mutation profiling of colorectal cancer for kras,...
TRANSCRIPT
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Mutation profiling of colorectal cancer for KRAS, BRAF, NRAS
and PIK3CA genes in Indian patient cohort
by
Harshali Anant Patil
(B.E. Biotechnology)
Submitted in fulfillment of the requirements for the degree of
Doctor of Philosophy
Deakin University
January 2017
iv
Dedicated to the Almighty and to all patients suffering from Colorectal Cancer
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Acknowledgements
It is a pleasant task to express my thanks to all those who contributed in many
ways to the success of this study and made it an unforgettable experience for me.
At this moment of accomplishment, I gratefully acknowledge Mr. K V
Subramaniam, President of Reliance Life Sciences Pvt Ltd (RLS), for giving me
this opportunity to pursue higher degree program while working in RLS. He has
always been very supportive and has always encouraged employees to further
their academic and professional aspirations.
I would like to express my sincere gratitude to my Principal Supervisor Prof. Colin
J. Barrow, Alfred Deakin Professor and Chair In Biotechnology, School of Life &
Environmental Sciences, for his invaluable guidance and mentorship.
I would like to thank my Co-Principal Supervisor Dr. Rupinder Kanwar, Senior
Research Fellow, School of Medicine, for all the support that she provided in these
years. I really admire her zeal for perfection. Her enthusiasm and drive for best
scientific research has always motivated me and has helped me grow in my
scientific career. I feel very fortunate to have you as my mentor in this PhD journey.
My sincere thanks to Prof. Jagat Kanwar, Professor In Nanomedicine, School of
Medicine, for giving me scientific advises and the feedback on the research work.
I would also like to thank Dr. Shailaja Gada Saxena, Head, Molecular Medicine
(MME). I have been associated with Molecular Medicine group for almost 10 years
and it has been a wonderful journey. Since the time she took up this responsibility
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of heading Molecular Medicine, she has always provided a constant support,
encouragement and valuable suggestions, which made my journey through
doctoral program a memorable one.
Dr. Arnab Kapat, my mentor and Director, Reliance Institute of Life Sciences, thank
you so very much for the belief you had in me and the encouragement you have
given me over the last few years will stay with me. Thank you for all the scientific
advises and prompt feedbacks given on the research work. I would like to specially
thank you for the care and guidance. My journey in these last four years under your
mentorship for PhD will be cherished for all the good times spent.
I am also thankful to Ms. Anuradha, Ms. Gayathri and Ms. Ruby from Deakin India
office for their support. I am also grateful to Ms. Helen Woodall, partnerships
coordinator of Deakin-India, for making me comfortable during my stay in Deakin.
Most of the results described in this thesis would not have been obtained without a
close collaboration with few hospitals. I owe a great deal of appreciation and
gratitude to Ruby Hall Clinic, Pune, and Hinduja Hospital for helping me with the
clinical details required.
I would like to thank all MMEiets, my colleagues, Kiran, Vrunda, Smita, Deepti and
team, Sandeep and Kundanben and team specially Mr. Ganesh. Dr. Rajesh Korde,
Dhanashree, Dr. Sonia, Dr. Harshal and Asha for the histopathology work. I would
also like to thank Rehana, Mitali, Tejas Mehta and team for helping me out in
completing my PhD.
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Also, a big thanks goes to all ex MMEites including Darshana and Akriti for their
constant support.
My heartfelt thanks to my friends who filled my life with love and laughter –
Sheena, Urvashi, Sanjukta, Dolly, Leena, Shivani, Pavani, Kavya and Moti. The
days spent with all of you would always be cherished in my life.
Above all, I would like to thank my husband, Anant, for his personal support and
great patience at all times. “Thank you for being there always!” My parents -
mummy and papa, my in-laws-aai and dada who supported me throughout these
years, my brother Dharmu and all my family members who have given me their
unequivocal support throughout, as always, for which my mere expression of
thanks likewise does not suffice. And finally my daughter Anushree, whose smiling
face has always encouraged me.
Harshali Anant Patil
02nd January 2017
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Table of Content
Acknowledgements ......................................................................................................... v
Table of Content ........................................................................................................... viii
List of Publications ........................................................................................................ xii
List of Abbreviations ...................................................................................................... xv
List of Tables ..................................................................................................................xx
List of Figures ............................................................................................................... xxii
Abstract ........................................................................................................................ xxv
Chapter 1 Introduction .......................................................................................... 1 1.1 Research Question ......................................................................................................... 3 1.2 Hypotheses .................................................................................................................... 3 1.3 Thesis Outline ................................................................................................................ 5
Chapter 2 Literature Review .................................................................................... 6 2.1 Introduction .................................................................................................................. 6 2.2 Epidemiology ............................................................................................................... 11
2.2.1 Colorectal cancer incidence varies globally ................................................................. 11 2.2.2 Mortality of colorectal cancer ..................................................................................... 13
2.3 Pathophysiology .......................................................................................................... 18 2.3.1 Hyperplastic polyps ...................................................................................................... 18 2.3.2 Adenomatous polyps .................................................................................................... 19 2.3.3 Molecular abnormalities in pathogenesis of CRC ........................................................ 21 2.3.4 WNT pathway ............................................................................................................... 25 2.3.5 Chromosomal Instability (CIN) ..................................................................................... 26 2.3.6 Microsatellite Instability .............................................................................................. 28 2.3.7 CpG island methylator phenotype (CIMP) pathway .................................................... 29 2.3.8 Clinical staging .............................................................................................................. 31
2.4 Risk factors .................................................................................................................. 34 2.4.1 Age ................................................................................................................................... 37 2.4.2 Hereditary Factors ........................................................................................................... 37 2.4.3 Inherited Syndrome ......................................................................................................... 37 2.4.4 Inflammatory bowel disease (IBD) ................................................................................... 39 2.4.5 Life Style Factors .............................................................................................................. 39 2.4.6 Dietary factors .................................................................................................................. 40 2.4.7 Race/ Ethinicity ................................................................................................................ 41
2.5 Diagnosis ..................................................................................................................... 42 2.6 Treatment strategies ................................................................................................... 44
2.6.1 Surgery ......................................................................................................................... 44 2.6.2 Conventional Chemotherapy ........................................................................................ 46
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2.6.1.1 5-Fluorouracil / Leucovorin .................................................................................................. 47 2.6.1.2 Oxaliplatin and Irinotican ..................................................................................................... 47 2.6.1.3 Capecitabine ........................................................................................................................ 50
2.6.2 Therapy options for colon cancer ................................................................................ 51 2.6.3 Therapy options for rectal cancer ................................................................................ 52 2.6.4 Treatment options for advanced disease i.e. mCRC .................................................... 54 2.6.5 Novel cytotoxic and targeted biologic therapeutics .................................................... 55
2.7 Targeting Vascular Endothelial Growth Factor (VEGF) .................................................. 57 2.7.1 Bevacizumab-Anti VEGF monoclonal antibody ............................................................ 60 2.7.2 Aflibercept- a novel antiangiogenic fusion protein ...................................................... 62 2.7.3 Regorafenib-small molecule inhibitor .......................................................................... 63 2.7.4 Identification of predictive biomarkers for anti-angiogenic agents: A priority for mCRC
management ............................................................................................................................. 64 2.8 Epidermal growth factor receptor targeting agents ...................................................... 66
2.8.1 Cetuximab ..................................................................................................................... 67 2.8.2 Panitumumab ............................................................................................................... 73 2.8.3 Predictive biomarkers for anti-EGFR agents ................................................................. 75
2.8.3.1 KRAS ..................................................................................................................................... 75 2.8.3.2 BRAF ..................................................................................................................................... 79 2.8.3.3 NRAS ..................................................................................................................................... 80 2.8.3.4 PIK3CA .................................................................................................................................. 81 2.8.3.5 PTEN ..................................................................................................................................... 82 2.8.3.6 TP53 ..................................................................................................................................... 84
2.9 Predictive and prognostic biomarkers in development ................................................ 85 2.9.1 Micro RNAs .................................................................................................................. 85 2.9.2 Cell free nucleic acid .................................................................................................... 85 2.9.3 Circulating tumor cells ................................................................................................. 86 2.9.4 Protein Biomarkers ...................................................................................................... 86 2.9.5 Cancer stem cells.......................................................................................................... 87
2.10 Molecular Pathology Epidemiology: Emerging discipline to help in optimizing disease
prevention and treatment strategies ..................................................................................... 93
Chapter 3 Materials and Methods ......................................................................... 98 3.1 Study Population ......................................................................................................... 98 3.2 Methods ...................................................................................................................... 98
3.2.1 Haematoxylin and eosin (H&E) Analysis ..................................................................... 102 3.2.1.1 Principle .............................................................................................................................. 102 3.2.1.2 Method ............................................................................................................................... 102
3.2.2 DNA extraction ............................................................................................................ 104 3.2.2.1 Principle .............................................................................................................................. 104 3.2.2.2 Methodology ...................................................................................................................... 105
3.2.3 Primer selection for Polymerase Chain Reaction (PCR) .............................................. 106 3.2.3.1 PCR Assay Optimization ..................................................................................................... 107
3.2.4 Detection of PCR products by agarose gel electrophoresis ........................................ 115 3.2.4.1 Method .............................................................................................................................. 115
3.2.5 Sequencing of PCR products for Detection of mutations ............................................ 115
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3.2.5.1 Principle .............................................................................................................................. 115 3.2.5.2 Method ............................................................................................................................... 116
Chapter 4 Assay Validation .................................................................................. 119 4.1 Validation parameters: ............................................................................................... 121
4.1.1 Specificity .................................................................................................................... 121 4.1.2 Sensitivity .................................................................................................................... 122 4.1.3 Repeatability ............................................................................................................... 122 4.1.4 Reproducibility ............................................................................................................ 122
4.2 Validation of KRAS exon 2 and exon 3 (codons 12, 13 and 61) mutation detection assay
123 4.2.1 Specificity .................................................................................................................... 123 4.2.2 Sensitivity .................................................................................................................... 127 4.2.3 Reproducibility ............................................................................................................ 131 4.2.4 Repeatability ............................................................................................................... 134
4.3 Validation of BRAF exon 15 codon 600 mutation detection assay ............................... 146 4.3.1 Specificity .................................................................................................................... 146 4.3.2 Sensitivity ................................................................................................................... 147 4.3.3 Reproducibility ............................................................................................................ 149 4.3.4 Repeatability ............................................................................................................... 151
4.4 Validation of NRAS exon 2 and exon 3 (codons 12, 13 and 61) mutation detection assay
156 4.4.1 Specificity .................................................................................................................... 156 4.4.2 Sensitivity .................................................................................................................... 158 4.4.3 Reproducibility ............................................................................................................ 159 4.4.4 Repeatability ............................................................................................................... 163
4.5 Validation of PIK3CA exon 9 and exon 20 (codon 545 and codon 1047) mutation
detection assay .................................................................................................................... 171 4.5.1 Specificity .................................................................................................................... 171 4.5.2 Sensitivity .................................................................................................................... 171 4.5.3 Reproducibility ............................................................................................................ 172 4.5.4 Repeatability ............................................................................................................... 174
4.6 Laboratory methods used for detection of mutations ................................................. 177
Chapter 5 ..................................................................................................................... 180
Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological
features of CRC patients .............................................................................................. 180 5.1 Introduction ............................................................................................................... 180 5.2 Methodology .............................................................................................................. 182 5.3 Clinicopathological characteristics of CRC samples ...................................................... 184
5.3.1 KRAS mutations .......................................................................................................... 191 5.3.2 BRAF ............................................................................................................................ 203 5.3.3 NRAS ........................................................................................................................... 205 5.3.4 PIK3CA ......................................................................................................................... 207
5.4 Discussion ................................................................................................................... 210
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5.4.1 KRAS ........................................................................................................................... 211 5.4.2 BRAF ............................................................................................................................ 213 5.4.3 NRAS ........................................................................................................................... 215 5.4.4 PIK3CA ........................................................................................................................ 215
Chapter 6 Survival Analysis ................................................................................. 225 6.1 Introduction ............................................................................................................... 225 6.2 Patients ...................................................................................................................... 226 6.3 Statistics ..................................................................................................................... 227 6.4 Results: ....................................................................................................................... 228
6.4.1 Estimation of survival ................................................................................................. 232 6.4.2 Censored observations ................................................................................................ 235 6.4.3 Actuarial Life Table ..................................................................................................... 236 6.4.4 Kaplan – Meier Survival Curve .................................................................................... 238 6.4.4.1 Estimation of Progression free survival (PFS) and Overall Survival (OS) .................... 239
6.5 Discussion ................................................................................................................... 251
Chapter 7 Summary and Future Work .................................................................. 258 7.1 Mutation Studies in 203 CRC patients ......................................................................... 259 7.2 Correlation of mutations in KRAS, BRAF, NRAS and PIK3CA genes with clinico-
pathological data for a 203 Indian CRC patient cohort .......................................................... 261 7.3 Correlation of clinico-pathological data with survival in Indian patient cohort ............ 262
Appendix ..................................................................................................................... 268 List of Chemicals .................................................................................................................. 270 List of Instruments ............................................................................................................... 272 List of Software’s ................................................................................................................. 274
References .................................................................................................................. 275
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List of Publications
H.A. Patil, S. Gada Saxena, C. J. Barrow, J.R. Kanwar, A. Kapat, R.K. Kanwar
(2016). Chasing personalized medicine dream through biomarker validation in
colorectal cancer. Drug Discovery Today (2017) 22:111-119. Impact Factor – 6.12
Conference Presentations
Poster Presentation
H.A. Patil, S.Gada Saxena, C. J. Barrow, R.K. Kanwar, J.R. Kanwar, A. Kapat
(2016).
203P - Molecular analysis of predictive biomarkers –KRAS, BRAF, NRAS and
PIK3CA in colon cancer and correlation of clinicopathological features with
mutation profiling and therapeutic response in Indian patient cohort. ESMO ASIA
2016, Singapore, December 16-19, 2016
Citation:Annals of Oncology (2016) 27 (suppl 9): doi: 10.1093/annonc/mdw581.036
H.A. Patil, C. J. Barrow, R.K. Kanwar, J.R. Kanwar, A. Kapat (2015).
168P - Clinicopathological correlation with mutation profiling of colorectal cancer
for KRAS, BRAF, NRAS and PIK3CA genes in Indian patient cohort. ESMO ASIA
2015, Singapore, December 18-21, 2015
Citation: Annals of Oncology (2015) 26 (suppl_9): 42-70. 10.1093/annonc/mdv523.
xiii
H.A. Patil, C. J. Barrow, R.K. Kanwar, J.R. Kanwar, A. Kapat (2015).
Molecular analysis of predictive biomarkers-KRAS, BRAF, NRAS and PIK3CA in
colon cancer and correlation with clinicopathological features in Indian patient
cohort. DIRI International Symposium 2015, New Delhi, India, December 6-9, 2015
Conferences Attended
DIRI International Symposium organized by TERI-Deakin, New Delhi, India, Nov
29 -31, 2012.
DIRI Workshop on “Special higher degree by research”, New Delhi, India,
November 5-6, 2014.
DIRI International Symposium on Translational Research, TERI Deakin NanoBio
Centre, Gurgaon, India, December 6 – 9, 2015.
Manuscript in Preparation
H.A. Patil, S. Gada Saxena, C. J. Barrow, J.R. Kanwar, A. Kapat, R.K. Kanwar.
Mutation profiling of colorectal cancer for KRAS, BRAF, NRAS and PIK3CA genes
in Indian patient cohort.
Travel Grant Awards
1. ESMO ASIA 2015, Singapore, December 18-21, 2015 - Availed
2. EMBL PhD symposium, Life by Numbers, November 17-20, 2016 – Not
availed
3. ESMO ASIA 2016, Singapore, December 16-19, 2016 - Availed
xiv
Other publications
Patil, H., Korde, R. & Kapat, A. KRAS gene mutations in correlation with
clinicopathological features of colorectal carcinomas in Indian patient cohort.
Med. Oncol (2013) 30: 617. doi:10.1007/s12032-013-0617-5. Impact Factor-2.5
xv
List of Abbreviations
5-FU 5-fluorouracil
A Adenine
AJCC American Joint Committee on Cancer
APC Adenomatous polyposis coli
app. Approximately
ATP Adenosine-5'-triphosphate
BRAF V-raf murine sarcoma viral oncogene homolog B1
BSA Bovine serum albumine
CAPOX Capecitabine in combination with oxaliplatin
cDNA Complementary DNA
C Cytosine
CEA Carcinoembryonic antigen
CI Confidence interval
CIN Chromosomal instability
COSMIC Catalogue of somatic mutations in cancer
CRC Colorectal cancer
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CR Complete response
DCC Deleted in colorectal carcinoma
DMSO Dimethyl sulfoxide
DNA Deoxyribonucleic acid
dsDNA Double-stranded deoxyribonucleic acid
DSF Disease-free survival
FAP Familial adenomatous polyposis
FFPE Formalin-fixed paraffin-embedded
FOBT Fecal occult blood test
FOLFIRI Leucovorin (folinic acid), 5-FU, and irinotecan
FOLFOX Leucovorin (folinic acid), 5-FU, and oxaliplatin
G Guanine
GAPDH Glyceraldehyde 3-phosphate dehydrogenase
gDNA Genomic DNA
GDP Guanosine diphosphate
GTP Guanosine triphosphate
GTPase Hydrolyze guanosine triphosphate (GTP)
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H&E Haematoxylin and eosin
HER-2 Human epidermal growth factor receptor 2
HNPCC Hereditary non-polyposis colorectal cancer
IFL Irinotecan + 5-Fluorouracil/Leucovorin
IHC Immunohistochemistry
KRAS Kirsten rat sarcoma viral oncogene homolog
LNR Lymph node ratio
LOH Loss of heterozygosity
mAb Monoclonal antibody
MAPK Mitogen-activated protein kinase
mCRC Metastatic colorectal cancer patients
MEK Mitogen-activated protein kinase
MGB Minor groove binder
miR/miRNA microRNA
MLH1 MutL homolog 1
MMR Mismatch repair system
mRNA Messenger RNA
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MSI Microsatellite instability
NR Non-responders
NRAS Neuroblastoma RAS viral (v-ras) oncogene homolog
Nt Nucleotide
OR Odds ratio
ORR Overall response rate
OS Overall survival
PCR Polymerase chain reaction
PFS Progression-free survival
PIK3CA Phosphoinositides-3-kinase catalytic alpha polypeptide
PR Partial response
PTEN Phosphatase and tensin homolog
R – RESICT Responder response evaluation criteria in solid tumors
RNA Ribonucleic acid
RT Room temperature
SNPs Single nucleotide polymorphisms
UBC Ubiquitin C
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T Thymine
TGF-ß Transforming growth factor beta
TGFBR2 Transforming growth factor beta receptor II
TP53 Tumour protein 53
VEGFA Vascular endothelial growth factor A
WT Wild-type
XELIRI A combination of capecitabine and irinotecan
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List of Tables
Table 2.1: Summary of risk factors associated with colorectal cancer. ............................................35
Table 2.2: Summary of clinical trials undertaken for bevacizumab. ..................................................61
Table 2.3: Summary of clinical trials undertaken for cetuximab. .......................................................69
Table 2.4: Summary of Biomarker based studies in CRC (Patil et al., 2016) ...................................88
Table 2.5: Biological agents in clinical trials ......................................................................................92
Table 3.1: Master mixture for first round of PCR for KRAS, BRAF, NRAS and PIK3CA. .............. 108
Table 3.2: Master mixture preparation for nested PCR for KRAS, BRAF, NRAS and PIK3CA. .... 109
Table 3.3: KRAS exons 2 and 3 mutation detection assay. ........................................................... 109
Table 3.4: BRAF exon 15 mutation detection assay- First round PCR. ......................................... 110
Table 3.5: BRAF exon 15 mutation detection assay- Nested PCR. ............................................... 110
Table 3.6 NRAS exon 2 mutation detection assay- First round and Nested PCR. ........................ 110
Table 3.7: NRAS exon 3 mutation detection assay- First round PCR. .......................................... 111
Table 3.8: NRAS exon 3 mutation detection assay- Nested PCR. ................................................ 111
Table 3.9: PIK3CA exon 9 mutation detection assay- First round and Nested PCR. .................... 111
Table 3.10: PIK3CA exon 20 mutation detection assay- First round PCR. .................................... 112
Table 3.11: PIK3CA exon 20 mutation detection assay- Nested PCR .......................................... 112
Table 4.1: Loading pattern for Figures 4.3A and 4.3B. .................................................................. 125
Table 4.2: Loading pattern on agarose gel electrophoresis for figures 4.5 and 4.6 ...................... 129
Table 4.3: Results of samples analysed by two different analysts for reproducibility. ................... 132
Table 4.4: Results of samples analysed by two different analysts for repeatability. ...................... 135
Table 4.5: Results of samples analyzed for sensitivity parameter for BRAF assay. ...................... 147
Table 4.6: Results of clinical samples analyzed by two different analysts for reproducibility. ....... 149
Table 4.7: Results of clinical samples analyzed by two different analysts for repeatability. .......... 151
Table 4.8: Results of clinical samples analysed by two different analysts for reproducibility for
NRAS codon 12/13 ................................................................................................................. 159
Table 4.9: Results of clinical samples analysed by two different analysts for reproducibility for
NRAS codon 61. ..................................................................................................................... 161
Table 4.10: Results of clinical samples analyzed by two different analysts for repeatability. ........ 164
Table 4.11: Results of clinical samples analysed by two different analysts for repeatability for NRAS
codon 61. ................................................................................................................................. 166
Table 4.12: Results of clinical samples analysed by two different analysts for reproducibility for
PIK3CA. ................................................................................................................................... 172
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Table 4.13: Results of clinical samples analysed by two different analysts for repeatability for
PIK3CA. ................................................................................................................................... 174
Table 5.1: Frequency of mutations in Catalogue of Somatic Mutations In Cancer (COSMIC)
database. ................................................................................................................................. 181
Table 5.2: The clinicopathological characteristics of Indian colorectal cancer samples (n=203). . 184
Table 5.3: Spectrum of KRAS mutations in 203 CRC cases determined by Sanger sequencing. 192
Table 5.4: Correlation of mutation frequency in KRAS gene with clinicopathological factors of
colorectal cancer patients (n=203). ......................................................................................... 201
Table 5.5: Clinicopathological characteristics’ correlation with mutation frequency in BRAF gene in
colorectal cancer patients (n=203). ......................................................................................... 204
Table 5.6: Correlation of mutation frequency in NRAS gene with clinicopathological characteristics
in CRC cases (n=203). ............................................................................................................ 206
Table 5.7: Correlation of mutation frequency in PIK3CA gene with clinicopathological characteristics
in colorectal cancer cases (n=203). ........................................................................................ 208
Table 5.8: Multivariate Logistic Regression Analysis for the correlation between gene mutations and
clinicopathological features in Indian CRC patients (n=203) .................................................. 209
Table 5.9: KRAS, NRAS, BRAF and PIK3CA mutation frequencies in reported studies worldwide.
................................................................................................................................................ 217
Table 6.1a: Basic Data of CRC patients (n=30) ............................................................................. 228
Table 6.1b: Clinicopathological Data of CRC patients (n=30) ....................................................... 230
Table 6.2: Actuarial Life Table for patients (n=30) ......................................................................... 236
Table 6.3: Univariate and Multivariate analysis of PFS and OS .................................................... 245
Table 6.4: Patient, disease and treatment characteristics (n=30). ................................................ 247
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List of Figures
Figure 1.1: Schematic outline of Aims and Objectives of the study. ...................................................4
Figure 2.1: Global cancer incidence and mortality according to Globocan 2012(Ferlay, 2012). ........7
Figure 2.2: Gender wise global cancer incidence and mortality according to Globocan 2012(Ferlay,
2012). ...........................................................................................................................................8
Figure 2.3: Increasing trend of cancers in India (Image source ICMR(Rath and Gandhi, 2014)). .....9
Figure 2.4: Cancer prevalence in metropolitan cities of India (Image source: Marimutthu et al, 2008
(Marimuthu, 2008)). ....................................................................................................................10
Figure 2.5: Age standardized Incidence and Mortality of colorectal cancer in men and women per
100,000 across geographical zones (Kupiers et al, Primer, 2015). ...........................................16
Figure 2.6A: Incidence rates of colorectal cancer. Data according to (Ferlay, 2012).......................16
Figure 2.6B: Incidence rates of colorectal cancer in US. Data according to (Jemal et al., 2010b). .17
Figure 2.7A: Mortality Rates of colorectal cancer. Data according to (Ferlay et al., 2010). .............17
Figure 2.7B: Mortality rates of colorectal cancer in US. Data according to (Jemal et al., 2010b). ...18
Figure 2.8: Diagrammatic representation of hyperplastic and adenomatous polyps. .......................20
Figure 2.9: Schematic overview of molecular abnormalities in pathogenesis of colorectal cancer
(Image source: (Patil et al., 2016). .............................................................................................21
Figure 2.10: Events in transformation of serrated polyps to adenocarcinomas. ...............................23
Figure 2.11: The WNT pathway: Image source: Reya T et al, Nature 2005(Reya and Clevers,
2005). .........................................................................................................................................24
Figure 2.12: TNM staging as maintained by American Joint Committee on Cancer (AJCC) and
Union for International Cancer Control (UICC) (Source-(Edge and Compton, 2010). ...............32
Figure 2.13: Schematic overview of incidence of colorectal cancer. ................................................33
Figure 2.14: Advantages and disadvantages of different screening modalities used for diagnosis of
CRC (Image source- Kuiper’s et al., 2013). ...............................................................................43
Figure 2.15: Schematic overview of advances made in treatment strategies for colorectal cancer. 44
Figure 2.16: Adjuvant therapy options for colon cancer patients with no metastasis i.e Mo as per
Indian Council for Medical Research (Sirohi et al., 2014). .........................................................52
Figure 2.17: Neo adjuvant and Adjuvant therapy options for rectal cancer with no metastasis i.e Mo
as per Indian Council for Medical Research (Sirohi et al., 2014). ..............................................53
Figure 2.18: EGFR and VEGF Signaling Pathways in CRC development and tumor survival. .......56
Figure 2.19: Activation of RAS pathway. ..........................................................................................76
Figure 2.20: Association of anti-EGFR therapy and KRAS mutations. .............................................77
Figure 2.21: Estimated Response Rate to EGFR inhibitors in Western population with activating
mutations in KRAS, BRAF, NRAS and PIK3CA Data according to -(Frattini et al., 2007). .......83
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Figure 2.22: Difference between traditional epidemiological studies and Molecular pathologic
epidemiology (MPE) (Image Source-Ogino et. al., Gut, 2011). .................................................94
Figure 2.23: Three approaches of MPE (Image source-Ogino et.al., Gut, 2011). ............................95
Figure 3.1: H&E stained tissue samples ........................................................................................ 104
Figure 4.1: Components of Assay Validation. ................................................................................ 119
Figure 4.2: Key assay considerations (Image Source-Steven Anderson, Expert Rev Mol Diagn,
2011). ...................................................................................................................................... 120
Figure 4.3A and 4.3B: Specificity experiment for KRAS mutation detection assay. ..................... 124
Figure 4.4: Clustal W for KRAS exon 2 codon 12 and 13 for specificity parameter. ..................... 126
Figure 4.5: KRAS Sensitivity 1 ....................................................................................................... 128
Figure 4.6: KRAS Sensitivity 2 ....................................................................................................... 128
Figure 4.7: Clustal W for KRAS exon 2, codons 12 and 13 for sensitivity parameter. .................. 130
Figure 4.8: Clustal W for KRAS for reproducibility parameter. ...................................................... 133
Figure 4.9: Clustal W for KRAS for repeatability parameter. ......................................................... 136
Figure 4.10: Agarose gel electrophoresis of exon2 and exon3 of KRAS gene after optimization of
the assay. ................................................................................................................................ 137
Figure 4.11: Electropherogram of KRAS exon 2 with no mutations detected. .............................. 138
Figure 4.12: Electropherogram of KRAS exon 2 with codon 12 mutation (GGT;GAT) Glycine to
Aspartic acid substitution. ....................................................................................................... 139
Figure 4.13: Electropherogram of KRAS exon 3 with no mutations detected. .............................. 140
Figure 4.13 A: KRAS-G12D Original-50% ..................................................................................... 142
Figure 4.13 B: KRAS-G12D 25% ................................................................................................... 143
Figure 4.13 C: KRAS-G12D 20% ................................................................................................... 144
Figure 4.13 D: KRAS-G12D 10% ................................................................................................... 145
Figure 4.14: Clustal W for BRAF for specificity parameter ............................................................ 146
Figure 4.15: Clustal W for BRAF for sensitivity parameter. ........................................................... 148
Figure 4.16: Clustal W for BRAF for reproducibility parameter. ..................................................... 150
Figure 4.17: Clustal W for BRAF for repeatability parameter. ....................................................... 152
Figure 4.18: Agarose gel electrophoresis of exon15 of BRAF gene after optimization of the BRAF
assay ....................................................................................................................................... 153
Figure 4.19: Electropherogram of BRAF exon 15 with no mutations detected. ............................. 154
Figure 4.20: ClustalW of exon15 of BRAF gene after sequencing. ............................................... 154
Figure 4.21: Representative Agarose gel electrophoresis of NRAS gene exon 2. ........................ 156
Figure 4.22: ClustalW of NRAS gene after sequencing for specificity. .......................................... 157
Figure 4.23: ClustalW of NRAS gene after sequencing for reproducibility. ................................... 160
Figure 4.24: ClustalW of nras exon 3 gene after sequencing for reproducibility. .......................... 162
Figure 4.25: ClustalW of NRAS gene after sequencing for repeatability ....................................... 165
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Figure 4.26: ClustalW of NRAS gene after sequencing for repeatability ....................................... 167
Figure 4.27: Agarose gel electrophoresis of exon2 and exon 3 of NRAS gene after optimization of
the NRAS assay. ................................................................................................................... 168
Figure 4.28: ClustalW of exon 2 and 3 of NRAS gene after sequencing. ...................................... 169
Figure 4.29: ClustalW of PIK3CA gene after sequencing for reproducibility. ................................ 173
Figure 4.30: ClustalW of PIK3CA gene after sequencing for repeatability .................................... 175
Figure 4.31: Agarose gel electrophoresis of exon 9 and exon 20 of PIK3CA gene after optimization
of the assay. ............................................................................................................................ 176
Figure 4.32: Key features of different methodologies used for mutation analysis (Image Source-
Steven Anderson, Expert Rev Mol Diagn, 2011). ................................................................... 178
Figure 4.33: Comparison of properties of different methodologies used for mutation analysis. .... 179
Figure 5.1: Clinicopathological and demographic characteristics of the CRC patient cohort
employed in this study (n=203). .............................................................................................. 186
Figure 5.2: Distribution of CRC tumor samples according to the anatomic location. .................... 188
Figure 5.3: Overall mutation frequency rate of four genes (KRAS, BRAF, NRAS and PIK3CA) in
Indian CRC patient samples (n=203). ..................................................................................... 189
Figure 5.4: Venn Diagram showing overall mutation distribution in CRC patient samples (n=203).
................................................................................................................................................ 190
Figure 5.5: Representative Sequencing electropherograms of KRAS showing KRAS codon 12 and
codon 13. ................................................................................................................................. 193
Figure 5.6 (a-f): Representative Sequencing electropherograms of NRAS, BRAF and PIK3CA
showing Wild type and mutant sequences. ............................................................................. 197
Figure 6.1: The data of 30 patients aligned in order of their survival time. Red bars indicate the
dead patients and yellow ones indicate the alive patients. ..................................................... 233
Figure 6.2: Tree diagram for CRC patients (n=30). ....................................................................... 234
Figure 6.3: Tree diagram showing 30 CRC cases of which 13 are dead, 6 have lost follow up and
11 are alive. ............................................................................................................................. 235
Figure 6.4: Actuarial survival curve (n=30) .................................................................................... 237
Figure 6.5: Kaplan-Meier survival curve for CRC patients (n=30). ................................................ 238
Figure 6.6: Kaplan Meier plots of PFS and OS for CRC patients (n=30). ..................................... 239
Figure 6.7: Kaplan Meier plots for comparison between patients with tumor mutation or wild type
tumors. .................................................................................................................................... 248
Figure 6.8: Kaplan Meier plots for comparison between different therapy options. ....................... 249
xxv
Abstract
Colorectal cancer (CRC) is one of the leading causes of cancer mortality
worldwide. It is the most common of all gastrointestinal malignancies.
Unfortunately, most CRC tumors are benign, grow slowly and do not show
symptoms until they grow larger. Wide geographical, racial and ethnic differences
in incidence are observed for this type of cancer. In rapidly developing nations as
India, the incidence of CRC is 4.2 and 3.2 per 100,000 in males and females,
respectively, which is comparatively much lower compared with rates in developed
nations. Despite many advances in early diagnosis, surgical techniques, molecular
and therapeutic characterization made over the past decade, CRC still remains a
major health burden with high unmet medical, diagnostic and clinical needs.
The past decade has uncovered various new pathways in the development of
CRC. Several studies have been performed to identify the prognostic impact of
various clinico-pathological factors. The survival rates of CRC in Asian countries
are lower as compared to European and other Western developed countries. This
can be attributed mainly to late diagnosis and less awareness about the disease in
low socioeconomic populations.
Early detection and proper treatment are some of the key strategies for improving
overall survival. In this direction some improvement in the CRC patient survival rate
has been observed due to the combinatorial use of chemotherapeutics alone/and
or with targeted therapy. 5-Fluoro Uracil (5-FU) was the only drug used for
xxvi
decades for CRC treatment. With the use of combination drugs in chemotherapy
for example, 5-FU plus Leucovorin (LV), either with irinotican (FOLFIRI) and
oxaliplatin (FOLFOX), has led to an improvement in survival. Two FDA approved
monoclonal antibodies (MAb) targeted at epidermal growth factor receptor (EGFR),
the chimeric IgG1 MAb cetuximab and the fully humanized IgG2 panitumumab,
and a VEGF inhibitor bevacizumab have proven to be effective in combination with
chemotherapy or as single agent for the treatment of metastatic colorectal cancer
(mCRC). However, clinicians require accurate outcome prediction to adopt
appropriate therapeutic regime.
The efficacy of MAb is not consistent for every patient; some patients experience a
dramatic response to MAb, whereas others show no response. Accurate
biomarkers are not yet available for differentiating between responders and non-
responders, i.e. resistant population. V-Ki-ras2 Kirsten rat sarcoma viral oncogene
homolog (KRAS) has been a well-established predictive biomarker and it is
mandatory to test for KRAS gene mutations before giving cetuximab or
panitumumab to CRC patient. However, recent studies have demonstrated that not
all KRAS wild type tumors are responders of targeted therapy. Therefore, further
molecular characterization of the EGFR signaling pathway is required. Various
signaling molecules downstream of KRAS, such as v-Raf murine sarcoma viral
oncogene homolog B (BRAF) and Neuroblastoma RAS viral oncogene homolog
(NRAS), have been extensively studied to evaluate the effect of molecular
alterations in these genes on the response of therapy. The majority of these
studies have been carried in Western developed nations like USA, UK. Only three
xxvii
studies have been published on the Indian population, which have tried to evaluate
the rate of mutations in RAS-RAF or PIK3 signaling pathway in CRC.
Therefore, the current study is taken up to better understand the molecular
mechanism that may predict the efficacy of EGFR targeted mAb therapy in Indian
population. In this study the frequency of KRAS, BRAF, NRAS and PIK3CA was
studied by analysis of a larger cohort of CRC patients (n=203). To study the
mutation profiling in Indian CRC patients, these four molecular markers were
assessed in 203 histologically confirmed CRC patients and the correlation of
different clinicopathological factors was evaluated.
Formalin-fixed paraffin-embedded colorectal cancer tissues (n = 203) were
prospectively collected from Indian CRC patients from all over India between the
period from January 2013 to July 2016. Genomic DNA was isolated from tissue
sections using Invitrogen DNA extraction kit and screened for mutations in KRAS
(exon 2, exon 3), BRAF (exon 15), NRAS (exon 2, exon 3) and PIK3CA (exon 9,
exon 20) genes using automated DNA sequencing. Treatment data were also
studied on the basis of different clinico-pathological features of the tumor.
Correlation between these molecular signatures and clinico-pathological
characteristics was further studied so as to meet the unmet medical need for
personalized medicine and to help in improvisation of survival rates in the Indian
population.
In 36% of CRC cases, at least one mutation in the analyzed hot spot region was
observed. The prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in the
Indian population was found to be 24%, 6%, 2% and 4%, respectively. In KRAS
xxviii
wild type (WT) population, approximately 12% of CRC patients have mutations in
NRAS, BRAF or PIK3CA. BRAF mutations were found to be mutually exclusive
with KRAS mutations (n=12/203). Coexistence of PIK3CA and KRAS mutations
was observed. The statistical analysis of clinico-pathological characteristics and
mutation showed significant association between KRAS mutations with age and
tumor differentiation (p<0.05).
In the case of BRAF, a more statistically significant correlation was observed in
moderately differentiating and poorly differentiating adenocarcinomas than in well-
differentiated adenocarcinomas. No significant association was observed between
any of the clinico-pathological features with NRAS or PIK3CA mutations. To study
the effect of clinicopathological features on survival 30 patients were
retrospectively analyzed. Overall survival of 37% with median survival of 25
months was observed for the Indian population, which is much lower than that
observed in developed nations, where the overall survival rates are 64%.
Significant association was observed in the survival rate of patients and grade of
tumor i.e. poorly differentiated adenocarcinoma (PDA). Significantly lower survival
rates were observed in PDA in comparison to moderately differentiated
adenocarcinoma (MDA).
The current experimental evidence of survival rates of CRC patients in relation to
different clinico-pathological features in the Indian population indicates that there
are factors which influence prognosis of colorectal cancer patients. However, life
expectancy has not increased drastically in recent years. This study further
indicates that KRAS, BRAF, NRAS and PIK3CA mutations in Indian CRC patients
xxix
occur at lower levels compared to those of Western developed nations. Our
findings are consistent with the published literature that differences in patients'
origins and related genetic backgrounds contribute to and even determine the
incidence rate of somatic mutations in candidate cancer genes. This study
supports the existing data that clinical and pathological characteristics of a tumour
are important determinants of prognosis in CRC patients worldwide.
Chapter 1. Introduction
1
Chapter 1 Introduction
Colorectal cancer (CRC) is a major health burden worldwide. It is one of the
leading causes of mortality from cancer, affecting both males and females. The
optimal approach to the diagnosis, management and treatment of CRC involves
multidisciplinary and integrated management practices. The field is rapidly
changing because of recent advancements in molecular characterization of CRC
and introduction of targeted therapy and diverse patient response, better biological
drugs and the effective combination regimes being employed for treatment (Patil et
al., 2016).
Two monoclonal antibodies (MAb) targeted at epidermal growth factor receptor
(EGFR), the chimeric IgG1 MAb cetuximab and the fully humanized IgG2
panitumumab, have proven to be effective in combination with chemotherapy or as
single agent for treatment of metastatic colorectal cancer (mCRC) (Cunningham et
al., 2004). However, the efficacy of MAb is not consistent for every patient; some
patients experience dramatic response to MAb, whereas others show no response.
In order to facilitate selection of mCRC patients who may benefit from anti-EGFR
MAbs treatments, a clear need for identifying predictive biomarkers that indicate
likelihood of response amongst potential recipients is currently widely appreciated
(Patil et al., 2016).
It has been reported that oncogenic activations of intracellular signaling pathways
downstream of EGFR, including the RAS-RAF- MAPK plays an important role in
Chapter 1. Introduction
2
generating resistance to anti-EGFR MAbs. To date, KRAS mutations have been
identified as a predictive marker of resistance to anti-EGFR MAbs in patients with
mCRC, and the use of anti-EGFR MAbs is now restricted to CRC patients with
wild-type KRAS (Karapetis et al., 2008).
In patients with KRAS wild type tumors, it remains unclear why a large number of
patients are still not responsive to the treatment. Other oncogenic mutations, such
as BRAF, NRAS and PIK3CA are found likely to be promising predictors for the
resistance in mCRC patients with wild-type KRAS (Moroni et al., 2005, Karapetis et
al., 2008).
Most of the studies that investigated the predictive value of KRAS, BRAF, NRAS,
and PIK3CA mutations were performed in western developed countries (De Roock
et al., 2011, Bozzao et al., 2011, Kawazoe et al., 2015). Little is known about the
relation of these biomarkers with the clinical outcomes of MAb treatment in Indian
patients with CRC.
Hence, this study was undertaken to investigate the status of KRAS, BRAF,
NRAS and PIK3CA mutations in Indian CRC patients, in order to clarify the
rate of mutations and to detect the correlation between mutations and
clinico-pathological factors.
Chapter 1. Introduction
3
1.1 Research Question
In the case of the Indian patient cohort, how different will the mutation profiling of
KRAS, BRAF, NRAS and PIK3CA genes involved in CRC, and how the correlation
with clinico-pathological data, impact the clinical outcome and survival?
1.2 Hypotheses
This project investigated the hypothesis that rate of mutations in critical CRC genes
involved in the tumor growth and survival i.e. KRAS, BRAF, NRAS and PIK3CA
differ according to racial differences.
It also investigated the hypothesis that different clinicopathological factors would
have impact on clinical outcome of the patient in the context of Indian patient
cohort. This hypothesis was addressed by pursuing the following aims and
objectives.
I. IA. Performing immunohistochemistry on CRC patient tumor samples
(n=203) to investigate the tumor percentage and also to grade and
categorize the tumors according to World Health Organization (WHO)
criteria.
IB. Screening all these colorectal cancer patient samples for
i) KRAS mutations in exon 1 (codons 12 and 13) and exon 2 (codon 61)
of KRAS gene.
ii) BRAF mutations in exon 15 (codon 600)
Chapter 1. Introduction
4
iii) NRAS gene mutations exon 1 (codons 12 and 13) and exon 2 (codon
61) and
iv) PIK3CA gene mutations exon 9 (codons 542 and 545) and exon 20
(codon 1047)
IC. Analysing the frequency of mutations in these four gene in Indian
CRC patients.
II. Investigate the possible correlation between the mutations observed and clinico-
pathological factors.
III. Examine the correlation between clinicopathological characteristics and survival
outcome in the Indian CRC patient cohort (n=30).
Figure 1.1: Schematic outline of Aims and Objectives of the study.
Chapter 1. Introduction
5
1.3 Thesis Outline
Chapter two discusses the literature review related to the thesis topic with main
focus on the diagnosis, biomarker analysis and treatment of metastatic CRC
(mCRC). Also, reviewed here is the biomarker validation for personalized medicine
and new candidate predictive and prognostic biomarkers requiring further
investigations in prospective trials.
Chapter three lists the materials and methodologies used in the experiments.
Chapter four describes the standardization and validation of the assays for KRAS,
BRAF, NRAS and PIK3CA mutation detection in CRC patient samples.
Chapter five investigates the correlation between the mutations observed in
KRAS, BRAF, NRAS and PIK3CA genes with the clinico-pathological data of the
patients.
Chapter six examines the correlation between clinicopathological characteristics
and survival outcome in the Indian CRC patient cohort.
Chapter seven provides the conclusion of the thesis and discusses future
perspectives.
Chapter 2. Literature Review
6
Chapter 2 Literature Review
2.1 Introduction
Cancer is one of the major cause of death worldwide. According to the World
Cancer report, cancer rates will increase by 50% from 2015 to 2020. There will
be 24 million people with cancer by 2035 (Stewart and Wild, 2016). According
to the World Health Organization (WHO), one in five men and one in six women
develop cancer before the age of 75 and one in eight men and one in twelve
women die of this disease. Hence, WHO has labelled it as global ‘tidal wave’ of
cancer (Siegel et al., 2015). Cancer is also a leading cause of economic loss.
The annual economic cost of cancer is around 1.16 trillion USD (Siegel et al.,
2015).
Cancer is mostly associated to Western developed countries. However, it has
been observed recently that less developed or developing countries have
shown a dramatically rise in new cancer cases. According to Globocan 2012,
the highest incidence of new cancer cases and highest mortality is observed in
Asian countries with 48% and 54.9% respectively (Ferlay, 2012) (Ferlay et al.,
2015). Figure 2.1 describes the global cancer incidence and mortality rate and
Figure 2.2 describes the prevalence of different types of cancer in males and
females.
Chapter 2. Literature Review
7
Figure 2.1: Global cancer incidence and mortality according to Globocan
2012(Ferlay, 2012).
Figure Legend: According to the estimates of International agency for Research on
Cancer (IARC) 2012, global cancer incidence was estimated as 14.1 million cases with
highest percentage observed in Asian countries (48%) followed by European (24.4%),
American (20.5%), African (6%) and Oceania (1.1%) countries. Similarly, overall
mortality was estimated to be 8.2 million cases with highest percentage observed in
again Asian countries (54.9%) and lowest in Oceania (0.7%).
Chapter 2. Literature Review
8
Figure 2.2: Gender wise global cancer incidence and mortality according to
Globocan 2012(Ferlay, 2012).
Figure Legend: According to the estimates of International agency for Research on
Cancer (IARC) 2012, in females, highest incidence of breast cancer (25.2%) is
observed followedy colorectum (9.2%) and lung (8.8%) whereas in males, lung cancer
(16.7%) cases are the highest followed by prostate (15%) and colorectum (10%).
Similarly, in case of mortality, breast cancer (14.7%) is leading cause of mortality in
females followed by lung (13.8%) and colorectum (9%) whereas in males lung cancer
(23.6%) is leading cause of mortality.
The prevalence of cancer in India is estimated to be around 2.5 million
with 800,000 new cases and 500,000 deaths due to cancer per year (Rath
and Gandhi, 2014). The cancer numbers in the Indian subcontinent are
increasing due to inadequate medical facilities, less patient awareness or
education and late detection. An increasing trend has been observed in cancer
incidence in the past few years as seen in the data compiled by the Indian
Council of Medical Research (ICMR) (Rath and Gandhi, 2014) (Figure 2.3)
Incidence and mortality number in thousands
Chapter 2. Literature Review
9
Figure 2.3: Increasing trend of cancers in India (Image source ICMR(Rath and
Gandhi, 2014)).
Figure Legend: According to Indian Council of Medical Research (ICMR), increasing
trend is observed in cancer incidence in India. By 2020, it has been estimated that the
cancer incidence would reach to around 1100 thousand cases with increasing
incidence observed in females in comparison to males.
According to the study published by Marimutthu et al in 2008, the highest Indian
incidence of cancer is seen in Delhi (Figure 2.4) with the most frequently
observed cancers being lung, breast, stomach, oral, colon and rectum
(Marimuthu, 2008).
Chapter 2. Literature Review
10
Figure 2.4: Cancer prevalence in metropolitan cities of India (Image source:
Marimutthu et al, 2008 (Marimuthu, 2008)).
Figure Legend: Amongst the five metropolitan cities highest incidence of cancer is
recorded in Delhi with around 14000 cases followed by Mumbai, Chennai, Bangalore
and Bhopal. Higher cancer incidence is observed in females in comparison to males
except in case of Bhopal.
Colorectal cancer is one of the leading causes of cancer mortality worldwide. It
is the most common of all gastrointestinal malignancies. It is the third most
common cancer in men and the second in women with 60% of cases occurring
in developed regions (Ferlay et al., 2010). Colorectal cancer develops as a
result of stepwise progression through several genetic alterations (Migliore et
al., 2011). The past decade has uncovered various new pathways in the
development of colorectal cancer. Despite this, an integrated view of the
genetic and genomic changes and their implication on colorectal tumorigenesis
remains to be obtained.
Chapter 2. Literature Review
11
2.2 Epidemiology
Colorectal cancer is an important public health problem. In 2012, there were an
estimated total of 1,234,000 incident cases of colorectal cancer diagnosed
worldwide (Ferlay, 2012). It is the third most common cancer in men with
746,000 cases diagnosed and second in women with 614,000 cases diagnosed
worldwide (Ferlay, 2012). This cancer affects men and women almost equally
and accounts for 10% of total cancer burden in men and 9.2% in women (M.P
Curado, 2008). Age standardized rates of colorectal cancer incidence are
higher in men than in women (overall sex ratio of the ASR’s 1.4:1) (Ferlay,
2012). About 41% of new cases of colorectal cancer occur outside
industrialized countries, indicating that colorectal cancer is not just a disease of
the developed world. Rapidly increasing populations, changes in lifestyle
associated with economic development, together with the epidemiologic
transition of less developed countries is leading to increasing numbers of
colorectal cancer cases.
2.2.1 Colorectal cancer incidence varies globally
There is a large geographic variation in the global distribution of colorectal
cancer (World Cancer Research Fund et al., 2007). Countries with highest
incidence rates include Australia, New Zealand, Canada, the United States and
parts of Europe. The countries with the lowest risk include China, India and
parts of Africa and South America (Boyle and Ferlay, 2005). The incidence
rates vary up to 10-fold between countries with the highest rates and those with
the lowest rates (Center et al., 2009b). The incidence of colorectal cancer
Chapter 2. Literature Review
12
ranges from more than 40 per 100,000 people in US, Australia, New Zealand,
and Western Europe to less than 5 per 100,000 in Africa and some parts of
Asia (World Cancer Research Fund et al., 2007). Slovakia had the highest
incidence rate in 2012 of colorectal cancer in men followed by Hungary and the
Czech Republic, with age standardized rates per 100,000 as 61, 56 and 54,
respectively. New Zealand, Israel, Denmark and Norway had the highest
incidence rates with 38, 36 and 34, respectively (Figure. 2.5 and 2.6A). The
incidence shows considerable variation among racially or ethnically defined
populations in multiracial or ethnic countries. For example, in 2009, 136,717
people in the US were diagnosed with colorectal cancer, including 70,223 men
and 66,494 women. African Americans have the highest incidence of colorectal
cancer, followed by White, Hispanic, Asian Pacific Islander and American
Indian/Inuits. Incidence rates reported (per 100,000) for all the races, Whites,
African American, Asia/Pacific Islander, American Indian/Inuit, Hispanic are
42.5, 41.3, 50.8, 33.7, 30.6, 36.4, (per 100,000), respectively (Figure. 2.6B)
(Jemal et al., 2010a). Although the majority of colorectal cancer incidence rates
in men are observed in Europe and North America, select registries in Asia also
have recorded two to four fold increases in the incidence of colorectal cancer in
men (Sung et al., 2005, Shin et al., 2012). However, among women colorectal
cancer incidence rates have declined in New Zealand and Australia but have
continued to increase in Israel (Center et al., 2009a). Lower rates of colorectal
cancer are observed in women when compared to men. This may be due to
behavioral differences, such as smoking rates and differing effects of obesity in
men and women (McCormack and Boffetta, 2011, Frezza et al., 2006).
Chapter 2. Literature Review
13
Asia has greatest diversity with regards to incidence of colorectal cancer High
incidence rates are observed in China, Japan, South Korea, Singapore and
Israel (Shin et al., 2012, Yiu et al., 2004) comparatively much lower are
observed in Nepal, Bhutan and India (3.2, 3.5 and 6.1 per 100,000
respectively). These variations are due to different socioeconomic levels.
However, increased incidence rates are observed in the developing countries
which can be attributed to westernization, including consumption of high calorie
food and physical inactivity. Among ethnic groups in Asia, the incidence of
colorectal cancer is higher in Chinese (Lu et al., 2003, Yang et al., 2004). A
rapid increase in incidence has also been observed in Taiwan and Iran in recent
years (Moghimi-Dehkordi et al., 2008, Safaee et al., 2012, Su et al., 2012).
Iranian data suggests a younger age distribution compared to Western
countries, probably related to lifestyle changes (Mahmodlou et al., 2012,
Khayamzadeh et al., 2011, Malekzadeh et al., 2009)
In India, the annual incidence rate of colon cancer is 4.9 and that of rectal
cancer is 4.1 in men per 100,000 and in women it is 3.9 per 100,000. The
annual incidence rate in men, as recorded in 2013, is highest in
Thiruvananthapuram (state of Kerala) (4.9) followed by Bengaluru (state of
Karnataka) (3.9) and Mumbai (state of Maharashtra) (3.7) whereas in women, it
is highest in the state of Nagaland (5.1) (Rath and Gandhi, 2014).
2.2.2 Mortality of colorectal cancer
In 2013, around 771,000 people died globally of CRC. The age standardized
mortality rate is higher in men (10 per 100,000) as compared to women (6.9 per
100,000). The mortality rate is higher in developed countries (11.6 per 100,000)
Chapter 2. Literature Review
14
than in less developed countries (6.6 per 100,000) which could be due to the
stage distribution at diagnosis (early diagnosis facility), awareness and
availability of population screening programs and level of health care in each
country. Western Africa has the lowest mortality rate (3.0 per 100,000) in the
world and highest is seen in Central and Eastern Europe (11.7 per 100,000)
(Figure 2.5) (Kuipers et al., 2015).
However, recently, mortality trend analysis has shown that the colorectal cancer
mortality has reduced in both men and women over the last several years.
Along with the US, Australia, New Zealand and Western Europe, colorectal
cancer mortality rates have also decreased in a few Asian (Japan and
Singapore) and Eastern European countries. The decreasing mortality rates
may be due to improvements in colorectal cancer treatments or to early
detection i.e by introduction of colonoscopy. Increased mortality rates of
colorectal cancer have been observed in South American countries, Russia,
Romania, China, Croatia, Latvia and Spain (Figure 2.7A). The increasing
mortality trends in these countries may be a reflection of increasing colorectal
cancer trends observed in economically transitioning countries. This increase
may also reflect lack of screening programs and changes in lifestyle, dietary
factors and urbanization (Kuipers et al., 2015).
Chapter 2. Literature Review
15
Mortality rates for colorectal cancer also vary with race and ethnicity. There is a
six-fold variation in male mortality rates among different regions of the world
and a five-fold variation in female versus male rates. In the US in 2009 the
mortality rates reported (per 100,000, age adjusted to 2000 US standard
population) for all races, whites, African Americans, Asia/Pacific Islanders,
American Indian/Inuit and Hispanic are 15.7, 15.3, 22.1, 10.4, 12.6 and 12.1,
respectively (Figure 2.7B) (Jemal et al., 2010a). Among men, African Americans
are more likely to die of colorectal cancer followed by white, Hispanic,
American, Indian/Inuit and Asia/Pacific Islander. In women, African Americans
are more likely to die of colorectal cancer, followed by white, American
Indian/Inuit, Hispanic and Asia/Pacific Islander. In Singapore, where different
ethnic groups live in a similar environment, the incidence of colorectal cancer is
lower in Indian and Malay population, in comparison to Chinese (Yang et al.,
2003).
In 2012, mortality rates were the highest in Central and Eastern Europe (20 and
12 per 100,000 in males and females, respectively) and the lowest in middle
Africa and South central Asia (3-4 per 100,000) in both the sexes (Ferlay,
2012).
Chapter 2. Literature Review
16
Figure 2.5: Age standardized Incidence and Mortality of colorectal cancer in
men and women per 100,000 across geographical zones (Kupiers et al, Primer,
2015).
Figure Legend: Highest incidence and mortality rates of colorectal cancer are
observed in Australia and New Zealand, Europe and Northern America with males
showing the higher rates in comparison to females.
Figure 2.6A: Incidence rates of colorectal cancer. Data according to (Ferlay,
2012).
Figure Legend: In males the highest incidence of CRC is observed in Slovakia where
as in females its observed in New Zealand
0
10
20
30
40
50
60
70
Inci
de
nce
Rat
e p
er
10
0 0
00
Males
Females
Chapter 2. Literature Review
17
Figure 2.6B: Incidence rates of colorectal cancer in US. Data according to
(Jemal et al., 2010b).
Figure Legend: In U.S highest incidence of CRC is observed in African Americans and
lowest is observed in American Indians/ Alaska Native.
Figure 2.7A: Mortality Rates of colorectal cancer. Data according to (Ferlay et
al., 2010).
Figure Legend: The highest mortality rate is observed in males compared to females.
In Hungary and Slovakia the mortality rate is highest amongst males whereas in case
of females the mortality rate is highest in Hungary, New Zealand and Uruguay.
0 20 40 60 80
All
White
Black
Asia/Pacific Islander
American Indian/Alaska Native
Hispanic
Incidence Rate per 100 000
Races
Females
Males
Both
0
5
10
15
20
25
30
35
Mo
rtal
ity
Rat
e p
er
10
0 0
00
Males
Females
Chapter 2. Literature Review
18
Figure 2.7B: Mortality rates of colorectal cancer in US. Data according to
(Jemal et al., 2010b).
Figure Legend: In U.S highest mortality rate of CRC is observed in African Americans
and lowest is observed in Asia/Pacific Islanders.
2.3 Pathophysiology
Colorectal cancer usually develops over a period of 10-15 years where most of
colorectal cancers are silent tumors, which grow slowly and do not produce
symptoms until they grow to a large size. Colorectal cancer genesis is a
multistep process involving accumulation of genetic and epigenetic changes
that transform normal glandular epithelial cells into invasive adenocarcinoma.
Colorectal cancer arises from mucosal colonic polyp. Some individuals are
more prone to develop polyp, especially those with personal or family history of
polyp and or colorectal cancer and those who carry specific genes for hereditary
forms of colorectal cancer (Lanza et al., 2011). The two most common
histologic types of polyps found in colon and rectum are described below.
2.3.1 Hyperplastic polyps
0 10 20 30
All
White
Black
Asia/Pacific Islander
American Indian/Alaska Native
Hispanic
Mortality Rate per 100 000
Races
Females
Males
Both
Chapter 2. Literature Review
19
These polyps are non-dysplastic and have little potential for malignant
transformation (Figure 2.8). Histologically, these polyps contain an increased
number of glandular cells with decreased cytoplasmic mucous, but lack nuclear
hyperchromatism, stratification or atypia (Lanza et al., 2011). Recent evidence
shows that serrated variants similar in morphology to hyperplastic polyps, but
with malignant potential. These exhibit hyper methylation and arise primarily in
proximal colon and may account for one third of all colorectal cancers
(Torlakovic et al., 2003) .
2.3.2 Adenomatous polyps
These polyps most likely turn into colon cancer. These are considered pre-
cancerous. Adenomatous nuclei are mostly hyper chromatic, enlarged and
crowded together in a palisade manner as seen in Figure 2.8 (Lanza et al.,
2011). Adenomas are common; an estimated one-third to one-half of all
individuals will eventually develop one or more adenomas (Bond and Practice
Parameters Comm Amer, 2000). Adenomas are further classified as tubular,
villous or tubulovillious (Flejou, 2011). Adenomas usually grow on stalk and
resemble mushrooms (Figure 2.8). They tend to grow slowly over a decade or
more. The risk of adenoma developing into cancer increases with the size of
adenoma and the time for which it is actually growing. In the early stages the
abnormal cells are contained in the polyp, which can be removed. However, as
cancer cells grow and divide inside the polyp, they can eventually invade
nearby colon tissue and can grow into and beyond the walls of colon and
rectum (Arnold et al., 2005)
Chapter 2. Literature Review
20
Figure 2.8: Diagrammatic representation of hyperplastic and adenomatous
polyps.
Figure Legend: Polyps are finger like projections growing from the lining of colon.
There are two types of polyps-Hyperplastic and adenomatous. Hyperplastic polyps are
pale, curved elevations and may rarely develop into cancer. However, adenomatous
polyps are hyper chromatic, large, cigar shaped, crowded and resemble mushroom.
These polyps turn into colon cancer.
Chapter 2. Literature Review
21
2.3.3 Molecular abnormalities in pathogenesis of CRC
Dr. Bert Vogelstein first described that colorectal cancer and associated polyps
develop as a result of genetic mutations or other chemical modifications causing
inactivation or promotion of specific genes known as tumor suppressing genes
and tumor promoter genes (Fearon and Vogelstein, 1990) (Bosman, 2013). The
most common molecular abnormalities that have been identified in the
pathogenesis of colorectal cancer (Figure 2.9) (Patil et al., 2016).
Figure 2.9: Schematic overview of molecular abnormalities in pathogenesis of
colorectal cancer (Image source: (Patil et al., 2016).
Figure Legend: This figure depicts the stepwise genetic events that lead to
transformation of normal colonic epithelium to CRC. This progression requires
accumulation of stepwise genetic changes that drive metastasis. Abbreviations: APC
Adenomatous polyposis coli; COX, Cyclooxegenase; KRAS, v-ki-ras2 Kristen rat
sarcoma viral oncogene homologue; MLH1, MutL homolog 1; MSH2, MutS protein
homolog 2; DCC, Deleted in Colorectal Carcinoma; MGMT, O6-methylguanine DNA
methyltransferase; p16 INK4A, Inhibitors of Cyclin Dependent Kinase 4; SMAD,
Mothers against decapentaplegic homologue.
According to this classical model, majority of colorectal cancers arise from
aberrant crypt polyp which then converts into early adenoma with tubular or
tubulovillious histology. The early adenoma then progresses to advanced
Chapter 2. Literature Review
22
adenoma with/without villous histology and finally becomes a colorectal cancer.
This process takes around 10-15 years except in patients with Lynch syndrome,
where it can progress more rapidly (Jones et al., 2008). However, the
understanding of molecular pathology has advanced in this past decade and
has led to several revisions of this theory. The original theory proposed that only
tubular and tubulovillious adenomas had the potential to progress towards
invasive adenocarcinomas, however, it has been observed that hyperplastic
polyps and sessile serrated polyps, accounting to around 5-10%, also have a
potential of malignant transformation. Serrated polyps arise from histological
and molecular events different from tubular polyps. These are classified into
three categories- hyperplastic polyps, sessile serrated polyps and traditional
serrated adenomas (Rex et al., 2012) (Figure 2.10). Serrated polyps that arise
in right colon (caecum, ascending colon and transverse colon) commonly show
Microsatellite Instability (MSI) and CpG island methylator phenotype (CIMP).
Polyps arising in left colon (descending colon, sigmoid and rectum) show MSS
phenotype with KRAS mutations and have attenuated form of CIMP (Bettington
et al., 2013).
Chapter 2. Literature Review
23
Figure 2.10: Events in transformation of serrated polyps to adenocarcinomas.
Figure Legend: Hyperplastic polyps and sessile serrated polyps have potential of
malignant transformation. Serrated polyps arise from histological and molecular events
different from tubular polyps and are classified into three categories- hyperplastic
polyps, sessile serrated polyps and traditional serrated adenomas. Polyps in left colon
show presence of KRAS mutations, MGMT methylation and MSS/MSI-L status
whereas polyps in right colon show BRAF mutations along with presence of MLH-1
methylation and MSI-H state.
Chapter 2. Literature Review
24
Figure 2.11: The WNT pathway: Image source: Reya T et al, Nature
2005(Reya and Clevers, 2005).
Figure Legend: WNT (Wingless plus int) pathway signaling pathway plays a vital role
in embryonic development, proliferation and differentiation. Beta-catenin is the main
component of WNT pathway. In normal cell, the level of beta-catenin is regulated by a
complex of proteins - actin and tumor suppressor Adenomatous polyposis coli (APC).
Mutation in APC leads to accumulation of APC, which travels to the nucleus and binds
to TCF wherein it activates the genes. This process is considered as trigger for
transforming event in colon cancer. Colorectal cancer is initiated by mutations in WNT
signaling pathway and then progress upon deregulation of other signaling pathways
including-RAS-RAF-MAPK, TGFB and PIK3-AKT pathway (Grady and Pritchard,
2013).
Chapter 2. Literature Review
25
2.3.4 WNT pathway
The most common initiator of colorectal cancer is aberrant activation of WNT
(Wingless plus int) pathway. Beta-Catenin, a major mediator of WNT pathway,
is a membrane associated protein with function of regulation of cellular
adhesion (Figure 2.11). In the absence of Wnt ligand, cytoplasmic beta-catenin
forms a multiprotein complex with two other cellular proteins; axin and APC
(Adenomatous polyposis coli). Beta-catenin then is phosphorylated by GSK3β
(glycogen synthase kinase 3β), leading to destruction of beta-catenin by
proteolysis. Hence, leading to low and steady state concentrations of beta-
catenin in the cytoplasm. On the other hand, when the WNT signaling is
activated by the Wnt ligand binding to Frz receptor (Frizzled family of
transmembrane proteins), GSK3β is blocked and beta-catenin is saved from
rapid destruction, leading to accumulation of unphosphorylated beta-catenin in
the cytoplasm. This accumulation leads to translocation into the nucleus where
beta-catenin binds to transcription factors, and activates transcription of target
genes, including those involved in cell proliferation, for example cyclin D1,
contributing to tumour progression. Constitutive WNT signaling leads to an
expansion of the proliferative compartment of the crypt by mutation of the
tumour suppressor gene APC, there by destroying the equilibrium between
proliferation and differentiation, leading to the development of precancerous
lesions (Reya and Clevers, 2005).
In addition to genetic mutations, epigenetic alterations seem to cooperate with
genetic mutations to drive polyp to carcinoma transformation. There are three
major pathways for colorectal carcinogenesis- Chromosomal instability (CIN),
Microsatellite instability (MSI) and CpG island methylator phenotype (CIMP).
Chapter 2. Literature Review
26
2.3.5 Chromosomal Instability (CIN)
CIN is the most common and well-characterized pathway of carcinogenesis. It is
characterized by high grade of differentiation, distal location and intermediate
prognosis. Around 70% of colorectal cancer arise from CIN pathway (Markowitz
and Bertagnolli, 2009). CIN pathway is associated with mutations in
Adenomatous polyposis coli (APC) and/loss of chromosome 5q which harbors
APC gene, loss of chromosome 18q, deletion of chromosome 17p, which
contains tumor suppressor gene TP53, and mutations in KRAS oncogene. Only
a small minority of CRC having CIN, have a full complement of these
abnormalities (Grady and Carethers, 2008).
APC is an important tumor suppressor gene. Pathogenic mutations in APC
truncate the APC protein and hence interrupts the binding of APC to beta
catenin involved in Wnt –signaling pathway (Cadigan and Liu, 2006). It is also
seen that loss of functional APC may interfere with regulation of mitosis
contributing to CIN. Frequency of APC mutations is observed in around 80% of
early adenomas. Mutations of APC are observed in around 60% colonic
cancers and 82% rectal cancers (Jass et al., 2002).
DCC, SMAD2 and SMAD4 are located on chromosome 18q21.1. Allelic loss of
18q is observed in around 60% of colorectal cancers. SMAD2 and SMAD4 are
involved in TGF-β signaling pathway, which plays an important role in regulating
growth and apoptosis. Mutations of SMAD4 cause juvenile polyposis syndrome
associated with CRC (Worthley and Leggett, 2010b).
Chapter 2. Literature Review
27
KRAS, a proto oncogene, plays an important role in CIN pathway. It is located
on chromosome 12p12 and encodes a GTP binding protein. When mutated
results in constitutive signaling through RAS-RAF-MAPK signaling pathway and
thus permits cell to evade apoptosis and acquire growth advantage. Activating
KRAS mutations are observed in 35-42% of colorectal cancer cases (Shen et
al., 2007).
p53 gene, is located on chromosome 17p13. This gene is also known as
‘guardian of genome’. Impairment of TP53 is often a late event in colorectal
carcinogenesis and usually occurs through allelic loss of 17p. Mutations or loss
of heterogeneity in TP53 is observed in around 4-26% of adenomas, 50% of
adenomas with invasive foci and 50-75%of CRC (Worthley and Leggett,
2010a). TP53 abnormalities increase relative to advancing histological state of
lesion. In normal functioning, p53 plays an important role in cell recovery form
genetic damage. It increases the expression of cell cycle genes, slows down the
cell cycle and hence provides time for DNA repair. Further, whenever there is
great extent of DNA damage, p53 induces pro apoptotic gene, thus promoting
early programmed cell death (Mills, 2005).
These alterations of CIN pathway are the followed by subsequent events that
promote and facilitate the progression towards malignant state.
Chapter 2. Literature Review
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2.3.6 Microsatellite Instability
Microsatellite instability (MSI) is another important genomic instability in
colorectal cancers. Microsatellites are nucleotides repeat DNA sequences
distributed throughout the genome and MSI refers to the instability in these
repeat sequences in germline DNA versus tumor DNA. These replication errors
occur due to DNA polymerase activity while copying and inserting the repeat
sequences during DNA replication. One of the methods to repair such
replication errors is DNA Mismatch repair (MMR) system. Hence, dysfunction of
MMR results in MSI. MSI is involved in development of about 15% of sporadic
CRC cases and in about 95% of HNPCC syndrome associated tumors. CRC
which develops through MSI pathway has distinct features like location in
proximal colon, poor differentiation and/or mucinous histology and increased
number of tumor infiltrating lymphocytes (Jass et al., 2002).
MMR system consists of seven proteins, mlh1, mlh3, msh2, msh3, msh6, pms1
and pms2, which associate with specific partners to form heterodimers which
are then responsible for surveillance and correction of replication errors.
Mutations in MLH1, MSH2, MSH6 and PMS2 are associated with HNPCC
(Boland and Goel, 2010).
To detect the frameshift mutations at the microsatellite regions in CRC, a
standardized panel of microsatellites was created to provide uniformity in
research and practice. This panel consists of two mono- nucleotide (BAT25,
BAT26) and three dinucleotide microsatellites (D5S346, D2S123, and
Chapter 2. Literature Review
29
D17S250). MSI-High (MSI-H) is defined as instability in more than 2 (40%) sites
out of the five sites mentioned and MIS-Low (MSI-L) is instability at one site.
And MSS i.e. microsatellite stable CRC has no instability in the mentioned five
markers (Boland et al., 1998). HNPCC cause pure form of MSI, however,
majority of MSI positive tumors i.e. MSI-H tumors occur sporadically due to
methylation of MLH-1 promoter. Such cancers exhibit both MSI and CIMP.MSI-
H tumors share similar biology irrespective of been sporadic or inherited (Zhang
and Li, 2013).
MSI-H CRC patients show good prognosis and survival and MSI is relatively
uncommon in metastatic CRC (Kawakami et al., 2015). MSI is frequently
observed in women especially older women in comparison to men (Kim et al.,
2015). It has also been observed that MSI-H CRC are less responsive to 5-
flurouracil based chemotherapy (Kurzawski et al., 2004).
2.3.7 CpG island methylator phenotype (CIMP) pathway
CIMP pathway is second most common pathway for sporadic CRC. It is
observed in around 15% of sporadic cases and in 70-80% of all dysplastic
serrated lesions of right colon (Issa, 2008). It is observed that CIMP is closely
associated with older age, female gender, mucinous histology, smoking, MSI,
KRAS and BRAF mutations (Issa, 2008).
CpG islands in DNA, are the regions which are proximally located in the
transcription start site of genes. These islands contain high frequency of CG
Chapter 2. Literature Review
30
dinucleotide. In various tumor suppressor genes in tumor cells, these CpG
islands are frequently methylated resulting in repression of transcription.
Subgroups of CRC show a wide range of hypermethylation of MLH1 gene, a
mismatch repair gene, which is known as CpG island methylator phenotype.
Tumors are classified as CIMP-high (CIMP-1) or CIMP-low (CIMP-2) depending
on the extent of methylation. This methylation can be detected by a panel of
CpG markers assessed by PCR (Shen et al., 2007).
CIMP positive CRC, which show presence of MSI-H exhibit MSI-H
characteristics like good prognosis. However, CIMP positive CRC which show
absence of MSI-H are characterized by advanced pathology, poor clinical
outcome and absence of tumor infiltrating lymphocytes (Jass, 2007). CRCs
which develop via CIN pathway, including HNPCC, originate from adenomatous
polyp. However, in CIMP pathway sessile serrated adenomas are primary
precursors of CRC (Jass, 2007).
Hence, all these three pathways of carcinogenesis have distinct clinical,
pathological and genetic features, all being potentially useful for molecular
characterization of CRC for improved diagnostic, prognostic and treatment
prediction.
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31
2.3.8 Clinical staging
In the advanced stage of cancer the tumor metastasizes, shedding cells in the
circulatory system and spreading the cancer to other organs like liver and lungs.
The extent to which the cancer has spread is described as its stage. Staging is
essential to determine the choice of treatment and to assess prognosis. At early
stages, that is Stage I and II, a curative treatment is achieved by surgical
resection or by chemotherapy, which is often applied in an adjuvant manner.
The metastatic stage IV disease indicates a more advanced cancer and is
usually incurable. The Stage I and II have moderate risk of relapse after surgical
resection, whereas patients with stage III and IV have higher chance of
recurrence (Libutti et al., 2008).
In past, Dukes and Astler-Coller classification systems was most commonly
used for staging (Dukes and Bussey, 1958, Astler and Coller, 1954). However,
this system was not considered to be elaborate enough, hence, recently TNM
staging system is used which is maintained by American Joint Committee on
Cancer (AJCC). This system codes the extent of the primary tumor (T), regional
lymph nodes (N), and distant metastases (M) and provides a stage grouping
based on T, N, and M (Edge and Compton, 2010) (Figure 2.12).
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32
Figure 2.12: TNM staging as maintained by American Joint Committee on
Cancer (AJCC) and Union for International Cancer Control (UICC) (Source-
(Edge and Compton, 2010).
Figure Legend: TNM Staging system is the most widely used staging system. It is
based on extent of tumor (T), the extent of spread of lymph nodes (N) and the
presence of metastasis (M).The status of T, N and M together decide the stage of
cancer. The stages of cancer are further subdivided as II a or b, III a,b ,c etc.
Chapter 2. Literature Review
33
Colorectal cancer may also present histologically with poorer prognostic cell
types, such as adenosquamous or signet ring cells, and may have
undifferentiated cells (Secco et al., 2009). Carcinoid tumors and mucosa-
associated lymphoid tumor (MALT) lymphomas may also present as tumors in
the colon but are not usually considered types of CRC (Oberhuber and Stolte,
2000). Colorectal cancers can be characterized according to their location. They
have a better prognosis rate when detected before bowel obstruction or
perforation occurs. Incidence rates of colorectal cancer differ by sub-sites
(Figure 2.13). Previously, tumors of the rectum were common but now the
highest frequency of tumors are present in right ascending colon (CO and IN,
2011). This change in incidence is attributed to improved screening and
detection techniques to discover early polyps.
Figure 2.13: Schematic overview of incidence of colorectal cancer.
Figure Legend: According to anatomy the prevalence of CRC is predominantly higher
in the left side of the bowel as compared to the right side (Macrae et al., 2015).
Chapter 2. Literature Review
34
2.4 Risk factors
There are many known factors that increase or decrease the risk of colorectal
cancer. Summary of risk factors associated with colorectal cancer is provided in
Table 2.1. Incidence and death rate for colorectal cancer increase with age.
Further as reviewed earlier in this chapter (Section 2.2), incidence and mortality
rates of colorectal cancer are about 35-40% higher in men than in women. As
suggested by Taunk and Audrey, the reason for this is not understood, but is
likely related to the “complex interactions between gender related differences in
exposure to hormones and risk factors” (Murphy et al., 2011). Further as
detailed in Table 2.1, factors that increase risk also include a personal or family
history of chronic inflammatory bowel disease (Crohn’s disease or ulcerative
colitis), genetic mutations, high consumption of red or processed meat,
smoking, physical inactivity, obesity and moderate to heavy alcohol
consumption. Additional risk factors include exposure to asbestos, radiation,
synthetic fibers, halogens (anesthetics), printing supplies and fuel (Moradi et al.,
2008).
Chapter 2. Literature Review
35
Table 2.1: Summary of risk factors associated with colorectal cancer.
Risk Factors References
Genetics-Hereditary Colorectal Cancers (Ghazi, 2012, Dunlop and Farrington,
2009)
Familial adenomatous polyposis
MUTYH associated polyposis
Lynch Syndrome
Gardner’s Syndrome
Peutz-Jeghers Syndrome
Hyperplastic polyposis
Family history of non-syndromic colon
cancer
(Lynch et al., 2008, Jass, 2000)
Cowden syndrome
Bannayan Zonana Syndrome
Inflammatory bowel disease (Bewtra et al., 2013)
Ulcerative colitis
Crohn’s Disease
History of neoplasia (Niell et al., 2004)
Prior colon cancer
Breast cancer
Other (Sun and Yu, 2012, Abdulamir et al., 2011,
Sonnenberg and Genta, 2013, Dutta et al.,
2012, Zhao et al., 2012)
Diabetes mellitus and insulin resistance
Strepotococcus bacteremia
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Helicobacter pylori
Acromegaly
Prior cholecystectomy
Use of androgen deprivation therapy
Additional Factors
(Dong et al., 2010, Hendifar et al., 2009,
Bardou et al., 2013)
Age
Gender
Race/ethnicity
Obesity
Physical inactivity
Diet
Smoking
Alcohol
Chapter 2. Literature Review
37
Recent studies found that about one quarter of colorectal cancer cases could
be avoided by following a healthy lifestyle (Anderson et al., 2013). Also, non-
steroidal anti-inflammatory drugs (Walker et al., 2012), estrogen and calcium
(Lappe et al., 2007) have been found to protect against colorectal cancer.
2.4.1 Age
Age is one of the most important factors strongly related to CRC. The peak
incidence of colorectal cancer occurs in the sixth and seventh decade of life.
Incidence and mortality rate of CRC specifically increases after the age of 50
years. Around 75% of cases of CRC occur in people above 65 years of age
(Ferlay, 2012). However, recently it has been observed that the incidence of
CRC is increasing in younger population. This could be due to lack of
screening, behavioural factors such as alcohol consumption, smoking and
lifestyle factor like obesity. It has also been observed that in the young patients
CRC is more aggressive and has poor pathological features (Chou et al., 2011).
2.4.2 Hereditary Factors
Majority of CRC are sporadic, however about 20-30% of cases have a negative
family history. People with their first degree relative having colorectal cancer or
adenomatous polyps, diagnosed at the age of <50 years are at higher risk. Risk
increases in individuals having two or more family members affected by
colorectal cancer. The reason for increased risk is still unclear, however it may
be due to genetic or environmental factors or due to combination of both
(Boardman et al., 2007).
2.4.3 Inherited Syndrome
Around 5% of CRC cases are due to hereditary conditions. The most common
inherited conditions include Hereditary nonpolyposis colorectal cancer (HNPCC)
Chapter 2. Literature Review
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or Lynch syndrome and Familial adenomatous polyposis (FAP) (Lynch et al.,
2008). HNPCC is caused by mutations DNA mismatch repair genes-MLH1,
MSH2, MSH6, PMS2 or EPCAM (Lynch et al., 2008). Inheritance is in
autosomal dominant pattern accounting for 2-5% of CRC and carriers of the
mutations have around 50-80% lifetime risk of developing CRC.HNPCC display
specific characteristics like mucinous or signet ring histology, poor
differentiation, lymphoid infiltration and predominance of right side tumors. One
of the most common clinical feature of HNPCC is that multiple generations get
affected by CRC at an early age around 45 years (Jass et al., 2002). To identify
individuals at risk, personal and family cancer history analysis, CRC molecular
testing for MSI and MMR gene mutation analysis should be performed.
Familial adenomatous polyposis (FAP), second most common hereditary
colorectal cancer syndrome accounting for 1% of all colorectal cancer cases, is
caused by mutations in Adenomatous polyposis coli (APC) and is inherited in
autosomal dominant manner (Jasperson et al., 2010). Patients with FAP
develop large number of adenomatous colorectal polyps that develop after first
decade of life, unlike individuals with HNPCC who develop only few adenomas
(Half et al., 2009). Carriers of APC gene mutations or individuals with known
family history of FAP should start colorectal cancer screening for polyps at the
age of 10-12 years with flexible sigmoidoscopy and should undergo annual
colonoscopy once polyps are detected (Kastrinos and Syngal, 2011). If there
are numerous polyps which cannot be managed by endoscopy then
prophylactic colectomy is recommended (Galiatsatos and Foulkes, 2006).
Other hereditary colorectal cancer syndrome include polyposis associated with
mutations in the mutY DNA glycosylase (MUTYH) gene, Gardner Syndrome, a
Chapter 2. Literature Review
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type of FAP, Peutz–Jeghers syndrome, caused by mutations in STK1 gene,
serrated polyposis and juvenile polyposis.
2.4.4 Inflammatory bowel disease (IBD)
Chronic colitis and Crohn’s disease are significant risk factors of CRC. The risk
increases with longer duration of IBD and is highest in patients with early onset
and widespread manifestation (Jess et al., 2012). However, recently it has been
observed that the incidence of CRC in IBD patients is reducing which may be
attributed to effective anti-inflammatory treatment and improved surveillance
(Castaño‐ Milla et al., 2014).
2.4.5 Life Style Factors
Life style related risk factors for CRC include lack of exercise, smoking, alcohol
consumption and obesity. Intensity of physical activity is inversely proportional
to risk of CRC. Increased risk is seen more in case physical inactivity and colon
cancer than in comparison to rectal cancer. The mechanisms responsible for
the association of reduced physical activity and increased risk to CRC are still
not fully understood. However, there are few studies, which indicate reduction in
insulin resistance, effects on endogenous steroid hormone metabolism and
reduced gut transit time as possible biological mechanism responsible for
increasing the risk of CRC (Samad et al., 2005).
Carcinogens from cigarette smoking cause irreversible damage in normal
colorectal mucosa, however, many years are required for completion of all
carcinogenic events from the time of initiation. The association between
smoking and CRC is stronger in case of rectal cancer in comparison to colon
Chapter 2. Literature Review
40
cancer. Smoking initiates formation of adenomatous polyps which are one of
the precursor lesion of CRC (Giovannucci, 2001).
Studies have found increased risk of CRC with regular high alcohol intake
(>45g/day) in comparison to nondrinkers (Cho et al., 2004). Studies have
shown that obesity is associated with increased risk of CRC. Insulin and insulin
like growth factors, leptin, adipose tissue induced changes of estrogen and
androgen and inflammatory molecules are proposed to be the putative link
between obesity and CRC. High insulin and IGF levels seen in obese people
activate certain signaling pathways favoring pro-carcinogenic processes
(Khandekar et al., 2011). Elevated levels of leptin in obese have shown to
suppress apoptosis and stimulate proliferation of colonic epithelial cells (Stattin
et al., 2004).
2.4.6 Dietary factors
Dietary factors potentially increase risk of CRC. Diet high in fats is associated
with increased risk of CRC. Diet rich in fats seem to favor development of
bacterial flora that degrade bile salts to potentially carcinogenic nitrogen
compounds (Larsson and Wolk, 2006). Consumption of red and processed
meat is also associated with increased risk of CRC. In The ERBITUX Plus
Irinotecan for Metastatic Colorectal Cancer (EPIC) study, positive association
between consumption of red meat and CRC was observed and also an inverse
association between consumption of fish and CRC was seen (Norat et al.,
2005). Similarly, diet rich in fibers is inversely associated with CRC. Possible
anti-carcinogenic effects of dietary fibers include reduction of secondary bile
Chapter 2. Literature Review
41
acid production, reduction in intestinal transit time, increased fecal bulk,
formation of short chain fatty acids from formation by colonic bacteria and
reduction in insulin resistance (Trock et al., 1990).
2.4.7 Race/ Ethinicity
As reviewed earlier in Epidemiology section in this chapter, African Americans
have the highest incidence and mortality rate of CRC in all racial groups. Also,
the Eastern European Jewish population i.e. Ashkenazi Jews has one of the
highest CRC risk (Besterman-Dahan, 2008). Several mutations leading to
increased risk of CRC in this ethnic group have been observed. The most
common mutation is observed in APC gene, I1307K, observed in around 6% of
American Jewish population. Individual preferences, social or cultural biases
contribute to racial and ethic disparities, cancer prevalence in few groups is also
linked to socio-economic status (Mitchell, 2013).
Chapter 2. Literature Review
42
2.5 Diagnosis
Colorectal cancer is associated with classical symptoms like blood in stool, pain
in abdomen and altered bowel habits. Other symptoms include fatigue,
shortness of breath, weight loss and anemia related symptoms. Diagnosis of
CRC results from assessment of patient showing above mentioned symptoms
or as a result of screening.
Colonoscopy is a preferred method of investigation in symptomatic individuals,
however, endoscopic methods are also available like-high definition white-light
endoscopy, chromo-endoscopy, magnification endoscopy, narrow band
imaging, intelligent colour enhancement and iScan imaging, autofluorescence
endoscopy and microendoscopy. Colonoscopy is a gold standard for CRC
diagnosis. This method has high accuracy, can enable simultaneous biopsy
sampling and can access location of tumor. Over the past decade, colonoscopy
has efficiently helped in reduction of CRC incidence and mortality. Past 20 year
follow up data from UN National Polyp study has showed 53% reduction in CRC
related mortality (Zauber et al., 2012).This method provides both diagnostic and
therapeutic effect. The quality of colonoscopy has improved drastically over the
past decade. The current standards utilize high power endoscopes with high
resolution video screens to yield high definition endoscopy to improve the
diagnosis of polyps and CRC. The invasive nature of this methodology possess
a burden to the patients and might affect the participation in screening
programs. However, recently various diagnostic methods and screening
biomarkers have been introduced, such as capsule endoscopy and biomarker
tests as mentioned in Figure 2.14 (Kuipers et al., 2015).
Chapter 2. Literature Review
43
Figure 2.14: Advantages and disadvantages of different screening modalities
used for diagnosis of CRC (Image source- Kuiper’s et al., 2013).
FIT, fecal immunochemical test; gFOBT, guaiac faecal occult blood test. *Less
problematic with newer-generation tests.
There are few serological markers that allow early detection and diagnosis of
CRC. The most widely studied marker is carcinoembryonic antigen (CEA).
Serial CEA measurements can detect recurrent CRC and liver metastasis
(Harrison et al., 1997). High preoperative levels of CEA are associated with
poor prognosis.
Chapter 2. Literature Review
44
2.6 Treatment strategies
Although CRC is highly treatable if diagnosed in the early stages and surgically
removed, recurrence after surgery and adjuvant therapy and metastatic disease
are still major problems with a median overall survival of approximately 24
months (Howlader et al., 2011). Nevertheless, there has been improvement in
survival due to the introduction of new cytotoxic and targeted agents (Figure
2.15). Systematic therapeutic efficacy is central to determining the outcome for
patients.
Figure 2.15: Schematic overview of advances made in treatment strategies for
colorectal cancer.
Figure Legend: CRC management has evolved evidently over the last few decades.
Incorporation of new therapeutic concepts has led to improved survival [Image Source-
(Patil et al., 2016)].
2.6.1 Surgery
Surgery is the mainstay curative treatment for non-metastatic colorectal cancer
patients. The extent of surgery is determined by blood supply and distribution of
regional lymph nodes and the choice of surgical method depends on
Chapter 2. Literature Review
45
preoperative TNM staging. Tumors located at caecum and right colon are
removed by right hemicolectomy followed by ileocolic anastomosis. Similarly,
tumors located at hepatic flexure and transverse colon are removed by
extended right hemicolectomy. Tumors of descending or sigmoid region are
treated with left hemicoloectomy. Recently, complete mesocolic excision (CME)
technique, similar to TME (Total mesorectal excision), has been introduced
which has shown to improve overall survival and has also reduced the
recurrence rate. To minimize surgical complications, perioperative protocols like
fast track protocols and enhanced recovery after surgery have been designed.
These protocols describe the list of requirements for taking care of patients at
various steps in perioperative process (Willemsen et al., 1999).
In case of operable disease primary surgery is carried out with or without
adjuvant chemotherapy. For locally advanced disease, primary curative
resection is unlikely hence, preoperative chemotherapy is considered (Kuipers
et al., 2015).
In case of isolated metastatic disease, resection of primary disease is carried
out followed by metastasectomy with or without neoadjuvant and/or adjuvant
chemotherapy. For widespread metastatic disease palliative chemotherapy
along with supportive care is considered (Kuipers et al., 2015).
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46
2.6.2 Conventional Chemotherapy
It is unfortunate that surgical resection is not suitable for majority of metastatic
cases, and the only option to prolong survival is systemic therapy directed at
metastatic colonies. The conventional chemotherapy drugs used to treat
metastatic colorectal cancer include:
5-Fluorouracil (abbreviated FU)
Capecitabine (Xeloda®)
Oxaliplatin (Eloxatin®)
Irinotecan (Camptosar®)
These drugs work by interfering with the ability of rapidly growing cells (like
cancer cells) to divide or reproduce themselves. Because most of an adult's
normal cells are not actively growing, they are less affected by chemotherapy,
with the exception of bone marrow (where the blood cells are produced), the
hair, and the lining of the gastrointestinal tract. Effects of chemotherapy on
these and other normal tissues cause side effects during treatment. The
introduction of combination regimes of oxaliplatin or irinotican and 5-FU/LV
have improved response rates, progression free survival and overall survival by
15-20%, 5-6 and 10-12 months to 30-40%, 8 and 20-24 months, respectively
(Colucci et al., 2005).
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47
2.6.1.1 5-Fluorouracil / Leucovorin
5-Fluorouracil (5-FU) has been the first choice of treatment options for
colorectal cancer patients for over 40 years. It is used in combination with
leucovorin, a vitamin, which makes 5-FU more effective. 5-FU is given
intravenously, although recently a pill form of 5-FU, capecitabine (Xeloda®) has
been developed, which is used in metastatic colorectal cancer patients. 5-
FU/LU became the standard of care for patients with stage III and selected
stage II colon cancer in the early 1990’s. Many clinical trials have shown that
these regimens improve overall survival, but how they affect the risk of
recurrence over time is not clear. Results of multiple randomized trials that have
enrolled more than 4,000 patients comparing adjuvant chemotherapy with 5-FU-
leucovorin (5FU/LV) to surgery or 5-FU-semustine-vincristine demonstrate a
relative reduction in mortality of between 22% and 33% (3-year OS of 71%–
78% increased to 75%–84%). Later studies have refined the use of 5-FU-
leucovorin in adjuvant settings (Gill et al., 2004).
2.6.1.2 Oxaliplatin and Irinotican
In early 2000’s, the introduction of oxaliplatin and irinotican resulted in
meaningful improvement in the management of colorectal cancer patients.
Irinotecan, a topoisomerase I inhibitor, was initially introduced as monotherapy
for patients with metastatic colorectal cancer refractory to 5-FU. irinotican has
demonstrated clinical efficacy and tolerability in multiple non randomized and
randomized Phase II and Phase III studies both as single agents and in
combinations with bolus or infusional 5 FU/LV or in concomitant versus
alternative schedules of administration (de Gramont et al., 2000).
Chapter 2. Literature Review
48
In two of the second line trials of irinotican, 2.3 month (p>0.03) improvement in
survival and 2.7 month (p>0/001) median survival improvements were observed
(Rougier et al., 1998, Cunningham et al., 1998).
In four Phase III trials, therapy with single agent irinotican resulted in longer
survival time than BSC or FU/LV therapy in FU refractory patients(Cunningham
et al., 1998, Fuchs et al., 2007, Rougier et al., 1998). A large trial involving 683
patients compared 5-FU/LV with irinotican/bolus 5-FU/LV or single-agent
irinotican (125 mg/m2) in weekly X 4, every-6-weeks schedules. Irinotecan/5-
FU/LV resulted in a higher response rate, a longer median time to disease
progression, and longer median survival compared with 5-FU/LV, which showed
efficacy comparable to that of irinotican. Grade 3 or 4 diarrhea occurred in 31%
of patients who received irinotican alone versus 23% and 13% of patients who
received IFL and 5-FU/LV, respectively. However, significantly greater
incidence rates for Grade 4 neutropenia, fever, and severe mucositis were
associated with 5-FU/LV. Combining irinotecan with 5-FU/LV did not affect
quality-of-life scores compared with 5-FU/LV alone (Fuchs et al., 2007). Based
on data from these trials, the United States Food and Drug Administration
(USFDA) and European regulatory authorities approved irinotecan in
combination with both bolus and infusional FU/LV as first-line therapy for
mCRC, replacing FU/LV as the standard of care. Interim data from a pair of
North American randomized trials have raised concerns regarding the safety of
irinotecan containing regimens, with gastro-intestinal and thromboembolic
syndromes accounting for the unexpectedly high rates of treatment-induced or
treatment-exacerbated death (2.5% and 3.5%, respectively)(Rougier et al.,
Chapter 2. Literature Review
49
1998, Cunningham et al., 1998). However, European oncologists continued to
use infusional FU/LV.
To define the optimal first-line irinotecan regimen, the BICC-C trial compared
5FU infusion plus irinotecan (FOLFIRI) to IFL and capecitabine plus irinotecan
(CapeIri) (Fuchs et al., 2007). FOLFIRI was associated with improved
progression-free survival (PFS) as compared with IFL (7.6 versus 5.9 months;
p=0.004) and trended toward improved overall survival (23.1 versus 17.6
months; p=0.09). In addition, FOLFIRI was associated with the most favorable
toxicity profile of the three regimens, thereby establishing it as a reference
standard for treatment of naive patients with colorectal cancer (Fuchs et al.,
2007).
Similarly for oxaliplatin, a second generation platinum analogue, in an initial
Phase III study oxaliplatin plus infusional 5FU (FOLFOX) was compared to
infusional 5FU in the first-line setting (de Gramont et al., 2000). In the 2,246
patients with resected stage II or stage III colon cancer in the completed
Multicenter International Study of Oxaliplatin/5-Fluorouracil/Leucovorin in the
Adjuvant Treatment of Colon Cancer (MOSAIC [NCT00275210]) study, the toxic
effects and efficacy of FOLFOX4 were compared with the same 5-FU-
leucovorin (5FU/LV) regimen without oxaliplatin administered for 6 months
(André et al., 2004). Based on results from the MOSAIC trial, adjuvant
FOLFOX4 demonstrated prolonged OS for patients with stage III colon cancer
compared with patients receiving 5-FU-leucovorin without oxaliplatin (André et
Chapter 2. Literature Review
50
al., 2009). Finally, the NCCTG-intergroup established 5FU/LV and oxaliplatin
(FOLFOX4) as the new first-line standard regimen compared with the previously
used irinotecan and bolus 5FU/LV (IFL) regimen (Alberts et al., 2005). Hence,
FOLFOX has become the reference standard for the next generation of clinical
trials for patients with stage III colon cancer.
2.6.1.3 Capecitabine
Later in the year of 2000, capecitabine was introduced and was found that for
stage III colon cancer it provides equivalent outcome to intravenous 5-FU-
leucovorin (Twelves et al., 2005). Several Phase II and III randomized trials also
investigated the substitution of 5FU/LV by capecitabine (XELOX) and showed
similar PFS and OS, but lower ORR (odds ratio = 0.85; 95% CI: 0.74–0.97; p =
0.02) for XELOX compared with FOLFOX (Ducreux et al., 2011) .
Grade 3 hand- foot syndrome was reported more frequently with capecitabine,
although the condition was tolerated with a reduced dose (Van Cutsem et al.,
2001). Based on these data, capecitabine was approved in the U.S. as first-line
therapy for patients with mCRC for whom combination therapy is not warranted.
Oxaliplatin can be combined with synergistic efficacy with fluropyrimidines,
irinotecan, bevacizumab and EGFR antibodies to further enhance treatment
efficacy in metastatic colorectal cancer (Assenat et al., 2011, Tveit et al., 2010).
In recent data from the Adjuvant Colon Cancer Endpoints (ACCENT) group,
individual patient data from 18 phase III clinical trials of adjuvant 5-FU based
chemotherapy for colon cancer was reviewed to show that the regimes provide
Chapter 2. Literature Review
51
the beneficiary effect primarily by reducing the high risk of reoccurrence within
the first two years of surgery. By five years after treatment with 5-FU based
adjuvant therapy, the risk of recurrence dropped to 1.5% and dropped again to
0.5% after eight years of treatment (Sargent et al., 2009).
As sequential therapies cannot be predefined in treatment protocols, overall
survival may no longer be regarded as the most sensitive end point for
assessing the efficacy of first-line therapy; other factors, such as PFS and TTP,
should be considered.
2.6.2 Therapy options for colon cancer
When the patients have undergone potentially curative resection with no
residual disease then the Five year survival rate without adjuvant chemotherapy
for Stage I is >90%, for Stage II is 70-80% and for Stage III is 50-60% (Mitry et
al., 2008). In most cases adjuvant chemotherapy is well tolerated however, it
may cause potential morbidity. Before administrating adjuvant treatment few
patient selection criteria should be considered. A minimum of 8 lymph nodes
and ideally >12 should be examined to determine the metastatic spread
(Eisenhauer et al., 2009). Other factors like poorly differentiated tumors,
presence of extramural vascular invasion or perineural invasion and T4
classification are reportedly associated with relatively high risk of recurrence
(Tsai et al., 2008). In addition to this MMR/MSI status should be evaluated in
patients before administration of adjuvant therapy. 5-FU is not effective in MSI-
positive and deficient MMR patients (Sinicrope, 2010) .For patients with high
risk Stage II tumors, capecitabine monotherapy is appropriate therapy.
Colonoscopy should be performed after 1 year of surgery and every three years
Chapter 2. Literature Review
52
thereafter (Sirohi et al., 2014). Figure 2.16 describes various adjuvant therapy
options available to colon cancer.
Figure 2.16: Adjuvant therapy options for colon cancer patients with no
metastasis i.e Mo as per Indian Council for Medical Research (Sirohi et al.,
2014).
5-FU-5-Fluorouracil, FOLFOX-5-FU plus oxaliplatin, CAPEOX-Capecitabine plus
oxaliplatin.
2.6.3 Therapy options for rectal cancer
In case of rectal cancers, rate of local recurrence is higher in comparison to
colon cancer due to limited availability to obtain wide radial resection margins at
the time of surgery due to presence of pelvic bone (Sagar and Pemberton,
1996). Surgery is the first option for tumors with low of positive or uninvolved
mesorectal fascia. Short course of preoperative ratio therapy may reduce the
local recurrence rate. Surgery is performed soon after completion of
radiotherapy. In case of tumors associated with poor prognosis as assessed by
MRI, T3 and T4 tumors with 4 or more lymph node involvement neoadjuvant
chemotherapy along with radiotherapy is recommended. For Stage II and III
rectal cancers, adjuvant chemotherapy is recommended following neoadjuvant
Chapter 2. Literature Review
53
therapy (Sirohi et al., 2014). Adjuvant chemotherapy should be initiated soon
after surgery so as to improve survival rate (Sirohi et al., 2014) (Figure 2.17).
Figure 2.17: Neo adjuvant and Adjuvant therapy options for rectal cancer with
no metastasis i.e Mo as per Indian Council for Medical Research (Sirohi et al.,
2014).
5-FU-5-FluoroUracil, LV- Leucovorin, FOLFOX-5-FU plus oxaliplatin, RT- Radiotherapy
Chapter 2. Literature Review
54
2.6.4 Treatment options for advanced disease i.e. mCRC
In case of patients with resectable metastatic disease, surgery is the first option
alternative approach been neoadjuvant chemotherapy. Adjuvant chemotherapy
is usually recommended to reduce the rate of recurrence. The therapy options
include FOLFOX, FOLFIRI, CAPOX or FOLFIRI or FOLFIRINOX with or without
bevacizumab and cetuximab (in case of wild type RAS).
For patients with unresectable mCRC, single agent chemotherapy is
recommended. In first line treatment various options are recommended like
capecitabine alone or 5-FU/LV alone or CAPOX with or without bevacizumab,
FOLFOX with or without bevacizumab, FOLFIRI with or without bevacizumab or
cetuximab, CAPIRI with or without bevacizumab, FOLFOX with or without
Panitumumab. Before administration of Cetuximab and Panitumumab RAS
testing is recommended (Sirohi et al., 2014) (Rossi et al., 2013).
In case of second line treatment option single agent irinotecan or FOLFIRI,
oxaliplatin + 5-FU, cetuximab or panitumumab with irinotecan, 5-FU with or
without bevacizumab are the options which are taken into consideration.
Aflibercept is also used in few cases (Sirohi et al., 2014, Carethers, 2008).
In third line treatment option for mCRC, cetuximab with or without irinotecan has
demonstrated to improve survival. In certain cases treatment may be offered ‘off
study’ i.e. retreatment with previously successful regime after long disease free
interval. Other options include reference of patients to clinical trials, treatment
with regorafenib and best supportive care alone (Sirohi et al., 2014) (Carethers,
2008).
Chapter 2. Literature Review
55
2.6.5 Novel cytotoxic and targeted biologic therapeutics
Recently cytotoxic drugs showed promising activity in the first-line treatment of
patients with advanced CRC. Studies have demonstrated that the efficacy and
safety of S-1, a novel oral fluropyrimidines, are comparable with those of 5-FU
and capecitabine for metastatic colorectal cancer patients (Kusaba et al., 2010).
However S-1 has mainly been studied among Asian population. Thus, at least
for now, S-1 cannot be recommended in global populations with metastatic
colorectal cancer as observed for many chemotherapeutic agents that the dose
recommendation and safety vary significantly with ethnicity. A novel antifolate,
Pemetrexed, that inhibits TS as well as folate dependent enzymes involved in
purine synthesis, showed modest efficacy in a pair of frontline Phase II studies,
with response rates of 15–17% (Braun et al., 2004).
Advances in the understanding of tumor cell biology have fostered the
development of novel biologic modifiers and molecular targeted therapeutics
that interfere with specific tumor cell propagation mechanisms. These range
from monoclonal antibodies to fusion proteins and small molecule inhibitors. As
depicted in Figure 2.15, the USFDA since 2004 has approved targeted agents
for example an antivascular endothelial growth factor (anti-VEGF) monoclonal
antibody (mAb), bevacizumab (Avastin®; Genentech, Inc., South San
Francisco, CA, http://www.gene.com) and a human epidermal growth factor
receptor (HER-1/EGFR)-targeted mAb, cetuximab (Erbitux®; Imclone Systems,
Inc., New York, NY, http://www.imclone.com), as first- and second-line mCRC
therapy, respectively (Figure 2.18).
Chapter 2. Literature Review
56
Figure 2.18: EGFR and VEGF Signaling Pathways in CRC development and
tumor survival.
Figure Legend: EGFR and VEGF signaling pathway play a pivotal role in tumor
initiation, progression and survival, including CRC. EGFR gene is located on
chromosome 7 and encodes a 170 kDa transmembrane receptor EGFR. EGFR
belongs to ErBb family of receptor tyrosine kinases and is activated by several ligands,
including EGF, transforming growth factor-α, amphiregulin (AREG), heparin-binding
EGF, epiregulin (EREG), and betacellulin. Consequently, two pathways are activated
by EGFR namely RAS–RAF–MAP kinase pathway and the PI3K–PTEN–Akt pathway
which in turn leads to tumor initiation and progression. Activating mutations in RAS,
RAF and PIK3CA can affect patients response to EGFR inhibitors. VEGF belongs to
family of angiogenic growth factors and comprises of 5 VEGF glycoproteins. VEGFA is
well characterized VEGF family member. Its receptor is VEGFR2. Binding of ligand
leads to further activation of MAPK through a cascade of events leading to
angiogenesis, proliferation, migration and survival of cells.
Chapter 2. Literature Review
57
2.7 Targeting Vascular Endothelial Growth Factor (VEGF)
VEGF is a critical regulator of angiogenesis. Since the growth and spread of
tumors require angiogenesis, inhibiting this process makes an interesting
strategy for the treatment of cancer (Folkman, 1971). The VEGF-A –targeting
mAb bevacizumab (Avastin®) and VEGF targeting Ziv-aflibercept (Zaltrap®),
and regorafenib (Stivarga®) have all been FDA approved for use in mCRC and
studied in combination with chemotherapy.
To date, there are no clinically validated biomarkers which are in routine use,
despite the widespread use of antiangiogenic therapy for mCRC (Duda et al.,
2013) (Mousa et al., 2015). The exact mechanism of benefit of anti-VEGF drugs
is unclear, which has resulted in lack of biomarker based selection of patients
for antiangiogenic therapy (Lambrechts et al., 2013).
A retrospective analysis of baseline plasma/serum sample data for 88–97% of
patients/study (>2000 patients), from two randomized Phase III studies
HORIZON II and III investigating cediranib (an oral VEGFR TKI) in mCRC,
reveals that baseline VEGF levels were treatment-independent prognostic
biomarker for PFS and OS in both the studies (Jürgensmeier et al., 2013).
Similarly, another retrospective analysis of the AVF2107 study data from phase
III trial of bevacizumab in mCRC, reports the prognostic value of total circulating
VEGF-A levels (pretreatment), but not predictive for bevacizumab based
therapeutic benefit (Jannuzzi et al., 2015).
In one of the phase II trials it was observed that when CRC patients were
treated with FOLFIRI in combination with bevacizumab, significant increases in
fibroblast growth factor 2 (FGF2), phosphatidylinositol-glycan biosynthesis class
Chapter 2. Literature Review
58
F protein (PIGF), stromal cell-derived factor 1 (SDF-1) and macrophage chemo
attractant protein 3 were observed, which may represent the mechanism of
resistance. Also, increases in the baseline interleukin -8, (a promoter of
angiogenesis), was observed and this was associated with decreased
progression free survival (Kopetz et al., 2010).
Furthermore, in the direction of achieving goal of personalized medicine, for
host-specific variability single-nucleotide polymorphisms (SNPs) are also
studied as potential biomarkers for response to anti angiogenic agents. Single
nucleotide polymorphisms like VEGF1154G>A and VEGF405C>1 are found to
be associated with improved overall survival and PFS (Formica et al., 2011).
Also, in another study it was observed that low gene expression levels of
VEGFA,VEGFR1 and VEGFR2 in colon cancer patients is associated with
longer mean disease free survival (Zhang et al., 2015b). Other biomarkers
such as angiopoietin-2 (a key regulator of vascular remodeling along with
VEGF) and CD133, identified as a potential biomarker of bevacizumab therapy
outcome, need future validation for their true predictive rather than prognostic
value reviewed in Patil et al, 2016.
Recently, mathematical models have also been developed to address this issue
(Finley and Popel, 2013), which could give new insight into the use of VEGF
isoforms as predictive biomarkers. These mathematical models might shed
some light on resistance mechanisms to anti-VEGF therapy and also be useful
in the analysis of future large prospective studies to address these predictive
biomarkers.
Chapter 2. Literature Review
59
Hence, inhibiting the VEGF pathway has become an important strategy in the
treatment of metastatic malignancies including metastatic colorectal cancer.
Studies reveal that VEGF expression is elevated in a wide variety of tumor
types including colorectal cancer. Hyper expression of VEGF is also seen to be
associated with progression, invasion and metastasis of colorectal
cancer(Martins et al., 2011). In case of metastatic colorectal cancer, along with
VEGF, growth factors such as prostaglandin E2, EGF as well as molecular
mediators of the epithelial-mesenchymal transition have been identified as
potentiators of metastatic spread.
Shaked et al 2008, have identified several hypothesis to explain the chemo
sensitization action of anti-VEGF drugs. Anti-VEGF drugs work via several
mechanisms, including increasing the delivery of cytotoxic drug via vessel
normalization. Another possible mechanism is control of the repopulation of
tumor cells during the chemotherapy-free intervals in between treatment cycles.
A third hypothesis is inhibiting the mobilization of marrow derived circulating
endothelial cells or their progenitors. This slows down the tumor growth and
makes chemotherapy more effective (Shaked et al., 2008).
Chapter 2. Literature Review
60
2.7.1 Bevacizumab-Anti VEGF monoclonal antibody
Bevacizumab (Avastin) is a humanized monoclonal antibody that inhibits VEGF-
A through inhibition of blood vessel formation, normalization of vasculature and
by reducing the intratumoral hydrostatic pressure (Krämer and Lipp, 2007).
Bevacizumab binds directly to VEGF to form a protein complex which is
incapable of further binding to VEGF receptor sites (which would initiate vessel
growth) effectively reducing available VEGF. The Bevacizumab/VEGF complex
is both metabolized and excreted directly.
It received its first approval in 2004 by the US FDA for combination use with
standard chemotherapy (as a first line treatment) and with 5-fluorouracil-based
therapy for second line treatment for metastatic colorectal cancer. This
recommendation was based on the E3200 trial which examined the addition of
bevacizumab to oxaliplatin/5-FU/leucovorin (FOLFOX4) in therapy. The addition
of bevacizumab was associated with improved progression free survival and a
4.7 month survival advantage (20.3 versus 15.6 months, Harzard
Ratio=0.66,p<0/001)(Kabbinavar et al., 2003). Other studies have also shown
that bevacizumab can be safely combined with capecitabine plus oxaliplatin
(XELOX, CAPOX), or irinotecan based regimens (Saltz et al., 2008, Hurwitz et
al., 2004). Summary of clinical trials undertaken for bevacizumab is shown in
Table 2.2. This strategy provides an extra option for patients with metastatic
colorectal cancer. The challenge remains to determine which patients benefit
most from continuing bevacizumab beyond progression. Hence, further
investigations are required to clarify how this strategy can be used in present
clinical scenarios, such as in v-ki-ras2 Kristen rat sarcoma viral oncogene
homologue (KRAS) wild type tumors.
Chapter 2. Literature Review
61
Table 2.2: Summary of clinical trials undertaken for bevacizumab.
Study No. of
patients
Chemotherapy Overall
Survival (in
months)
E3200- FOLFOX4
with or without
bevacizumab as
second-line
therapy in patients
who have
progressed on
irinotecan-based
therapy(Giantonio
et al., 2007)
829 Bevacizumab+FOLFOX4(n=286)
versus FOLFOX4 alone (n=291)
and bevacizumab alone (n=252)
12.9 vs 10.8
and 10.2
BRiTE-
Bevacizumab
Regimens:
Investigation of
Treatment Effects
and
Safety(Grothey et
al., 2008)
1953 Bevacizumab as first and
second line(n=642) versus
bevacizumab as first line and
chemotherapy as second
line(n=531)
31.8 versus
19.9
Chapter 2. Literature Review
62
ARIES-
Avastin in
Combination With
Chemotherapy for
Treatment of
Colorectal Cancer
and Non-Small Cell
Lung Cancer(Cohn
et al., 2010)
1550 FOLFOX+ Bevacizumab
(n=968)versus FOLFIRI
+Bevacizumab (n=243)
23.7 versus
25.5
TML18147(Helwick,
2012)
820 Second line therapy with or
without concomitant
bevacizumab
11.2 versus
9.8
FOLFOX4-Oxaliplatin/5-Fluorouralcil/Leucovorin, FOLFOX- oxaliplatin +5Flurouracil/Leucovorin, FOLFIRI-
5Fluorouracil/Leucovorin + Irinotican
2.7.2 Aflibercept- a novel antiangiogenic fusion protein
Aflibercept (Zaltrap, VEGF trap) is a fully human recombinant fusion protein
composed of both VEGFR-1 and VEGFR-2 ligand binding components fused to
the Fc portion of human IgG1 (W Stewart, 2011). It functions as a decoy VEGF
receptor, binds VEGF-A, VEGF-B, and placental growth factors 1 and 2 with
high affinity, prevents their binding to native VEGF receptors, and therefore
inhibits angiogenesis through downstream signaling. In 2012, the USFDA
approved Zaltrap for use in combination with 5-fluorouracil, leucovorin and
irinotecan to treat adults with metastatic colorectal cancer that are resistant to or
has progressed following an oxaliplatin containing regimen. This approval was
based on a recent randomized Phase III study VELOUR, in which a total of
1226 patients who had previously received oxaliplatin-based chemotherapy
Chapter 2. Literature Review
63
were randomized to either FOLFIRI plus aflibercept or FOLFIRI plus placebo
(Van Cutsem et al., 2012b). The study showed a significant increase in OS
(13.5 vs. 12.1 months; HR 0.81) and PFS (6.9 vs. 4.7 months). There was a
significant improvement in overall response rate in the aflibercept group when
compared to FOLFIRI group (19.8% vs. 11.1%, p=0.0001). However, the side
effects include hemorrhage, GI perforation and compromised wound healing.
This agent is being evaluated in the first-line setting in combination with
modified FOLFOX6 (mFOLFOX6) in the phase 2 Aflibercept And Modified
FOLFOX6 As First-Line Treatment In Patients With Metastatic Colorectal
Cancer (AFFIRM) trial (Wang and Lockhart, 2012). Hence, aflibercept is an
important new option in oxaliplatin failing metastatic colorectal cancer patients
in combination with FOLFIRI.
2.7.3 Regorafenib-small molecule inhibitor
Regorafenib (Stivarga) is an oral multi-kinase inhibitor which targets
angiogenic, stromal and oncogenic receptor tyrosine kinase. Regorafenib can
also inhibit c-KIT, RET, and BRAF. In September 2012, the USFDA approved
regorafenib for the treatment of chemorefactory metastatic colorectal cancer
patients. This approval was based on the CORRECT study (an international,
multicentre, randomised, placebo-controlled, phase III trial) which investigated
the use of regorafenib (160 mg orally daily for 3 out of 4 weeks) or placebo in
760 patients (Van Cutsem et al., 2012a). The study revealed a significant
improvement in overall survival by 29% (6.4 versus 5 months, Hazard
Ratio:0.77). Regorafenib also significantly prolonged median progression-free
survival (PFS) from 1.7 months to 1.9 months when added to best supportive
care (p < 0.000001, 1-sided). The 0.2-month difference in PFS belies the 51%
Chapter 2. Literature Review
64
reduction in the risk of disease progression with regorafenib. The most common
side effects grade 3 or higher reported in patients treated with Stivarga
included weakness or fatigue (9.6%), hand-foot syndrome (also called palmar-
plantar erythrodysesthesia) (16.6%), diarrhea (7.2%), hypertension (7.2%) ,
rash or desquamation (5.8%). Other side effects included loss of appetite,
mouth sores (mucositis), weight loss, high blood pressure, and changes in voice
volume or quality (dysphonia). Approximately 8% of patients assigned to
regorafenib permanently discontinued treatment due to adverse events,
compared with 1% in the placebo arm. CORRECT study provides evidence that
regorafenib is the first small-molecule multikinase inhibitor with survival benefits
in metastatic colorectal cancer which has progressed after all standard
therapies. It also therefore, highlights for a continuing role of targeted treatment
after disease progression, with regorafenib offering a potential new line of
therapy in this treatment-refractory population. Unlike bevacizumab and
alfibercept, regorafinib is given by itself and not with other chemotherapy
agents. The precise mechanism of action of regorafenib in mCRC remains
unclear, and the predictive biomarkers are also not yet available for optimal
patient selection.
2.7.4 Identification of predictive biomarkers for anti-angiogenic agents: A
priority for mCRC management
Introduction of anti-angiogenic drugs over the last decade has established anti-
angiogenic therapy as a novel therapeutic modality, but their implementation
has raised several important questions on whether we can find biomarkers to
identify the patients who would benefit from these drugs. Validated biomarkers
are not yet available for routine clinical use (Duda et al., 2013). The exact
Chapter 2. Literature Review
65
mechanism of benefit of anti-VEGF drugs is unclear, which has resulted in lack
of biomarker based selection of patients for antiangiogenic therapy(Lambrechts
et al., 2013). This makes the identification of mechanistic biomarkers of
response to anti-angiogenic therapy a priority. Researchers are currently
studying tissue markers, blood derived markers, imaging parameters,
genotypes and systemic measurements (Jain et al., 2009). There are limited
and inconsistent clinical data for VEGF expression levels as the natural
candidate for biomarker for anti-VEGF drugs. Some studies suggest that
circulating VEGF may predict response to anti-VEGF therapy in hepatocellular
carcinoma (Zhu et al., 2009).
Some phase III studies have found no association between circulation VEGF
and response to bevacizumab (Hegde et al., 2013). In one of the phase II trials
it was observed that when colorectal cancer patients were treated with FOLFIRI
in combination with bevacizumab, significant increases in Fibroblast Growth
Factor 2 (FGF2) , Phosphatidylinositol-glycan biosynthesis class F protein
(PIGF), stromal cell-derived factor 1 (SDF-1) and macrophage chemoattractant
protein 3 were observed, which may represent the mechanism of resistance.
Also, increases in base line interleukin 8, which is the promoter of angiogenesis,
was observed and this was associated with decreased progression free survival
(Kopetz et al., 2010). Furthermore, in the direction of achieving goal of
personalized medicine, for host-specific variability single-nucleotide
polymorphisms (SNPs) are also studied as potential biomarkers for response to
these anti angiogenic agents, and several SNPs have been studied as
candidates for predictive markers for bevacizumab in non-colorectal cancers
(Lambrechts et al., 2012). However, these have not been validated as good
Chapter 2. Literature Review
66
markers to response to anti angiogenic agents in metastatic colorectal cancer
primarily due to conflicting reports, disparity in detection/measurement of genes
and enzyme activity, and variation in data analysis and interpretation. Recent
mathematical models have also been developed to address this issue (Finley
and Popel, 2013), which could give new insight into the use of VEGF isoforms
as predictive biomarkers. These mathematical models might shed some light on
mechanisms of resistance to anti-angiogenic therapy and also be useful in the
analysis of future large prospective studies to address these predictive
biomarkers.
2.8 Epidermal growth factor receptor targeting agents
The epidermal growth factor receptor (EGFR) is a transmembrane glycoprotein
which belongs to the human epidermal growth factor receptor (Her)-erbB family
of receptor tyrosine kinases. It is composed of an extracellular ligand-binding
domain, a hydrophobic trans-membrane region and an intracellular domain with
tyrosine kinase activity. EGFR is activated by ligands belonging to EGF family
of peptide growth factors which include TGF-α, EGF, amphiregulin, betacellulin
or epiregulin. Binding of these ligands to its extracellular domain leads to
formation of both homo or heterodimers with its family members ErbB2/Neu,
Erbb3/Her3and Erbb4/Her4, which in turn leads to auto phosphorylation of
intracellular tyrosine kinase domains and subsequent activation of downstream
signaling (Ciardiello and Tortora, 2008). The most commonly activated
downstream signaling pathways are the RAS-RAF-MAPK, the PI3K/AKT and
the Jak2/Stat3 pathways, which in turn stimulate cancer cell proliferation,
Chapter 2. Literature Review
67
survival, invasion, metastasis and neoangiogenesis (Ciardiello and Tortora,
2008). Over-activation has been shown to induce tumorigenesis. Such
overexpression is observed in 25% to 77% of colorectal cancers and has shown
to be associated with tumor aggressiveness, poor prognosis and
chemoresistance (Capdevila et al., 2009). Several mechanisms have been
reported to contribute to this phenomenon, including mutations in the kinase
domain of EGFR, overexpression of EGFR, and its ligands, and gene copy
number changes. Hence, these findings led to a rational to target EGFR as a
therapeutic strategy for colorectal cancer. EGFR inhibitors act by preventing
ligand binding and subsequent downstream signaling of the oncogenic
pathway.
Cetuximab and panitumumab are two anti-EGFR mAbs that by targeting the
extracellular domain of the receptor inhibit its dimerization and subsequent
phosphorylation and signal transduction. These mAbs have improved patient
outcomes and hence have been incorporated into routine clinical practice with
the finding that the KRAS oncogene is a predictive biomarker for anti-EGFR
therapy (Chee and Sinicrope, 2010). The therapeutic benefit of anti-EGFR
treatment is restricted to tumors with wild-type KRAS. The use of molecular
targeted agents has fewer yet more specific toxicities compared with
conventional cytotoxic drugs and enables a more personalized approach to
cancer therapy.
2.8.1 Cetuximab
Cetuximab (Erbitux) is a chimeric human/mouse recombinant immunoglobin (Ig)
G1 that specifically binds to extracellular domain II of EGFR and blocks the
ligand binding induced receptor dimerization and its further tyrosine kinase
Chapter 2. Literature Review
68
activation. It also elicits antibody dependent cellular cytotoxicity against cancer
cells (Kimura et al., 2007). In 2004, the FDA granted approval for cetuximab for
use in combination with irinotecan for first line treatment of irinotecan refractory
advanced colorectal cancer in patients. In 2012, FDA granted approval for
cetuximab in combination with FOLFIRI (irinotecan, 5-FU and leucovorin) for
first line treatment of patients with KRAS mutation negative mCRC. This
approval was based on retrospective analyses of tumor samples from patients
enrolled in the CRYSTAL trail and in two supportive studies, CA225025 and
EMR 62 202 -047(OPUS). These studies led to the American and European
health authorities restricting the use of cetuximab to patients with KRAS wild-
type tumors. The most common side effects of this medication are acne like
rash and hypomagnesaemia (Van Cutsem et al., 2012c). A summary of clinical
trials is shown in Table 2.3.
Chapter 2. Literature Review
69
Table 2.3: Summary of clinical trials undertaken for cetuximab.
Study No. of patients Chemotherapy Overall Survival
(in months)
CRYSTAL-
Cetuximab
Combined with
Irinotecan in First-
Line Therapy for
Metastatic
Colorectal
Cancer(KGaA,
2005)
1198
63%-wild type
KRAS
37%-mutant
KRAS
Cetuximab +
FOLFIRI (n=599)
Versus FOLFIRI
(n=599)
23.5 versus 19.5-
wild type KRAS
16.0 versus 16.7 –
mutant KRAS
CA225025(Huang
et al., 2013)
572 Cetuximab
+BSC(n=287)
versus BSC(n=285)
8.6 versus 5-wild
type KRAS
No improvement
for mutant KRAS
EMR62 202-047
OPUS-
Oxaliplatin and
Cetuximab in
First-Line
Treatment of
Metastatic
Colorectal
Cancer(Chang et
al., 2013)
337
57%-wild type
KRAS
43%-mutant
KRAS
Cetuximab +
FOLFOX-4
(n=169)versus
FOLFOX-4 (n=168)
22.8 versus 18.5-
wild type KRAS
No improvement
for mutant KRAS
Chapter 2. Literature Review
70
BOND-
Bowel Oncology
with Cetuximab
Antibody(Moroni
et al., 2005)
329 Cetuximab +
Irinotecan (n=218)
versus Cetuximab
(n=111)
4.1 versus 1.5
EPIC-
ERBITUX Plus
Irinotecan for
Metastatic
Colorectal
Cancer(Martinelli
et al., 2009)
1298 Cetuximab +
Irinotecan(n=628)
versus
Irinotican(n=650)
10.7 versus 10
COIN-
Continuous
Chemotherapy
Plus Cetuximab,
or Intermittent
Chemotherapy
With Standard
Continuous
Palliative
Combination
Chemotherapy
With Oxaliplatin
1630
57%-wild type
KRAS
43%-mutant
KRAS
Fluorouracil
+Oxaliplatin
+Cetuximab (n=815)
versus Fluorouracil
+Oxaliplatin (n=815)
17.9 versus 17-
KRAS wild type
14.4 versus 20.1-
KRAS mutant
Chapter 2. Literature Review
71
and a
Fluoropyrimidine
in First-Line
Treatment of
Metastatic
Colorectal
Cancer(Tejpar et
al., 2012)
NORDIC VII-
Cetuximab with
Continuous or
Intermittent
Fluorouracil,
Leucovorin and
Oxaloplatin
(NORDIC FLOX)
versus FLOX
Alone in First-Line
Treatment of
metastatic
colorectal cancer
(Tveit et al., 2012)
571
39%-mutant
KRAS
12%-mutant
BRAF
Cetuximab+
Oxaloplatin+
Fluorouracil+
Leucouracil versus
Oxaliplatin+
Fluorouracil+
Leucouracil
9.5 versus 22-
BRAF mutant –
strong prognostic
marker 20.5
versus 21-KRAS
mutant – no
significant
difference
Chapter 2. Literature Review
72
NORDIC VII-
Cetuximab With
Continuous or
Intermittent
Fluorouracil,
Leucovorin, and
Oxaliplatin
(Nordic FLOX)
Versus FLOX
Alone in First-Line
Treatment
of Metastatic
Colorectal
Cancer(Tveit et al.,
2012)
571-
39%-mutant
KRAS
12%-mutant
BRAF
Cetuximab +
Oxaliplatin+
Fluorouracil+
Leucouracil versus
Oxaliplatin+
Fluorouracil+
Leucouracil
9.5 versus 22-
BRAF mutant-
strong prognostic
marker
20.5 versus 21-
KRAS mutant-no
significant
difference
FOLFIRI-5Fluorouracil/Leucovorin + Irinotican, BSC-Best Supportive Care, FOLFOX4-Oxaliplatin/5-
Fluorouralcil/Leucovorin,
Chapter 2. Literature Review
73
2.8.2 Panitumumab
Panitumumab (Vectibix) is a human IgG2 monoclonal antibody targeting the
extracellular domain of EGFR with high affinity and preventing its activation.
Panitumumab is responsible for inhibition the of proliferation, angiogenisis and
downregulation of EGFR expression (Foon et al., 2004). The most common
side effects are skin rash and hypomagnesaemia, which are similar to those for
cetuximab. Panitumumab was approved by the USFDA in 2006 for the
treatment of EGFR-expressing metastatic colorectal cancer for cases where
disease progression continues despite prior treatment. It was also approved by
the European Medicine Agency (EMA) in 2007 and by Health Canada in 2008
for the treatment of refractory EGFR-expressing metastatic colorectal cancer in
patients with non-mutated (wild-type) KRAS. The approval was based on the
result of single randomized multinational study which had 463 metastatic
colorectal cancer patients. Patients were randomly assigned to either best
supportive care (BSC) alone or BSC plus panitumumab (Van Cutsem et al.,
2007). The mean progression free survival was 96 days for patients receiving
panitumumab and 60 days for patients receiving BSC alone. The median times
of progression were similar (~8 weeks). The objective response rate for
panitumumab monotherapy was 10 %, comparable with cetuximab
monotherapy. A longer progression free survival with panitumumab was
observed in wild type KRAS group (12.3 weeks) as compared to KRAS mutant
group (7.3 weeks).
Furthermore, a phase III randomized trial, called the PRIME study, evaluated
the role of panitumumab in combination with oxaliplatin in first line chemo-naïve
Chapter 2. Literature Review
74
metastatic colorectal cancer patients (Douillard et al., 2010). In this study 1183
patients were enrolled, of which 40% had mutated KRAS. The treatment
outcome was analyzed according to KRAS mutational status. The proportion of
wild type and mutated patients was preserved in both of the study groups.
There was a significant increase in progression free survival in KRAS wild-type
patients who received panitumumab plus FOLFOX-4, as compared with the
FOLFOX alone group (9.6 vs. 8 months; Hazard Ratio: 0.80, p=0.02). Similarly
to cetuximab, KRAS status was predictive of panitumumab resistance.
Another phase III randomized trial the 20050181 study, evaluated the role of
panitumumab in combination with FOLFIRI (Peeters et al., 2008). A total of
1186 fluropyrimidine refractory patients were randomized to receive either
panitumumab in combination with FOLFIRI or FOLFIRI alone. The KRAS status
was analyzed for all patients. A significant prolonged progression free survival
was observed in KRAS wild-type patients who received panitumumab in
combination with FOLFIRI (5.9 months), as compared to patients who received
FOLFIRI alone (3.9 months). There was no significant improvement in overall
survival (14.5 vs 12.5 months; Hazard ratio: 0.85; p=0.12).
Chapter 2. Literature Review
75
2.8.3 Predictive biomarkers for anti-EGFR agents
The studies described above emphasize the role of anti-EGFR inhibitors for the
treatment of metastatic colorectal cancer. From the results of these studies it is
clear that primary resistance probably plays a pivotal role, and that only a
specific subset of metastatic colorectal cancer patients might benefit from these
anti-EGFR monoclonal antibodies. The discovery of biomarkers has led to
improvements in the therapeutic index for these anti- EGFR antibodies. There
are both positive and negative predictors of response which have been
identified.
2.8.3.1 KRAS
v-ki-ras2 Kristen rat sarcoma viral oncogene homologue (KRAS), a proto
oncogene, is a signal transducer modulated by the EGFR signaling pathway. It
is the most frequently mutated gene in colorectal cancer (20%-40%)(Karapetis
et al., 2008). KRAS activation induces activation of downstream components of
the RAF-MAPK signaling pathway. KRAS is a cytoplasmic GTP-binding protein
with low inherent GTPase activity. When the KRAS protein is bound to GTP, it
relays signals of cellular proliferation and inhibition of apoptosis, acting as a
typical oncogene as described in Figure 2.19 and 2.20. KRAS mutations were
observed mainly in gene exon 2, resulting in abrogated GTPase activity and
locking the KRAS protein in the active KRAS-GTP conformation. By activating
the RAS/RAF/MAPK axis downstream of EGFR, these mutations render
therapeutic modulation of EGFR irrelevant. The most frequent mutations are
observed in codons 12 and 13 (Vaughn et al., 2011). Mutations in codons 61
and 146 are under investigation.
Chapter 2. Literature Review
76
Figure 2.19: Activation of RAS pathway.
Figure Legend: The ligand EGF (Epidermal growth factor) binds to EGFR (Epidermal
growth factor receptor) and leads to phosphorylation of tyrosine kinase domain of the
receptor. Upon stimulation of EGF receptor, Grb2 an adaptor protein binds to the
tyrosine kinase domain through its SH2 domain and simultaneously binds to another
protein SOS. This process catalyzes removal of GDP from RAS .RAS then binds to
GTP acquiring an active conformation leading to further downstream RAF and MEK
activation through phosphorylation.
Binding of growth factors to receptor tyrosine kinases stimulates the
autophosphorylation of specific tyrosines on the receptors. The phosphorylated
receptor then binds to an adaptor protein called GRB2 which, in turn, recruits
SOS to the plasma membrane. SOS is a guanine nucleotide exchange factor
which displaces GDP from Ras, subsequently allowing the binding of GTP and
consequently activating RAS.
Chapter 2. Literature Review
77
Figure 2.20: Association of anti-EGFR therapy and KRAS mutations.
Figure Legend: Anti EGFR drugs block receptor signals thus preventing downstream
events. In case of Wild type KRAS, when EGFR receptor is blocked, it stops signaling
and tumor cells do not proliferate. Whereas in case of mutant KRAS, it is permanently
turned on allowing the tumor to continue to proliferate.
Many retrospective studies and trials have shown the impact of KRAS
mutational status on treatment efficacy with anti-EGFR monoclonal antibodies
in metastatic colorectal cancer patients (Qiu et al., 2010). The studies have
suggested that treatment with anti –EGFR produces better outcomes only in
patients with wild type KRAS, whereas these drugs had no effect on mutant
KRAS patients. Analysis of data from CRYSTAL and OPUS trials showed that
addition of cetuximab with chemotherapy provided significant improvement in
progression free survival and overall survival, in comparison to chemotherapy
alone. Addition of cetuximab significantly reduced the risk of disease
Chapter 2. Literature Review
78
progression by 34% in KRAS wild type patients and increased the likelihood of
achieving a response by greater than 2 fold (Odds ratio:2.16, p<0.0001). In the
re-analysis of CRYSTAL study done in 2015 additional RAS mutations were
examined at KRAS exon 3 (codons 59 and 61), KRAS exon 4 (codons 117 and
146), RAS status was evaluable in 430 of 666 patients (64.6%) (Van Cutsem et
al., 2015) (Allegra et al., 2015). Recently, reports have suggested that different
KRAS mutations may have different biological characteristics with respect to
treatment sensitivity. Tumors having mutations of KRAS codon 13, glycine to
aspartate (G13D), have been suggested to retain cetuximab sensitivity and has
improved outcomes in some patients during cetuximab therapy (Tejpar et al.,
2012). In the reported studies from different population backgrounds the KRAS
mutations frequency varies from 14%-40% (Ozen et al., 2013, Mao et al.,
2012a, D. Lambrechts, 2009). These variations in patterns of KRAS mutations
may be due to the racial differences and etiological factors. In view of the
results of several clinical trials, KRAS mutation screening in codons 12 and 13
for metastatic colorectal cancer treatment has been recommended (Allegra et
al., 2009). The use of cetuximab and bevacizumab has been approved only for
patients with KRAS wild type tumors.
Chapter 2. Literature Review
79
2.8.3.2 BRAF
V-raf murine sarcoma viral oncogene homologue B1 (BRAF) is a member of the
RAF family acting downstream of KRAS in the MAPK cascade. The BRAF gene
is another potential predictive factor. BRAF and KRAS mutations are mutually
exclusive events in tumor (Fransen et al., 2004). The most frequently reported
BRAF tumor mutation is a valine-to-glutamic acid amino acid (V600E)
substitution that leads to the aberrant activation of the MEK–ERK pathway
(Ikenoue et al., 2003). This mutation leads to a 500-fold increase in BRAF
activity compared to the wild type form. BRAF mutations are used as exclusion
criteria in the diagnosis of hereditary nonpolyposis colorectal cancer syndrome.
Also, BRAF mutation is closely associated with MSI-H phenotype, MLH1
hypermethylation and CIMP high status (Zlobec et al., 2010). The predictive
value of BRAF mutations in KRAS wild-type patients treated with anti-EGFR
therapy has been demonstrated by several groups. In the study carried out by
Di Nicanlotonio et al., it was seen that among 79 patients with wild type KRAS,
86% had wild type BRAF. No patient with a mutated BRAF had objective tumor
response compared to 32% in patients with wild type BRAF(Di Nicolantonio et
al., 2008). In the PETACC-3 study, BRAF mutations occurred in 7.9% of tumors
in stage II and stage III colon cancer patients. These mutations were found to
be prognostic for overall survival (Hazard ratio=2.2, p=0.003)(Roth et al., 2010).
An analysis of 724 patients treated with irinotecan plus cetuximab showed
mutated BRAF was present in 5% of patients and that it was associated with
reduced responses compared with wild-type BRAF (6% versus 24%) (Tejpar
and De Roock, 2009). In the CAIRO-2 study, the predictive and prognostic
Chapter 2. Literature Review
80
value of BRAF was analyzed in 516 patients. 8% of patients had BRAF
mutations and had decreased median free survival compared to those without
mutation (5.9 versus 12.2 months, P = .003 without cetuximab; and 6.6 vs 10.4
months, P = .010 with cetuximab, respectively) (Tol et al., 2009). This finding
suggests that BRAF can be a prognostic factor and not a predictive factor of
cetuximab efficacy. Pooled analysis from CRYSTAL and OPUS data confirms
that patients with mutated BRAF have worse prognosis than those with wild
type BRAF. Based on these findings, BRAF genetic screening has been
recommended in patients negative for KRAS mutations before treatment with
anti- EGFR drugs.
2.8.3.3 NRAS
Neuroblastoma Ras viral oncogene homolog (NRAS) is a member of RAS
family. Along with KRAS and BRAF, NRAS has also been evaluated recently as
a potential predictive marker in metastatic colorectal cancer. The most
frequently reported NRAS mutations are observed in codons 12, 13, 61, 117
and 146 (D. Lambrechts, 2009). Recently in a retrospective study the predictive
value of NRAS was evaluated in KRAS wild type metastatic colorectal cancer
patients. As opposed to KRAS mutations, the NRAS mutation frequency was
low (3-5%). In the pooled retrospective analysis led by the European
Consortium, the rates of response to cetuximab in a large cohort of patients
were lower in patients with KRAS wild-type tumors bearing NRAS mutations
(De Roock et al., 2010). The only randomized dataset available demonstrates a
numeric lack of benefit from panitumumab, another EGFR-targeted monoclonal
antibody, for the NRAS mutant population, but trying to extract statistical
Chapter 2. Literature Review
81
significance from findings obtained in a 14-patient population is not feasible. In
one recent study, it was observed that in metastatic colorectal cancer the Q61K
NRAS mutation had a favorable response to bevacizumab (Janku et al., 2013).
These results suggest that NRAS mutations merits further investigation as a
potential biomarker predicting the efficacy of bevacizumab-based treatment.
2.8.3.4 PIK3CA
Phosphatidylinositide-3-kinases (PIK3) are lipid kinases that are divided in three
classes, I, II and III. Only the a-type isoform of the catalytic subunit, PI3KCA,
harbors oncogenic mutations that are present in 15–20% of all colorectal
cancers(Jehan et al., 2009). PIK3CA mutations occurring in the “hotspots”
located in exon 9 (E542K, E545K) and exon 20 (H1047R) (Samuels et al.,
2005). It has been demonstrated that PIK3CA mutations confer resistance to
apoptosis, whilst enhancing invasion capacity and metastatic potential. Several
studies have demonstrated that PIK3CA mutations do not respond to anti-
EGFR therapy and these mutant colorectal cancer patients have shorter
progression free survival than wild type patients (Lièvre et al., 2010). The
findings of a European consortium suggest that response to EGFR treatment
can be predicted only if specific PIK3CA mutation status is co-evaluated with
KRAS status (De Roock et al., 2010). These investigations suggest that
combining mutational analysis for KRAS and PIK3CA could identify up to 70%
patients with metastatic colorectal cancer who are unlikely to respond to
treatment with an EGFR targeted monoclonal antibody (Sartore-Bianchi et al.,
2009). In one of the recent studies a contradictory evidence was reported in
which it was found that there was no strong rationale for using PIK3CA
Chapter 2. Literature Review
82
mutations as a single marker for sensitivity to cetuximab in chemotherapy
refractory metastatic colorectal cancer (Prenen et al., 2009). Since tumors with
oncogenic PIK3CA are likely to be driven by PI3K as the primary source of
growth, proliferation and survival, the use of selective PI3K inhibitors is being
tested in ongoing trials. Several PI3K inhibitors are progressing from pre-clinical
studies to phase I trials. These include XL147,GDC-0941,BGT226, XL765 and
NVP-BEZ235 (Yuan and Cantley, 2008). This data from various trials needs to
be validated in clinical applications in larger study groups due to occurrence of
low frequency of PIK3CA mutations.
2.8.3.5 PTEN
Phosphatase and tensin homolog (PTEN) acts as a tumor suppressor gene
through the action of its phosphatase protein product. This phosphatase is
involved in the regulation of the cell cycle, preventing cells from growing and
dividing too rapidly (Chu and Tarnawski, 2004). It negatively regulates
intracellular levels of phosphatidylinositol-3,4,5-trisphosphate in cells and
functions as a tumor suppressor by negatively regulating Akt/PKB signaling
pathway. PTEN loss or inactivation leads to hyperactivation of the PI3K
signaling pathway. Loss of PTEN expression occurs in 30% of sporadic CRCs
(Thomas and Grandis, 2004). There are only few studies which have
demonstrated that loss of PTEN expression may be useful in predicting
response to cetuximab. Frattini et al. reported that none of 11 patients with
tumor PTEN loss responded to cetuximab-based treatment, whereas 10 (63%)
of 16 patients with intact PTEN protein expression were partial responders
(Frattini et al., 2007, Sartore-Bianchi et al., 2009). Further studies are required
to confirm these findings.
Chapter 2. Literature Review
83
Figure 2.21: Estimated Response Rate to EGFR inhibitors in Western
population with activating mutations in KRAS, BRAF, NRAS and PIK3CA Data
according to -(Frattini et al., 2007).
Chapter 2. Literature Review
84
2.8.3.6 TP53
The TP53 gene encodes a tumor suppressor protein p53 which is one of the
most frequently mutated genes in human cancer. Activated p53 binds to the
regulatory sequences of a number of target genes to initiate a program of cell
cycle arrest, DNA repair, apoptosis, and angiogenesis (Vogelstein et al., 2000).
Loss of function of TP53 is critical in tumorigenesis, and mutations which result
in overexpression of the protein are frequent events in colorectal cancer. p53
alterations are more frequent in tumors that are aneuploid, non-mucinous, and
do not show any MSI or CIMP molecular phenotypes (Westra et al., 2005).
Associations of TP53 tumor alterations with patient prognosis and response to
adjuvant chemotherapy have been widely studied. The majority of translational
studies carried out which aimed at determining whether TP53 mutation and
overexpression of p53 have prognostic value in colorectal cancer (Popat et al.,
2006). Few studies in colon cancer patients failed to demonstrate correlations
between TP53 alterations and benefit from adjuvant therapy (Allegra et al.,
2003). Similarly, a subset of functionally inactive mutations in TP53 predict poor
survival in late stage colorectal cancer (Iacopetta et al., 2006). Oden- Gangloff
et al. suggests that TP53 mutations may be predictive of increased likelihood of
response to cetuximab treatment, particularly in patients with wild-type KRAS
status (Slevin et al., 2008).
Chapter 2. Literature Review
85
2.9 Predictive and prognostic biomarkers in development
2.9.1 Micro RNAs
MicroRNAs (miRNAs) are small non-coding RNA molecules involved in the
post-transcriptional and translational regulation of gene expression. miRNAs are
emerging as ideal disease biomarkers for diagnosis as well as therapeutics in
CRC patients. Several studies have demonstrated that the expression of
various miRNAs in plasma may be indicative of presence of CRC. High plasma
miR-29a and miR-92a expression are useful non-invasive biomarkers to
distinguish CRC from healthy controls. Studies have shown a total of 362
differentially expressed miRNAs in colorectal cancer of which 242 are
upregulated and 120 are down regulated (Ma et al., 2012). miRNAs have also
been evaluated as therapeutic targets. Two general strategies for miRNA-based
therapeutics are seen: blocking oncogenic miRNAs and restoration of tumor-
suppressor miRNAs. Blocking of oncogenic miRNAs by anti-miRNAs has
suppressed cell proliferation and has enhanced chemo sensitivity (Akao et al.,
2011). Similarly, studies have shown that restoration of miR-143 using miR-143
precursor has reduced tumor growth (Ng et al., 2009).
2.9.2 Cell free nucleic acid
Circulating cell free nucleic acids has been reported in blood, stool and urine of
colorectal cancer patients in higher levels in comparison to healthy individuals.
Hence, presence of circulating DNA or RNA expression can provide valuable
molecular information about the tumor. It has been shown that cfDNA levels
decreased after tumor resection and increased in patients with recurrence and
metastasis (Frattini et al., 2008). More studies are required to evaluate cfNA
Chapter 2. Literature Review
86
levels in same cohort and different sample types to select the best possible
panel for diagnosis.
2.9.3 Circulating tumor cells
Circulating tumor cells (CTC) levels in peripheral blood have shown a significant
correlation with more advanced disease. CTC detection is significantly
associated with depth of tumor invasion, venous invasion, lymph node
metastasis, liver metastasis and stage (Iinuma et al., 2006).Metastatic
colorectal cancer patients with liver metastasis and poorer performance had
higher baseline CTC levels. Baseline CTC is an independent prognostic factor
in metastatic colorectal cancer. Patients with unfavorable levels of CTCs at
baseline had significantly shorter median disease-free and overall survival than
patients with fewer CTCs (Aggarwal et al., 2012). Also, patients with low
baseline and post-treatment CTC counts had longer progression-free and
overall survival than patients with an initially high baseline CTC count which
decreased after chemotherapy. Hence, in multiple studies CTCs have shown to
be prognostic and predictive biomarker.
2.9.4 Protein Biomarkers
Due to limited clinical applicability of CEA and CA19-9, additional proteins have
been proposed as colorectal cancer protein markers. These include TIMP-1,
MAPKAPK3, ACVR2B, CCSA-2, CCSA-3, CCSA-4 and matrix
metalloproteinase 9, S100A8, and S100A9. These proteins show very
promising results as CRC diagnostic biomarkers. However, these proposed
biomarkers need to be validated individually or in panel so as to make it
clinically relevant diagnostic tool (García-Bilbao et al., 2012).
Chapter 2. Literature Review
87
2.9.5 Cancer stem cells
Recently, compelling evidence has emerged in support of the cancer stem cell
(CSC) hypothesis in several solid organ epithelial malignancies including CRC.
CSC's are responsible for tumor initiation, metastases and resistance to
treatment leading to disease relapse following surgery and/or chemo
radiotherapy. As CSC have a potential to self-renew, capacity to differentiate
and initiate tumor and also allow asymmetric cell division via non-random
chromosomal co-segregation researchers have used these properties to isolate
colorectal cancer stem cells (Langan et al., 2013). Till date various putative
CRC stem cell markers have been identified- CD133, CD24, CD29, CD44,
CD166 (ALCAM), EpCAM, Lgr5, ALDH1A1 and ALDH1B1. These CSC markers
have shown to predict disease progression, and identify patients at risk for
recurrence. However, their prognostic significance as not been effectively
evaluated(Lugli et al., 2010). Further as reviewed by Lagan et al, 2013 in order
to translate the CSC based findings into clinical practice, comprehensive
analysis of a panel of CSC expression in large groups of colorectal cancer
patients is crucial.
Chapter 2. Literature Review
88
Table 2.4: Summary of Biomarker based studies in CRC (Patil et al., 2016)
Type Biomarker Biological Role Clinical use References C
he
mo
the
rap
y
TS : Thymidylate
Synthetase
Response to 5-
Fluorouracil
Predictive (Choueiri et
al., 2015)
Nuclear excision repair
pathway - ERCC1
expression
Absence or low
expression, prolonged
disease free survival with
Cisplatin based adjuvant
chemotherapy
Prognostic (Choueiri et
al., 2015)
DPD : Dihydropyrimidine
dehydrogenase
5-FU Prognostic (Falvella et
al., 2015)
TP : Thymidine
Phosphorylase
High expression, poor
prognosis with
Capecitabine
Prognostic (Bai et al.,
2015)
MTHFR :
Methylenetetrahydrofola
te Reductase
Genetic polymorphisms;
low risk to CRC
Prognostic (Zhao et
al., 2013)
UGT1A1 : Uridine
diphosphate
glucuronosyl transferase
1A1
Genetic polymorphisms ;
predicting toxicity to
Irinotecan
Prognostic (Lu et al.,
2015,
Hirose et
al., 2012)
GSTP1 : Glutathione S-
transferase P1
Genetic polymorphism
Ile105Val ,increased risk
to CRC
Prognostic (Song et
al., 2014)
Ta
rgete
d t
hera
py
KRAS : v-ki-ras2 Kristen
rat sarcoma viral
oncogene homologue
Genetic mutations in
exon2, 3 and 4. Predicting
resistance to anti-EGFR
moAB
Predictive (Allegra et
al., 2015)
BRAF : V-raf murine Genetic mutations in exon Prognostic (Fransen et
Chapter 2. Literature Review
89
sarcoma viral oncogene
homologue B1
15 V600 E, poor
prognosis
al., 2004)
NRAS : Neuroblastoma
Ras viral oncogene
homolog
Genetic mutations in
exon2, 3 and 4. Predicting
resistance to anti-EGFR
moAB
Predictive (De Roock
et al., 2010)
PI3K:
Phosphatidylinositide-3-
kinases
Genetic mutations.
Predicting resistance to
anti-EGFR moAB
Predictive (Jehan et
al., 2009)
PTEN : Phosphatase
and tension homolog
Loss of expression.
Predicting response to
anti-EGFR moAB
Predictive (Thomas
and
Grandis,
2004)
TP53 Genetic mutations.
Predicting response to
anti-EGFR moAB
Predictive (Slevin et
al., 2008)
Ep
ige
ne
tic
ma
rke
rs
MSI : Microsatellite
Instability
Lynch Syndrome Prognostic (Lech et al.,
2016)
COX2 : Cyclooxegenase
-COX2
COX2 inhibitors
associated with worst
outcome to treatment and
low risk to CRC
Prognostic (Rahman et
al., 2012)
CIMP : CpG island
methylator phenotype
Methylation of CpG
islands. Indicator of poor
prognosis
Prognostic (Bae et al.,
2013)
18q LOH : 18q loss of
heterogeneity
Allelic loss of 18 q, poor
prognosis
Prognostic (Colussi et
al., 2013)
CIN : Chromosomal
instability
Abnormal chromosome
number, poor prognosis
Prognostic (Reimers et
al., 2013)
Chapter 2. Literature Review
90
Pro
tein
ma
rke
rs
CEA : Carcinoembryonic
antigen
Monitoring therapy and
prognosis
Prognostic (García-
Bilbao et
al., 2012)
CA19-9 : Cancer
antigen 19-9
Monitoring therapy and
prognosis
Prognostic (García-
Bilbao et
al., 2012)
TIMP : Tissue Inhibitors
of Metalloproteinases
Monitoring therapy and
prognosis
Prognostic (García-
Bilbao et
al., 2012)
Bio
ma
rkers
In
de
ve
lop
men
t
Micro RNA (miRNA) Regulating gene
expression, poor
prognosis
Predictive and
prognostic
(Akao et
al., 2011)
Cell free nucleic acid High levels of cfNA, poor
outcome
Predictive and
prognostic
(Frattini et
al., 2008)
Circulating tumor cells High CTC, poor outcome Predictive and
prognostic
(Aggarwal
et al., 2012)
Cancer stem cells Predict disease
progression and risk of
reoccurrence
Predictive and
prognostic
(Lugli et al.,
2010)
Chapter 2. Literature Review
91
In summary as reviewed in this chapter, over the past decade significant
advances have been made in the management of colorectal cancer. Various
genes and pathways have been identified in colorectal cancer and extensive
knowledge has been gained about initiation and progression of the disease.
These recent advancements have attributed to an increase in overall survival of
metastatic colorectal cancer patients, with current overall survival averaging at
approximately 2 years. Advances in the development of chemotherapy and
biological agents to treat colorectal cancer have resulted in incremental gains
for patients survival (Table 2.5).
Recently several dynamic predictive markers have been identified which partly
solve the challenge of selecting patients who will respond to the high cost
targeted therapy (Table 2.4). However, currently there are only a few predictive
tools which are available to select patients who would best respond to specific
tumor treatments. Hence, there is a strong need to develop and validate more
biomarkers to assist with clinical decision making.
Population based studies are required to assess the most recent benefits of
clinical trials and also to determine the meaningful survival improvements in
colorectal cancer. With the recent genomic profiling of colorectal cancer and the
development of new proteomic and modeling studies, selecting and stratifying
colorectal cancer patients based on their molecular profile will be improved,
resulting in better patient management and individualized and personalized
health care.
Chapter 2. Literature Review
92
Table 2.5: Biological agents in clinical trials
Biological Agent Clinical Trial Stage Biological Agent Clinical Trial Stage
Edrecolomab Phase III-Completed MEHD7945A Phase I-Recruiting
Adecatumumab Phase II-Completed R05083945 Phase II
Cixutumumab Phase II-Completed OMP-21M18 Phase I-Active not
recruiting Conatumumab Phase II-Recruiting MGA271 Phase I-Recruiting
Figitumumab Phase II-Completed Dalotuzumab Phase II-Completed
Ganitumab Phase II Drozitumab Phase Ib
Necitumumab Phase II-Completed Ensituximab Phase I-Recruiting
Nimotuzumab Phase II-Terminated Etaracizumab Phase I-Completed
Trastuzumab Phase II-Completed Ramucirumab Phase II-Completed
Tremelimumab Phase II-Completed Tigatuzimab Phase II
Zalutumumab Phase II-Terminated CDX-1127 Phase I-Recruiting
AMG386 Phase I-Completed CEP37250/KHK280
4
Phase I-Active not
recruiting
CNTO328 Phase I/II-Completed Hu3S193 Phase I-Completed
CT011 Phase II-Completed ING-1 Phase I-Completed
GS-6624 Phase II-Recruiting KRN330 Phase I/II-Completed
IMC18F1 Phase II-Active not
recruiting
MDX-1105 Phase I-Active not
recruiting
IVIG Phase II-Unknown MDX-1106 Phase I-Completed
L19 Phase II-Completed MGA-271 Phase I-Recruiting
Chapter 2. Literature Review
93
2.10 Molecular Pathology Epidemiology: Emerging discipline to help in
optimizing disease prevention and treatment strategies
Molecular Pathological Epidemiology (MPE) or Molecular Pathologic
Epidemiology is a promising interdisciplinary research field that deals with the
integration of molecular signatures with epidemiological studies to elucidate
disease aetiologies. Its concept was consolidated in the year 2010 by Ogino
and Stampfer (Ogino and Stampfer, 2010), and has been designed to reveal
how various lifestyle exposures affect initiation, transformation and progression
of neoplasia (Ogino and Stampfer, 2010, Ogino et al., 2016).
CRC comprises of heterogeneous group of diseases with varied genetic and
epigenetic alterations. With the introduction of MPE, interactive effect of
molecular features of the tumor and lifestyle or other exposure factors on
prognosis and clinical outcome can be examined. In traditional epidemiological
studies, the risk of developing CRC in accordance with the different genetic,
environmental or lifestyle related factors was examined. However, MPE
investigates the genetic or molecular variation in population, its interaction with
lifestyle or environmental factors to evaluate possible causative links by
encompassing Genome wide association studies (GWAS) (Figure 2.22).
Chapter 2. Literature Review
94
Figure 2.22: Difference between traditional epidemiological studies and
Molecular pathologic epidemiology (MPE) (Image Source-Ogino et. al., Gut,
2011).
As mentioned by Ogino and co-workers, there are three approaches to
investigate exposure and molecular change- Case-Case approach, Case-
Control Study and Prospective Cohort Study (Ogino et al., 2011). The same has
been demonstrated in Figure 2.23. There is one more approach as mentioned
by Ogino et. al. which is known as Interventional Cohort study. This approach is
considered as a ‘gold standard’, however, no data has been published yet.
Chapter 2. Literature Review
95
Figure 2.23: Three approaches of MPE (Image source-Ogino et.al., Gut, 2011).
In ‘case- case’ approach, the tumor is classified according to the subtypes and
the effect of the variable of interest is compared amongst the different subtypes.
The limitation of this approach is that the direction of any association between
the exposure variable and the molecular subtype cannot be ascertained. Like
for example as provided by Ogino et al, if smoking an exposure variable is
studied along with KRAS mutations which acts as molecular subtype, then in
case- case approach it cannot be ascertained if smoking protects form KRAS
mutations or causes KRAS mutations.
Chapter 2. Literature Review
96
The second approach is ‘Case-Control’ approach. In this approach the
distribution of the exposure variable is studied in the cases with specific genetic
or epigenetic alterations as well as in the control cases without those
alterations.The third approach of Prospective cohort study combines case-case
and case-control approach. This approach requires substantial number of
participants, frequent follow up time, large funding and substantial efforts by
researchers.
Hence, MPE with its distinctive strengths can provide insights into the
pathogenic process of a disease and help optimize personalized therapy and
prevention.
However, there are few challenges of MPE such as sample size limitations,
validations of assays and study findings, paucity of interdisciplinary experts and
standardized guidelines (Ogino et al., 2015). To overcome these challenges,
Professor Shuji Ogino has taken an initiative and has introduced International
MPE meeting series since April 2013.He has also started many projects like-
Strengthening the Reporting of Observational Studies in Epidemiology-MPE
guideline project and ongoing efforts for multidisciplinary consortium projects.
These approaches are similar to other existing initiatives like Big Data to
Knowledge (BD2K), Genetic Associations and Mechanisms in Oncology
(GAME-ON), and Precision Medicine initiatives. Hence, in oncology, MPE, a
different approach from traditional epidemiological studies, has led to address
the interactive effect of exposure factors and molecular changes on the tumor
aggressiveness.MPE will in future help in providing substantial insights in
Chapter 2. Literature Review
97
carcinogenic processes and will also help in optimizing prevention and
treatment strategies.
Chapter 3. Materials and Methods
98
Chapter 3 Materials and Methods
3.1 Study Population
A total of 203 formalin fixed paraffin embedded (FFPE) colorectal cancer tissue
samples of patients from Indian origin were analyzed for mutations in KRAS,
BRAF, NRAS and PIK3CA genes.
The age range for males was 21-90 years and of females was 27-76 years and
median age was 54 years. These samples were resected at various hospitals all
over India and were sent to us for testing during January 2013 till June 2016.
Demographic and clinicopathological features were obtained along with the test
requisition form. Each sample has been designated with a unique accession
number. The project has been approved by scientific committee of Reliance Life
Sciences. The samples were processed according to guidelines of College of
American Pathologists (CAP) and National Accreditation Board for testing and
calibration Laboratory (NABL) and the samples were collected from patients
with informed consent.
3.2 Methods
The detection of mutations in KRAS exons 2 and 3, BRAF exon 15, NRAS
exons 2 and 3 and PIK3CA exons 9 and 20 was developed and standardized
in-house for PCR amplification and direct nucleotide sequencing of PCR
products. The assay was validated in order to comply with the guidelines of
Chapter 3. Materials and Methods
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National Accreditation Board for testing and calibration Laboratory (NABL),
India and College of American Pathologists (CAP), USA. The validation
parameters included sensitivity, specificity, repeatability and reproducibility as
defined below. The performance characteristics were determined at Molecular
Medicine Group, Reliance Life Sciences Pvt. Ltd., Navi Mumbai, India, after the
completion of validation of the assay.
Sensitivity: Analytical sensitivity represents ability of the assay to consistently
detect presence or absence of mutations in minimum percentage of tumor
present in the sample and also to detect the mutant allele burden in normal
allele.
Specificity: Analytical specificity represents ability of the assay to consistently
amplify only specific exons or regions of the mentioned genes and absence of
non-specific additional fragments.
Repeatability: The agreement in the results of an assay performed by the same
analyst on two different occasions in independent assay is defined as
repeatability of the assay.
Reproducibility: The agreement in results of an assay performed by different
analysts on different occasions in independent assay is defined as
reproducibility of the assay.
Chapter 3. Materials and Methods
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The mutation detection assays for KRAS, BRAF, NRAS and PIK3CA were
based on six major processes outlined below.
1. Haematoxylin and eosin (H&E) analysis for histological assessment of
tumor in FFPE tissue samples.
2. Specimen preparation: DNA extraction from FFPE tissues, quality check
and quantification of the DNA.
3. PCR amplification of exon 2 and exon 3 for KRAS, exon 15 for BRAF,
exon 2 and exon3 for NRAS and exon 9 and exon 20 for PIK3CA genes.
4. Detection of amplified products by agarose gel electrophoresis.
5. Direct sequencing of the amplified products.
6. In-silico data analysis for mutation screening.
Chapter 3. Materials and Methods
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Flow chart of Methodology
Formalin Fixed Paraffin Embedded Tissue (FFPE) specimen collection (n=203)
(Demographic and clinico-pathological details)
Immunohistochemistry (Tumor % + Tumor grading)
DNA isolation from FFPE samples
PCR assay standardization, validation and set up for all four genes (KRAS,
BRAF, NRAS and PIK3CA genes)
Sequencing and analysis for mutation profiling of four genes
Correlation between mutations and clinico-pathological characteristics
Survival Analysis
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3.2.1 Haematoxylin and eosin (H&E) Analysis
H & E staining is an essential first step to confirm the presence of
representative tumor tissue before further analysis can be started. It is the gold
standard and allows the typing and grading of tumors as well as gauging the
extent of tissue involvement by tumor.
3.2.1.1 Principle
Tissue processing leaves the tissue structurally enabled to undergo
downstream processes of sectioning and staining for diagnostic purpose. The
H&E is the most widely used stain for histopathology in which nuclei is stained
by oxidized haematoxylin (haematin) through mordant bonds such as aluminum
followed by counter-staining by xanthene dye eosin, which colors in various
shades the different tissue fibers and cytoplasm. Haematoxylin is basic in
nature and binds to basophilic substances i.e. DNA or RNA, whereas eosin is
acidic in nature and binds to acidophilic substances like amino acid side chains.
3.2.1.2 Method
Pre-cooled FFPE block was immobilized on a microtome. The angle of high
profile microtome blade was adjusted. Trimming of the paraffin blocks was done
on 10 µM. Then microtome setting was adjusted for section thickness of 3-4
µM. Ribbons of tissue sections taken and were layered on a floatation water
bath (temperature = 50° C) from where they were mounted on egg albumin
coated ordinary double frosted slide for H & E analysis.
Slides were then placed in oven at 60°C overnight and then transferred into
xylene bath and three serial changes were performed at an interval of 5 min.
Rehydration of slides was done using 100 % ethanol in two changes at 5 min
Chapter 3. Materials and Methods
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interval and then the slides were placed in freshly prepared 90 % ethanol for 5
min followed by 70% and 50%. Slides were gently rinsed under tap water for 5
min and were kept in Harris haematoxylin solution for 10 min followed by gentle
wash under tap water for 5 min. The slides were then dipped in 1% acid alcohol
for differentiation and then gently rinsed under tap water followed by treatment
with 1% ammonia solution with single change followed by gentle wash under
tap water. These slides were then placed in 70% ethanol followed by absolute
ethanol for 30 s each and were then dipped in Eosin stain for one min. Slides
were dehydrated in two changes of fresh 100% ethanol for 5 min each followed
by blot drying and were kept in xylene overnight and mounted in DPX. These
were then reviewed by the pathologist for tumor content and grading.
Chapter 3. Materials and Methods
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Figure 3.1: H&E stained tissue samples
A
B
Figure Legend:
A Representative H&E photomicrograph of colorectal cancer tissue sample with well
differentiated adenocarcinoma, WHO grade I;
B H&E photomicrographs of normal tissue.
3.2.2 DNA extraction
3.2.2.1 Principle
The PureLink Genomic DNA kits (Invitrogen, Cat No. K1820-02, Carlsbad, CA,
USA) are based on the selective binding of DNA to silica-based membrane in
the presence of chaotropic salts. The lysate was prepared from a variety of
starting material such as tissues. The cells were digested with Proteinase K at
55C using an optimized digestion buffer formulation that aids in protein
denaturation and enhances Proteinase K activity. Any residual RNA was
removed by digestion with RNase A prior to binding samples to the silica
membrane. The lysate was mixed with ethanol and Purelink Genomic Binding
Buffer that allows high DNA binding Purelink Spin Column (Mini kit). The DNA
Chapter 3. Materials and Methods
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binds to the silica based membrane in the column and impurities were removed
by thorough washing with Wash Buffers. The genomic DNA was then eluted in
low salt Elution Buffer.
3.2.2.2 Methodology
The protocol was followed as per the manufactures instructions (Invitrogen, Cat
No. K1820-02, Carlsbad, CA, USA)
1. Specimen preparation
Four sections each of 8 µM of FFPE tissue specimen were taken in 1.5 ml
microfuge tube. Tumor sections having 20% tumor content, as assessed by the
pathologist, were processed further for macrodissection. Sections on the slide
containing tumor were marked by the pathologist which were further scraped
and taken for DNA extraction. One ml xylene was added followed by vortex for
10 s and centrifugation at 13000 rpm for 3 min. The supernatant was removed
and the xylene wash was repeated again to remove paraffin from the tissue.
Then 1 ml of absolute ethanol was added to the tube followed by vortexing for
10 s and centrifugation at 13000 rpm for 3 min. The supernatant was removed
and the ethanol wash step was repeated again. Supernatant was removed and
the tube with open lid was placed at 37 °C dry block until all residual ethanol
was evaporated. Pellet was re-suspended in 180 µl digestion buffer and 20 µl
Proteinase K followed by incubation at 50° C overnight.
Chapter 3. Materials and Methods
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2. DNA extraction
After overnight incubation, the lysate was centrifuged at 13000 rpm for 3 min.
The supernatant was transferred to 1.5 ml micro centrifuge tube. 20 ul of RNase
A solution was added followed by one min incubation at RT.200 µl of
Lysis/Binding buffer and 200 µl of ethanol was added to the lysate, vortexed to
yield a homogenous solution and spun briefly. The 620 µl solution was then
layered on Purelink spin column and centrifuged at 10000 rpm for 1 min. The
spin column was then transferred to fresh collection tube. Wash Buffer WB1
500 µl was added to the spin column and centrifuged at 10000 rpm for 1 min.
The column was then placed in fresh collection tube and 500 µl of Wash Buffer
WB2 was added and centrifuged at 13000 rpm for 3 min. The column was then
placed in 1.5 ml microfuge tube and 50 µl of elution buffer was added to the
center of the column, incubated for 1 min at RT followed by centrifugation at
10000 rpm for 1 min. The spin column was discarded and the DNA was
collected in 1.5 ml tube.
The extracted DNA was quantified by Nanodrop spectrophotometer using the
Nanodrop 26 ND-1000 spectrophotometer (Nanodrop Technologies Inc.
Wilmington, NC, USA). Purified DNA with the A260/A280 ratio of 1.7-1.9 and
absorbance scans with symmetric peak at 260 nm confirmed the purity of the
DNA.
The DNA was further processed for PCR amplification followed by nucleotide
sequencing.
3.2.3 Primer selection for Polymerase Chain Reaction (PCR)
The primers were selected from literature survey for amplification of KRAS exon
2 and exon 3, BRAF exon 15, NRAS exon 2 and exon 3 and PIK3CA exon 9
Chapter 3. Materials and Methods
107
and exon 20. The described primers were modified , when necessary using
Primer Express software (Applied Bio systems, Foster City, USA) to check the
secondary structure and critical parameters like primer length, melting
temperature (Tm), specificity, complimentary primer sequences, G/C content
polypyrimidine (T,C) or polypurine (A,G) stretches, 3’- end sequence, primer-
dimer formation.
3.2.3.1 PCR Assay Optimization
PCR assay optimization for KRAS, BRAF, NRAS and PIK3CA mutation
detection included optimization of DNA concentration, tumor percentage, and
Taq polymerase, MgCl2, DMSO and BSA. The PCR conditions including
temperature and time for denaturation, annealing and extension were
standardized and were finalized on observation of the PCR products on
agarose gel electrophoresis.
The nested PCR was standardized for all four genes. Table 3.1 and 3.2
summarize the thermal cycling conditions:
Chapter 3. Materials and Methods
108
Table 3.1: Master mixture for first round of PCR for KRAS, BRAF, NRAS and
PIK3CA.
Stock Solution 1 reaction (µl)
Sterile MilliQ water 11.7
5 X Buffer (Promega) 10
2mM d NTPs (Takara) 5
25mM MgCl2 (Promega) 3
DMSO (Sigma) 3
0.1% BSA (Sigma) 5
Forward Primer (Sigma) 1
Reverse Primer (Sigma) 1
Taq DNA Polymerase (5U/µl) Promega 0.3
Total Volume 40
10 µl of extracted DNA was added to the reaction mix. Each assay had an
amplification control, and reaction control to check the assay validity.
Chapter 3. Materials and Methods
109
Table 3.2: Master mixture preparation for nested PCR for KRAS, BRAF, NRAS
and PIK3CA.
Stock Solution 1 reaction (µl)
Sterile MilliQ water 7.7
5 X Buffer (Promega) 5
2mM d NTPs (Takara) 2.5
25mM MgCl2 (Promega) 1.5
DMSO (Sigma) 1.5
0.1% BSA (Sigma) 2.5
Forward Primer (Sigma) 1
Reverse Primer (Sigma) 1
Taq DNA Polymerase (5U/µl) Promega 0.3
Total Volume 23
About 2 µl of the first round product was added to the reaction mix
Thermal cycling conditions are given in Table 3.3 - Table 3.11.
Table 3.3: KRAS exons 2 and 3 mutation detection assay.
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 52°C – 30sec
Extension 72°C – 30sec
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
First round and Nested PCR for KRAS exons 2 and 3 have same conditions.
Chapter 3. Materials and Methods
110
Table 3.4: BRAF exon 15 mutation detection assay- First round PCR.
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 58°C – 30sec
Extension 72°C – 30sec
Number of cycles 22
Final extension 72°C – 7min
Hold 15°C - ∞
Table 3.5: BRAF exon 15 mutation detection assay- Nested PCR.
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 58°C – 30sec
Extension 72°C – 30sec
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
Table 3.6 NRAS exon 2 mutation detection assay- First round and Nested PCR.
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 58°C – 30sec
Extension 72°C – 30sec
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
First round and Nested PCR for NRAS exons 2 and 3 have same conditions.
Chapter 3. Materials and Methods
111
Table 3.7: NRAS exon 3 mutation detection assay- First round PCR.
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 60°C – 30sec
Extension 72°C – 30sec
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
Table 3.8: NRAS exon 3 mutation detection assay- Nested PCR.
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 53°C – 30sec
Extension 72°C – 30sec
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
Table 3.9: PIK3CA exon 9 mutation detection assay- First round and Nested
PCR.
Initial denaturation 95°C- 5 min
Denaturation 95°C – 30sec
Annealing 60°C – 30sec
Extension 72°C – 1min
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
First round and Nested PCR for PIK3CA exon 9 have same conditions.
Chapter 3. Materials and Methods
112
Table 3.10: PIK3CA exon 20 mutation detection assay- First round PCR.
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 55°C – 30sec
Extension 72°C – 1min
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
Table 3.11: PIK3CA exon 20 mutation detection assay- Nested PCR
Initial denaturation 94°C- 5 min
Denaturation 94°C – 30sec
Annealing 64°C – 30sec
Extension 72°C – 30sec
Number of cycles 35
Final extension 72°C – 7min
Hold 15°C - ∞
Chapter 3. Materials and Methods
113
Primer Details:
Gene Primer Name
Primer sequence 5'-3' Base
pairs
KRAS KRAS E2-FP TACTGGTGGAGTATTTGATAGTG 23
KRAS E2-RP TGTATCAAAGAATGGTCCTG 20
KRAS-E2IF TAGTGTATTAACCTTATGTGTGAC 24
KRAS-E2IR ACCTCTATTGTTGGATCATATTC 23
KRAS-E3 F AAGGTGCACTGTAATAATCCA 21
KRAS-E3 R CATGGCATTAGCAAAGACTC 20
KRAS-E3IF AATCCAGACTGTGTTTCTCC 20
KRAS-E3IR TTTAAACCCACCTATAATGG 20
(Patil et al., 2013)
BRAF BRAFEX15F1 CATAATGCTTGCTCTGATAGG 21
BRAFEX15R1 GGCCAAAAATTTAATCAGTGGA 22
BRAFEX15F2 CATAATGCTTGCTCTGATAGG 21
BRAFEX15R2 TAGCCTCAATTCTTACCATC 20
(Houben et al., 2004)
NRAS NRAS2F1 TAATCCGGTGTTTTTGCGTTCTC 23
NRAS2R1 GCTACCACTGGGCCTCACCTCTA 23
NRAS2F2 AGTACTGTAGATGTGGCTCGC 21
NRAS2R2 ACTGGGCCTCACCTCTATG 19
NRAS3F1 CCCCCTTACCCTCCACACC 19
NRAS3R1 GAGGTTAATATCCGCAAATGACTT 24
NRAS3F2 GATTCTTACAGAAAACAAGTG 21
NRAS3R2 ATGACTTGCTATTATTGATGG 21
(Houben et al., 2004)
PIK3CA PIKEX9 P1 TTGCTTTTCTGTAAATCATCTGTG 24
Chapter 3. Materials and Methods
114
PIKEX9 P2 GGGAAAAATATGACAAAGAAAGC 23
PIKEX9 P3 GAATCTCCATTTTAGCACTTACCTG
TGACT
30
PIKEX20 P1 TTTTCTCAATGATGCTTGGC 20
PIKEX20 P2 GGATTGTGCAATTCCTATGC 20
PIKEX20 P3 AATCTTTTGATGACATTGCATACAT
TCG
28
PIKEX20 P4
(Bisht et al., 2014)
TCAGTTATCTTTTCAGTTCAATGCA
TG
27
Chapter 3. Materials and Methods
115
3.2.4 Detection of PCR products by agarose gel electrophoresis
3.2.4.1 Method
The agarose was made in 1X TBE buffer after initial homogenization in
microwave. After cooling to 55°C, Ethidium bromide (0.5µg/ml) was added to
agarose, mixed thoroughly and poured in Bio-Rad gel casting tray (Bio-Rad
Laboratories Inc., Hercules, USA) and the gel was allowed to solidify.
About 10 µl of PCR product is loaded into the wells of the gel along with 4 µl of
DNA ladder. The gel was run at 200 volts with 30-35 min run time and
visualized under Gel Documentation system (Amersham Pharmacia Biotech,
Sweden). Image was captured and saved as *tif file and documented.
3.2.5 Sequencing of PCR products for Detection of mutations
Direct Nucleotide Sequencing, the gold standard for detection of mutations was
used to determine the mutations in KRAS, BRAF, NRAS and PIK3CA genes.
3.2.5.1 Principle
Sanger dideoxy sequencing method, developed by Nobel Laureate Fredrick
Sanger, is an enzymatic procedure in which DNA synthesis is carried out by
DNA polymerase. DNA polymerase requires both a primer, to which nucleotides
are added and a template strand to guide selection of each nucleotide. The
primer binds to the 3’hydroxyl group of the DNA to be synthesized. The 3’ end
of the primer reacts with the incoming deoxynucleoside phosphates (dNTPs). In
Sanger sequencing procedure dideoxynucleoside triphosphate (ddNTP)
analogues are used to interrupt DNA synthesis as these lack 3’-hydroxyl group
needed for addition of subsequent nucleotide. Thus, these ddNTPS prematurely
terminate the reaction. This process synthesizes DNA strands of varying length.
Chapter 3. Materials and Methods
116
Each of the resulting fragment is analyzed by electrophoresis and a base
sequence is elucidated from the results.
3.2.5.2 Method
Five major processes are involved in nucleotide sequencing
1. Pre sequencing clean up
2. Sequencing clean up
3. Post sequencing clean up
4. Capillary electrophoresis
5. In-Silico Data Analysis
1. Pre sequencing clean up:
After the PCR amplification was completed, unincorporated primers, excess
dNTPs interfere with subsequent sequencing reaction. For removing these
contaminants pre sequencing clean-up was carried out before setting up
sequencing reaction. The volume of PCR products to be sequenced was
adjusted to 100 µl by MilliQ and was layered on Millipore Montage µPCR 96
cleanup plate. These plates incorporate Millipore’s size exclusion technology for
sample clean-up. The plate having PCR product was then placed on a vacuum
manifold and vacuum of 20 inches of Hg was applied for 5 min. The products
were subsequently washed with 50 µl MilliQ water at same vacuum pressure for
5 min. In the dried well the 25 µl of MilliQ was added and the purified PCR
products were retrieved from each well by pipetting.
2. Sequencing PCR:
The purified PCR products were subjected to cycle sequencing using Big Dye
Terminator v3.1 Cycle sequencing kit. The PCR included initial denaturation at
Chapter 3. Materials and Methods
117
96°C for 10 sec, 50° C for 5 sec and 60° C for 4 min. The reaction was carried
out in 10 µl volume, containing 1 µl of BigDye Terminator, 3 µl of 2.5X
sequencing buffer, 4 µl of MilliQ water, 1 µl each of primer and template.
3. Post sequencing clean-up:
Unincorporated fluorescence dye terminators in the sequencing reaction
interfere with the quality of sequence by forming unwanted dye blobs in the
sequence read. Hence, it is important to remove these from the reaction before
loading it ABI Prism 3100 Genetic Analyzer.
Sequencing reaction cleanup was carried using Montage SEQ 96 sequencing
reaction clean up kit. Each cycle sequencing product was mixed with 30 ul of
injection solution provided in the kit and transferred to Montage SEQ 96 plate.
Vacuum pressure of 20 inches of Hg was applied until wells become dry. The
wash was given again with 30 µl of injection solution at same vacuum pressure.
The purified sequencing products were re-suspended n 25 µl of injection
solution and were transferred to ABI 96 well Optical plates.
4. Capillary electrophoresis:
The 96 well ABI Optical plate having purified products was loaded on the BI
3100 Genetic analyzer. Electro kinetic injection was done at 1kv for 22 sec,
voltage of 12.2 kV was applied and 50°C constant temperature was maintained
in the oven throughout the run time of 2 hrs. 22 min. For correct data collection,
analysis and extraction, following settings were made in the software. Run
module: StdSeq50_POP6_1 Default Module, Dye set Z-BigDyeV3, Analysis
Chapter 3. Materials and Methods
118
Protocol: 3100POP6_BDTv-3-kb-dEnOvO_V5.1, Base Caller KB.bcp, Mobility
File: KB_3100_POP6_BDTv3.mob.
After the run was completed, the data was extracted and analyzed
automatically by the software provided with the instrument.
5. In-silico Sequence Analysis:
The sequences of KRAS, BRAF, NRAS and PIK3CA genes obtained were
aligned with respective exonic sequences obtained from NCBI to screen for
mutations using BioEdit software.
6. Statistical analysis:
Statistical analysis was performed using GraphPad and GraphPad Prism
software. The comparative distribution of mutations in all four genes was
studied and its correlation with clinico-pathological parameters was analyzed.
Chi square test was employed and a finding of p<0.05 was considered as
statistically significant.
Chapter 4. Assay Validation
119
Chapter 4 Assay Validation
According to National Accreditation Board for testing and calibration Laboratory
(NABL), India and College of American Pathologists (CAP), USA. Guidelines,
assay validation involves three critical components- Assay development, Assay
validation and Validation retention system.
Figure 4.1: Components of Assay Validation.
Assay development phase involves performing a thorough background search,
developing a rationale, aims and objectives. The key assay requirements which
should be taken in consideration include-Pre analytical, Analytical and Post
analytical components. The Pre-analytical component includes- Specimen Type
and tumor content which includes parameters like Frozen tissue or FFPE tissue,
primary or metastatic tissue, necrotic or inflammatory and if there is any need
for macro dissection. Analytical phase includes methodology i.e in-house
developed or kit based, assay validation includes sensitivity, robustness and
reproducibility of the assay and spectrum of mutations covered. The post
Chapter 4. Assay Validation
120
analytical parameter includes the cost of testing i.e. the reagent and labor cost
and the report content. The assay requirements are summarized in Figure 4.2.
Figure 4.2: Key assay considerations (Image Source-Steven Anderson, Expert
Rev Mol Diagn, 2011).
Further, assay validation involves four parameters – Specificity, Sensitivity,
Reproducibility and Repeatability. Then comes the assay validation retention,
which comprises of monitoring and maintenance of the assay.
Chapter 4. Assay Validation
121
4.1 Validation parameters:
4.1.1 Specificity
Specificity is primarily a function of primer selection in PCR assays. Twenty
samples and ten plasmid DNA samples were tested to establish that the assay
did not generate false-positive reactions. Cross contamination concerns were
addressed by testing the panel containing alternating panel of the plasmid DNA
to be tested after every two human DNA samples. The PCR products were
loaded on 2% agarose gel along with a 100 bp molecular weight marker and
reaction controls. The results were observed, and recorded. All the positive
samples were subjected to nucleotide sequencing for the critical point mutations
at exons 2 and 3 for KRAS and NRAS, exon 15 for BRAF and exons 9 and 20
for PIK3CA and the sequences were analyzed.
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4.1.2 Sensitivity
The detection limit of the qualitative assay must account for the detection of
gene mutation in excess of normal DNA. The positive cut-off point was the
minimum percentage of malignant cells in the tissue as visualized in H&E
staining of the tissue sample, for detection of mutation in the tissue sample. The
test was performed on 20 samples with malignant cell percentages ranging from
80% to 10%. The PCR products were observed visually on gel documentation
system and gel images recorded. The positive PCR products were sequenced
on ABI 3100 Genetic Analyzer, and analyzed with Sequence Analysis Software
version 5.1.1. The allelic sensitivity was also established using commercially
available standards each of 50% allele burden from Horizon Diagnostics. The
nucleotide sequences obtained were screened for mutations. The sequences
were aligned using BioEdit tool against respective exonic sequences of
reference sequence.
4.1.3 Repeatability
Twenty samples comprising wild-type and mutated were analyzed for specific
exons of all four genes, on two different days in independent assays, by two
different analysts. The results were recorded.
4.1.4 Reproducibility
Twenty samples comprising wild-type and mutated were analyzed for specific
exons of all four genes, twice on different days in independent assays, by the
same analyst. The results were recorded.
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4.2 Validation of KRAS exon 2 and exon 3 (codons 12, 13 and 61)
mutation detection assay
4.2.1 Specificity
Twenty samples comprising wild-type and mutated used for specificity assay
showed specific amplimers for exons 2 and 3 of K-ras gene. The ten plasmid
DNA samples did not show amplification in exons 2 or 3. The nucleotide
sequences showed >99% homology with the reference sequence of K-ras
(Accession No: NC_000012) indicating 100% specificity of the assay. The
variations obtained were due to mutations. The results are shown in Figures
4.3A and 4.3B and the description for the same is mentioned in Table
4.1.Figure 4.4 is the ClustalW of KRAS exon 2, codons 12 and 13.
Chapter 4. Assay Validation
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Figure 4.3A and 4.3B: Specificity experiment for KRAS mutation detection
assay.
Figure 4.3A
Figure 4.3B
Figure Legend (4.3A and 4.3B): Twenty samples were analysed using PCR followed
by Sanger sequencing along with ten plasmid DNA samples having insert other than
KRAS gene. The samples showed amplification whereas the plasmid DNA samples did
not show any amplification. Two bands were observed in few sample lanes like lane
nos. 7, 14, 16, 17 of Figure 4.3A and lane nos.2, 7, 11, 13, 14, 16 in Figure 4.3B which
could be due to non specific amplification. This non specific amplification was
overcome by increasing the annealing temperature to 52°C.The loading pattern is
mentioned in Table 4.1.
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Table 4.1: Loading pattern for Figures 4.3A and 4.3B.
Figure 4.3A: KRAS Spec 1 Figure 4.3B: KRAS Spec 2
Lane No.
Sample Lane No. Sample
1 100 bp Ladder 1 100 bp Ladder
2 Positive Control 2 Positive Control
3 Reaction Control 3 Reaction Control
4 7C40344 4 7C51922
5 7C49157 5 7C47808
6 Plasmid 1 6 Plasmid 6
7 7C49142 7 7C47583
8 7C42607 8 7C50956
9 Plasmid 2 9 Plasmid 7
10 7C49133 10 7C47333
11 7C49132 11 7B35583
12 Plasmid 3 12 Plasmid 8
13 7C50984 13 7C47933
14 7C51169 14 7C43426
15 Plasmid 4 15 Plasmid 9
16 7C47888 16 7C49168
17 7C43404 17 7C43424
18 Plasmid 5 18 Plasmid 10
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Figure 4.4: Clustal W for KRAS exon 2 codon 12 and 13 for specificity
parameter.
Codon 12/13
KRAS_E2 GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 85
7C40344_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 100
7C49157_K2F GTTGGAGCTG RTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92
7C49142_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92
7C42607_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 80
7C49133_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 98
7C49132_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 91
7C50984_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92
7C51169_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 79
7C47888_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 85
7C43404_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 84
7C51922_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92
7C47808_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 85
7C47583_K2F GTTGGAGCTK GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 98
7C50956_K2F GTTGGAGCTK GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92
7C47333_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92
7B35583_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 89
7C47933_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92
7C43426_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 98
7C49168_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 98
7C43424_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 91
Clustal Consensus ********* ********* ********** ********** ********** 77
Figure Legend: The PCR amplified samples were sequenced and aligned with the
reference sequence using BioEdit software. The highlighted nucleotides in the box
correspond to codon 12 (GGT) and codon 13 (GGC) and the heterozygous G/A is
depicted by R and heterozygous G/T is depicted by K.
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4.2.2 Sensitivity
The test was performed on 20 samples with malignant cell percentages ranging
from 80% to 20%, for exon 2, codons 12/13 and exon3, codon 61. The PCR
products are observed visually on gel documentation system and gel images
recorded. The KRAS positive PCR products are sequenced on ABI 3100
Genetic Analyzer, and analyzed with Sequence Analysis Software version
5.1.1. The nucleotide sequences obtained were screened for mutations at
exon2, codons12/13 and/or exon3, codon 61. The sequences are aligned using
BioEdit tool against respective exonic sequences of reference sequence of
KRAS gene (Accession No: NC_000012).
The minimum percentage of malignant cells in the tissue as visualized in H&E
staining, for detection of KRAS mutation in the tissue sample was found to be
20%. The results are shown in Figures 4.5 and 4.6 and the description for the
same is mentioned in Table 4.2. Clustal W for KRAS is shown in Figure 4.7 for
sensitivity parameter.
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Figure 4.5: KRAS Sensitivity 1
Figure 4.6: KRAS Sensitivity 2
Figure Legend: Figure 4.5 and 4.6: Sensitivity experiment for KRAS mutation
detection assay. Twenty samples with malignant cell percentage from 20 to 80% were
analysed using PCR. The amplified products were further sequenced. The loading
pattern is mentioned in Table 4.2.
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Table 4.2: Loading pattern on agarose gel electrophoresis for figures 4.5 and
4.6
Figure 4.5: KRAS Sensitivity 1 Figure 4.6: KRAS Sensitivity 2
Lane No.
Sample % Tumor
Lane No.
Sample % Tumor
1 100 bp Ladder 1 100 bp Ladder
2 Positive Control 2 Positive Control
3 Reaction Control 3 Reaction Control
4 7C55300 80 4 7C43458 60
5 7C61089 70 5 7C59701 50
6 7C58028 70 6 7C60972 50
7 7C58014 70 7 7C46780 50 8 7C49157 70 8 7B35569 50
9 7C49110 70 9 7C61075 40 10 7C42621 70 10 7C58194 40 11 7C60966 60 11 7C58062 30 12 7C52618 60 12 7C61886 20
13 7C52160 20
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Figure 4.7: Clustal W for KRAS exon 2, codons 12 and 13 for sensitivity
parameter.
KRAS_E2 AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 84
7C55300_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100
7C61089_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 99
7C58028_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 83
7C58014_k2f AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 61
7C49157_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 99
7C49110_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92
7C42621_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92
7C60966_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91
7C52618_k2f AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100
7C43458_K2F AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91
7C59701_k2f AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 78
7C60972_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 83
7C46780_K2F AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 79
7B35569_K2F AGTTGGAGCT GGTGRCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 63
7C61075_K2F AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 64
7C58194_k2f AGTTGGAGCT RGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100
7C58062_K2F AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 60
7C61886_K2F AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 62
7C52160_K2F AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91
Clustal Consensus ********** ** ***** ********** ********** ********** 57
Figure Legend: The PCR amplified samples were sequenced and aligned with the
reference sequence using BioEdit software. The highlighted nucleotides in the box
correspond to codon 12 (GGT) and codon 13 (GGC). The mutation was observed in
the sample with 20% malignant cells (Sample accession no.7C52160) indicating the
sensitivity as 20%.The heterozygous G/A is depicted by R, heterozygous G/T is
depicted by K and heterozygous G/C is depicted by S.
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4.2.3 Reproducibility
Twenty samples comprising wild-type and mutated were analysed for codons
12/13 and 61 mutations at exons 2 and 3 of KRAS gene, on two different days
in independent assays, by two different analysts, using KRAS gene mutation at
codons 12/13 and 61 testing protocol. Detection of KRAS gene mutation at
codons 12/13 and 61 was observed to be 100% reproducible assay as, all the
samples showed same nucleotide sequence when performed by two different
analysts (Table 4.3).
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Table 4.3: Results of samples analysed by two different analysts for
reproducibility.
Accession No Test Result Mutation Detected
Test Result Mutation Detected
Analyst 1 Analyst 2
7C42621 Detected GGT ; GTT Detected GGT ; GTT
7C46549 Detected GGT ; GTT Detected GGT ; GTT
7C49823 Detected GGT ; GAT Detected GGT ; GAT
7C50956 Detected GGT ; TGT Detected GGT ; TGT
7C58194 Detected GGT ; AGT Detected GGT ; AGT
7C52484 Detected GGT : GAT Detected GGT : GAT
7C52681 Detected GGT ; TGT Detected GGT ; TGT
7C43458 Detected GGT ; GCT Detected GGT ; GCT
7A12168 Not Detected - Not Detected -
7C46456 Not Detected - Not Detected -
7C46867 Not Detected - Not Detected -
7B35559 Not Detected - Not Detected -
7B35575 Not Detected - Not Detected -
7B35583 Not Detected - Not Detected -
7C27862 Not Detected - Not Detected -
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Figure 4.8: Clustal W for KRAS for reproducibility parameter.
KRAS_E2 AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 84
7c42621_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 77
7c46549_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 78
7c47823_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 83
7C50956_K2F AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 88
7c58194_k2f AGTTGGAGCT RGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 82
7c52484_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92
7C52618_k2f AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 98
7C43458_K2F AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 61
7A12168_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 99
7c46456_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 61
7c46867_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100
7B35559_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91
7c35575_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 64
7B35583_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 79
7c47087_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92
7c27862_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 83
Clustal Consensus ********** ******** ********** ********** ********** 59
Figure Legend: The samples were processed independently by two analyst to validate
the reproducibility parameter and the results were concordant. The highlighted
nucleotides in the box correspond to codon 12 (GGT) and codon 13 (GGC). R depicts
the heterozygous G/A, K depicts heterozygous G/T and S depicts heterozygous G/C.
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4.2.4 Repeatability
Twenty samples comprising wild-type and mutated were analyzed for codons
12/13 and 61 mutations at exons 2 and 3 of KRAS gene, twice on different days
in independent assays, by the same analyst, using KRAS gene mutation at
codons 12/13 and 61 testing protocol. The samples showed identical nucleotide
sequences in two independent assays, by the same analyst, indicating 100%
repeatability (Table 4.4).
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Table 4.4: Results of samples analysed by two different analysts for
repeatability.
Accession No Test Result Mutation Detected
Test Result Mutation Detected
Analyst 1 Analyst 1
7C42621 Detected GGT ; GTT Detected GGT ; GTT
7C46549 Detected GGT ; GTT Detected GGT ; GTT
7C49823 Detected GGT ; GAT Detected GGT ; GAT
7C50956 Detected GGT ; TGT Detected GGT ; TGT
7C58194 Detected GGT ; AGT Detected GGT ; AGT
7C52484 Detected GGT : GAT Detected GGT : GAT
7C52681 Detected GGT ; TGT Detected GGT ; TGT
7C43458 Detected GGT ; GCT Detected GGT ; GCT
7A12168 Not Detected - Not Detected -
7C46456 Not Detected - Not Detected -
7C46867 Not Detected - Not Detected -
7B35559 Not Detected - Not Detected -
7B35575 Not Detected - Not Detected -
7B35583 Not Detected - Not Detected -
7C27862 Not Detected - Not Detected -
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Figure 4.9: Clustal W for KRAS for repeatability parameter.
KRAS_E2 AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 84
7c42621_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 83
7c46549_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 99
7c49823_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 61
7c50956_k2f AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91
7c58194_k2f AGTTGGAGCT RGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92
7c52484_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 83
7c52618_k2f -GTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 49
7c43458_k2f AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92
7a12168_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100
7c46456_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100
7c46867_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 64
7b35559_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 63
7B35575_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 62
7B35583_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91
7C47087_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 79
7c27862_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 60
Clustal Consensus ********* ******** ********** ********** ********** 47
Figure Legend: To validate the repeatability parameter of the KRAS assay, the same
analyst processed the samples used in reproducibility assay on two different days and
concordant results were observed. The highlighted nucleotides in the box correspond
to codon 12 (GGT) and codon 13 (GGC). R depicts the heterozygous G/A, K depicts
heterozygous G/T and S depicts heterozygous G/C.
Thus, a nested PCR protocol was used to amplify exons 2 and 3 of KRAS gene
with specific primers. Annealing temperature used for both the rounds of PCR
was 52° C. Base pair size for KRAS exon 2 is 189 bp and for exon 3 is 218 bp.
The agarose gel images after optimization of the assay for each exon fragments
is given in Fig. 4.10. A representative electropherogram obtained after
sequencing of the amplified product is shown in Figure 4.11, 4.12 and 4.13.
Hence, the data suggests the KRAS gene mutation at codons 12/13 and 61
testing protocol showed 100% specificity, 100% reproducibility and 100%
repeatability and lower detection limit of 20% tumor involvement, in the samples
studied.
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Figure 4.10: Agarose gel electrophoresis of exon2 and exon3 of KRAS gene
after optimization of the assay.
Figure Legend: KRAS mutation detection assay was optimised at 52°C annealing
temperature with a nested PCR protocol complying all the validation parameter
requirements of specificity, sensitivity, reproducibility and repeatability. The base pair
size of exon 2 amplified product was 189 bp and for exon 3 -218 bp. Lane 1-100 bp
molecular weight ladder, lane 2-amplification control for KRAS exon 2, lane 3- reaction
control, lane 4 till lane 10- KRAS exon 2 PCR amplified samples, lane 11-blank well,
lane 12-amplification control for KRAS exon 3, lane 13- reaction control, lane14till lane
18-KRAS exon 3 PCR amplified samples.
100 bp ladder Exon 2(189 bp) Exon 3(218bp)
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Figure 4.11: Electropherogram of KRAS exon 2 with no mutations detected.
Figure Legend: Highlighted region is codon 12 –GGT at position 84, 85 and 86 and
codon 13- GGC at position 87, 88 and 89 on the electropherogram.
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Figure 4.12: Electropherogram of KRAS exon 2 with codon 12 mutation
(GGT;GAT) Glycine to Aspartic acid substitution.
Figure Legend: Highlighted region is codon 12 –GGT at position 83, 84 and 85 and
codon 13- GGC at position 86, 87 and 88 on the electropherogram. R depicts the
heterozygous G/A change in the system as seen at the position number 84. This
change results in Glycine (GGT) to Aspartic acid (GAT) substitution.
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Figure 4.13: Electropherogram of KRAS exon 3 with no mutations detected.
Figure Legend: Highlighted region is codon 61 –CAA at position 96, 95 and 94 on the
electropherogram. The sample is sequenced using reverse primer.
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Mutations of clinical significance for KRAS:
According to published findings critical KRAS mutations at codon 12/13, and
one at codon 61 have been associated with resistance to anti-EGFR therapy,
as tabulated:
Exon Codon Nucleotide Change Amino acid change
2 12 GGT - GAT Gly - Asp
2 12 GGT - GTT Gly - Val
2 12 GGT - CGT Gly - Arg
2 12 GGT - GCT Gly - Ala
2 12 GGT - AGT Gly - Ser
2 12 GGT - TGT Gly - Cys
2 13 GGC - GAC Gly - Asp
2 13 GGC-TGC Gly - Cys
3 61 CAA - CAC Gln - His
3 61 CAA- AAA Gln - Lys
3 61 CAA- GAA Gln - Glu
3 61 CAA- CTA Gln - Leu
3 61 CAA- CCA Gln - Pro
3 61 CAA- CGA Gln - Arg
3 61 CAA-CAT Gln - His
Presence of the above mentioned mutations indicate resistance to anti-EGFR
therapy. Absence of the mutations indicates wild-type allele indicating
responsiveness to anti-EGFR therapy.
Establishment of allelic sensitivity on Sanger sequencer
Commercially available standards for KRAS G12D and NRAS G12V each of
50% allele burden from Horizon Diagnostics’ were mixed with wild type genomic
DNA to obtain 50%, 25%, 20%, 10% and 5% tumor DNA content. PCR was set
up followed by Sanger sequencing. Samples were processed in triplicates.
Sanger sequencing yielded an optimum sensitivity of 20%. Representative
electropherograms of KRAS G12D allelic sensitivity establishment experiment
are shown below.
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Figure 4.13 A: KRAS-G12D Original-50%
Figure Legend: An electropherogram of the original sample received from Horizon
Diagnostics’ having 50% allele burden for the mutation KRAS G12D having Glycine
(GGT) to Aspartic acid (GAC) substitution. The G/A substitution is marked with R,
highlighted in red, on the electropherograms at position 84.
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Figure 4.13 B: KRAS-G12D 25%
Figure Legend: An electropherogram obtained after titration of KRAS G12D reference
sample received from Horizon Diagnostics’ having 50% allele burden with wild type
genomic DNA to obtain 25% allele burden. The G/A substitution is marked with R,
highlighted in red, on the electropherograms at position 87.
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Figure 4.13 C: KRAS-G12D 20%
Figure Legend: An Representative electropherogram obtained after titration of KRAS
G12D reference sample received from Horizon Diagnostics’ having 50% allele burden
with wild type genomic DNA to obtain 20% allele burden. The G/A substitution is
marked with R, highlighted in red, on the electropherograms at position 86.
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Figure 4.13 D: KRAS-G12D 10%
Figure Legend: An electropherogram obtained after titration of KRAS G12D reference
sample received from Horizon Diagnostics’ having 50% allele burden with wild type
genomic DNA to obtain 10% allele burden. The G/A substitution is marked with R,
highlighted in red, on the electropherograms at position 87.
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4.3 Validation of BRAF exon 15 codon 600 mutation detection assay
4.3.1 Specificity
Twenty samples and ten plasmid DNA samples with no BRAF insert were used
to test BRAF specificity. All the 20 samples showed specific amplimers for exon
15 of BRAF gene. Ten plasmid DNA samples did not show amplification in exon
15. The nucleotide sequences showed >99% homology with the reference
sequence of BRAF (Accession No: NG_007378) indicating 100% specificity of
the assay. The variations obtained were due to mutations.
Figure 4.14: Clustal W for BRAF for specificity parameter
BRAF_EX15 AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179
7D67172_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64
7D86257_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 65
7D86258_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 65
7D97202_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71
7D95188_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 75
7D96711_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 64
7E00488_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 68
7D95189_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 60
7D91488_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 70
7E01934_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 66
7C82482_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 72
7D95360_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 80
7D67181_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 82
7D97108_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64
7D86665_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 72
7D95218_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 96
7D86359_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 64
7D95373_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 85
7D86360_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71
Clustal Consens ********** ********** ******** * ********** ********** ********** 82
Figure Legend: The PCR amplified samples were sequenced and aligned with the
reference sequence using BioEdit software. The highlighted nucleotides in the box
correspond to codon 600 of exon 15 and the K depicts the heterozygous G/T on the
sequence.
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4.3.2 Sensitivity
The minimum percentage of malignant cells in the tissue as visualized in HNE
staining, for detection of BRAF mutation in the tissue sample was found to be
20%.
Table 4.5: Results of samples analyzed for sensitivity parameter for BRAF
assay.
Sample % Tumor Sample % Tumor
7D83666 80 7D28772 60
7D92670 70 7D28773 50
7E01745 70 7D87169 50
7D87179 70 7A15581 50
7E01761 70 7E01840 50
7D92584 70 7D83668 40
7E04378 70 7D52242 40
7D52160 60 7A15506 30
7D97202 60 7D71912 20
7D97108 10
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Figure 4.15: Clustal W for BRAF for sensitivity parameter.
BRAF_EX15 AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 180
7D83666_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 78
7D92670_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 80
7E01745_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 85
7D87179_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 90
7E01761_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 95
7D92584_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 100
7E04378_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 85
7D52160_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 75
7D97202_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 90
7D28772_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 75
7D28773_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 77
7D87169_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 69
7A15581_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 75
7E01840_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 85
7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 88
7D52242_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 76
7A15506_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 80
7D71912_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 85
7D97108_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 92
Clustal Consens ********** ********** ******** * ********** ********** ********** 76
Figure Legend: The PCR amplified samples were sequenced and aligned with the
reference sequence using BioEdit software. The highlighted nucleotides in the box
correspond to codon 600 in exon 15. The mutation was observed in the sample with
20% malignant cells (Sample accession no.7D71912) indicating the sensitivity as 20%.
K depicts the heterozygous G/T.
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4.3.3 Reproducibility
The reproducibility of the assay, as observed in 15 samples was 100%
concordance in two independent assays by two analysts. The results in both the
assays were identical, indicating 100% reproducible.
Table 4.6: Results of clinical samples analyzed by two different analysts for
reproducibility.
Accession No
Test Result Mutation Detected
Test Result Mutation Detected
Analyst 1 Analyst 2
7D83668 Detected GTG ; GAG Detected GTG ; GAG
7D83666 Not Detected - Not Detected -
7D96917 Detected GTG ; GAG Detected GTG ; GAG
7E01702 Not Detected - Not Detected -
7E01703 Detected GTG ; GAG Detected GTG ; GAG
7D28772 Not Detected - Not Detected -
7D87169 Detected GTG ; GAG Detected GTG ; GAG
7D52242 Not Detected - Not Detected -
7D28773 Not Detected - Not Detected -
7D92760 Not Detected - Not Detected -
7D91871 Not Detected - Not Detected -
7D52160 Not Detected - Not Detected -
7D71911 Not Detected - Not Detected -
7D94781 Not Detected - Not Detected -
7E00505 Not Detected - Not Detected -
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Figure 4.16: Clustal W for BRAF for reproducibility parameter.
BRAF_EX15 AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179
7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 65
7D83666_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRKCAGTT 69
7D96917_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC GATCAGTTTT 75
7E01702_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 78
7E01703_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 71
7D28772_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 80
7D87169_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGRTCAGTT 86
7D52242_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRTCAGTT 83
7D28773_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71
7D92760_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 80
7D91871_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 71
7D52160_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRKCAGTT 75
7D71911_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 88
7D94781_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 75
7E00505_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 79
Clustal Consens ********** ********** ******** * ********** ********** ********** 80
Figure Legend: The samples were processed independently by two analyst to validate
the reproducibility parameter and the results were concordant. The PCR amplified
samples were sequenced and aligned with the reference sequence using BioEdit
software. The highlighted nucleotides in the box correspond to codon 600 (GTG) in
exon 15. K depicts the heterozygous G/T.
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4.3.4 Repeatability
The repeatability of the assay, as observed in 15 samples was 100%
concordance in the independent assays by the same analyst. The results in
both the assays were identical, indicating 100% repeatability.
Table 4.7: Results of clinical samples analyzed by two different analysts for
repeatability.
Accession No Test Result Mutation Detected
Test Result Mutation Detected
Analyst 1: HP Analyst 1: HP
7D83668 Detected GTG ; GAG Detected GTG ; GAG
7D83666 Not Detected - Not Detected -
7D96917 Detected GTG ; GAG Detected GTG ; GAG
7E01702 Not Detected - Not Detected -
7E01703 Detected GTG ; GAG Detected GTG ; GAG
7D28772 Not Detected - Not Detected -
7D87169 Detected GTG ; GAG Detected GTG ; GAG
7D52242 Not Detected - Not Detected -
7D28773 Not Detected - Not Detected -
7D92760 Not Detected - Not Detected -
7D91871 Not Detected - Not Detected -
7D52160 Not Detected - Not Detected -
7D71911 Not Detected - Not Detected -
7D94781 Not Detected - Not Detected -
7E00505 Not Detected - Not Detected -
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Figure 4.17: Clustal W for BRAF for repeatability parameter.
BRAF_EX15 AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179
7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 57
7D83666_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 61
7D96917_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC GATCAGTTTT 75
7E01702_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 52
7E01703_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 57
7D28772_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 60
7D87169_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGRTCAGTT 57
7D52242_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRTCAGTT 65
7D28773_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 75
7D92760_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 75
7D91871_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 57
7D52160_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 67
7D71911_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 57
7D94781_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 76
7E00505_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 74
Clustal Consens ********** ********** ******** * ********** ********** ********** 71
Figure Legend: To validate the repeatability parameter of the BRAF assay, the same
analyst processed the samples used in reproducibility assay on two different days and
concordant results were observed. The PCR amplified samples were sequenced and
aligned with the reference sequence using BioEdit software. The highlighted
nucleotides in the box correspond to codon 600 (GTG) in exon 15. K depicts the
heterozygous G/T.
Thus, a nested PCR protocol is used to amplify exon 15 of BRAF gene with
specific primers. Annealing temperature used for both the rounds of PCR is 55°
C. Base pair size for BRAF exon 15 is 228 bp. The agarose gel image after
optimization of the assay for exon 15 fragment is given in Fig. 4.18. A
representative electropherogram obtained after sequencing of the amplified
product is shown in Fig.4.19 ClustalW of the sequenced product is shown in Fig
4.20.
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Figure 4.18: Agarose gel electrophoresis of exon15 of BRAF gene after
optimization of the BRAF assay
Figure Legend: BRAF mutation detection assay was optimised using nested PCR
protocol complying all the validation parameter requirements of specificity, sensitivity,
reproducibility and repeatability. The base pair size of exon 15 amplified product was
228 bp. Lane 1-100 bp molecular weight ladder, lane 2-amplification control for BRAF
exon 15, lane 3- reaction control, lane 4 till lane 8- BRAF exon 15 PCR amplified
samples.
100 base pair (bp) ladder
228 bp
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Figure 4.19: Electropherogram of BRAF exon 15 with no mutations detected.
Figure Legend: Highlighted region is codon 600(GTG) of exon 15 at position 90, 91
and 92 on the electropherograms.
Figure 4.20: ClustalW of exon15 of BRAF gene after sequencing.
Figure Legend: The highlighted nucleotides in the box correspond to codon 600
(GTG) in exon 15.
Chapter 4. Assay Validation
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Hence, the data suggests the BRAF gene mutation at codons 12/13 and 61
testing protocol showed 100% specificity, 100% reproducibility and 100%
repeatability and lower detection limit of 20% tumor involvement, in the samples
studied.
Mutation of clinical significance
V600E, GTG - GAG (Valine to Glutamic Acid)
Presence of the above mentioned mutations indicate resistance to anti-EGFR
therapy. Absence of the mutations indicates wild-type allele indicating
responsiveness to anti-EGFR therapy (Di Nicolantonio et al., 2008).
Chapter 4. Assay Validation
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4.4 Validation of NRAS exon 2 and exon 3 (codons 12, 13 and 61)
mutation detection assay
4.4.1 Specificity
Twenty samples used for specificity assay showed specific amplimers for exons 2 and
3 of NRAS gene. The ten plasmid DNA samples did not show amplification in exons 2
or 3. The nucleotide sequences showed >99% homology with the reference sequence
of NRAS (Accession No: NG_007572.1) indicating 100% specificity of the assay. The
variations obtained were due to mutations.
Figure 4.21: Representative Agarose gel electrophoresis of NRAS gene exon
2.
Figure Legend: NRAS
specific amplification was
observed in genomic DNA
samples (lanes 4-8) however
plasmid DNA samples did not
show any amplification (lanes
9-13) showing specificity of
primers used for NRAS
amplification.
Lane No. Sample
1 100 bp Ladder
2 Amplification control
3 Reaction control
4 7G23701(60%tumor)
5 7G23702(70%tumor)
6 7G23703(30%tumor)
7 7G23704(50%tumor)
8 7G23705(70%tumor)
9 Plasmid 1
10 Plasmid 2
11 Plasmid 3
12 Plasmid 4
13 Plasmid 5
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Figure 4.22: ClustalW of NRAS gene after sequencing for specificity.
Figure Legend: The PCR amplified samples were sequenced and aligned with the
reference sequence using BioEdit software. The highlighted nucleotides in the box
correspond to codons 12 (GGT) and 13 (GGT) of exon 2.
Chapter 4. Assay Validation
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4.4.2 Sensitivity
The detection limit of the qualitative assay must account for the detection of
NRAS gene mutation in excess of normal DNA. The positive cut-off point is the
minimum percentage of malignant cells in the tissue as visualized in HNE
staining of the tissue sample, for detection of NRAS mutation in the tissue
sample. The test was performed on 20 samples with malignant cell percentages
ranging from 80% to 10%, for exon 2, codons 12/13 and exon3, codon 61. The
PCR products are observed visually on gel documentation system and gel
images recorded. The NRAS positive PCR products are sequenced on ABI
3100 Genetic Analyzer, and analyzed with Sequence Analysis Software version
5.1.1. The nucleotide sequences obtained were screened for mutations at
exon2, codons12/13 and/or exon3, codon 61. The sequences are aligned using
BioEdit tool against respective exonic sequences of reference sequence of
NRAS gene (Accession No: NG_007572.1).
The minimum percentage of malignant cells in the tissue as visualized in HNE
staining, for detection of NRAS mutation in the tissue sample was found to be
20%.
Chapter 4. Assay Validation
159
4.4.3 Reproducibility
Twenty samples were analyzed for codons 12/13 and 61 mutations at exons 2
and 3 of NRAS gene, on two different days in independent assays, by two
different analysts, using NRAS gene mutation at codons 12/13 and 61 testing
protocol. Detection of NRAS gene mutation at codons 12/13 and 61 is 100%
reproducible assay as, all the samples showed same nucleotide sequence
when performed by two different analysts.
Table 4.8: Results of clinical samples analysed by two different analysts for
reproducibility for NRAS codon 12/13
Sample Codon Test Result (Analyst 1)
Test Result (Analyst 2)
7G15477 12/13 Not Detected Not Detected
7G15474 12/13 Not Detected Not Detected
7G23703 12/13 Not Detected Not Detected
7G01373 12/13 Not Detected Not Detected
7G15453 12/13 Not Detected Not Detected
7G23730 12/13 Not Detected Not Detected
7G23729 12/13 Not Detected Not Detected
7G23731 12/13 Not Detected Not Detected
7G23732 12/13 Not Detected Not Detected
7G23733 12/13 Not Detected Not Detected
7G23701 12/13 Not Detected Not Detected
7G23702 12/13 Not Detected Not Detected
7G23703 12/13 Not Detected Not Detected
7G23704 12/13 Not Detected Not Detected
7G23705 12/13 Not Detected Not Detected
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Figure 4.23: ClustalW of NRAS gene after sequencing for reproducibility.
Figure Legend: The samples were processed independently by two analyst to validate
the reproducibility parameter and the results were concordant. The highlighted
nucleotides in the box correspond to codon 12 (GGT) and codon 13 (GGT).
Chapter 4. Assay Validation
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NRAS EXON 3
Table 4.9: Results of clinical samples analysed by two different analysts for
reproducibility for NRAS codon 61.
Sample Codon Test Result (Analyst 1)
Test Result (Analyst 2)
7G15477 61 Not Detected Not Detected
7G15474 61 Not Detected Not Detected
7G23703 61 Not Detected Not Detected
7G01373 61 Not Detected Not Detected
7G15453 61 Not Detected Not Detected
7G23730 61 Not Detected Not Detected
7G23729 61 Not Detected Not Detected
7G23731 61 Not Detected Not Detected
7G23732 61 Not Detected Not Detected
7G23733 61 Not Detected Not Detected
7G23701 61 Not Detected Not Detected
7G23702 61 Not Detected Not Detected
7G23703 61 Not Detected Not Detected
7G23704 61 Not Detected Not Detected
7G23705 61 Not Detected Not Detected
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Figure 4.24: ClustalW of nras exon 3 gene after sequencing for reproducibility.
Figure Legend: The samples were processed independently by two analyst to validate
the reproducibility parameter and the results were concordant. The highlighted
nucleotides in the box correspond to codon 61 (CAA).
Chapter 4. Assay Validation
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4.4.4 Repeatability
Twenty samples comprising wild-type and mutated were analysed for codons
12/13 and 61 mutations at exons 2 and 3 of NRAS gene, twice on different days
in independent assays, by the same analyst, using NRAS gene mutation at
codons 12/13 and 61 testing protocol. The samples showed identical nucleotide
sequences in two independent assays, by the same analyst, indicating 100%
repeatability.
Chapter 4. Assay Validation
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NRAS EXON 2
Table 4.10: Results of clinical samples analyzed by two different analysts for
repeatability.
Sample Codon Test Result (Analyst 1)
Test Result (Analyst 2)
7G15477 12/13 Not Detected Not Detected
7G15474 12/13 Not Detected Not Detected
7G23703 12/13 Not Detected Not Detected
7G01373 12/13 Not Detected Not Detected
7G15453 12/13 Not Detected Not Detected
7G23730 12/13 Not Detected Not Detected
7G23729 12/13 Not Detected Not Detected
7G23731 12/13 Not Detected Not Detected
7G23732 12/13 Not Detected Not Detected
7G23733 12/13 Not Detected Not Detected
7G23701 12/13 Not Detected Not Detected
7G23702 12/13 Not Detected Not Detected
7G23703 12/13 Not Detected Not Detected
7G23704 12/13 Not Detected Not Detected
7G23705 12/13 Not Detected Not Detected
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Figure 4.25: ClustalW of NRAS gene after sequencing for repeatability
Figure Legend: To validate the repeatability parameter of the NRAS assay, the same
analyst processed the samples used in reproducibility assay on two different days and
concordant results were observed. The PCR amplified samples were sequenced and
aligned with the reference sequence using BioEdit software. The highlighted
nucleotides in the box correspond to codon 12 (GGT) and codon 13 (GGT) in exon 2.
Chapter 4. Assay Validation
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NRAS EXON 3
Table 4.11: Results of clinical samples analysed by two different analysts for
repeatability for NRAS codon 61.
Sample Codon Test Result (Analyst 1)
Test Result (Analyst 2)
7G15477 61 Not Detected Not Detected
7G15474 61 Not Detected Not Detected
7G23703 61 Not Detected Not Detected
7G01373 61 Not Detected Not Detected
7G15453 61 Not Detected Not Detected
7G23730 61 Not Detected Not Detected
7G23729 61 Not Detected Not Detected
7G23731 61 Not Detected Not Detected
7G23732 61 Not Detected Not Detected
7G23733 61 Not Detected Not Detected
7G23701 61 Not Detected Not Detected
7G23702 61 Not Detected Not Detected
7G23703 61 Not Detected Not Detected
7G23704 61 Not Detected Not Detected
7G23705 61 Not Detected Not Detected
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Figure 4.26: ClustalW of NRAS gene after sequencing for repeatability
Figure Legend: To validate the repeatability parameter of the NRAS assay, the same
analyst processed the samples used in reproducibility assay on two different days and
concordant results were observed. The PCR amplified samples were sequenced and
aligned with the reference sequence using BioEdit software. The highlighted
nucleotides in the box correspond to codon 61 (CAA) in exon 3.
Thus, a nested PCR protocol is used to amplify exon 2 and 3 of NRAS gene
with specific primers. Annealing temperature used for both the rounds of PCR is
58 ° C. Base pair size for NRAS exon 2 is 192 bp and for exon 3 is 157 bp. The
agarose gel images after optimization of the assay for each exon fragments is
given in Figure 4.27. ClustalW of the sequenced product is shown in Figure
4.28.
Chapter 4. Assay Validation
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Hence, the data suggests the NRAS gene mutation at codons 12/13 and 61
testing protocol showed 100% specificity, 100% reproducibility and 100%
repeatability and lower detection limit of 20% tumor involvement, in the samples
studied.
Figure 4.27: Agarose gel electrophoresis of exon2 and exon 3 of NRAS gene
after optimization of the NRAS assay.
Figure Legend: NRAS mutation detection assay was optimised at 58°C annealing
temperature with a nested PCR protocol complying all the validation parameter
requirements of specificity, sensitivity, reproducibility and repeatability. The base pair
size of exon 2 amplified product was 197 bp and for exon 3 -157 bp. Lane 1-100 bp
molecular weight ladder, lane 2-amplification control for NRAS exon 2, lane 3- reaction
control, lane 4 till lane 9- NRAS exon 2 PCR amplified samples, lane 10-amplification
control for NRAS exon 3, lane 11- reaction control, lane12 till lane 17-NRAS exon 3
PCR amplified samples.
100 bp ladder
NRAS exon2 (192bp) NRAS exon3 (157 bp)
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Figure 4.28: ClustalW of exon 2 and 3 of NRAS gene after sequencing.
Figure Legend: The PCR amplified samples were sequenced and aligned with the
reference sequence using BioEdit software. The highlighted nucleotides in the box
correspond to codon 12 (GGT), codon 13 (GGT) and codon 61 (CAA).
Chapter 4. Assay Validation
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Mutations of clinical significance
According to published findings critical NRAS mutations at codon 12/13, and
one at codon 61 have been associated with resistance to anti-EGFR therapy,
as tabulated below.
Exon Codon Nucleotide Change Amino acid change
2 12 GGT - GAT Gly - Asp
2 12 GGT - GTT Gly - Val
2 12 GGT - CGT Gly - Arg
2 12 GGT - GCT Gly - Ala
2 12 GGT - AGT Gly - Ser
2 12 GGT - TGT Gly - Cys
2 13 GGT - GAT Gly - Asp
2 13 GGT - GTT Gly - Val
2 13 GGT - CGT Gly - Arg
2 13 GGT - GCT Gly - Ala
2 13 GGT - AGT Gly - Ser
2 13 GGT - TGT Gly - Cys
3 61 CAA - CAC Gln - His
3 61 CAA- AAA Gln - Lys
3 61 CAA- GAA Gln - Glu
3 61 CAA- CTA Gln - Leu
3 61 CAA- CCA Gln - Pro
3 61 CAA- CGA Gln - Arg
3 61 CAA-CAT Gln - His
Presence of the above mentioned mutations indicate resistance to anti-EGFR
therapy. Absence of the mutations indicates Wild-type allele indicating
responsiveness to anti-EGFR therapy.
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4.5 Validation of PIK3CA exon 9 and exon 20 (codon 545 and codon
1047) mutation detection assay
4.5.1 Specificity
Twenty samples used for specificity assay showed specific amplimers for exons
9 and 20 of PIK3CA gene. The ten plasmid DNA samples did not show
amplification in exon 2 or 3. The nucleotide sequences showed >99% homology
with the reference sequence of PIK3CA (Accession No: NG_012113.2)
indicating 100% specificity of the assay. The variations obtained were due to
mutations.
4.5.2 Sensitivity
The test was performed on 20 samples with malignant cell percentages ranging
from 80% to 10%, for exon 9, codon 545 and exon 20, codon 1047. The PCR
products are observed visually on gel documentation system and gel images
recorded. The PIK3CA positive PCR products are sequenced on ABI 3100
Genetic Analyzer, and analyzed with Sequence Analysis Software version
5.1.1. The nucleotide sequences obtained were screened for mutations at
exon9, codon 545 and/or exon20, codon 1047. The sequences are aligned
using BioEdit tool against respective exonic sequences of reference sequence
of PIK3CA gene (Accession No: NG_012113.2).
The minimum percentage of malignant cells in the tissue as visualized in H&E
staining, for detection of PIK3CA mutation in the tissue sample was found to be
20%.
Chapter 4. Assay Validation
172
4.5.3 Reproducibility
Twenty samples were analysed for codons 12/13 and 61 mutations at exons 9
and 20 of PIK3CA gene, on two different days in independent assays, by two
different analysts, using PIK3CA gene mutation at codons 545 and 1047 testing
protocol. Detection of PIK3CA gene mutation at codons 545 and 1047 is 100%
reproducible assay as, all the samples showed same nucleotide sequence
when performed by two different analysts.
Table 4.12: Results of clinical samples analysed by two different analysts for
reproducibility for PIK3CA.
Accession No
Test Result Mutation Detected
Test Result Mutation Detected
Analyst 1 Analyst 2
7D83668 Detected GGT ; GAT Detected GGT ; GAT
7D83666 Not Detected - Not Detected -
7D96917 Detected GGT ; GAT Detected GGT ; GAT
7E01702 Not Detected - Not Detected -
7E01703 Detected CAA - CAG Detected CAA - CAG
7D28772 Not Detected - Not Detected -
7D87169 Detected GGT ; GAT Detected GGT ; GAT
7D52242 Not Detected - Not Detected -
7D28773 Not Detected - Not Detected -
7D92760 Not Detected - Not Detected -
7D91871 Not Detected - Not Detected -
7D52160 Not Detected - Not Detected -
7D71911 Not Detected - Not Detected -
7D94781 Not Detected - Not Detected -
7E00505 Not Detected - Not Detected -
Chapter 4. Assay Validation
173
Figure 4.29: ClustalW of PIK3CA gene after sequencing for reproducibility.
PIK3_EX09 AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179
7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64
7D83666_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 65
7D96917_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 65
7D97202_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71
7D95188_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 75
7D96711_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 64
7E00488_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 68
7D95189_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 60
7D91488_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 70
7E01934_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 66
7C82482_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 72
7D95360_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 80
7D67181_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 82
7D97108_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64
7D86665_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 72
7D95218_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 96
7D86359_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 64
7D95373_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 85
7E00505_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71
Clustal Consens ********** ********** ******** * ********** ********** ********** 82
Figure Legend: The samples were processed independently by two analyst to validate
the reproducibility parameter and the results were concordant. The PCR amplified
samples were sequenced and aligned with the reference sequence using BioEdit
software. The highlighted nucleotides in the box correspond to codon 545 (GGT) in
exon 15. R depicts the heterozygous G/A.
Chapter 4. Assay Validation
174
4.5.4 Repeatability
Twenty samples comprising wild-type and mutated were analysed for codons
545 and 1047 mutations at exons 9 and 20 of PIK3CA gene, twice on different
days in independent assays, by the same analyst, using PIK3CA gene mutation
at codons 545 and 1047 testing protocol. The samples showed identical
nucleotide sequences in two independent assays, by the same analyst,
indicating 100% repeatability.
Table 4.13: Results of clinical samples analysed by two different analysts for
repeatability for PIK3CA.
Accession No
Test Result Mutation Detected
Test Result Mutation Detected
Analyst 1 Analyst 2
7D83668 Detected GGT ; GAT Detected GGT ; GAT
7D83666 Not Detected - Not Detected -
7D96917 Detected GGT ; GAT Detected GGT ; GAT
7E01702 Not Detected - Not Detected -
7E01703 Detected CAA - CAG Detected CAA - CAG
7D28772 Not Detected - Not Detected -
7D87169 Detected GGT ; GAT Detected GGT ; GAT
7D52242 Not Detected - Not Detected -
7D28773 Not Detected - Not Detected -
7D92760 Not Detected - Not Detected -
7D91871 Not Detected - Not Detected -
7D52160 Not Detected - Not Detected -
7D71911 Not Detected - Not Detected -
7D94781 Not Detected - Not Detected -
7E00505 Not Detected - Not Detected -
Chapter 4. Assay Validation
175
Figure 4.30: ClustalW of PIK3CA gene after sequencing for repeatability
PIK3_EX09 AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179
7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64
7D83666_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 65
7D96917_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 65
7D97202_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71
7D95188_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 75
7D96711_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 64
7E00488_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 68
7D95189_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 60
7D91488_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 70
7E01934_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 66
7C82482_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 72
7D95360_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 80
7D67181_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 82
7D97108_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64
7D86665_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 72
7D95218_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 96
7D86359_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 64
7D95373_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 85
7E00505_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71
Clustal Consens ********** ********** ******** * ********** ********** ********** 82
Figure Legend: To validate the repeatability parameter of the PIK3CA assay, the
same analyst processed the samples used in reproducibility assay on two different
days and concordant results were observed. The PCR amplified samples were
sequenced and aligned with the reference sequence using BioEdit software. The
highlighted nucleotides in the box correspond to codon 545 (GGT) in exon 15. R
depicts the heterozygous G/A.
Thus, a nested PCR protocol was used to amplify exon 9 and 20 of PIK3CA
gene with specific primers. Annealing temperature used for both the rounds of
PCR is 56 ° C. Base pair size for PIK3CA exon 9 was 197 bp and for exon 20
was 225 bp. The agarose gel images after optimization of the assay for each
exon fragments is given in Figure 4.31. A representative electropherogram
obtained after sequencing of the amplified product is shown in Figure 4.32 and
Figure 4.33. ClustalW of the sequenced product is shown in Figure 4.34.
Chapter 4. Assay Validation
176
Figure 4.31: Agarose gel electrophoresis of exon 9 and exon 20 of PIK3CA
gene after optimization of the assay.
Figure Legend: PIK3CA mutation detection assay was optimised at 56°C annealing
temperature with a nested PCR protocol complying all the validation parameter
requirements of specificity, sensitivity, reproducibility and repeatability. The base pair
size of exon 9 amplified product was 197 bp and for exon 20 -225 bp. Lane 1-100 bp
molecular weight ladder, lane 2-amplification control for PIK3CA exon 9, lane 3-
reaction control, lane 4 till lane 10- PIK3CA exon 9 PCR amplified samples, lane 11-
blank well, lane 12-amplification control for PIK3CA exon 20, lane 13- reaction control,
lane14till lane 18- PIK3CA exon 20 PCR amplified samples.
Hence, the data suggests the PIK3CA gene mutation at codons 545 and 1047
testing protocol showed 100% specificity, 100% reproducibility and 100%
repeatability and lower detection limit of 20% tumor involvement, in the samples
studied.
100 bp ladder Exon 9(197 bp) Exon 20(225bp)
Chapter 4. Assay Validation
177
Mutations of clinical significance
According to published findings critical PIK3CA mutations at codon 545 and one
at codon 1047 have been associated with resistance to anti-EGFR therapy, as
tabulated below. :
Exon Codon Nucleotide Change
Amino acid change
9 545 GGT - GAT Gly - Asp
20 1047 CAA - CAG His – Arg
20 1047 CAA - CAT His - Leu
Presence of the above mentioned mutations indicate resistance to anti-EGFR
therapy. Absence of the mutations indicates Wild-type allele indicating
responsiveness to anti-EGFR therapy.
4.6 Laboratory methods used for detection of mutations
World wide a variety of laboratory methods have been used for detection of
mutations in KRAS, BRAF, NRAS and PIK3CA genes like, Allele specific PCR,
ARMS PCR, Realtime PCR, Sanger Sequencing, Pyrosequencing, Multiplex
PCR etc. each having specific characteristics and sensitivity as mentioned in
Figure 4.32.
Chapter 4. Assay Validation
178
Figure 4.32: Key features of different methodologies used for mutation analysis
(Image Source-Steven Anderson, Expert Rev Mol Diagn, 2011).
To provide a cost effective solution to CRC patients Sanger sequencing was
used in this study. The sensitivity of Sanger sequencing was established as
20% with a broad spectrum of mutations being detected in comparison to kit
based assays wherein the sensitivity is higher than Sanger sequencing
however the spectrum of mutation detection is limited. Also the reagent and
labor cost is minimal in case of Sanger Sequencing. The comparison of different
methodologies used recently is shown in Figure 4.33. More recently, with the
advent of Next generation sequencing (NGS) platform the mutation analysis
would be more cost efficient and time efficient however, larger studies are
required to assess its use in routine clinical setting.
Chapter 4. Assay Validation
179
Figure 4.33: Comparison of properties of different methodologies used for
mutation analysis.
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
180
Chapter 5
Correlation of KRAS, BRAF, NRAS and PIK3CA mutation
profiling with clinicopathological features of CRC patients
5.1 Introduction
As reviewed in Chapter 2, genetic alterations in KRAS, NRAS, BRAF and PIK3CA
have an important role in colorectal cancer evolution. Mutations in KRAS exons 2
and 3, BRAF exon 15, NRAS exons 2 and 3 and PIK3CA exon 9 and 20 are the
most frequently mutated hotspots. These alterations lead to tumorigenesis and
cause resistance to the targeted therapy.
The aim of this chapter was to screen for mutations in these four oncogenes
in Indian CRC patients (n=203) and to study the correlation with different
clinicopathological features.
As per the COSMIC database, the hotspot mutations analysed for KRAS in the
current study account for 99% of all KRAS mutation found in CRC. Similarly V600E
mutations of BRAF accounts for 98%, 9 mutations of NRAS account for 99% and 3
hotspot mutations of PIK3CA, two in exon 9 and one in exon 20 accounts for 77%
of all mutations observed in CRC. This information is summarised in the table
given below (Table 5.1).
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181
Table 5.1: Frequency of mutations in Catalogue of Somatic Mutations In Cancer
(COSMIC) database.
Gene Mutation COSMIC Database Frequency
KRAS
G12D 12.5%
G12V 8.0%
G12S 2.2%
G12C 3.0%
G12R 0.5%
G12A 2.3%
G13D 7.1%
G13C 0.2%
NRAS G12D 3.0%
G13D 0.9%
BRAF V600E 10.1%
PIK3CA E545K 4.1%
E542K 2.2%
H1047R 3.2%
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
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5.2 Methodology
In summary, a total of 203 Indian CRC patient samples were analysed for KRAS,
BRAF, NRAS and PIK3CA mutation status.
Formalin-fixed, paraffin-embedded tissue blocks were cut at 4μm thicknesses and
stained with haematoxylin and eosin (HE) for histopathological examination as
mentioned in Chapter 3, section 3.2.1. Histological examination was carried out to
ensure the tumor percentage was greater than 20% in the sections analysed. All
tissue samples were analyzed for their tumor content by NABL certified internal
pathologists. The tumor was further classified in poorly differentiated, well
differentiated and moderately differentiated tumor according to the WHO
classification (Jass and SOBIN, 2012). Mucinous and signet ring tumors were
classified separately. Subsequently for DNA extraction, 10μm section of tumor
tissue was used. DNA was extracted by using Purelink DNA extraction kit (Life
Technologies, Carlsbad, California, USA) according to manufacturer’s protocol as
described in Chapter 3, section 3.2.2.
In total, 25–50 ng of DNA was added to a volume of 2 mM deoxynucleotide
triphosphates (Takara Bio.Inc, Shinga, Japan), 10 pmol of each primer, 5U of DNA
polymerase (Promega, Madison, WI, USA), 0.1 % BSA (Sigma Aldrich, Missouri,
USA) , 6 % dimethylsulfoxide, 25 mM MgCl2 and 5X polymerase chain reaction
(PCR) buffer (Promega, Madison, WI, USA). The cycling conditions and primer
details for KRAS (exon 2 and 3), BRAF (exon 15), NRAS (exon 2 and 3) and
PIK3CA (exon 9 and 20) are mentioned in Chapter 3, section 3.2.3. The PCR
products were electrophoresed on 2 % agarose gels, visualized in Gel-
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
183
Documentation system (Amersham Pharmacia Biotech, Uppsala, Sweden) and
recorded as described in Chapter 3, section 3.2.4. The PCR product obtained after
amplification was subjected to nucleotide sequencing. The sequencing reactions
used 5 pmol of forward and reverse primers, 10l volume and Big Dye Terminator
v.3.3 cycle sequencing kit and analysed after sequencing using ABI 3100
Genetic Analyzer (Life Technologies, Carlsbad, CA, USA) refer Chapter 3, section
3.2.5.
The chi-square test using GraphPad Software was performed to examine statistical
differences between mutation distribution and clinicopathological data. It was also
used to compare the mutation frequency found in other studies to that in our
present study. P<0.05 was considered significant.
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
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5.3 Clinicopathological characteristics of CRC samples
The clinicopathological characteristics of collected 203 CRC patient samples from
Indian population are detailed in the Table 5.2.
Table 5.2: The clinicopathological characteristics of Indian colorectal cancer
samples (n=203).
Characteristics Total Samples Percentage
Age ≤50 64 31.5
>50 139 68.5
Gender Males 138 68.0
Females 65 32.0
Geographical location East 8 3.9
West 119 58.6
North 38 18.7
South 38 18.7
Primary or Metastatic Primary 129 63.5
Metastatic 74 36.5
Type of tumor Adenocarcinoma 174 85.7
Mucinous 19 9.4
Signet ring 10 4.9
Tumor Differentiation MDA 111 54.7
WDA 41 20.2
PDA 51 25.1
pT T1 3 1.5
T2 12 5.9
T3 136 67.0
T4 52 25.6
Lymph Node Metastasis Yes 152 74.9
No 51 25.1
Anatomic Site Colon 147 72.4
Rectum 56 27.6
MDA-Moderately differentiated adenocarcinoma, WDA-Well differentiated adenocarcinoma
and PDA-Poorly differentiated adenocarcinoma, pT-extent of primary tumor.
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185
In these CRC samples analysed, it was observed that the frequency of CRC
tumors was higher in patients with age above 50 years (68.5%). The frequency
was more in males (68%) as compared to females (32%) (Table 5.2). Figures 5.1
and 5.2 further show the distribution of samples as per the clinicopathological
features.
According to geographical location of India, 58.6% cases were from West followed
by 18.7% cases from North and South and 3.9% cases from East. With regards to
the malignancy status 63.5% cases were of primary tumor and 36.5% were
metastatic cases. When these samples were subtyped histologically, the tumor
samples were distributed as – 85.7% adenocarcinoma, 9.4% mucinous
adenocarcinoma and 4.9% signet ring carcinoma. The tumor samples were also
classified as moderately differentiated adenocarcinomas (MDA) (n=111, 54.7%),
well differentiated adenocarcinoma (WDA) (n=41, 20.2%) and poorly differentiated
adenocarcinoma (PDA) (n=51, 25.1%).
According to AJCC/UICC TNM staging system, these tumors were classified as T1
(1.5%), T2 (5.9%), T3 (67%) and T4 (25.6%). Lymph node metastasis was also
investigated in this study and 74.9% cases had lymph node metastasis and 25.1%
cases did not show any lymph node metastasis (Table 5.2 and Figure 5.1). On the
basis of anatomic location of tumors (colon or rectum) 72.4% cases were from
colon and 27.6% cases from rectum. The distribution of cases as per the anatomic
location is shown in Figure 5.2.
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186
Figure 5.1: Clinicopathological and demographic characteristics of the CRC
patient cohort employed in this study (n=203).
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
187
Figure Legend: The CRC cohort included in this study was investigated according to age,
gender, geographical location, tumour differentiation, lymph node metastasis and tumour
stage. MDA-Moderately differentiated adenocarcinoma, WDA-Well differentiated
adenocarcinoma and PDA-Poorly differentiated adenocarcinoma
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
188
Figure 5.2: Distribution of CRC tumor samples according to the anatomic location.
Figure Legend: In this study 35% of cases were from colon followed by 28% cases from
rectum and 20% from sigmoid. Rest of the cases belonged to caecum, transverse colon,
ascending colon, descending colon, appendix and jejunum.
Figure 5.3 shows the overall mutation frequency of KRAS, BRAF, NRAS and
PIK3CA genes in the CRC samples was found to be 36%. KRAS showed the
highest mutation rate (24%), followed by BRAF (6%) and PIK3CA (4%) while
NRAS gene had the lowest frequency rate (2%).
Colon35%
Rectum28%
Sigmoid20%
Transverse colon
4%
Appendix2%
Caecum5%
Descending colon 2%
Jejunum1%
Ascending Colon 3%
Anatomic Sitewise distribution of samples
Colon
Rectum
Sigmoid
Transverse colon
Appendix
Caecum
Descending colon
Jejunum
Ascending Colon
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
189
Figure 5.3: Overall mutation frequency rate of four genes (KRAS, BRAF, NRAS
and PIK3CA) in Indian CRC patient samples (n=203).
Figure Legend: Out of the 203 CRC samples, 36% of cases had mutation in either of the
four genes studied. KRAS mutation frequency was highest with 24% of cases having
KRAS mutation followed by BRAF, PIK3CA and NRAS.
The overall mutation rate was further represented in detail as a Venn diagram
(Figure 5.4)
KRAS, 24%
BRAF, 6%
NRAS, 2%
PIK3CA, 4%
Overall mutation frequency
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
190
Figure 5.4: Venn Diagram showing overall mutation distribution in CRC patient
samples (n=203).
Figure Legend: Out of the 203 CRC samples, 49 cases (24%) harboured KRAS mutation,
12 cases (6%) harboured BRAF mutation, PIK3CA mutations were observed in 8 cases
(4%) and 4 cases (2%) had NRAS mutations. BRAF, NRAS and KRAS mutations were
mutually exclusive. 3 cases harboured both KRAS and PIK3CA mutations.
KRAS Wild Type
n=130(64%)
PIK3CA Mutant
n=8 (4%)
NRAS Mutant
n= 4 (2%)
KRAS Mutant
n=49 (24%) n=5 (2.5%)
n=3 (1.5%)
BRAF Mutant
n=12 (6%)
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
191
5.3.1 KRAS mutations
KRAS mutational status tested by Sanger Sequencing showed that 49 cases out of
a total of 203 (24.1%) harboured KRAS mutation and the spectrum of these
mutations in KRAS gene is further analysed and summarised in Table 5.3. In these
49 samples, KRAS codon 12 mutations were most frequent (20.2%) followed by
codon 13 (3.9%). The most frequent mutation was Glycine to Aspartic acid
substitution G12D (6.9%) followed by Glycine to Valine substitution G12V (6.4%).
Other mutations observed in codon 12 were Glycine to Alanine G12A (3.94%),
Glycine to Cysteine G12C (2.46%) and Glycine to Serine substitution G12S
(0.49%). At codon 13, only one substitution i.e. Glycine to Aspartic acid G13D
(3.9%) was observed. Thus, in KRAS predominantly G>A transition mutations were
observed followed by G>T transversions. All mutations observed were in
heterozygous state. None of the concomitant mutations in codon 12 and 13 were
observed. Representative sequencing electropherograms are further shown below
in Figure 5.5.
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
192
Table 5.3: Spectrum of KRAS mutations in 203 CRC cases determined by Sanger
sequencing.
Codons
Type of mutation
No. of patients (%)
Wild Type codon (amino
acid)
Mutated codon
(amino acid)
KRAS cd12 G>A 14 (6.9) GGT (Gly) (G)
GAT (Asp) (D)
1 (0.49) AGT (Ser) (S)
G>T 13 (6.4) GTT (Val) (V)
5 (2.46) TGT (Cys) ( C)
G>C 8 (3.94) GCT (Ala) (A)
KRAS cd13 G>A 8 (3.94) GGC (Gly) (G) GAC (Asp) (D)
Gly glycine, asp aspartic acid, ser serine, val valine, cys cysteine, ala alanine, arg arginine, his histidine,
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
193
Figure 5.5: Representative Sequencing electropherograms of KRAS showing
KRAS codon 12 and codon 13.
a-KRAS Wild type
b-KRAS G12V
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c-KRAS G12D
d-KRAS G12A
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
195
e-KRAS G12C
f-KRAS G12S
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
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g- KRAS G13D
Figure Legend: Figure a- KRAS Wild type with no mutation, Figure b-KRAS Glycine
(GGT) to Valine (GTT) substitution G12V, Figure c-KRAS Glycine (GGT) to Aspartic acid
(GAT) substitution G12D, Figure d-KRAS Glycine (GGT) to Alanine (GCT) substitution
G12A, Figure e-KRAS Glycine (GGT) to Cysteine (TGT) substitution G12C and Figure f-
KRAS Glycine (GGT) to Serine (AGT) substitution G12SD and Figure g-KRAS Glycine
(GGC) to Aspartic acid (GAC) substitution at codon 13 G13D. Highlighted regions on the
electropherograms correspond to codon 12 (GGT) and codon 13(GGC).R depicts the
heterozygous G/A substitution, K depicts the heterozygous G/T substitution, S depicts
heterozygous G/C substitution on the electropherograms.
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Figure 5.6 (a-f): Representative Sequencing electropherograms of NRAS,
BRAF and PIK3CA showing Wild type and mutant sequences.
a-NRAS Wild Type
b-NRAS G12V
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c-BRAF Wild Type
d-BRAF V600E
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199
e- PIK3CA exon 9 – Wild Type
f- PIK3CA exon 20 – Wild Type
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
200
Figure Legend: a- Representative electropherogram of NRAS Wild type with no mutation,
b-NRAS Glycine (GGT) to Valine (GTT) substitution G12V, c-BRAF Wild type with no
mutation, d-BRAF Valine (GTG) to Glutamic acid (GAG) substitution V600E, e-PIK3CA
exon 9 wild type with no mutation for codon 545- (GAG) and f- PIK3CA exon 20 wild type
with no mutation for codon 1047-Histidine (CAT). Highlighted regions on the
electropherograms correspond to respective codon positions of NRAS, BRAF and
PIK3CA. R depicts the heterozygous G/A substitution, K depicts the heterozygous G/T
substitution, W depicts heterozygous A/T substitution on the electropherograms.
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
201
Table 5.4: Correlation of mutation frequency in KRAS gene with clinicopathological factors of colorectal cancer patients (n=203).
Characteristics Total Samples
% Total Detected
% KRAS Detected Codon12
KRAS Detected Codon13
Total KRAS
Detected
% Not detec
ted
% p value
Age ≤50 64 31.5 20 9.9 9 2 9 4.4 55 27.1 0.02*
>50 139 68.5 53 26.1 32 6 40 19.7 99 48.8
Gender Males 138 68.0 53 26.1 30 6 36 17.7 102 50.2 0.38
Females 65 32.0 20 9.9 11 2 13 6.4 52 25.6
Geographical
location
East 8 3.9 2 1.0 0 2 2 1.0 6 3.0 0.84
West 119 58.6 47 23.2 27 3 30 14.8 89 43.8
North 38 18.7 11 5.4 6 1 7 3.4 31 15.3
South 38 18.7 13 6.4 8 2 10 4.9 28 13.8
Primary or Metastatic tumor
Primary 129 63.5 45 22.2 26 5 31 15.3 98 48.3 0.99
Metastatic 74 36.5 28 13.8 15 3 18 8.9 56 27.6
Mucinous or Adeno or Signet
Adeno 174 85.7 66 32.5 38 7 45 22.2 129 63.5 0.35
Mucinous 19 9.4 5 2.5 2 1 3 1.5 16 7.9
Signet ring 10 4.9 2 1.0 1 0 1 0.5 9 4.4
Differentiation status MDA 111 54.7 43 21.2 18 6 33 16.3 78 38.4 0.04*
WDA 41 20.2 19 9.4 14 1 10 4.9 31 15.3
PDA 51 25.1 11 5.4 9 1 6 3.0 45 22.2
Stage T1 3 1.5 1 0.5 1 0 1 0.5 2 1.0 0.98
T2 12 5.9 3 1.5 2 1 3 1.5 9 4.4
T3 136 67.0 49 24.1 27 6 33 16.3 103 50.7
T4 52 25.6 20 9.9 9 1 12 5.9 40 19.7
Lymph Node Metastasis
Yes 152 74.9 61 30.0 32 7 39 19.2 113 55.7 0.45
No 51 25.1 12 5.9 9 1 10 4.9 41 20.2
Site Colon 147 72.4 51 25.1 28 7 35 17.2 112 55.2 0.86
Rectum 56 27.6 22 10.8 13 1 14 6.9 42 20.7 * statistically significant
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
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Statistical analysis of KRAS gene mutational status with clinico-pathological data
revealed that KRAS mutations were significantly higher in patients above 50 years
of age (p<0.05) (Table 5.4). KRAS mutations were more frequent in males (17.7%)
as compared to females (6.4%), however, this association was not statistically
significant (p=0.38). Furthermore there was no significant correlation seen between
KRAS mutations and geographical location of India.
In correlation with tumor differentiation, KRAS gene mutations were significantly
higher in moderately differentiated and poorly differentiated tumors (p<0.05). Also,
KRAS mutations were seen to be more frequent in Adenocarcinoma (22.17%), T3
stage (16.26%) and colon as the anatomic site (17.24%), however, no this
association was not statistically significant.
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5.3.2 BRAF
In this study, 5.9% (12/203) cases showed presence of BRAF gene mutations. All
these 12 samples showed presence of codon 600 GTG to GAG substitution
resulting in Valine to Glutamic acid substitution V600E. None of the mutations in
BRAF gene were overlap with KRAS, NRAS or PIK3CA mutations. All these BRAF
mutations were present in heterozygous state. Representative electropherograms
are shown in Figure 5.6.
In regards with clinicopathological status BRAF mutation frequency was similar in
patients above and below 50 years of age (3% each). Frequency of BRAF
mutations was higher in Western region of India (55.2%); however, there was no
significant association between the geographical location and BRAF mutations. In
correlation with tumor differentiation status, BRAF mutations were significantly
associated with moderately differentiated and poorly differentiated
adenocarcinomas (p>0.05). No significant association was observed in BRAF and
other clinico-pathological features like type of tumor, T stage, lymph node
metastasis and site of tumor (Table 5.5).
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
204
Table 5.5: Clinicopathological characteristics’ correlation with mutation frequency in BRAF gene in colorectal cancer
patients (n=203).
Characteristics Total Samples % Total Detected % Detected % Not detected
% p value
Age ≤50 64 31.5 20 9.9 6 3.0 58 28.6 0.2
>50 139 68.5 53 26.1 6 3.0 133 65.5
Gender Males 138 68.0 53 26.1 8 3.9 130 64.0 0.99
Females 65 32.0 20 9.9 4 2.0 61 30.0
Geographical location East 8 3.9 2 1.0 0 0.0 8 3.9 0.44
West 119 58.6 47 23.2 7 3.4 112 55.2
North 38 18.7 11 5.4 4 2.0 34 16.7
South 38 18.7 13 6.4 1 0.5 37 18.2
Primary or Metastatic tumor
Primary 129 63.5 45 22.2 8 3.9 121 59.6 0.99
Metastatic 74 36.5 28 13.8 4 2.0 70 34.5
Mucinous or Adeno or Signet
Adeno 174 85.7 66 32.5 9 4.4 165 81.3 0.55
Mucinous 19 9.4 5 2.5 2 1.0 17 8.4
Signet ring 10 4.9 2 1.0 1 0.5 9 4.4
Differentiation status MDA 111 54.7 43 21.2 4 2.0 107 52.7 0.02*
WDA 41 20.2 19 9.4 6 3.0 35 17.2
PDA 51 25.1 11 5.4 2 1.0 49 24.1
Stage T1 3 1.5 1 0.5 0 0.0 3 1.5 0.74
T2 12 5.9 3 1.5 0 0.0 12 5.9
T3 136 67.0 49 24.1 8 3.9 128 63.1
T4 52 25.6 20 9.9 4 2.0 48 23.6
Lymph Node Metastasis
Yes 152 74.9 61 30.0 10 4.9 142 70.0 0.73
No 51 25.1 12 5.9 2 1.0 49 24.1
Site Colon 147 72.4 51 25.1 8 3.9 139 68.5 0.74
Rectum 56 27.6 22 10.8 4 2.0 52 25.6
* Statistically significant
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5.3.3 NRAS
Out of 203 cases, NRAS mutations were observed in only 4 patients accounting to
1.9%. Three of the NRAS mutations were observed in codon 12 and one was seen
in codon 13. All of the codon 12 mutations were G12V substitutions and in codon
13 was G13D substitution. Representative electropherograms for these changes in
the gene are shown in Figure 5.6. All these NRAS mutations were observed in
adenocarcinomas, metastatic lymph nodes and colon; however, the presence was
not statistically significant. There was no statistically significant correlation between
other clinicopathological and demographic features (Table 5.6) ; such as primary
or metastatic tumor, tumor staging, distant metastasis or anatomical site.
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
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Table 5.6: Correlation of mutation frequency in NRAS gene with clinicopathological characteristics in CRC cases (n=203).
Characteristics Total Samples
% Total Detected
% Detected Codon 12
Detected Codon 61
Total Detected
% Not detected
% p value
Age ≤50 64 31.5 20 9.9 1 1 2 1.0 62 30.5 0.59
>50 139 68.5 53 26.1 2 0 2 1.0 137 67.5
Gender Males 138 68.0 53 26.1 2 0 2 1.0 136 67.0 0.59
Females 65 32.0 20 9.9 1 1 2 1.0 63 31.0
Geographical location
East 8 3.9 2 1.0 0 0 0 0.0 8 3.9 0.75
West 119 58.6 47 23.2 3 0 3 1.5 116 57.1
North 38 18.7 11 5.4 0 0 0 0.0 38 18.7
South 38 18.7 13 6.4 0 1 1 0.5 37 18.2
Primary or Metastatic tumor
Primary 129 63.5 45 22.2 2 0 2 1.0 127 62.6 0.62
Metastatic 74 36.5 28 13.8 1 1 2 1.0 72 35.5
Mucinous or Adeno or Signet
Adeno 174 85.7 66 32.5 3 1 4 2.0 170 83.7 0.71
Mucinous 19 9.4 5 2.5 0 0 0 0.0 19 9.4
Signet ring 10 4.9 2 1.0 0 0 0 0.0 10 4.9
Differentiation status
MDA 111 54.7 43 21.2 1 0 1 0.5 110 54.2 0.43
WDA 41 20.2 19 9.4 0 1 1 0.5 40 19.7
PDA 51 25.1 11 5.4 2 0 2 1.0 49 24.1
Stage T1 3 1.5 1 0.5 0 0 0 0.0 3 1.5 0.95
T2 12 5.9 3 1.5 0 0 0 0.0 12 5.9
T3 136 67.0 49 24.1 3 0 3 1.5 133 65.5
T4 52 25.6 20 9.9 0 1 1 0.5 51 25.1
Lymph Node Metastasis
Yes 152 74.9 61 30.0 3 1 4 2.0 148 72.9 0.57
No 51 25.1 12 5.9 0 0 0 0.0 51 25.1
Site Colon 147 72.4 51 25.1 3 1 4 2.0 143 70.4 0.57
Rectum 56 27.6 22 10.8 0 0 0 0 56 27.6
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5.3.4 PIK3CA
PIK3CA mutations were seen in 3.9% cases (8/203) in the current study. Seven of
the PIK3CA mutations were observed in exon 9 and all were GAG to AAG
substitution i.e. E545K mutation. One mutation was observed in exon 20 namely
H1047R resulting in CAT to CGT substitution. It was also observed that 3
mutations of exon 9 were overlap with KRAS mutant cases. This suggests that
KRAS and PIK3CA mutations can occur in the overlapping form in colon cancer.
No such concomitant mutations were observed in PIK3CA and NRAS and BRAF
suggesting that these mutations occur in exclusive manner. Interestingly, no
significant correlation was observed in PIK3CA mutations and any of the
clinicopathological characteristics (Table 5.7).
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
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Table 5.7: Correlation of mutation frequency in PIK3CA gene with clinicopathological characteristics in colorectal cancer
cases (n=203).
Characteristics Total Samples
% Total Detected
% Detected exon 9
Detected exon 20
Total Detected
% Not detected
% p value
Age ≤50 64 31.5 20 9.9 2 0 3 1.5 61 30.0 0.71 >50 139 68.5 53 26.1 5 1 5 2.5 134 66.0
Gender Males 138 68.0 53 26.1 5 1 7 3.4 131 64.5 0.44 Females 65 32.0 20 9.9 2 0 1 0.5 64 31.5
Geographical location
East 8 3.9 2 1.0 0 0 0 0.0 8 3.9 0.36 West 119 58.6 47 23.2 6 1 7 3.4 112 55.2 North 38 18.7 11 5.4 0 0 0 0.0 38 18.7 South 38 18.7 13 6.4 1 0 1 0.5 37 18.2
Primary or Metastatic tumor
Primary 129 63.5 45 22.2 4 0 4 2.0 125 61.6 0.46 Metastatic 74 36.5 28 13.8 3 1 4 2.0 70 34.5
Mucinous or Adeno or Signet
Adeno 174 85.7 66 32.5 7 1 8 3.9 166 81.8 0.49 Mucinous 19 9.4 5 2.5 0 0 0 0.0 19 9.4
Signet ring 10 4.9 2 1.0 0 0 0 0.0 10 4.9
Differentiation status
MDA 111 54.7 43 21.2 5 0 5 2.5 106 52.2 0.69 WDA 41 20.2 19 9.4 1 1 2 1.0 39 19.2 PDA 51 25.1 11 5.4 1 0 1 0.5 50 24.6
Stage T1 3 1.5 1 0.5 0 0 0 0.0 3 1.5 0.78 T2 12 5.9 3 1.5 0 0 0 0.0 12 5.9 T3 136 67.0 49 24.1 4 1 5 2.5 131 64.5 T4 52 25.6 20 9.9 3 0 3 1.5 49 24.1
Lymph Node Metastasis
Yes 152 74.9 61 30.0 7 1 8 3.9 144 70.9 0.21 No 51 25.1 12 5.9 0 0 0 0.0 51 25.1
Site Colon 147 72.4 51 25.1 4 0 4 2.0 143 70.4 0.22 Rectum 56 27.6 22 10.8 3 1 4 2.0 52 25.6
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
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Table 5.8: Multivariate Logistic Regression Analysis for the correlation between gene mutations and clinicopathological
features in Indian CRC patients (n=203)
Clinico-pathological features
KRAS BRAF NRAS PIK3CA
OR 95%CI P OR 95%CI P OR 95%CI P OR 95%CI P
Age 2.4691 1.1154 - 5.4660
0.018* 0.4361 0.1350 - 1.4091
0.17 0.4526 0.0623 - 3.2869
0.439 0.7587 0.1757 - 3.2768
0.715
Gender 0.7083 0.3459 - 1.4505
0.339 1.0656 0.3089 - 3.6754
0.92 2.1587 0.2973, 15.6757
0.452 0.2924 0.0352 - 2.4276
0.191
Geographic location
0.9777 0.6654 - 1.4366
0.909 0.9667 0.4791 - 1.9506
0.925 0.968 0.2940 - 3.1870
0.957 0.6283 0.2346 - 1.6826
0.327
Primary or metastatic
1.0161 0.5214 - 1.9802
0.963 0.8643 0.2512 - 2.9741
0.816 1.7639 0.2433 - 12.7904
0.577 1.7857 0.4332 - 7.3616
0.425
Mucinous or Adeno or Signet
0.5534 0.2409 - 1.2712
0.123 1.5807 0.6332 - 3.9458
0.36 The model could not be fit.# The model could not be fit.#
Differentiation 0.5921 0.3852 - 0.9102
0.012* 1.2045 0.6163 - 2.3539
0.59 2.077 0.6516 - 6.6208
0.206 0.7196 0.2835 - 1.8268
0.471
Stage 0.911 0.5293 - 1.5681
0.737 1.7155 0.6019 - 4.8890
0.304 1.284 0.2274 - 7.2593
0.775 1.9522 0.5421 - 7.0306
0.297
Lymph Node Mets
0.7067 0.3236 - 1.5435
0.375 0.5796 0.1227 - 2.7376
0.468 The model could not be fit.# The model could not be fit.#
Site 1.0667 0.5223 - 2.1785
0.86 1.3365 0.3861 - 4.6269
0.652 The model could not be fit.# 2.75 0.6635 - 11.3973
0.171
#Maximum likelihood estimates of parameters may not exist due to quasi-complete separation of data points. *-Statistically significant p<0.05 OR-Odds ratio, 95%CI-95% Confidence interval
In the multivariate logistic regression analysis it was observed that mutant KRAS was directly associated with increased
age i.e above 50 years (p=0.018) and greater differentiation (p=0.012) as seen in Table 5.8.
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5.4 Discussion
In this phase of the study, the mutation frequencies in KRAS, BRAF, NRAS and
PIK3CA genes in 203 Indian colorectal cancer patients was examined and also the
correlation between clinicopathological features of CRC patients with these
mutations was further investigated. To the best of my knowledge, the present study
is the first study to determine collectively the mutation status of KRAS, BRAF,
NRAS and PIK3CA genes along with clinicopathological and geographical
incidence in a cohort of 203 Indian CRC patients.
The estimated incidence of CRC worldwide is 1.3 million (Ferlay et al., 2015).
Incidence of CRC in India has been estimated as 4.2 and 3.2 per 100,000 in males
and females, respectively. Population based time trend studies show a rising trend
in incidence of CRC in India (Ferlay et al., 2015).
Significant developments have been made in the recent past in the field of treating
CRC with the use of monoclonal antibodies targeting Epidermal growth factor
receptor (EGFR) such as cetuximab and panitumumab (Velho et al., 2009). EGFR
pathway plays a very critical role in tumorigenesis and progression of CRC. EGFR
initiates cascade of downstream signalling pathways such as RAS-RAF-MAPK and
PIK3-AKT pathways, which are responsible for, cell proliferation, differentiation and
survival (Patil et al., 2016). However, these anti-EGFR monoclonal antibodies are
effective against a small subset of CRC patients. This is due to the presence of
activating oncogenic mutations downstream of EGFR like KRAS, BRAF, NRAS
and PIK3CA, which negatively predict the response to anti-EGFR therapy. Studies
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
211
have identified KRAS, BRAF, NRAS and PIK3CA gene mutations as predictive
biomarkers in response to anti-EGFR antibody therapy (D. Lambrechts, 2009, De
Roock et al., 2010).
The overall frequency of mutations in current study from 203 Indian CRC cases,
revealed the presence of one of at least gene mutation in 36% cases and
remaining 64% of cases did not have any mutations in the four genes tested. The
prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in this Indian cohort
cases was 24%, 6%, 2% and 4%, respectively. Hence, it can be seen further that
12% of KRAS wild type CRC patients had mutations in NRAS, BRAF or PIK3CA
genes.
5.4.1 KRAS
KRAS mutation frequency varies from 14% to 67% worldwide as seen in Table 5.8.
In the present study, the KRAS mutation frequency was observed to be 24%. In
Western countries such as USA, UK, France, Italy, Lithuania, Germany, Russia
and Australia, KRAS frequency ranges from 13%-67% where as in Asian countries
such as China, Korea, India, Japan and Taiwan it varies from 20%-66% as
mentioned in Table 5.8. The KRAS mutation frequency of 24% seen in the present
study is thus similar to those reported from our group as well as from Bagadi et.al.
and Bhist et.al. (20%-24%) (Patil et al., 2013, Bagadi et al., 2012, Bisht et al.,
2014) (Table 5.9). The variations seen in KRAS mutation frequency could be
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
212
attributed to ethnicity, geographical location, sample size, and techniques used and
other etiological factors.
In the current study, mutations in KRAS codons 12, 13 and 61 were evaluated.
From the past studies, it has been observed that point mutations in KRAS codon
12 are most common mutations in CRC (Vaughn et al., 2011). KRAS G12D is the
most frequent change observed which is trailed by G12V, G12C, G12S and G12A
(Vaughn et al., 2011, Neumann et al., 2009). In agreement with this, in present
study, codon 12 mutations were observed in 20.2% cases followed by codon 3.9%
in codon 13. No mutation was observed in codon 61.
The glycine residue at codon 12 has a very critical role in normal functioning of ras
protein. Hence, the single base substitutions that occur at this position cause
GTPase formation, which further gets locked at the activating site (Arrington et al.,
2012). In the current study, G12D substitution was the most frequent followed by
G12V, G12A, G12C and G12S. Similarly, G13D is the most frequent mutation
observed in codon 13, which was also seen as the only mutation observed in this
study in codon 13. (Vaughn et al., 2011)
Correlation of clinico-pathological characteristics was further investigated with
respect to KRAS mutations. It was observed that there was a significant positive
association between KRAS mutations and age of a CRC patient (p<0.05). Mutation
rate in patients with above 50 years of age was higher as compared to the patients
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
213
younger than 50 years. Furthermore, KRAS mutations were significantly
associated with tumor differentiation status and significantly associated
(moderately and poorly differentiated adenocarcinoma as compared to well-
differentiated adenocarcinoma (p<0.05) (Table 5.4). This observation is in
agreement with the previous reports (Zhang et al., 2015a). In present study, it was
further seen that KRAS mutations were more frequent in adenocarcinomas than in
mucinous or signet ring carcinoma, which is also interestingly observed in an
earlier study, however, statistically significant association was not observed in the
current study (Li et al., 2011). Other clinico-pathological characteristics showed no
significant association with KRAS mutations (p>0.05) which is in concordance with
the recent reports of Indian population study based data (Bisht et al., 2014).
5.4.2 BRAF
BRAF is a member of the serine-threonine protein kinase family i.e. RAF family. It
plays a very crucial role downstream of EGFR signalling pathway. The codon 600
Valine to Glutamic acid substitution is the most frequent alteration observed in
many human cancers including CRC (Di Nicolantonio et al., 2008). The BRAF
mutation frequency ranges from 0.2% to 25% worldwide. In the present study the
BRAF frequency is seen to be 5.9% which is in concordance with Asian studies
(Table 5.9). As mentioned earlier geographical location, etiological factors, genetic
makeup have an important role in these variations.
BRAF codon 600 V600E was the only mutation observed in our study. Other
mutations of BRAF i.e. V600K, V600Q or V600L were not observed which have
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
214
been reported in Western population (Mao et al., 2012b). Also, there was no
presence of concomitant BRAF mutation with KRAS mutant cases. This lies in
concordance with previous studies which show that BRAF and KRAS mutations
are mutually exclusive (Di Nicolantonio et al., 2008).
However, till today there is insufficient data to justify the predictive role of BRAF for
benefit from anti-EGFR monoclonal antibody therapy in KRAS wild type cases.
Few studies in past have shown worst outcome in case of BRAF mutant cases(Di
Fiore et al., 2010). In the recent past few studies have evaluated anti-BRAF/EGFR
combination regimes to elucidate the best treatment outcome (Connolly et al.,
2013, Yaeger et al., 2015). This combinatorial approach would emerge as a
potential strategy for future cancer treatment.
In the present study, it was seen that BRAF mutations were significantly observed
in moderately differentiating and poorly differentiating adenocarcinomas than in
well-differentiated adenocarcinomas. Presence of BRAF mutations in poorly
differentiated adenocarcinomas is similar to findings of previously reported studies
(Shen et al., 2013). No significant association was observed in BRAF mutations
and other clinico-pathological features which supports the reported studies (Li et
al., 2011, Mao et al., 2012b, Bisht et al., 2014).
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
215
5.4.3 NRAS
In human cancers RAS proto-oncogenes, KRAS and NRAS, are found mostly in
mutant oncogenic forms. In case of CRC unlike KRAS, which are most frequently
mutated, NRAS mutations are rare (Irahara et al., 2010). The mutation frequency
for NRAS in CRC varies from 2%-10% (Table 5.9). In the present study, NRAS
mutations were observed in 1.9% which lies in concordance with previously
reported studies from Asian countries (Bagadi et al., 2012, Zhang et al., 2015a).
There was no coexistence of KRAS , BRAF and NRAS mutations in the study. No
significant association was observed between NRAS mutations and patient
demographics.
5.4.4 PIK3CA
Alterations in phosphoinositide-3-kinase catalytic alpha; a catalytic domain in PIK3,
is seen in many cancers. In CRC these mutations range from 1%-37% and majority
of mutations are observed in exon 9 and exon 20 (Table 5.9). There are two hot
spot regions in exon 9 –codon 542 and codon 545 and one in exon 20-codon 1047.
Recently, PIK3CA has been observed as a potential predictive marker for targeted
therapy in CRC. A low response rate has been observed in patients having
PIK3CA mutations (De Roock et al., 2011). In the present study, the frequency of
PIK3CA mutations was found to be 3.9% similar to other Asian studies (Zhang et
al., 2015a, Bisht et al., 2014, Hsieh et al., 2012). Higher percentage of mutations
were observed in exon 9 which is similar to Western population (Palomba et al.,
2012). Further current study data shows that mutations were seen in exon 9 only
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
216
E545K and in exon 20 H1047R while other mutations of exon 9 namely E542K and
H1047L in exon 20 were absent. Mutations in exon 9 and 20 may affect differently
the response to anti-EGFR therapy. Mutations in exon 20 are associated with lower
response rates as seen in the study done by Mao et.al (Mao et al., 2012b, De
Roock et al., 2010).
It was also seen in the present study results that 3 cases showed overlapping
mutations in KRAS and PIK3CA. Such coexistence of KRAS and PIK3CA
mutations has been reported earlier in few studies (Mao et al., 2012b). However,
no significant correlation was seen in clinico-pathological characteristics and
PIK3CA mutations which corresponds to previous studies of Asian countries (Mao
et al., 2012b, Bisht et al., 2014) .
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
217
Table 5.9: KRAS, NRAS, BRAF and PIK3CA mutation frequencies in reported studies worldwide. Study Sample
size
Method Target Population KRAS
%
NRAS
%
BRAF
%
PIK3C
A %
(Baltruškevičienė et al.,
2016)
55 Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exons 9 and 20
Lithuania 67.3 0 1.8 5.5
(Zhang et al., 2015a) 1110 RT PCR and Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exon 9
China 45.4 3.9 3.1 3.5
(Foltran et al., 2015) 194 Pyro sequencing KRAS exons 2 and 3
BRAF exon 15
NRAS exons 2 and 3
PIK3CA exons 9 and 20
Italy 47.4 3.6 5.2 16.5
(Kawazoe et al., 2015) 264 Luminex assay KRAS exons 2,3 and 4
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Japan 34.1 4.2 5.4 6.4
(Normanno et al., 2015) 182 Next Generation Sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exons 9 and 20
Italy 24.7 7.1 8.2 13.2
(Kriegsmann et al., 2015) 93 Mass spectrometry KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
Germany 49 2 1 Not
done
(Negru et al., 2014) 2071 Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
Greek 46.56 9 14.4 Not
done
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
218
BRAF exon 15,
(Negru et al., 2014) 2071 Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
Romania 46.3 10.3 10.2 Not
done
(Bisht et al., 2014) 204 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
India 23.5 Not
done
9.8 5.9
(Guedes et al., 2013) 212 High resolution melting analysis KRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exons 9 and 20
Portugal 44.1 Not
done
18.3 37.3
(Rosty et al., 2013) 757 HRM and Sanger sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Australia 28.4 Not
done
15.9 14
(Patil et al., 2013) 1323 DNA sequencing KRAS exons 2 and 3 India 20.5 Not
done
Not
done
Not
done
(Sinha et al., 2013) 62 DNA sequencing KRAS exons 2 and 3 India 62.1 Not
done
Not
done
Not
done
(Malhotra et al., 2013) 30 PCR Restriction digestion KRAS exons 2 and 3 India 26.7 Not
done
Not
done
Not
done
(Smith et al., 2013) 1976 Mass spectrometry and pyro
sequencing
KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
UK 42.3 Not
done
9 12.7
(Yanus et al., 2013) 195 HRM/COLDPCR/Allele Specific
PCR and Sequencing
KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Russia 35.9 4.1 4.1 12.3
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
219
(Derbel et al., 2013) 98 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exon 9 and 20
France 23.5 Not
done
2 4
(Neumann et al., 2013) 171 Pyro sequencing KRAS exon 2,
BRAF exon 15
PIK3CA exons 9 and 20
Germany 40.9 Not
done
11.1 18.7
(Yip et al., 2013) 44 KRAS-DNA sequencing, BRAF-
Real Time
KRAS exons 2 and 3
BRAF exon 15
Malaysia 25 Not
done
2.3 Not
done
(Nakanishi et al., 2013) 254 DNA sequencing KRAS exon 2,
BRAF exon 15
Japan 33.5 Not
done
6.7 Not
done
(Soeda et al., 2013) 43 DNA sequencing KRAS exons 2 and 3,
BRAF exon 15
PIK3CA exons 9 and 20
Japan 27.9 Not
done
4.7 4.7
(Mao et al., 2012a) 69 Sanger sequencing KRAS codons 12,13,14
BRAF codon 600
PIK3CA exons 9 and 20
China 43.9 Not
done
25.4 8.2
(Bagadi et al., 2012) 100 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
India 23 2 17 Not
done
(Liao et al., 2012) 964 Pyro sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
USA 35 Not
done
13.7 16.7
(Bozzao et al., 2011) 209 HRM and Sanger sequencing KRAS exon 2
BRAF exon 15
PIK3CA exon 20
Italy 43.5 Not
done
0 2.3
(Palomba et al., 2012) 478 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
Sardinia 30.3 Not
done
0.26 17.4
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
220
PIK3CA exon 9 and 20
(Hsieh et al., 2012) 182 HRM KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Taiwan 33.5 Not
done
1.1 7.1
(Ling et al., 2012) 331 DNA sequencing KRAS exon 2
BRAF exon 11 and 15
PIK3CA exons 9 and 20
China 44.1 Not
done
5.8 2.6
(Balschun et al., 2011) 21 Sanger sequencing and Pyro
sequencing
KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exon 20
Germany 31.6 3.5 12.3 0
(Wong et al., 2011) 29 Real Time PCR KRAS exon 2
BRAF exon 15
PIK3CA exon 9 and 20
USA 34.9 Not
done
10.3 10.3
(Saridaki et al., 2011) 112 KRAS and PIK3CA-DNA
sequencing, BRAF - Real Time
PCR
KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Greece 33 Not
done
7.2 9.8
(Janku et al., 2011) 504 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
USA 19 8 9 11
(Baba et al., 2011) 717 Pyro sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
USA 37.7 Not
done
15.4 16.8
(Kwon et al., 2011) 92 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Korea 20.7 Not
done
3.3 1.1
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
221
(Aoyagi et al., 2011) 134 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Japan 30.6 Not
done
0.75 13.4
(De Roock et al., 2010) 1022 Mass spectrometry KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Belgium 40 2.6 4.7 14.5
(Di Nicolantonio et al.,
2010)
43 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Italy 43.5 Not
done
0 2.3
(Lurkin et al., 2010) 294 Multiplex PCR and sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Germany 48.6 2 5.3 13.1
(Perkins et al., 2010) 42 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exon 9
France 45.2 Not
done
2.4 14.3
(Baldus et al., 2010) 100 Pyro sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Germany 41 Not
done
7 21
(Berg et al., 2010) 181 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Chinese 32 Not
done
16 3
(Irahara et al., 2010) 225 Pyro sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
USA 41 2.2 14 11
(Roth et al., 2010) 1404 Real Time PCR KRAS exon 2 Swiss 37 Not 7.9 Not
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
222
BRAF exon 15 done done
(Souglakos et al., 2009) 168 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
UK 36.9 Not
done
7.7 15.5
(Ogino et al., 2009) 450 Pyro sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
USA 35.7 Not
done
15.8 18.2
(Perrone et al., 2009) 32 DNA sequencing KRAS exon 2
BRAF exon 11,15
PIK3CA exons 9 and 20
Italy 24.1 Not
done
9.7 12.9
(D. Lambrechts, 2009) 153 Squenome MALDI TOF
MassArray
KRAS exons 2,3 and 4
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Belgium 42 5.4 9.8 12
(Velho et al., 2008) 150 PCR and sequencing KRAS exon 2
BRAF exon 15
PIK3CA exon 20
Portugal 31 Not
done
18 14
(Simi et al., 2008) 116 HRM KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Italy 43 Not
done
9.5 17.2
(Freeman et al., 2008) 62 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
USA 38.7 Not
done
5.6 3.2
(Cappuzzo et al., 2008) 80 PCR and Suveyor digestion KRAS exon 2
BRAF exon 11,15
PIK3CA exons 9 and 20
Italy 52.5 Not
done
5.06 17.7
(Barault et al., 2008) 586 DNA sequencing KRAS exon 2
BRAF exon 15
France 33.8 Not
done
13.3 16.7
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
223
PIK3CA exons 1, 2,9 and
20
(Velho et al., 2005) 150 PCR-SSCP-Sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Portugal 20.7 Not
done
12 9.3
(Fransen et al., 2004) 130 PCR-SSCP-Sequencing KRAS exons 2 and 3
BRAF exon 11,15
Sweden 40 Not
done
10 Not
done
(K Servomaa, 2000) 118 PCR-SSCP-Sequencing KRAS exons 2 and 3 Finland 14 Not
done
Not
done
Not
done
Current Study 203 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
India 24 2 6 4
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
224
These variations in the mutation patterns could be due to racial differences,
geographical differences, environmental factors, and lifestyle factors, which include
obesity or physical inactivity, or other etiological factors. Nevertheless, future
studies on large cohorts are required for in depth investigations on the genetic and
epigenetic markers involved in colorectal cancer to aid in identification of new
targets for personalised medicine.
Chapter 6. Survival Analysis
225
Chapter 6 Survival Analysis
6.1 Introduction
Several studies have been performed to identify prognostic effects of various
clinico-pathological factors in colorectal cancer (Desolneux et al., 2010, Gharbi et
al., 2010, Laohavinij et al., 2010, Moghimi-Dehkordi et al., 2008, Rath and Gandhi,
2014). In Asia, the 5-year survival rate is around 42% as compared to the USA,
where it is around 60% (Jemal et al., 2010b). Early stage detection of disease
increases the patient survival rate to around 90%. However, in developing
countries early detection is possible only in 35% of CRC patients due to lack of
screening programs. Relative survival rates of CRC patients in Asian countries
ranges from 28%-42%, with the highest being in China and lowest in India (Siegel
et al., 2015). As reviewed in Chapter 2, the survival rates in Asian countries are
lower as compared to European and Western developed countries. This has been
attributed mainly to late diagnosis. Aggressive treatment besides early detection is
another key strategy for improving overall survival.
Further as summarized in Figure 2. 15, and reviewed in Treatment strategies
section 2.6, 5-FU was the only drug used for decades for the treatment of CRC.
With the use of combination of drugs in chemotherapy, the oncologists have
achieved improvement in patient’s overall survival. Use of 5-FU plus Leucovorin
(LV) either with irinotican (FOLFIRI) or oxaliplatin (FOLFOX) has led to
improvement in survival. Further, the addition of monoclonal antibodies to standard
Chapter 6. Survival Analysis
226
chemotherapy regime has improved the survival rates. The clinicians require
accurate outcome prediction to adopt appropriate therapeutic regime.
The aim of this phase of study was to examine the correlation between
clinicopathological characteristics and survival outcome in the Indian CRC patient
cohort (n=30). This may help in disease understanding and better patient
management for the Indian patients. Hence, evaluation of progression free and
overall survival of Indian CRC patients was carried out by exploring relevant
clinicopathological factors affecting prognosis like age, sex, site, stage etc.
6.2 Patients
This study is a retrospective observational study comprising of a total of 30 Indian
CRC patients studied between January 2013 till August 2016. All clinical and follow
up data were collected from medical records. The data included age, sex, tumor
differentiation, location of tumor, lymph node involvement, depth of invasion, date
of onset, cause of recurrence if any, date of death and treatment given. The data
was collected after every 3 months. Patients were treated with either FOLFOX
(Folinic acid, 5-FU and Oxaliplatin) or CAPOX (Oxaliplatin and Capecitabine) or
CAPIRI (Capecitabine and Irinotecan). Three patients were given bevacizumab
(Avastin) and three were given cetuximab (Erbutix) in combination with
chemotherapy. The study was conducted with approval from the scientific
committee of Reliance Life Science Pvt Ltd. The study design was shared with the
hospitals for obtaining clinical information of the patients who were referred to
Chapter 6. Survival Analysis
227
Reliance Life Sciences Pvt. Ltd. for mutational analysis of the samples. Formalin
fixed specimens was stained with haematoxylin – eosin (HE) and histological
assessment was done. Direct sequencing was performed for KRAS, NRAS, BRAF
and PIK3CA mutational analysis.
6.3 Statistics
Statistical analysis was done using GraphPad Prism 7 (GraphPad Software Inc,
CA, USA) and MedCalc for Windows, version 16.0 (MedCalc Software, Ostend,
Belgium). The Kaplan- Meier method was used for plotting survival curves.
Progression free survival (PFS) is defined as the time from initial administration
of treatment until the first objective evidence of disease progression or death from
any cause.
Overall survival (OS) is defined as the time from the initiation of the treatment
until the death of the patient. Patients were censored at the time of last follow up or
if they were alive after the end of the study, which was August 2016.
To assess the differences in survival, log rank test was used. Univariate hazard
ratios were identified along with multivariate using COX proportional hazard
analysis. p value less than or equal () to 0.05 was considered as significant.
Chapter 6. Survival Analysis
228
6.4 Results:
Basic information data from the CRC patients is given in Table 6.1.
Table 6.1a: Basic Data of CRC patients (n=30)
Pt
No. Age
Date of
diagnosis
Date of
death or lost
follow up
Alive (A) or
dead (D) or
left (L)
No of
years
Round
Down
No. of
years
No. of
days
1 66 5/03/13 5/03/14 D 1 1 365
2 74 18/06/12 1/07/13 D 1 1.04 378
3 44 1/04/11 1/03/15 L 3 3.92 1430
4 70 16/07/13 1/08/14 D 1 1.04 381
5 69 24/08/13 15/07/15 L 1 1.89 690
6 58 27/11/13 3/03/15 L 1 1.26 461
7 45 6/09/13 6/07/15 D 1 1.83 668
8 70 20/11/14 6/04/16 A 1 1.38 503
9 56 2/01/14 1/08/15 D 1 1.58 576
10 62 7/06/11 12/03/16 A 4 4.77 1740
11 57 30/07/13 12/01/16 A 2 2.45 896
12 59 6/07/15 12/05/16 A 0 0.85 311
13 66 12/04/13 4/04/16 A 2 2.98 1088
14 56 30/08/14 8/05/16 A 1 1.69 617
15 52 6/08/13 10/06/15 D 1 1.84 673
16 48 31/07/13 20/05/14 D 0 0.80 293
17 63 1/03/14 11/05/15 L 1 1.19 436
18 78 27/09/12 4/12/12 D 0 0.19 68
19 60 28/02/13 1/02/15 L 1 1.93 703
20 55 7/03/13 8/11/15 D 2 2.67 976
21 45 17/12/14 29/04/16 A 1 1.37 499
Chapter 6. Survival Analysis
229
22 51 26/01/13 25/02/16 A 3 3.08 1125
23 62 8/05/13 18/06/15 D 2 2.11 771
24 48 12/02/15 10/03/16 A 1 1.07 392
25 50 6/06/13 21/07/15 D 2 2.12 775
26 52 16/02/14 24/05/16 L 2 2.27 828
27 65 26/03/13 19/07/16 A 3 3.32 1211
28 67 27/05/13 13/08/16 A 3 3.22 1174
29 71 14/07/14 6/08/16 D 2 2.07 754
30 80 21/01/14 15/07/16 D 2 2.48 906
Chapter 6. Survival Analysis
230
Table 6.1b: Clinicopathological Data of CRC patients (n=30)
Pt No Age Date of diagnosis
Date of death or lost follow up
Alive or dead or left
No.of yrs Gende
r Site
Tumor differentiation
Lymphnode mets Therapy Mutation P/M
1 66 5/03/13 5/03/14 D 1 M Colon PDA pT3N2
CAPIRI+ERBUTIX BRAF M
2 74 18/06/12 1/07/13 D 1 F Rectum PDA ypT3N2b CAPOX ND P
3 44 1/04/11 1/03/15 L 3 M Rectum PDA ypT4N2b
FOLFOX+CAPIRI ND M
4 70 16/07/13 1/08/14 D 1 M Colon MDA pT3pN0pM1 CAPIRI ND M
5 69 24/08/13 15/07/15 L 1 F Rectum MDA pT4bN1aMx FOLFOX KRASCD12 M
6 58 27/11/13 3/03/15 L 1 F Colon MDA T3N0 FOLFOX KRASCD12 P
7 45 6/09/13 6/07/15 D 1 F Colon PDA T3N2MX FOLFOX ND P
8 70 20/11/14 6/04/16 A 1 M Rectum MDA T3N1MX FOLFOX ND P
9 56 2/01/14 1/08/15 D 1 F Colon PDA T3N2
FOLFOX+CAPIRI ND M
10 62 7/06/11 12/03/16 A 4 M Rectum MDA T3NOMX CAPOX ND P
11 57 30/07/13 12/01/16 A 2 M Rectum MDA T3N1MX FOLFOX KRASCD12 P
12 59 6/07/15 12/05/16 A 0 M Rectum MDA T3N0 CAPOX ND P
13 66 12/04/13 4/04/16 A 2 M Rectum MDA T4N2M1 CAPOX+CAPIRI ND M
14 56 30/08/14 8/05/16 A 1 M Rectum MDA T3N2MX
FOLFIRI+AVASTIN KRASCD12 M
15 52 6/08/13 10/06/15 D 1 M Rectum MDA T3N0 FOLFOX ND M
16 48 31/07/13 20/05/14 D 0 F Colon PDA T4N2
FOLFOX+AVASTIN ND P
17 63 1/03/14 11/05/15 L 1 F Colon MDA T4N2
FOLFOX+ERBUTIX ND P
18 78 27/09/12 4/12/12 D 0 M Rectum MDA T3N2MX CAPOX ND P
19 60 28/02/13 1/02/15 L 1 F Colon MDA T3N1MX CAPOX ND P
20 55 7/03/13 8/11/15 D 2 F Colon PDA T3N1 FOLFOX KRASCD12 P
Chapter 6. Survival Analysis
231
21 45 17/12/14 29/04/16 A 1 F Colon MDA T3N1 FOLFOX ND M
22 51 26/01/13 25/02/16 A 3 M Colon MDA T3N0
FOLFIRI+AVASTIN ND P
23 62 8/05/13 18/06/15 D 2 F Rectum MDA T3N2MX FOLFOX KRASCD12 P
24 48 12/02/15 10/03/16 A 1 M Rectum PDA T3N0 CAPOX ND P
25 50 6/06/13 21/07/15 D 2 F Colon PDA T3N1 FOLFOX ND M
26 52 16/02/14 24/05/16 L 2 F Colon MDA T3N0 CAPOX ND P
27 65 26/03/13 19/07/16 A 3 M Rectum MDA T3N1 FOLFOX ND P
28 67 27/05/13 13/08/16 A 3 F Rectum MDA T4N2
FOLFOX+ERBUTIX ND P
29 71 14/07/14 6/08/16 D 2 M Colon PDA T3N2MX FOLFOX ND M
30 80 21/01/14 15/07/16 D 2 M Colon PDA T3N1MX FOLFOX KRAS CD12 M
D-Dead, A-Alive, L-Lost follow up, P-Primary tumor, M-Metastatic tumor
Chapter 6. Survival Analysis
232
6.4.1 Estimation of survival
The first requirement for estimation of survival time is a well-defined starting point.
In this study, the date of diagnosis was taken as the starting time. The outcome
was defined as the death of the patient, so the death of patient was recorded.
Survival time is the time between the starting point (occurrence of disease) and the
outcome (death of the patient). The data of patients’ survival time in increasing
duration is shown in Figure 6.1. The X axis shows the time in years and Y axis
shows the patient. The bars in red denote the death of that particular patient.
Chapter 6. Survival Analysis
233
Figure 6.1: The data of 30 patients aligned in order of their survival time. Red bars
indicate the dead patients and yellow ones indicate the alive patients.
0 1 2 3 4 5 6
10
3
27
28
22
13
20
30
11
26
25
23
29
19
5
15
7
14
9
8
21
6
17
24
4
2
1
12
16
18
Years
Pat
ien
t n
um
be
r
Chapter 6. Survival Analysis
234
The data, in summary, is represented in the form of a tree diagram (Figure 6.2).
Figure 6.2: Tree diagram for CRC patients (n=30).
Figure Legend: The upper arm represents the dead patients and the lower arm
represents the alive patients. Based on this data, the probability of dying can be calculated
as 13/25=0.52=52%.
30
13-Dead
17-Alive
Chapter 6. Survival Analysis
235
6.4.2 Censored observations
A closed group consists of patients having all complete observations. However, in
the clinical scenario of a retrospective study this is not possible as there would be
some patients who would join the study at different time points and other patients
that would have been lost to follow up due to migration. Such patients who have
early termination of follow up are called as censored patients.
In the present study, there were 6 patients who left during the study period. These
patients were termed as censored. Hence, the results can be expressed as a tree
diagram having three arms, one for patients who are alive, one for dead patients
and another one for censored patients as shown in Figure 6.3. The probability of
dying can then be calculated as D/N-(0.5XL) i.e.13/25-(0.5X6) =0.59=59%.
Figure 6.3: Tree diagram showing 30 CRC cases of which 13 are dead, 6 have
lost follow up and 11 are alive.
30
13-Dead
11-Alive
06-Lost Follow up
Chapter 6. Survival Analysis
236
6.4.3 Actuarial Life Table
The data of these 30 patients is also presented in the form of Actuarial life table in
which cumulative survival is calculated along with probability of dying and
probability of survival. Table 6.2 summarises actuarial life table for 30 patients and
the survival curve is shown in Figure 6.4.
Table 6.2: Actuarial Life Table for patients (n=30)
No. of
years
No. of patients
(N)
No. of Dead (D)
No. of Alive or Left A/L
(N-0.5L) Prob. of Dying
Prob. of Surviving
Cumulative Survival
0 30 2 1 29.5 0.07 0.932 0.93
1 28 6 8 24 0.25 0.75 0.47
2 14 5 3 12.5 0.40 0.600 0.2
3 6 0 2 5 0.00 1.000 0.13
4 4 0 1 3.5 0.00 1.000 0
Chapter 6. Survival Analysis
237
Figure 6.4: Actuarial survival curve (n=30)
Figure Legend:
Probability of dying = D/N-(0.5L)
Probability of survival = 1- Probability of death
Cumulative survival = Multiplication of probability survivals of each year
Chapter 6. Survival Analysis
238
6.4.4 Kaplan – Meier Survival Curve
The method of estimation of survival probabilities when the exact time of death or
censored data is known is called Kaplan-Meier survival curve analysis. It is a
stepped line plot in which each step denotes the death of a patient.
Figure 6.5: Kaplan-Meier survival curve for CRC patients (n=30).
Figure Legend: The 3-year overall survival is 40% with median survival of 2.48 years i.e.
29 months as shown in Figure 6.5 .The dots on the plot represent the censored patients
and the drops are the patients, which died in the study.
0 2 4 6
0
5 0
1 0 0
T im e
Pe
rc
en
t s
urv
iva
l
Chapter 6. Survival Analysis
239
6.4.4.1 Estimation of Progression free survival (PFS) and Overall Survival
(OS)
Patients were divided according to the different clinicopathological characteristics.
PFS and OS were calculated using Kaplan-Meier curves with respect to different
clinicopathological features. Figure 6.6 summarises the PFS and OS curves of
Indian CRC patients.
Figure 6.6: Kaplan Meier plots of PFS and OS for CRC patients (n=30).
A
B
10
20
30
40
50
60
70
80
90
100
Age wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Sur
viva
l pro
babi
lity
(%)
Number at risk
Group: Below 60 years
15 15 10 7 2 0 0
Group: Above 60 years
15 14 10 5 2 1 0
Age
Below 60 years
Above 60 years
30
40
50
60
70
80
90
100
Age wise Overall Survival
0 1 2 3 4 5
Time in years
Sur
viva
l pro
babi
lity
(%)
Number at risk
Group: Below 60 years
15 13 6 2 0 0
Group: Above 60 years
15 11 7 3 1 0
Age
Below 60 years
Above 60 years
Chapter 6. Survival Analysis
240
C
D
0
20
40
60
80
100
Differentiation wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Sur
viva
l pro
babi
lity
(%)
Number at risk
Group: MDA
11 11 6 4 1 0 0
Group: PDA
19 18 14 8 3 1 0
Differentiation
MDA
PDA
10
20
30
40
50
60
70
80
90
100
Differentiation-Overall Survival
0 1 2 3 4 5
Time in years
Sur
viva
l pro
babi
lity
(%)
Number at risk
Group: PDA
11 8 5 1 0 0
Group: MDA
19 16 8 4 1 0
Differentiation
PDA
MDA
Chapter 6. Survival Analysis
241
E
F
0
20
40
60
80
100
Lymphnode metastasis wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: No
8 8 5 4 2 1 0
Group: Yes
22 21 15 8 2 0 0
Lymphnode_mets
No
Yes
30
40
50
60
70
80
90
100
Lymphnode metastasis-Overall Survival
0 1 2 3 4 5
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: No
8 6 3 2 1 0
Group: Yes
22 18 10 3 0 0
Lymphnode_mets
No
Yes
Chapter 6. Survival Analysis
242
G
H
10
20
30
40
50
60
70
80
90
100
Primary or Metastatic tumor wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Primary
18 17 12 8 3 1 0
Group: Metastatic
12 12 8 4 1 0 0
Primary_or_metastatic
Primary
Metastatic
20
30
40
50
60
70
80
90
100
Primary or metastatic tumor-Overall Survival
0 1 2 3 4 5
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Primary
18 14 8 4 1 0
Group: Metastatic
12 10 5 1 0 0
Primary_or_metastatic
Primary
Metastatic
Chapter 6. Survival Analysis
243
I
J
Figure Legend:
A and B: Age wise PFS and OS ; C and D : Tumor differentiation wise PFS and OS; E and
F :Lymph node metastasis wise PFS and OS ; G and H : Primary or metastatic tumor wise
PFS and OS ; I and J: Tumor site wise PFS and OS. MDA-Moderately differentiated
adenocarcinoma ; PDA-Poorly differentiated adenocarcinoma
10
20
30
40
50
60
70
80
90
100
Tumor site wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Rectum
15 14 11 7 3 1 0
Group: Colon
15 15 9 5 1 0 0
Tumor_site
Rectum
Colon
10
20
30
40
50
60
70
80
90
100
Tumor site wise Overall survival
0 1 2 3 4 5
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Rectum
15 12 7 4 1 0
Group: Colon
15 12 6 1 0 0
Tumor_site
Rectum
Colon
Chapter 6. Survival Analysis
244
According to age factor, two groups were made, less than 60 years old and other
more than 60 years. PFS and OS were compared in these two groups. The median
PFS of patients below 60 years was 0.7 years (n=15, HR=0.834,95%CI=0.3543 to
1.9636) and above 60 years was 0.6 years (n=15, HR=0.94,95%CI=0.3178 to
2.8032) . The median OS of patients below 60 years was 2.7 years (n=15,
HR=0.94,95%CI=0.3178 to 2.8032) and above 60 years was 2.5 years (n=15,
HR=1.06,95%CI=0.3567 to 3.1462). There was no significant association between
the two plots both by univariate and multivariate analysis (p>0.05) (Figure 6.6 A
and B, Table 6.3).
As per histological differentiation - moderately differentiated adenocarcinoma
(MDA) and poorly differentiated adenocarcinoma (PDA), median PFS of PDA
(n=11, 0.5 years, 95%CI= 0.40-0.70) was shorter than that of MDA (n=19, 0.8
years, 95%CI= 0.50-0.90), as verified in both univariate (HR=0.503, 95%CI=
0.2001 to 1.2643, p=0.08 ) and multi variate (HR= 0.331, 95%CI=0.1047 to 1.0468,
p=0.058) analysis.
The median OS of PDA was significantly shorter than that of MDA as verified both
by univariate (HR=0.25, 95%CI= 0.07682 to 0.7499, p=0.009 ) and multivariate
analysis (HR=0.1605, 95%CI= 0.0237 to 1.0895, p=0.05 ) (Figure 6.6 C and D,
Table 6.3)
In the case of location of tumor i.e colon or rectum, primary or metastatic tumor
and lymph node metastasis no significant association was observed for PFS and
OS both by univariate and multivariate analysis (p>0.05). The PFS and OS was
Chapter 6. Survival Analysis
245
shorter in case of tumors located in rectum, metastatic tumors and tumors with
lymph node metastasis (Figure 6.6 E-J, Table 6.3) .
Table 6.3: Univariate and Multivariate analysis of PFS and OS
Univariate
PFS OS
Clinicopathological features
Reference category
OR 95% CI p OR 95% CI p
Age >60/<60 yrs 0.834 0.3543 to 1.9636
0.65 0.94 0.3178 to 2.8032
0.72
Site Colon/Rectum 0.96 0.4061 to 2.2771
0.9 2.63 0.8756 to 7.911
0.08
Differentiation
PDA/MDA 0.503 0.2001 to 1.2643
0.08 0.25 0.07682 to 0.7499
0.009*
Lymph node involvement
Yes/No 0.55 0.2164 to 1.4007
0.23 0.53 0.1517 to 1.828
0.39
Metastatic or Primary
Primary/Metastatic 0.79 0.3264 to 1.9152
0.56 0.56 0.1875 to 1.727
0.3
Mutation status
Wild Type/ Any mutant
0.68 0.2566 to 1.8262
0.36 0.86 0.2548 to 2.918
0.81
Therapy Chemo/Chemo+biological agent
0.91 0.2984 to 2.8045
0.86 0.83 0.2021 to 3.4482
0.8
Multivariate
PFS OS
Clinicopathological features
Reference category
OR 95% CI p OR 95% CI p
Age >60/<60 yrs 1.4722
0.5944 to 3.6461
0.4033
0.456 0.1233 to 1.6866
0.2393
Site Colon/Rectum 0.6488
0.2294 to 1.8349
0.4147
1.7074
0.4122 to 7.0717
0.4606
Differentiation
PDA/MDA 0.3311
0.1047 to 1.0468
0.0598
0.1605
0.0237 to 1.0895
0.0542*
Lymph node involvement
Yes/No 0.76 0.1136 to 5.1044
0.779 0.7525
0.1133 to 4.9961
0.7684
Metastatic or Primary
Primary/Metastatic 0.9074
0.3490 to 2.3591
0.842 0.8336
0.2093 to 3.3207
0.7963
Mutation status
Wild Type/ Any mutant
1.5031
0.5732 to 3.9417
0.4073
0.6749
0.1709 to 2.6663
0.5749
Therapy Chemo/Chemo+biological agent
0.5973
0.1827 to 1.9529
0.3939
0.77 0.1450 to 4.0739
0.76
*Statistically significant
Chapter 6. Survival Analysis
246
PFS-Progression free survival, OS-Overall survival, OR-Odds ratio, 95%CI-95%Confidence interval, PDA-Poorly differentiated adenocarcinoma, MDA-Moderately differentiated adenocarcinoma Further in this study, twenty two patients had tumors with no mutations (all wild-
type tumors) and 8 had tumors with mutation in either KRAS codons 12 or 13 or
BRAF (any of the mutations). Among the 8 patients with any of the mutations, 7
had KRAS codon 12 or 13 mutations one had BRAF mutation (Table 6.4).
Patients with tumor mutations were more likely to have worse PS in comparison
with all wild-type tumors. No other significant difference was seen between the two
groups (Table 6.4).
Chapter 6. Survival Analysis
247
Table 6.4: Patient, disease and treatment characteristics (n=30).
Clinicopathological features
Wild type (n=22) Any mutant (n=8) p value
Age
>60 11 (50.0) 4 (50.0) 0.99
<60 11 (50.0) 4 (50.0)
Gender
Male 12 (54.5) 4 (50.0) 0.99
Female 10 (45.5) 4 (50.0)
Dead/Alive
Dead 9 (40.9) 4 (50.0) 0.69
Alive 13 (59.1) 4 (50.0)
Site
Colon 11 (50.0) 4 (50.0) 0.99
Rectum 11 (50.0) 4 (50.0)
Tumor Diff
MDA 14 (63.6) 5 (62.5) 0.99
PDA 8 (36.4) 3 (37.5)
Lymph node metastasis
YES 15 (68.1) 7 (87.5) 0.39
NO 7 (31.9) 1 (12.5)
Therapy
Chemo 18 (81.8) 6 (75.0) 0.64
Chemo+ Biological agent
4 (18.2) 2 (25.0)
Primary/metastasis
Primary 14 (63.6) 4 (50.0) 0.67
Metastasis 8 (36.4) 4 (50.0)
Mutation
KRAS 0 7
NRAS 0 0
BRAF 0 1
PIK3CA 0 0
Chapter 6. Survival Analysis
248
Figure 6.7: Kaplan Meier plots for comparison between patients with tumor
mutation or wild type tumors.
A
B
Figure Legend:
A- PFS plot and B-OS plot
Any mutation- KRAS +BRAF mutation
0
20
40
60
80
100
Mutation wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Wild Type
22 21 13 9 4 1 0
Group: Any Mutant
8 8 7 3 0 0 0
Mutation
Wild Type
Any Mutant
0
20
40
60
80
100
Mutationwise Overall Survival
0 1 2 3 4 5
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Wild type
22 17 9 5 1 0
Group: Any mutation
8 7 4 0 0 0
Mutation
Wild type
Any mutation
Chapter 6. Survival Analysis
249
Figure 6.8: Kaplan Meier plots for comparison between different therapy options.
A
B
Figure Legend: A-PFS plot and B-OS plot.
10
20
30
40
50
60
70
80
90
100
Therapy wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Chemo + Biological agent
6 6 3 2 1 0 0
Group: Chemotherapy
24 23 17 10 3 1 0
Therapy
Chemo + Biological agent
Chemotherapy
30
40
50
60
70
80
90
100
Therapy wise Overall survival
0 1 2 3 4 5
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Chemo+ Biological agent
6 4 2 2 0 0
Group: Chemotherapy
24 20 11 3 1 0
therapy
Chemo+ Biological agent
Chemotherapy
Chapter 6. Survival Analysis
250
Among the 8 patients with mutations, one was treated with second-line anti-EGFR-
containing regimen, one was treated with second line anti VEGF treatment i. e
bevacizumab and six were treated with chemotherapy.
The median PFS of patients with KRAS or BRAF mutations (n = 8; 6 months; 95%
CI, 5-7 months) was shorter than that of patients with all wild-type tumors (n = 22; 7
months; 95% CI, 4-9 months), as verified in both univariate (HR 1.46; 95% CI,
0.5476 to 3.8978; P = 0.36) and multivariate analyses (HR 1.50; 95% CI, 0.5732 to
3.9417; P = 0.407) (Figure 6.7 A, Table 6.3).
The median OS of patients with KRAS or BRAF mutations (n = 8; 2.1 years; 95%
CI, 1.800 to 2.1 years) was shorter than that of patients with all wild-type tumors (n
= 22; 2.5 years; 95% CI, 2.100 to 2.700 years), as verified in both univariate (HR
1.1; 95% CI, 0.3316 to 3.6657; P = 0.86) and multivariate analyses (HR 0.75; 95%
CI, 0.2033 to 2.7878; P = 0.6) (Figure 6.7 B, Table 6.3).
Chapter 6. Survival Analysis
251
6.5 Discussion
In the past few years, there have been several studies done to determine the
association of a range of variables with the survival of CRC patients (Laohavinij et
al., 2010, Moghimi-Dehkordi et al., 2008, Ratto et al., 1998, Desolneux et al., 2010,
Ghazali et al., 2010). The age, gender, site of CRC, tumor differentiation, invasion
of tumor, lymph node metastases and other variables have been studied. However,
determination of prognostic factor is still a challenge. The overall survival rate
currently in Asian countries is approximately 60% as the majority of
adenocarcinomas are still diagnosed at the later stages. If the disease is
diagnosed at an early stage then the survival rate is observed as 90% (Moghimi-
Dehkordi and Safaee, 2012). In this study, the effect of different clinico-pathological
features on the Indian patients survival rate was evaluated.
It was observed that overall survival was 37% with median survival of 29 months
which is similar to the survival rate observed in a study published by Yeole et al. in
2001 on the Indian population (Yeole et al., 2001). The incidence rates of colon
and rectal tumors are low in comparison to the population in Western developed
countries. In India, rectal tumors are more common than colon (Mohandas and
Desai, 1998). However, a significant increase has been noted in colon cancer
cases over the past two decades. The present study data shows that the incidence
of colon cancer in India was more in comparison to rectal cancer (colon -35% and
rectum – 28%).
Few other researchers from China have also reported the decline in rectal cancer
cases (Xu et al., 2006, Wan et al., 2001).The reason for this change is unclear, it
Chapter 6. Survival Analysis
252
could be due to improvisation in early diagnosis, changing dietary habits or
etiological changes (McMichael and Potter, 1985, Mohandas and Desai, 1998). For
example, alcohol consumption has been associated with rectal cancers while
family history is strongly associated with colon cancers (Bongaerts et al., 2008,
Andrieu et al., 2004). Literature indicates that patients having colon as the site of
tumor have a better prognosis than those having rectum (Moghimi-Dehkordi et al.,
2008, Ratto et al., 1998, Wang et al., 2008). Further, it has been observed that
poorer survival has been associated with proximal colon, rather than distal colon
and as such there is not much difference in the survival rate of distal colon and
rectal tumors (Hemminki et al., 2010, Meguid et al., 2008, Wray et al., 2009).
These differences in survival according to tumor site could instead be due to
differences in tumor aggressiveness or due to screening methodologies used. Few
studies have shown that colonoscopy and sigmoidoscopy are associated with
higher incidence and mortality with proximal colon cancers in comparison to distal
colon cancers (Newcomb et al., 2003, Atkin et al., 2010, Brenner et al., 2009,
Baxter et al., 2009). Proximal colon cancers exhibit rapid tumor progression, which
could be due to their diagnosis as interval cancers. Also these tumors frequently
show the presence of CIMP, MSS/MSI-L along with BRAF mutated status. These
all factors are associated with poorer survival in proximal tumors (Baxter et al.,
2011, Shaukat et al., 2010, Phipps et al., 2013). Similarly it has been reported MSI-
H tumors are significantly present in proximal colons and MSI-H tumors have
favourable survival outcome (Guastadisegni et al., 2010).
Chapter 6. Survival Analysis
253
In the current study the median overall survival of patients with colon tumors was
observed to be 21 months. There was however no significant association observed
for PFS and OS with respect to site of tumor origin.
The likelihood of being diagnosed with CRC increases after the age of 40 years
and the occurrence of CRC cases is higher in patients after the age of 50 years
(Fund and Research, 2007). Incidence rate is seen to higher in patients with age
above 60 years, however, the incidence of CRC is seen to be increasing in the
younger population of 40 years and below (O'Connell et al., 2004, You et al., 2012,
Siegel et al., 2009). Some studies have shown that early onset of the disease is
associated with poorer survival outcome (Fang et al., 2010, Gharbi et al., 2010,
Moghimi-Dehkordi et al., 2008, Zhang et al., 2010).
The increasing incidence of CRC in younger populations could be due to lack of
screening at a younger age, behavioural factors such as alcohol consumption,
smoking and lifestyle factor like obesity. The pesticide consumption in India has
increased several hundred folds from 154 metric tons in 1954 to 41,822 metric tons
in 2009-2010. In low income countries like India only 10% of the contaminated
water is treated rest all is discharged into water bodies. This highly contaminated
water can cause adverse health effects including cancer. In an epidemiological
study from Egypt, researchers have shown prevalence of young onset CRC in
people with exposure to pesticides (Lo et al., 2010).
Some studies that have shown the better survival in younger patients which could
be due to aggressive therapy regimes used for younger patients, low risk of
Chapter 6. Survival Analysis
254
postoperative complications and higher treatment completion rates of surgery and
adjuvant therapy. Also the younger patient group may include hereditary CRC like
Lynch syndrome which is seen to have better survival rates (O'Connell et al.,
2004).
To study the effect of age on survival the patients were divided in two categories
above 60 years and below 60 years. No significant association was observed
between the survivals of these two groups. However, the median overall survival
for patients above the age of 60 was 28 months and below 60 years was 30
months which concurs with few studies reporting poor survival rate in older patients
when compared with that of younger ones (Rosenberg et al., 2008, Laohavinij et
al., 2010). It has been observed that in the young patients CRC is more aggressive
and has poor pathological features (Chou et al., 2011).
In this study, significant association was observed between patients survival rates
and poorly differentiated adenocarcinoma (PDA). PDA is associated with poor
survival in comparison to moderately differentiated adenocarcinoma (p<0.05)
(Figure 6.8) as seen in other studies (Laohavinij et al., 2010, Moghimi-Dehkordi et
al., 2008). Histologically, PDA account for around 4.8% to 23% of all colorectal
cancer cases (Benedix et al., 2010, YOSHIDA et al., 2011, Xiao et al., 2013). PDA
is directly associated with a poor prognosis as reported in previous studies(Ishihara
et al., 2012, Bjerkeset et al., 1987). PDA cases mostly occur in advanced stages or
metastatic stages which could be the reason for poor survival.
Chapter 6. Survival Analysis
255
Lymph node metastasis is a critical predictor of survival and recurrence in CRC.
Several studies have found a significant association between numbers of lymph
node resected and improved survival (Chang et al., 2007, Gunderson et al., 2010,
Chen and Bilchik, 2006). As increased survival is noted in patients with lymph node
involvement, better therapeutic advantage is suggested in higher lymph node
retrieval. In the current study, the overall survival of patients with lymph node
involvement was 25 months, which was seen to be better than patients with no
lymph node involvement. However, the survival curves are non-significant. ASCO,
NCCN and the American College of Surgeons Commission on Cancer (CoC)
indicate that a minimum of 12 lymph node count is associated with improved
outcome in the patients (Nelson et al., 2001).The actual mechanism between
lymph node count and survival is unclear. However, there are various factors that
affect the number of lymph node examined like patient age, extent of surgical
resection and tumor location. It has been observed that right-sided tumors show
the presence of higher number of lymph nodes (Chang et al., 2007). Numbers of
lymph nodes involved reflect the patients improved immune response. More lymph
node involvement indicates a greater immune response and hence improved
survival (Pagès et al., 2005).
CRC can be prevented if detected at an early phase and if adenomatous polyps
are removed early. If the tumor is diagnosed when it is in localised stage then the
survival outcome of the patient is better than in those cases where in the diagnosis
occurs at the metastatic stage (Fatemi et al., 2010). In the current study, it was
Chapter 6. Survival Analysis
256
observed that if the tumor is localised i.e. present at the primary stage then the
median survival is 31 months in comparison to the metastatic tumors in which the
median survival is 26 months. However, the survival curve was not statistically
significant. In one study published on a Dutch population, it was observed that
metastatic tumors have significantly improved survival. This change observed
could be due to increased used and improvisation in chemotherapy, use of
adjuvant and neoadjuvant therapies and better selection of patients eligibility for
surgery (Meulenbeld et al., 2008).
Together with the clinico-pathological features, mutation in the RAS –RAF pathway
is observed in around 30-50% of colorectal cancer tumors implying that only the
remaining 50% of patients would benefit from anti-EGFR therapy. Cetuximab plus
FOLFOX helps in improving the survival rate and disease free progression
(Bokemeyer et al., 2008). Cetuximab and FOLFIRI both improve the survival and
response rate both in KRAS wild type tumors (Assenat et al., 2011). In current
study, the overall survival rate in mutated tumors with mutation in KRAS or BRAF
genes studied was 25 months and in wild type was 29 months although statistical
significance was not observed.
These experimental evidences of survival rates of CRC patients in relation to
different clinico-pathological features in this retrospective analysis of the Indian
population suggests that there are many factors which could influence the
prognosis of colorectal cancer patients. However, the present study has limitations.
Due to a smaller sample size, current study may not exactly reflect the prevalence
Chapter 6. Survival Analysis
257
of colorectal cancer in the entire Indian population, however it reflects the nature of
disease and the effect of different clinico-pathological features on survival for
Indian CRC cases. This study indicates the differences in presentation of CRC in
Indian population and also the effect of various factors on survival that may differ
from the population in Western developed nations. One of the major limitation of
the present study was that owing to the small number of patients, no data was
available specifying BRAF, NRAS and PIK3CA mutation due to which impact of
these mutations on the survival could not be studied. As seen in Chapter 5, the
frequency of BRAF, NRAS and PIK3CA mutations in Indian population is 5.9%, 2%
and 4% respectively. Further the survival analysis involved only seven patients for
KRAS and single patient for BRAF out of a small sample size selected (n=30), from
the total patients studied for mutation analysis (n=203). According to the published
literature patients with BRAF mutations are often refractory to systemic
chemotherapy and have poor prognosis hence screening for BRAF mutations has
become important. However, the study findings are extrapolative and hypothesis
generating which can be further analysed in larger cohort.
This study supports the hypothesis that clinical and pathological characteristics are
better determinants of prognosis in CRC patients. Amongst all the
clinicopathological features studied through univariate and multivariate analyses,
the feature that has the significant impact on the survival outcome, is the tumor
differentiation status. Thus, early detection and timely evaluation of tumor becomes
extremely important in CRC, which can further lead to improved survival.
Chapter 7. Summary and Future Work
258
Chapter 7 Summary and Future Work
Colorectal cancer is one of the leading causes of cancer worldwide. It is the most
common of all gastrointestinal malignancies. The chapter 2 highlights that in
existing literature wide geographical, racial and ethnic difference in incidence are
observed for this cancer. The majority of studies showing genetic and epigenetic
changes correlation with CRC have been carried out in the population of Western
developed nations. Very little data is available on the Indian population. This study
was hence undertaken with an aim to evaluate the genetic alterations in KRAS,
BRAF, NRAS and PIK3CA genes and the correlation of these molecular alterations
with clinicopathological characteristics in 203 CRC patients. Further, the correlation
between clinicopathological features and survival was studied in in a subset of
Indian population sample size (n=30). The percentage of molecular alterations
observed in this study corresponds with those reported in literature for CRC cases
described in COSMIC database.
All the molecular analysis performed in this study was according to current
recommendations of CAP and NABL guidelines. Hence, the molecular data
obtained from this study can be associated to the clinical data and errors possibly
related to technical issues are unlikely. Also, the samples analyzed in the current
study constituted a random fraction of Indian CRC patients and is hence a
balanced representation of entire Indian population.
Chapter 7. Summary and Future Work
259
7.1 Mutation Studies in 203 CRC patients
Analysis of mutation distribution in KRAS, BRAF, NRAS and PIK3CA genes was
carried out using Sanger sequencing, which is the cost effective methodology and
‘Gold Standard’ for mutation analysis. Sanger sequencing allowed evaluation of all
the hotspot regions in all four genes. Various steps were undertaken in our
laboratory to ensure optimal procedures for mutational testing through direct
sequencing. As mentioned above, these included strict adherence to current
recommendations by CAP and NABL guidelines and involvement of experienced
pathologists in representative tissue sample section and for performing tumour
macrodissection. Furthermore, mutational analyses were performed using widely
accepted protocols. The laboratory is also registered in external quality control
audits. The minimum allelic sensitivity of Sanger sequencing was established as
20% using commercially available reference standards. Samples having tumor
percentage of 20% were processed by macro dissection to enrich the tumor
content. Samples below the tumor percentage of 20 were not included in the study.
In a total of 36% of CRC cases at least one mutation in the analyzed hot spot
region was observed. The prevalence of KRAS, BRAF, NRAS and PIK3CA
mutations in the present study were 24%, 6%, 2% and 4%, respectively which
concurs well with the COSMIC database reported frequencies. Hence, it can be
seen that approximately 12% of CRC patients have mutations in NRAS, BRAF or
PIK3CA in KRAS wild type population. However, it was observed that the mutation
frequency of BRAF V600E was relatively lower in the Indian population as
Chapter 7. Summary and Future Work
260
compared to what is reported in the COSMIC database. The reason for this
difference may be the structure of COSMIC database that screens for the
information available in literature for the somatic mutations and displays its
relationship to the particular human cancer. This amino acid substitution of V to E
at codon position 600 in BRAF was observed in 6% of cases versus 10.1% in the
COSMIC database. However, this observed frequency of 6% was in concordance
with the study performed by Bagadi et. al. and Bisht et. al. on the Indian population
(Bagadi et al., 2012, Bisht et al., 2014).BRAF mutations were found to mutually
exclusive with KRAS mutations. Three cases showed coexistence of PIK3CA and
KRAS mutations together, which confirms the reported observations that PIK3CA
mutations can coexist with other molecular alterations (Thesis Chapters 4 and 5).
Chapter 7. Summary and Future Work
261
7.2 Correlation of mutations in KRAS, BRAF, NRAS and PIK3CA genes
with clinico-pathological data for a 203 Indian CRC patient cohort
The statistical analysis of clinicopathological characteristics and mutation analysis
was performed using Chi-square tests. Significant positive association was
observed for KRAS mutations with age and tumor differentiation (p<0.05). The
mutation rate in patients above 50 years was higher than the rate in patients below
50 years. Also, KRAS mutations were significantly associated positively with
moderately and poorly differentiated adenocarcinoma, as compared to well-
differentiated adenocarcinoma. Other clinicopathological findings like gender,
tumor location, stage and lymph-node metastasis, showed no significant
association with KRAS mutations (p>0.05), which is in accordance with recent
reports for the Indian population.
In the case of BRAF, a statistically significant correlation was observed in
moderately differentiating and poorly differentiating adenocarcinomas, but not in
well-differentiated adenocarcinomas. This study supports previous reports that
found that BRAF mutation status is correlated with specific clinicopathological
features and hence identifies a distinctive subgroup of patients having specific
clinico-pathological features (Li et al., 2011).
No significant association was observed between any of the clinico-pathological
features with NRAS or PIK3CA mutations. However, this study agrees well with
other population based studies not only in terms of distribution of mutations and
clinical and pathological features but also in terms of association between these
mutations and the clinical data (Thesis Chapter 5).
Chapter 7. Summary and Future Work
262
7.3 Correlation of clinico-pathological data with survival in Indian patient
cohort
In the past few years, there have been many studies carried out to determine the
association of numerous variables with the survival in case of CRC (Laohavinij et
al., 2010, Moghimi-Dehkordi et al., 2008, Ratto et al., 1998, Desolneux et al., 2010,
Ghazali et al., 2010). The age, gender, site of CRC, tumor differentiation, invasion
of tumor, lymph node metastases and other variables have been studied. However,
determination of prognostic factor is still a challenge. The overall survival rate
currently in Asian countries is approximately 60% as the majority of
adenocarcinomas are still diagnosed at the later stages while the highest survival
rate is observed in the USA as 64% (Moghimi-Dehkordi and Safaee, 2012).
Amongst the Asian countries, the highest survival rate is seen in China and the
lowest in India (Shiono et al., 2005, Yeole et al., 2001). In this study, it was
observed that overall survival was 37% with median survival of 25 months. In terms
of anatomic location, the median survival for colon was seen to be 21 months. The
differences in survival according to tumor site could be due to differences in tumor
aggressiveness or due to screening methodologies used. Further, the median
survival for patients above the age of 60 was 25 months and below 60 years was
30 months which concurs with few studies reporting poor survival rate in older
patients when compared with that of younger ones. However, recently it has been
observed that the incidence of CRC is rising in the younger population in India. It
has been observed that in the young patients CRC is more aggressive and has
poor pathological features (Chou et al., 2011). With regard to differentiation of
Chapter 7. Summary and Future Work
263
tumor, the current study data reveals a significant association in the survival rate of
patients with poorly differentiated adenocarcinoma (PDA). PDA is positively
associated with poor survival (median survival of 21 months) in comparison to
MDA (p0.05) as observed in previous studies. PDA cases mostly occur in
advanced stages or metastatic stages which could be the reason for poor survival.
The overall survival in the case of patients with lymph node involvement was found
to be 25 months, which was higher than patients with no lymph node involvement.
Numbers of lymph nodes involved reflect the patients’ improved immune response.
The more the lymph node involvement, the greater the immune response and
hence improved survival.
If the tumor is diagnosed when it is in a localised stage then the survival outcome
of the patient is better than those cases where in the diagnosis occurs at the
metastatic stage. In the current study, it was observed that if the tumor is
localised, i.e. present at primary stage, then the median survival was 31 months in
comparison to the metastatic tumors in which the median survival was 26 months.
The survival rate in mutated tumors was 25 months and in wild type was 29
months though statistical significance was not observed (Thesis Chapter 6).
Patients’ selection has recently entered a new era of personalised therapy. The
establishment of biomarkers and clinicopathological features prior to treatment can
lead to improved survival. The impact of different genetic and epigenetic alterations
such as mutations, SNP’s, methylation status and copy number, required for
efficacy of treatment, requires further study to determine the mechanisms of action
for the specific drug molecule used. This thesis investigated a variety of CRC
Chapter 7. Summary and Future Work
264
cases in the Indian population for studying the effect of different factors on survival.
Taken together, the findings of current study shows for the first time that at the
genetic level, mutations in one of the four genes (KRAS, BRAF, NRAF and
PIK3CA) occur at a lower frequency than in the population in Western developed
countries. Data supports the hypothesis that (i) rate of mutations in critical CRC
genes involved in the tumor growth and survival i.e. KRAS, BRAF, NRAS and
PIK3CA differ according to racial differences, and (ii) that different
clinicopathological factors would have impact on clinical outcome of the patient in
the context of Indian patient cohort. Results from therapeutic data analysis
(Chapter 6) shows that the knowledge of tumor differentiation status can influence
decision making for patient and hence improve response rate and outcome of CRC
patient. This data needs to be validated in larger cohort to potentially influence
treatment decisions in Indian patients, and hence is a step forward towards
personalised treatment.
In brief, the following conclusions can be drawn from this study:
1. The prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in CRC
patients in the present study was 24%, 6%, 2% and 4%, respectively,
which is at a much lower frequency when compared to the data available
for populations in Western developed nations.
2. BRAF mutations were found to be mutually exclusive with KRAS
mutations. However, coexistence of PIK3CA mutations with KRAS
mutant patients was observed. These results concur with the published
literature on populations in Western developed nations.
Chapter 7. Summary and Future Work
265
3. Significant statistical association (p<0.05) was observed between the
following parameters:
a. KRAS mutations with age and tumor differentiation. Mutation rate in
patients above 50 years was higher than for patients below 50 years
of age. KRAS mutations were significantly associated positively with
moderately and poorly differentiated adenocarcinoma, as compared
to well-differentiated adenocarcinoma.
b. BRAF statistically significant positive correlation was observed in
moderately differentiating and poorly differentiating adenocarcinomas
than in well-differentiated adenocarcinomas.
4. No significant association was observed between any of the
clinicopathological features with NRAS or PIK3CA mutations in Indian cases.
5. In terms of correlation between survival and clinicopathological features, the
following observations were made:
a. The 3- year overall survival in Indian patients was observed to be 37%, with
median survival of 25 months, which is much lower in comparison to that in
developed nations.
b. Significant association was observed in the survival rate of patients with
poorly differentiated adenocarcinoma (PDA). PDA is inversely associated with poor
survival (median survival of 21 months) in comparison to moderately differentiated
adenocarcinoma (MDA) (p0.05).
c. It was observed that if the tumor is localised, that is, present at the primary
stage, then the median survival is 31 months, in comparison to metastatic tumors
Chapter 7. Summary and Future Work
266
in which the median survival is 26 months. The survival rate in mutated tumors was
25 months and in wild type was 29 months, although statistical significance was
not observed.
The current study data reflects the nature of disease and the effect of different
clinicopathological features on survival in case of Indian CRC cases. These
experimental evidences of survival rates of CRC patients in relation to different
clinico-pathological features in the Indian population indicates that there are
numerous factors that influence the prognosis of colorectal cancer patients.
However, life expectancy has not increased much in these years. Though the data
has inherent limitations due to small sample size analysis, this retrospective study
supports the hypothesis (based on existed literature) that clinical and pathological
characteristics, especially tumor differentiation are good determinants of prognosis
in CRC patients.
Recently mutations in KRAS exon4 and NRAS exon 4 have been shown to have
an effect on therapeutic response. However, the reported percentage of these
mutations is low, ranging from 0.5 to 2.2%. Further studies are required to
establish the prevalence and effect of these mutations of exon 4 in the Indian
population.
In terms of a forward path, additional studies are required in the Indian CRC
population to determine the effect of additional genetic and epigenetic markers,
such as AKT, PTEN, MAPK, and other receptors molecules such as MET, MSI,
CIMP, which could provide an alternative pathway to survival. Also, larger cohort
Chapter 7. Summary and Future Work
267
needs to be studied to evaluate the effect of different mutations in correlation with
clinicopathological features on survival. Thus the future strategy to improve survival
outcomes and clinical management of CRC, lies in personalized therapy which is
still an evolving approach with a focus to identify highly specific and sensitive
predictive biomarkers. Hence, there is a strong need to identify, develop and
validate more biomarkers that will assist with clinical decision-making. As reviewed
recently (Patil et al 2016), Next Gen Sequencing and multi-gene sequencing
(parallel sequencing technology), data reveals that along with the mutations in
genes of EGFR pathway, mutations SMAD-4 and FBXW7 are also responsible for
resistance to therapy. Also, advances in imaging techniques such as FDG-PET,
DWI, DCE-MRI could potentially serve as predictive imaging biomarkers to anti-
angiogenesis inhibitors(Atreya and Goetz, 2013). Considering the current progress
and focus in personalized medicine, and with the recent genomic profiling of CRC
patient tumors and the development of new proteomic and modeling studies,
selecting and stratifying CRC patients based on their molecular profile will be
improved in future.
Appendix
268
Appendix
I. Haematoxylin and eosin (H&E) Analysis
Preparation of reagents:
1. Haematoxylin Solution (Harris):
Potassium or ammonium (alum): 100 g
Distilled water: 1000 ml
Heat to dissolve. Add 50 ml of 10% alcoholic haematoxylin solution and heat to boil
for 1 min. Remove from heat and slowly add 2.5 g of mercuric oxide (red). Heat to
the solution and until it becomes dark purple color. Cool the solution in cold water
bath and add 20 ml of glacial acetic acid (concentrated). Filter before use and store
at room temperature.
2. Eosin-Phloxine B Solution:
Eosin Stock Solution:
Eosin Y: 1 g
Distilled water: 100 ml
Mix to dissolve.
Phloxine Stock Solution:
Phloxine B: 1 g
Distilled water: 100 ml
Mix to dissolve.
3. Eosin-Phloxine B Working Solution:
Eosin stock solution: 100 ml
Phloxine stock solution: 10 ml
Appendix
269
Ethanol (95%): 780 ml
Glacial acetic acid: 4 ml
Mix well and store at room temperature.
4. 1% Acid Alcohol Solution (for differentiation):
Hydrochloric acid: 3 ml
70% ethanol: 300 ml
Mix well and store at room temperature.
II. Reagents required for DNA extraction using Invitrogen Purelink Genomic DNA
kit:
Preparation of Reagents:
1. Purelink Wash Buffer WB1:
Buffer WB1 was diluted with 80 ml of 100% ethanol to make the volume to 200 ml.
2. Purelink Wash Buffer WB2:
Buffer WB2 was diluted with 80 ml of 100% ethanol to make the volume to 185 ml.
III. Gel electrophoresis Reagent Preparation:
1. 1XTBE Buffer
Tris-180 grams
EDTA-9.3 grams
Boric Acid-55 grams
pH-7.5
Volume adjusted to 1 liter with MilliQ water
Appendix
270
List of Chemicals
Sr
No. Chemical Company Catalogue
1 SeaKem LE Agarose Lonza 50004
2 FG, BDT V3.1 RR1000 ABI 4337456
3 Boric acid Merck 6505001730
4 BSA Sigma A7030- 100 g
5 DMSO Sigma D2650
6
DNA Extraction kit:
Purelink Invitrogen K1820-02
7 EDTA Thomas Baker 74298
8 Ethidium bromide Sigma 160539
9 Ethanol Changshu Yangyuan XK-13-201-00185
10 Generuler 100bp Ladder MBI Fermantas SM0241
11
GOTAQ(R)Flexi DNA
Polymerase Promega M829B
12 Hi Di Formamide ABI 4311320
13
N-RAS G12V Reference
Standard Horizon Diagnostics HD203
14
N-RAS Q61K Reference
Standard Horizon Diagnostics HD247
15 PCR Nucleotide Mix Promega C114H
Appendix
271
16 10 X EDTA Buffer Thermo Scientific 402824
17 5X Seq Buffer Thermo Scientific 4339843
18 HPLC grade water Merck O61765010001730
19
Montage PCR u96
Cleanup Plates Millipore LSKMPCR50
20
Montage PCR u96 Seq.
Rxn Cleanup Kit Millipore LSKS09624
21 POP6 Thermo Scientific 4316357
22 Primers Sigma -
23 1XPBS Gibco 20012-050
24 5X Seq Buffer Thermo Scientific 4339843
25
Flat Deck Thermo-Fast 96
detection plate Thermo Scientific AB-1400
26 DPX Mountant Merck AF2 AF 52226
27 Formaldehyde solution Qualigens 12755
28 Formamide SRL 62758
29 Hydrogen Peroxide Qulaigens 15465
30 Tri Sodium citrate Qulaigens 14005
31 Hydrogen Peroxidase Thomas Baker 90383
32 Xylene Xlar Qualigens 32295
33 Reference Standards Horizon Diagnostics HD203
34 Trizma base Sigma T1503
Appendix
272
List of Instruments
Sr
No. Instrument Model Company
1 Biosafety Cabinate 1590V Klenzaids
2 Fume Hood AFA1000 Kewaunee
3
Gel Electrophoresis
Appratus SubcellGT Biorad
4 pH Meter PICO+ LabIndia
5 Incubator 450X450 mm Trishul Equipment
6 Hot Air Oven PEW180ASS Pathak Electric Work
7 Floatation bath 3120058
Thermo Electron
Corporation
8 Microtome Finesse ME
Thermo Electron
Corporation
9
Bright field Olympus
Microscope BX51 BX51 Olympus
10
CCD camera for
microphotography ProgRes C3 Olympus
11 Genetic Analyser 3100 3100 Life Technologies
12 Dry Bath DB-3D Techne
13 PCR Thermal Cycler MyCycler Biorad
14 Micro Centrifuge 5415D Eppendorf
Appendix
273
15 Gel Documentation System
Pharmacia Biotech
011E991 GE Biosciences
16
Nano drop
Spectrophotometer ND-1000 Nanodrop
17 Laminar Hood 1560R Klenzaids
18 Microwave CE118KF Samsung
19 Analytical Balance BP121S Sartorius
20 Semianalytical Balance BP1200 Sartorius
Appendix
274
List of Software’s
Sr.
No. Software Company
1 Sequencing Analysis Software Life Technologies
2 Image Total Master GE Biosciences
3 BioEdit www.mbio.ncsu.edu/bioedit/bioedit.html
4 GraphPad QuickCals GraphPad Software, Inc
5 GraphPad Prism GraphPad Software, Inc
6 MedCalc MedCalc Software, Ostend, Belgium
Accreditations and Approvals
i. Accreditation with College of American Pathologist (CAP) - LAP No: 7194405,
AU ID-1449073, CLIA No: 99D20118815, Issue date: 12 Sep 2013
ii. Accreditation with National Accreditation Board for testing and calibration
Laboratory (NABL) in accordance with ISO15189:2007 - NABL No. M-0090,
Issue date:19 Dec 2015
iii. Registration with Maharashtra Industrial Development Corporation,
Registration No.11/24/MIDC/001.IEM No. 686,687,688/SIA/IMO/2008/
References
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