
supernatant by using a transfer pipette, without disturbing pellet.
6. Add 300µl of 10mM TrisHCl, pH 8.0 to the falcon tube and break up the
pellet using a transfer pipette. Ensure pellet is completely broken
up/resuspended.
7. Transfer to fresh microfuge tubes and resuspend by vortexing vigorously.
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8. Centrifuge at 2500rcf for 20 mins at 4°C temperature.
9. Discard supernatant, and add 330µl of 10mM TrisHCl, pH 8.0; 330 µl of
laundry powder (30mg/ml) and a glass bead. Vortex the samples for 1 min
and add 250 µl of 6M NaCl and vortex again for 20 seconds.
10. Transfer sample to a fresh 2ml microfuge tube and centrifuge tube at
15000rcf for 10 min.
11. Transfer supernatant to fresh microfuge tubes (about 900µl) and precipitate
by addition of 900µl of chilled 100% ethanol. Gently swirl and observe the
DNA strands precipitate.
12. Using an inoculating loop, washed twice in 70% ethanol, transfer the DNA
into one labelled 2ml tube containing 1ml 1X TE buffer (10 mM Tris pH 8.0,
1 mM EDTA).
13. Parafilm the 2ml tube and allow the DNA to dissolve by incubating at 70°C
for 5 minutes or at 37 °C overnight.
14. Discard the blood waste into the beaker containing Decon90®, cover with
aluminium foil and let sit overnight. It can be poured down the sink with
copious amounts of water the next day.
15. Vortex and quantitate using the Thermo Scientific NanoDropTM 8000 UV-Vis
Spectrophotometer (Thermo Fisher Scientific Inc., Wilmington, DE. USA) or
other methods (picogreen, fluorometer) and keep a record of concentrations.
Store DNA tubes in appropriate boxes in -20°C freezer.
0 Appendices 217
16. Prepare DNA to a concentration of 20 ng/µl dilution from 1X TE stock DNA
using sterilised deionised water and use in PCRs (store this at 4°C).
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Appendix B Protocol for genotyping using multiplex PCR and matrix-assisted laser
desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) analysis.
Primary Multiplex PCR
1. Set up multiplex PCR reactions to a final volume of 5 µl using 10 ng of
genomic DNA, 0.1 µM of PCR Primers mix (IDT® Pte. Ltd., Baulkham Hills,
NSW, Australia), 1 U PCR enzyme (Roche®, Indianapolis, IN, USA), 1X
SEQUENOM PCR Buffer (SEQUENOM Inc., San Diego, CA, USA), 2 mM
SEQUENOM MgCl2 (SEQUENOM Inc., San Diego, CA, USA) and 500 µM
SEQUENOM 2´-deoxynucleoside- 5´-triphosphate (dNTP) mix
(SEQUENOM Inc., San Diego, CA, USA) in an Applied Biosystems®
MicroAmp® EnduraPlate™ Optical 96-Well Clear Reaction Plates with
Barcode (Life Technologies Australia Pty Ltd., Mulgrave, VIC, Australia).
2. Place reaction plate in Applied Biosystems® Veriti® 96-Well Thermal Cycler
(Life Technologies Australia Pty Ltd., Mulgrave, VIC, Australia) and
perform primary PCR using the following conditions: initial denaturation at
95°C for 2 min; 45 cycles at 95°C for 30 s, 56°C for 30 s, 72°C for 1 min;
and final extension at 72°C for 5 min.
Shrimp Alkaline Phosphatase (SAP) dephosphorylation
3. Remove plate from thermal cycler and add 0.5 U of shrimp alkaline
phosphatase (SAP) (iPLEX® GOLD reaction kit, SEQUENOM Inc., San
Diego, CA, USA).
0 Appendices 219
4. To neutralize unincorporated dNTPs run dephosphorylation reaction using
the following cycling conditions: 37°C for 40 min and 85°C for 5 min.
iPLEX® primer extension reactions
5. Remove reaction plate from thermal cycler and perform primer extension
reactions using the iPLEX® Gold Reagent Kit according to the SEQUENOM
linear adjustment method described in iPLEX™ GOLD genotyping
application guide.
6. Prepare the iPLEX® reaction cocktail using: 0.222X iPLEX® Buffer Plus
(SEQUENOM Inc., San Diego, CA, USA), 1X iPLEX® Termination Mix
(SEQUENOM Inc., San Diego, CA, USA), 1X iPLEX® Enzyme
(SEQUENOM Inc., San Diego, CA, USA) and an extend primer mixture
made according to the manufacturer’s linear adjustment method.
7. Add iPLEX® reaction cocktail to the amplification products and place
reaction plate in the thermal cycler. Run iPLEX® primer extension reactions
using the following conditions: initial denaturation at 94°C for 30s; 40 cycles
of 94°C for 5s, annealing at 52°C for 5s and extension 80°C for 5s. Annealing
and extension procedure repeats five-times (to give a total of 200 short
cycles), followed by a 5 s denaturation step, and a final extension step of 3
min at 72°C.
8. Remove reaction plate and add 41 µl of sterile water and 15 mg of Clean
Resin (SEQUENOM Inc., San Diego, CA, USA) to each iPLEX® reaction.
Seal reaction plate and mix continuously by rotation for at least 15 minutes.
9. Centrifuge reaction plate at 3200g for 5 minutes.
220 0 Appendices
MALDI-TOF MS analysis and data analysis
10. Transfer reaction plate to the SEQUENOM® MassARRAY® Nanodispenser
(SEQUENOM Inc., San Diego, CA, USA) and dispense a total of 12-16 nl of
each iPLEX® reaction product onto a SpectroCHIP® II G96 (SEQUENOM
Inc., San Diego, CA, USA).
11. Run SpectroCHIP® analysis using SEQUENOM® MassArray® Analyzer 4
and laser desorption/ionisation, automated spectra acquisition using
SpectroAcquire Version 4.0 (SEQUENOM Inc., San Diego, CA, USA).
12. Perform genotype data analysis with the MassARRAY® Typer software
version 4.0 (SEQUENOM Inc., San Diego, CA, USA).
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Appendix C Protocol for genotyping using automated Sanger sequencing
1. Prepare a 10 µl Exo-SAP-IT Clean-up reaction using 5ul of HRM product, 4
µl of Reverse Osmosis water and 1µl of Exo-SAP-IT. Mix reaction and
incubate in Applied Biosystems® Veriti® 96-Well Thermal Cycler (Life
Technologies Australia Pty Ltd., Mulgrave, VIC, Australia) using the
following cycling conditions: 37°C for 15 min and 80°C for 15 min.
2. Subsequently, prepare a 20 µl reaction mixing 15 ng/µl of the Exo-SAP-IT
product, 650nM of each primer (separate reactions for forward and reverse
primer), 3.5ul of BDT v3.1 Ready Reaction Mix (100RR) and 1x Sequencing
Buffer. Incubate in Applied Biosystems® Veriti® 96-Well Thermal Cycler
(Life Technologies Australia Pty Ltd., Mulgrave, VIC, Australia) using the
following cycling conditions: 1 cycle of 96°C for 1 min, 30 cycles of 96°C
for 10 s, 50°C for 5 s. and 60°C for 4 min, 1 cycle of 4°C for 5 min and 1
cycle of 10°C for 5 min.
3. Perform standard ethanol precipitation on the products and load them onto the
ABI-3130 Genetic Analyzer (Life Technologies Australia Pty Ltd., Mulgrave,
VIC, Australia) to perform automated Sanger sequencing.
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