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Identification of REST regulated genes in prostate cancer via high-throughput technologies Yue Sun Thesis to obtain the Master of Science Degree in Biotechnology Examination Committee Chairperson: Professor Arsénio do Carmo Sales Mendes Fialho Departamento de Bioengenharia (DBE) Supervisor: Professor Isabel Maria de Sá Correia Leite de Almeida (DBE) Co-supervisor: Professor Colin Collins (University of British Columbia/Vancouver Prostate Centre) Member of the Committee: Professor Miguel Nobre Parreira Cacho Teixeira (DBE) December 2012

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Page 1: Identification of REST regulated genes in prostate cancer via high-throughput technologies · Identification of REST regulated genes in prostate cancer via high-throughput technologies

Identification of REST regulated genes in prostate cancer

via high-throughput technologies

Yue Sun

Thesis to obtain the Master of Science Degree in

Biotechnology

Examination Committee

Chairperson: Professor Arsénio do Carmo Sales Mendes Fialho

Departamento de Bioengenharia (DBE)

Supervisor: Professor Isabel Maria de Sá Correia Leite de Almeida (DBE)

Co-supervisor: Professor Colin Collins

(University of British Columbia/Vancouver Prostate Centre)

Member of the Committee: Professor Miguel Nobre Parreira Cacho Teixeira (DBE)

December 2012

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Acknowledgements The experimental work reported in this thesis was carried out at the Laboratory for

Advanced Genome Analysis (LAGA), Vancouver Prostate Centre (VPC), through he

Erasmus Mundus Master's Program in Systems Biology (euSYSBIO).

I would like to express my gratitude to my supervisors Prof. Isabel Sá Correia

(Instituto Superior Técnico IST) and Prof. Erik Aurell (Royal Institute of Technology

KTH), for giving me the opportunity to make this thesis in an external institute.

I am very grateful to my supervisor Prof. Colin Collins (University of British

Columbia/VPC), who supervised me and gave me good suggestions on this thesis

work. I would also like to thank the staff VPC for their kind help through my work.

Within the two years study, the Erasmus Mundus euSYSBIO program navigated me

through a cruise of the adventures of the Great Voyage, from the heavy snow in

Sweden to the beach and sunshine of Portugal. I believe, being a student in this

program will be a treasured experience for the rest of my life.

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Dedication For my parents and 天海祐希.

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Abstract Prostate cancer (PCa) is one of the most commonly diagnosed cancers in the world:

males have 17% risk of developing PCa during lifetime. Androgen deprivation

therapy (ADT) has been the leading form of PCa therapy [1]. Unfortunately, the

effectiveness of this treatment is usually temporary, with a subpopulation of cells

surviving. Growing evidence indicates that neuroendocrine differentiation (NED)

occurs in 30-100% of disease recurrence after ADT [2, 3]. However, the mechanism is

still poorly understood.

Repressor element-1 silencing transcription factor (REST) is responsible for

restricting neuronal gene expression to the nervous system. REST was recently shown

to be absent in various types of cancer. Blockade of REST function in human LNCaP

cells is found to cause NED [4], indicating REST may be one of important

determinants of neuroendocrine prostate cancer (NEPC). The aim of this study is to

investigate the potential role of REST in PCa progression. We combined

state-of-the-art sequencing technologies and bioinformatics with patient-derived

next-generation models of PCa to deal with this problem. An integrated data analysis

of RNA-seq on mouse xenograft model and expression microarray with RNA

interference on human LNCaP cells was performed to identify potential REST targets.

Experimentally, we validated most likely targets by QRT-PCR of LNCaP mRNA. In

addition, we also estimated the clinical significance of the targets using public

datasets. Our results help to elucidate the mechanism that drives development of

androgen independent and aggressive NEPC after castration. Furthermore, it could

provide a new prognosis factor for PCa and also provide a new target for the PCa

treatment.

Key words: REST, prostate cancer, neuroendocrine differentiation, gene expression

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Resumo O cancro da próstata (CaP) é um dos cancros mais diagnosticados em todo o mundo:

Cerca de 17% dos homens correm o risco de desenvolver CaP durante a vida. A

terapia de privação de andrógenios (ADT) tem sido a principal forma de tratamento

do CaP [1]. Infelizmente, a eficácia deste tratamento é normalmente temporária, com

uma sub-população de células sobreviventes. Várias evidências indicam que a

diferenciação neuroendócrina (NED) ocorre em 30-100% das recorrências da doença

após a ADT [2, 3]. No entanto, o mecanismo ainda é pouco compreendido.

O “Repressor element-1 silencing transcription factor ” (REST) é responsável por

restringir a expressão do gene neuronal no sistema nervoso. Evidências recentes

mostraram que o REST estava ausente em vários tipos de cancro. O bloqueio da

função do REST em células humanas LNCaP causa NED [4], o que indica que o

REST pode ser um importante determinantedo cancro da próstata neuroendócrino

(NEPC). O objetivo deste estudo é investigar o potencial papel do REST na

progressão do CaP, através da combinação de tecnologia de sequenciação e

bioinformática com modelos baseados em pacientes de CaP. Para tal, foi realizada

uma análise integrada de dados de RNA-seq em ratos modelo xenoenxerto e de

expressão com RNA de interferência em células humanas LNCaP com o objectivo de

identificar potenciais alvos de REST. Foram validados experimentalmente osalvos

mais prováveis de QRT-PCR de mRNA de LNCaP. Além disso, também foi avaliada a

importância clínica dos alvos utilizando conjuntos de dados públicos. Os nossos

resultados ajudam a elucidar o mecanismo que conduz ao desenvolvimento das

formas de NEPC androgénio-independente e agressiva após a castração. Além disso,

pode fornecer um novo fator prognóstico para o CaP, bem comor um novo alvo para o

tratamento do CaP.

Palavras-chave: REST, câncer de próstata, diferenciação neuroendócrina, expressão

gênica

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I

Table of contents

Table of contents ............................................................................................................ I 

List of tables ................................................................................................................. III 

List of figures ............................................................................................................... IV 

List of abbreviations .................................................................................................... VI 

1.  Prostate cancer ........................................................................................................ 1 

1.1  The prostate and the development of prostate cancer .................................... 1 

1.1.1 Gleason score ........................................................................................ 2 

1.1.2 Prostate specific antigen (PSA) ............................................................ 4 

1.2  Androgen receptor (AR) ................................................................................ 6 

1.3  Castrate-resistance prostate cancer (CRPC) .................................................. 6 

1.4  Neuroendocrine prostate cancer (NEPC) ....................................................... 7 

2  The transcription factor REST .............................................................................. 10 

2.1 REST and its alternative isoforms ................................................................. 10 

2.2 RE1 motif ....................................................................................................... 12 

2.3 REST Co-repressors ....................................................................................... 14 

2.4 REST and non-coding RNAs ......................................................................... 18 

2.5 REST function and cancer ............................................................................. 19 

3  Aims of research ................................................................................................... 22 

4  Materials and methods .......................................................................................... 25 

4.1  Cell lines ...................................................................................................... 25 

4.2  Prostate tissue samples ................................................................................. 25 

4.3  Mouse xenograft model ............................................................................... 25 

4.4  Public dataset ............................................................................................... 26 

4.5  siRNA and shRNA experiments ................................................................... 26 

4.6  Microarray .................................................................................................... 27 

4.7  Transcriptome sequencing ........................................................................... 27 

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II

4.8  Statistics Analysis ........................................................................................ 28 

4.9  Data analysis ................................................................................................ 28 

4.10 Experimental RT-PCR validation ................................................................. 28 

5  Results ................................................................................................................... 30 

5.1  Specific and efficient knock-down of REST mRNA in LNCaP cells .......... 30 

5.2  High fidelity of mouse xenograft model for neuroendocrine

transdifferentiation ............................................................................................... 32 

5.3  Microarray and RNA-seq analysis for identification of differential

expressed genes .................................................................................................... 33 

5.4  Functional categorization analysis ............................................................... 37 

5.5  Network analysis .......................................................................................... 38 

5.6  Experimental RT-PCR validation ................................................................. 43 

5.7  Kaplan Meier survival analysis .................................................................... 44 

6  Discussion ............................................................................................................. 47 

References .................................................................................................................... 50 

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III

List of tables

Table 2.1 Molecular weight of REST alternative isoforms (Uniprot.org) ........... 12 

Table 4.1 RT-PCR primer sequence ..................................................................... 29 

Table 5.1 Profiling of overlap genes differentially expressed ............................. 35 

Table 5.2 IPA-generated molecular networks ...................................................... 39 

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IV

List of figures

Figure 1.1 Human prostate gland. .......................................................................... 1 

Figure 1.2 Gleason scale. ....................................................................................... 3 

Figure 1.3 Functions of PSA in the normal prostate and in carcinogenesis. ......... 5 

Figure 1.4 Neuroendocrine cells in the development of prostate cancer. .............. 8 

Figure 2.1 REST Gene in genomic location: bands according to Ensembl. ........ 10 

Figure 2.2 Model of REST domains. ................................................................... 11 

Figure 2.3 REST mRNA and alternative splicing variants. ................................. 11 

Figure 2.4 Sequence logo of canonical RE1 motif. ............................................. 13 

Figure 2.5 Histone modification on H3 N-terminus. ........................................... 15 

Figure 2.6 Mechanism of repression/activation via REST-RE1 interaction. ....... 16 

Figure 2.7 REST-ncRNA interactions in neural networks. .................................. 18 

Figure 2.8 Experimental models and possible routes for REST dysfunction in

cancers. ......................................................................................................... 20 

Figure 5.1 siRNA knockdown of REST in LNCaP cells. .................................... 30 

Figure 5.2 shRNA knockdown of REST in LNCaP cells. ................................... 31 

Figure 5.3 The LTL-331 xenograft model of neuroendocrine transdifferentiation.

...................................................................................................................... 32 

Figure 5.4 Summary of microarray data on LNCaP model. ................................ 33 

Figure 5.5 Summary of RNA-seq data on xenograft model. ............................... 34 

Figure 5.6 The overlap between genes differentially expressed from microarray

data and from RNA-seq data. ....................................................................... 35 

Figure 5.7 Functional categorization analysis of most significant pathways and

diseases. ....................................................................................................... 37 

Figure 5.8 Ingenuity pathway analysis Network 1. ............................................. 41 

Figure 5.9 Combination of Network 1, Network 2 and Network 3. .................... 42 

Figure 5.10 BEX1 expression level in REST shRNA knockdown LNCaP cells. 43 

Figure 5.11 SRRM3 expression level in REST shRNA knockdown LNCaP cells.

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V

...................................................................................................................... 44 

Figure 5.12 Kaplan–Meier survival plots on 3 selective genes in a public dataset

prostate tumors (p<0.01). ............................................................................. 46 

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VI

List of abbreviations

ADT: androgen deprivation therapy

AR: androgen receptor

CNS: central nervous system

CRPC: castrate-resistance prostate cancer

DRE: digital rectal examination

FC: fold-change

HAT: histone acetyltransferase

HDAC: histone deacetylase

H3K4: histone H3 lysine 4

LNCaP: lymph node carcinoma of prostate

LSD1: lysine-specific demethylase 1

miRNA: microRNA

ncRNA: non-coding RNA

NEPC: neuroendocrine prostate cancer

NOC/SCID: nonobese diabetic/severe combined immunodeficiency

NRSF: neuron-restricted silencing factor

PCa: prostate cancer

PSA: prostate-specific antigen

RD: repression domains

RE1: repressor element 1

REST: repressor element 1- silencing transcription factor

RIN: RNA Integrity Number

RISC: RNA induced silencing complex

shRNA: short hairpin RNA

siRNA: small interfering RNA

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1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland. 1

1. Prostate cancer

Prostate cancer (PCa) is the most common non-skin cancer in North American men

and a leading cause of cancer death (http://www.pcf.org/faq). Approximately, 1 in 7

North American men will be diagnosed with prostate cancer in his lifetime. On

average, everyday 73 Canadian men are diagnosed with PCa and on average 11

Canadian men die from the disease (http://www.prostatecancer.ca). Approximately

26,500 Canadians will be diagnosed with PCa in 2012.

1.1 The prostate and the development of prostate cancer

The prostate is a male reproductive gland. The physiological role of prostate in

reproduction is to serve as a secretory gland, primarily to maintain semen gelation,

coagulation and liquefaction for successful fertilization [5]. Prostate gland surrounds

the urethra and is located inferior to the bladder and anterior to the rectum (Fig. 1.1). At

birth, the size of the prostate in humans is approximately 1-2 grams and after puberty

androgens act to mature the organ to its adult size of approximately 20 grams [6].

Figure 1.1 Human prostate gland.

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2

Development of the prostate originates in utero from the ambisexual endodermal

urogenital sinus (UGS) when androgens such as testosterone and dihydrotestosterone

(DHT) stimulate the androgen receptor (AR) in the surrounding embryonic connective

tissue, urogenital sinus mesenchyme (UGM) [7]. Androgen stimulation of the AR in the

UGM supports the differentiation of stem cells in the UGS.

What differentiates a cancer cell from a normal cell is its ability to grow

uncontrollably and invade surrounding tissues, as well as its potential to spread to

other distant organs [8]. Within the stromal microenvironment an epithelial stem cell

destined for differentiation into a normal basal or luminal secretory cell can instead be

forced to undergo differentiation into a cancerous cell [9]. Many factors contribute to

PCa development, including the disruption in the interactions between mesenchymal

and epithelial cells, which is mediated through changes in growth factors expression

and overexpression of proteases acting on the mesenchyme to increase metastasis [5,

10]. The presence of PCa may be indicated by symptoms, physical examination, and

elevation of serum levels of prostate-specific antigen (PSA), which can result in

biopsy. The severity of PCa, and therefore the treatment regime, can then be

determined by Gleason score.

1.1.1 Gleason score

Gleason score is a histomorphometric method that was developed by Donald Gleason

to classify adenocarcinoma of the prostate by evaluating the pattern of glandular

differentiation [11]. The tumour grade is the sum of the dominant and secondary

patterns, each numbered on a scale of 1 to 5 Gleason patterns are associated with the

following features [12] (Fig. 1.2):

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Figure 1.2 Gleason scale. Pattern 1 - The cancerous prostate glands are small, well-formed, and closely packed. Pattern 2 - The gland tissues are still well-formed, but they are larger and have more space between them. Pattern 3 - The tissue still has recognizable glands, but the cells are darker. At high magnification, some cells have left the glands and are beginning to invade the surrounding tissue. Pattern 4 - The tissue has few recognizable glands. There are irregular masses of cells with few glands. Pattern 5 - The tissue does not have recognizable glands. There are often just sheets of cells throughout the surrounding tissue.

The pathologist assigns a grade to the most common tumor pattern, and a second

grade to the next most common tumor pattern. The two grades are added together to

get a Gleason score. The Gleason grade is also known as the Gleason pattern, and the

Gleason score is also known as the Gleason sum. For example, if the most common

tumor pattern was grade 3, and the next most common tumor pattern was grade 4, the

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Gleason score would be 3+4 = 7. As Gleason pattern ranges from 1 to 5, with 5

having the worst prognosis, the Gleason score ranges from 2 to 10, with 10 having the

worst prognosis. For Gleason score 7, Gleason 4+3 is a more aggressive cancer than

Gleason 3+4.

1.1.2 Prostate specific antigen (PSA)

In North America, routine screening for PCa involves a digital rectal examination

(DRE), a Prostate Specific Antigen (PSA) blood test and a biopsy. The DRE is

relatively simple procedure: the doctor gently puts a lubricated, gloved finger of one

hand into man’s rectum to check for problems with organs or other structures in the

pelvis and lower belly. The PSA test, which was originally approved by the FDA in

1986, is a simple blood test screen for elevated levels of PSA. PSA, prostate specific

member of kallikrein-related protease family, is produced specifically in the luminal

secretory epithelial cells of the gland. There is no specific normal or abnormal level of

PSA in the blood. In the past, most doctors considered PSA levels of 4.0 ng/mL and

lower as normal. Therefore, if a man had a PSA level over 4.0 ng/mL, doctors would

often recommend a prostate biopsy to determine whether PCa was present. But more

recent studies have shown that some men with PSA levels below 4.0 ng/mL have PCa

and that many men with higher levels do not have PCa [13]. Although experts’

opinions vary, there is no clear consensus regarding the optimal PSA threshold for

recommending a prostate biopsy for men of any racial or ethnic group. In general,

however, the higher a man’s PSA level, the more likely it is that he has PCa.

From the perspective of molecular pathology, PSA is highly expressed by normal

prostatic epithelial cells, however overexpression of PSA accelerates carcinogenesis

in the prostate. PSA plays many crucial roles in the development of prostate cancer

(Fig. 2.3) [14]. Firstly, it is thought to cleave insulin-like growth-factor-binding

protein 3 (IGFBP3), thereby liberating insulin-like growth-factor 1 (IGF1), which is a

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5

mitogen to prostatic stromal and epithelial cells. Also, it can activate latent

transforming growth factor (TGF) to stimulate cell detachment and facilitate tumour

cell spread. Moreover, PSA can directly proteolyse components of the basement

membrane, which leads to tumour-cell invasion and metastasis [14].

Figure 1.3 Functions of PSA in the normal prostate and in carcinogenesis. (a). In the normal prostate, PSA acts to liquefy spermatozoa. (b). In carcinogenesis of the prostate, PSA acts on various molecules to potentially enhance proliferation, cell detachment, invasion and metastasis through increasing the availability of insulin-like growth-factor 1 (IGF1), through proteolysis of insulin-like growth-factor-binding protein (IGFBP) and activation transforming growth factor-β (TGF-β).

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1.2 Androgen receptor (AR)

Androgen receptor (AR) is a member of the steroid and nuclear receptor superfamily,

which is activated by the binding of androgens, mainly testosterone and

5α-dihydrotestosterone (DHT) [15].

In the normal prostate, the predominant role of AR is to promote differentiation of

luminal epithelial cells and to regulate the transcription of genes encoding proteins

necessary for prostate function, such as PSA. In PCa, the proliferation of cancer cells

with a low concentration of androgen may involve the overexpression of the AR [16].

Furthermore, AR activity could modulate the expression of genes associated with

cancer cell cycle regulation, survival and growth [17, 18]. In addition, AR is known to

stimulate the expression of TMPRSS2-ERG [19]. TMPRSS2-ERG fusions are the

predominant molecular subtype of PCa. Approximately 50% of PCa from PSA

screened surgical cohorts are TMPRSS2-ERG fusion-positive, and greater than 90% of

PCa overexpress TMPRSS2-ERG fusions [20]. High levels of ERG and other

members of the ETS oncogene family contribute to PCa development.

1.3 Castrate-resistance prostate cancer (CRPC)

There are several different treatment options for PCa patients, depending on the grade and

stage of the disease at diagnosis, and the aggressiveness of the tumor after first-line

treatment. If the cancer is locally confined, treatments such as radical prostatectomy,

radiation therapy or active surveillance may be employed. Radical prostatectomy involves

surgically removing the prostate and closely surrounding tissues [21]. For patients with

low to intermediate-risk disease, radiation therapy is often successful (70-90% at 10-year

progression-free survival) [22]. If the cancer at diagnosis has spread well beyond the

gland or is not responsive to these localized therapies, then androgen deprivation therapy

(ADT) is the most common treatment for patients.

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Unfortunately, the effectiveness of this type of treatment is usually temporary due to

the progression of surviving tumor cells which become castration-resistant prostate

cancer (CRPC). This extremely painful form of PCa has no curative treatment options

and a median life expectancy of around 18 months [23]. Globally, CRPC claims the

lives of 32,000 men annually [24]. In actual fact, only a few chemotherapeutic agents that

act to both prolong survival and improve quality of life are available for patients with

metastatic CRPC. The cancer chemotherapeutic docetaxel has been used as treatment

for CRPC with a median survival benefit of 2 to 3 months [25]. The oral drug

ketoconazole is sometimes used as a way to further manipulate hormones with a

therapeutic effect in CRPC. However, there are many side effects associated with

ketoconazole usage. Abiraterone, a specific inhibitor of the cytochrome p450 enzyme

17A1, is likely to supplant ketoconazole as it shown to be more effective, with less

toxic side effects [26]. On April 28, 2011, the U.S. FDA approved abiraterone acetate

in combination with prednisone to treat patients with late-stage (metastatic)

castration-resistant prostate cancer.

Only through understanding the molecular biology underlying disease progression

have we been able to develop drugs, such as the ones listed above. Furthering our

understanding by exploring known and yet to be understood mechanisms by which

prostate cancer cells become resistant to castration will allow us to develop and

optimize new, more effective drugs of CRPC.

1.4 Neuroendocrine prostate cancer (NEPC)

Neuroendocrine tumors have been recognized as a unique subcategory of cancers of

various epithelial organs. Neuroendocrine prostate cancer (NEPC) is an aggressive

subtype of PCa, evolving from preexisting prostate adenocarcinoma. NE cells express

potent neuropeptides that mediate diverse biological processes such as cell growth,

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differentiation, transformation and invasion. Although NE cells do not stain for

proliferative antibodies, they may be a source of paracrine factors that support

prostate cancer growth and progression (Fig. 1.4).

Figure 1.4 Neuroendocrine cells in the development of prostate cancer.

Prostate epithelium is interspersed with neuroendocrine cells. These cells produce specialized neuropeptides, including chromogranin-A (CgA), which stimulate and regulate the normal prostatic-cell secretory functions [27]. On the contrary, inappropriate neuroendocrine regulation might facilitate carcinogenesis, aiding proliferation and other tissue changes such as loss of basal cells, angiogenesis, piling up of prostatic luminal epithelium and invasion, which are characteristic of prostatic carcinoma.

Prostate cancer can be broadly divided into two groups at diagnosis, based on the AR

expression level [28]. AR and PSA positive adenocarcinoma accounts for over 95% of

initial prostate cancer diagnoses, while NEPC which are typically AR and PSA

negative (also known as small cell carcinoma) accounts for 2-3% of primary prostate

cancer. However, at the late stage of PCa, 30-100% of advanced type show

enrichment of neuroendocrine-like cells [3]. One hypothesis shows that ADT may

induce micro-environmental changes that increase the activity of these cells.

Concomitantly, NE cells may allow continued PCa growth through paracrine

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stimulation of neoplastic epithelial cells. Indeed, mitogenic and oncogenic activity has

been demonstrated for many of the factors NE cells are known to produce.

Interestingly, blockade of REST function in the human LNCaP cells is found to cause

NED [4]. As reported by Tawadros’ team, during NE phenotype acquisition of LNCaP,

REST binding activity studies demonstrated a decreased binding activity even after 8h

of differentiation. Furthering, they evaluated the regulation of the human

synaptophysin (SYP) gene, one REST-target gene, which is also known to be a marker

of NE differentiation in human prostate. The result shows that SYP is expressed at

low level in LNCaP cells and is largely increased during NE phenotype acquisition.

Therefore, these data indicate REST may be one of the important determinants of the

NE phenotype in PCa [4].

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2 The transcription factor REST

Repressor element 1- silencing transcription factor (REST), also known as

neuron-restricted silencing factor (NRSF), is a prominent transcriptional repressor,

regulating a myriad of genes throughout the human body. In 1995, two independent

groups identified that REST/NRSF was a protein that binds a DNA sequence element,

called the neuron-restrictive silencer element (NRSE) which represses neuronal gene

transcription in non-neuronal cells [29, 30]. The REST gene was assigned to the

chromosomic region 4q12 by fluorescence in situ hybridization (Fig. 2.1) [31]. REST

has been shown to play critical roles during embryonic development and neurogenesis.

However, during tumorigenesis REST shows paradoxical roles in different type of

cells. REST acts as a tumor suppressor in human epithelial cancers while it plays an

oncogenic role in the development of brain tumors and other non-epithelial cancers.

Figure 2.1 REST Gene in genomic location: bands according to Ensembl.

2.1 REST and its alternative isoforms

The structure of REST includes a DNA-binding domain and two repression domains

(RD): one at the N-terminal, RD1 and one at the C-terminal, RD2 [32]. REST

recognizes repressor element 1 (RE1) motifs within the control regions of target genes

through its eight finger DNA-binding domain and recruits multiple co-factors,

including mSin3 and CoREST via these two repression domains (Fig. 2.2).

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Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

11

Figure 2.2 Model of REST domains.

The REST gene spans 24 kb of genomic DNA. According to Entrezgene the REST is

composed of four exons. However, the literature indicates that at least six different

splice variants of REST exist (Fig.2.3) [33] and are associated with neural gene

expression and certain disease states [34].

Figure 2.3 REST mRNA and alternative splicing variants.

The human REST gene is shown in Fig. 2.3 (top) illustrating the three main exons (dark blue), an alternative exon (light blue) and one of three alternative 5' non-coding exons (white). The approximate position of sequence encoding eight zinc fingers are illustrated by boxes, these are associated with DNA binding (white) and nuclear localization (red). REST reference sequence (NM_005612.3) codes the major isoform 1. REST 1 and REST-5FΔ code isoform 2 or isoform 4 respectively. The alternative exon in REST-N62, REST-N4 and sNRSF (N50) introduces a premature stop codon (white) and all three transcripts encode isoform 3 (REST4).

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Table 2.1 Molecular weight of REST alternative isoforms (Uniprot.org)

Isoform Amino acids Protein Molecular Weight

(kDa)

Isoform 1 1097 122

Isoform 2 (Δ314-1097), 313 35

Isoform 3 (Δ330-1097), 329 37

Isoform 4 (Δ304-326), 1074 119

Isoform 1 is the canonical sequence, which comprises two repression domains,

lysine-rich (400-603) and proline-rich (595-815) domains, a DNA binding domain

(159-412) of eight zinc fingers. As previously mentioned, REST is able to recruit a

variety of transcriptional co-repressors through binding at RD1 or RD2; examples are

illustrated including Sin3A and CoREST. Isoform 2, which lacks RD2, is truncated

and retaining only zinc fingers 1-4 and localizes to the cytoplasm. Isoform 3, also

called REST4 or sNRSF, is expressed in neuroblastoma and SCLC; it also lacks RD2

and has a truncated DNA binding domain, but it retains zinc fingers 1-5 and nuclear

localization. Isoform 4 has selective deletion of zinc finger 5 [34, 35].

REST4 contains only five zinc fingers of the eight in full length REST and it lacks the

C-terminal zinc finger repression domain. The biological function of REST4 is

controversial. In fact, it does not act as a transcriptional repressor like REST but

rather regulates the repressor activity of REST. In fact, REST4 inhibits the binding of

REST to the RE1 sequence, instead of binding to the RE1 directly [36]. Furthermore,

it has been proposed that different isoforms of REST can regulate distinct patterns of

gene expression and, especially in certain cancer types, may function antagonistically.

2.2 RE1 motif

RE1 is a silencing element which was originally found in the 5’ flanking region of

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NaV1.2 (voltage-gated sodium type II channel) and SCG10 (superior cervical

ganglion 10) genes [37]. RE1 is a general DNA element for the control of

neuron-specific gene expression in vertebrates.

REST interacts with 21-bp RE1 sites via its DNA-binding domain [30]. However,

individual RE1 sites interact differentially with REST under a particular cellular

content, which leads to various RE1-binding profiles. Several independent groups

have performed genome-wide analysis of RE1s and potential REST target genes

across different genomes [38-40].

Figure 2.4 Sequence logo of canonical RE1 motif.

The canonical RE1 sequence is actually a subset of a much larger, more complex

motif. RE1 shows an uneven distribution to the binding of REST of the 21 positions

(Fig. 2.4). The position 2 to 9 and 12 to 17 nucleotides are referred as core positions

of RE1 while position 10 and 11 are two of the least-highly conserved nucleotides

across mammalian species. Furthermore, to address transcriptional regulation by

REST in a global and unbiased manner, serial analysis of chromatin occupancy

(SACO) was utilized to identify the transcriptome regulated by REST [41]. There

were novel REST-binding motifs discovered at the position of 10 and 11: one is the

expanded site where additional nucleotides were inserted; the other one is the

compressed site where nucleotides were removed. These results strongly suggest a

complex mechanism for REST/RE1 interaction for normal neuronal gene repression.

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2.3 REST Co-repressors

The nucleosome is the basic unit of DNA packaging in eukaryotes. The nucleosome

core particle consists of approximately 147 base pairs of DNA wrapped in 1.67

left-handed superhelical turns around a histone octamer consisting of 2 copies each of

the core histones H2A, H2B, H3, and H4. Fundamentally, nucleosomes form the basic

repeating units of eukaryotic chromatin, which is used to pack the large eukaryotic

genomes into the nucleus while still ensuring appropriate access to it. Since they were

discovered in the mid-1960s, histone modifications have been predicted to affect

cellular transcription in different ways [42]. Two of the most important types of

histone modifications are acetylation and methylation of lysine residues [43].

Acetylation of histones on lysine residues neutralizes their positive charge, is a mark

for active transcription. Typically, this kind of reaction is catalyzed by enzymes with

histone deacetylase (HDAC) or histone acetyltransferase (HAT). On the other hand,

methylation of lysine is more complex. Several lysine residues on the tails of histones

H3 and H4 can be mono-, di- or even tri-methylated. The position of the lysine and

the exact state of methylation (in conjunction with other epigenetic marks) determines

whether the region is transcriptionally active, repressed, or silenced. The methylation

of lysine prevents its acetylation. That is to say, acetylation and methylation may act

antagonistically. Histone H3 lysine 4 (H3K4) can be mono-, di- or tri-methylated, and

the two states of di- and tri-methyated H3K4 are hallmarks of transcriptional activity.

H3K9 can be either methylated or acetylated. Acetylation of H3K9 inhibits the ability

of LSD1 (lysine-specific demethylase 1) to demethylate H3K4 (Fig. 2.5) [44].

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Figure 2.5 Histone modification on H3 N-terminus.

The structure of REST includes a DNA-binding domain and two repression domains

(RD): one at the N-terminal, RD1 and one at the C-terminal, RD2 [32].

REST-mediated gene repression is achieved by the recruitment of two separate

co-repressor complexes, mSin3 and CoREST, in addition to other co-repressors such

as the histone H3 lysine 9 (H3K9) methylase (G9a) which enables methyl groups to

add on lysine 27 as well as lysine 9 in H3 [45]. And the NADH-sensitive co-repressor,

carboxyl-terminal binding protein (CtBP) is also included [46]. G9a and CtBP can be

recruited directly by REST but also have been found associated with the CoREST

complex. The mSin3 complex contains two class I histone deacetylases (HDACs),

HDAC1 and HDAC2 while the CoREST complex contains HDAC1 and HDAC2, a

histone H3K4 demethylase (LSD1), the chromatin-remodeling enzyme, (BRG1), also

known as SMARCA4, as well as CtBP, G9a, and a methyl-CpG-binding protein

(MeCP2) [44]. In summary, REST interacts with several complementary

chromatin-modifying activities, with the recruitment of some of these enzymes being

regulated by intracellular signals such as CAMK-mediated phosphorylation and

NADH levels.

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Figure 2.6 Mechanism of repression/activation via REST-RE1 interaction.

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A. REST binds to RE1 sequence through eight zinc finger domains. The N-terminal domain of REST interacts with Sim3A, which in turn recruits HDAC and associated proteins to the promoter region. The C-terminal domain of REST interacts with CoREST which recruits HDAC1 and HDAC2, resulting in inhibition of RNA polymerase activity (RNA PII). B. Mechanism for activation of gene expression by derepression. REST4 inhibits the binding of REST to the RE1 sequence through association with Sin3 co-repressor complex. Then the CoREST-HDAC complex is sequestered away from interacting with REST, which fails to remove acetyl groups from nucleasomal core histones. Thus the RNA PII is able to form a transcriptionally active complex with the promoter.

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2.4 REST and non-coding RNAs

A non-coding RNA (ncRNA) is a functional RNA that is not translated into a protein

such as transfer RNA (tRNA) and ribosomal RNA (rRNA), as well as RNAs such as

small nucleolar (snoRNA), microRNA (miRNA), short interfering RNA (siRNA) and

long non-coding RNA (long ncRNA). It is increasingly clear that ncRNAs are far

from being just transcriptional noise but are involved in chromatin accessibility,

transcription and post-transcriptional processing, trafficking, or RNA editing. REST

and its cofactor CoREST are both highly regulated by various ncRNAs.

Figure 2.7 REST-ncRNA interactions in neural networks. A yet to be identified mechanism initiates the binding of BRG1 to REST, resulting in the exposure of the RE1 motif and the subsequent occupation of it by REST. With the presence of Sin3 and CoREST, the RE1-bound REST complexes recruit HDAC1/2 and MeCP2, to promote histone deacetylations, G9a and LSD1. REST can recruit additional histone methylases and demethylases to target lysine residues of histones; arrows link cofactors to their chromatin remodeling and modifying activities. Inhibition of these histone modifying components results in an attenuation of REST recruitment, a decrease in H3K9me2, an increase in H3K9/K14 and H4 acetylation, an increase in H3K4me3 and a decrease in binding of MeCP2 to methylated DNA. In neural progenitors, REST represses miR-124a expression, allowing the persistence of non-neuronal transcripts. Upon differentiation of the neuronal progenitors into neurons, REST dissociation from the miR-124a gene loci induces the activity of gene derepression, which leads to selective degradation of non-neuronal gene transcripts.

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Initially, REST was found to modulate genes for neuronal differentiation, homeostasis

and plasticity. A similar role has been demonstrated for miRNA and ncRNA networks

in neuronal differentiation and plasticity [47]. The miRNA miR-124 was originally

identified as a miRNA specifically expressed in the brain, and later was shown to

negatively regulate the expression of many non-neuronal genes. Interestingly,

expression of miR-124 is repressed by the transcriptional repressor REST [47]. Fig.

2.7 illustrates the major components of REST-ncRNA interactions in neural networks

[48]. REST-mediated regulation of neuronal gene expression relies on the dissociation

of the REST repressor complex from the RE1 site. In neural progenitors, REST

represses miR-124a expression, allowing the persistence of non-neuronal transcripts.

Upon differentiation of the neuronal progenitors into neurons, REST dissociation

from the miR-124a gene loci induces the activity of gene derepression, which leads to

degrade non-neuronal gene transcripts selectively. Therefore, REST links

transcriptional and post-transcriptional events to fine-tune the balance of phenotype

between neuronal and non-neuronal cells, partly by controlling miR-124 and other

ncRNAs that act in a network associated with miR-124 [48].

2.5 REST function and cancer

Transcription factors often coordinately control complex programs of gene expression

during development as well as the aberrant activation of developmental programs in

cancer. As REST plays crucial roles in the regulation of gene expression, abnormal

expression or dysfunction of REST has been associated with diverse diseases,

including Down’s syndrome [49], Huntington’s disease [50] and various types of

cancer [51]. Interestingly, REST appears to have opposite roles in non-nervous

cancers and tumors in central nervous system (CNS): REST acts as a tumor

suppressor to inhibit tumorigenesis in epithelial cells, while it plays an oncogenic role

during the development of brain tumors.

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Figure 2.8 Experimental models and possible routes for REST dysfunction in cancers. (A,B) In cell line models experimental strategies that reduce REST levels or inactivate REST are associated with transformation or progression of the cancer phenotype. (C) On the contrary, active REST is overexpressed in medulloblastoma. (D–F) In other cancers, genetic deletion or depletion of REST may be accompanied by mutation (D) or alternative splicing (E, F) generating different isoforms such as REST-FS, sNRSF or hREST-N62, lacking the C-terminal repression domain.

REST was identified as a human tumor suppressor in epithelial cells during an

unbiased RNA interference (RNAi) library screening [52]. Functional inhibition of

REST in human mammary epithelial cells increased cellular capability for oncogenic

transformation. Furthermore, a splice variant of REST transcript was highly expressed

in established cell lines and primary small cell lung cancer cultures as well as in some

primitive neuroectodermal tumor biopsies [35]. Both breast and colon cancer can

display some neuroendocrine features, where many neuroendocrine genes that would

normally be restricted through RE1 motifs in non-neuronal cells are aberrantly

expressed [51, 52]. All these findings are corroborated by the loss of REST function

accompanying the acquisition of the neuroendocrine phenotype in LNCaP prostate

cancer cells via transdifferentiation [4]. In human cell lines derived from

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neuroblastoma, additional exons introduce a frameshift and premature termination,

resulting in a truncated DNA-binding domain lacking the C-terminal repression

domain. Therefore these tumors would employ independent strategies to generate the

C-terminal deleted variants that eschew key transcriptional silencing mechanisms as

an alternative route to modulate REST function (Fig. 2.8).

On the contrary, it has been demonstrated that REST acts as an oncogenic promoter in

several types of brain tumors, including medulloblastoma [53] and glioblastoma.

REST is upregulated in the development of medulloblastoma relative to differentiated

neurons or neural progenitors. Ectopic expression of REST-VP16 mutant in

medulloblastoma cells significantly reduced tumorigenic potential of these cancer

cells in mice [54]. And inhibition of REST in medulloblastoma cells triggers apoptosis

through the activation of caspase cascades [53]. Therefore, the effect of REST

inhibition on cell survival in medulloblastoma cells and human epithelial cancer cells

appears totally opposite. Inhibition of REST activity promotes survival of human

epithelial cell but induces apoptosis of medulloblastoma cells. It is not well

understood why REST plays differential roles in different cancer types. The precise

molecular mechanisms associated with this cell type-specific role of REST need to be

further elucidated. Probably, REST forms different dynamic repressor complex

through recruiting other co-activators in different cell types to exert its diverse

biological roles. For the importance of REST in regulating tumor development, more

characterizations on cell type-specific transcription of REST as well as

REST-mediated signaling pathways are necessary.

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3 Aims of research

Prostate cancer (PCa) is one of the most commonly diagnosed cancers in the world:

males have 17% risk of developing PCa during their lifetime. Pharmacological

androgen deprivation therapy (ADT) has been the leading form of PCa therapy since

Huggin’s discovery that castration induced the regression of PCa tumors in 1941[1].

Unfortunately, the effectiveness of this type of treatment is usually temporary, with a

subpopulation of cells surviving and becoming castrate resistant prostate cancer

(CRPC). This form of PCa is most often terminal, with a median life expectancy of

approximately 18 months [23]. Globally, CRPC claims the lives of 32,000 men

annually [24]. The mortality rate of PCa could be lowered by more effective early

detection due to identification of novel molecular markers and better understanding

the molecular mechanisms underlying tumor progression and metastasis. Growing

evidence indicates that neuroendocrine differentiation (NED) occurs in 30-100% of

disease recurrence after ADT [2, 3]. However, the mechanism is still poorly

understood.

Repressor element-1 silencing transcription factor (REST) is generally expressed

throughout the body, which is in charge of restricting neuronal gene expression to the

nervous system by silencing expression of neuronal genes in non-neuronal cells.

REST expression was recently shown to be absent in various types of cancer, such as

small cell lung cancer (SCLC) [55] and colorectal cancer [52]. Blockade of REST

function in the human LNCaP cells is found to cause NED [4].

Here, we aim to investigate the possible role of REST in PCa progression from the

following aspects:

a. Use expression microarray with RNA interference (RNAi) technology to

indentify potential REST targets under human LNCaP cell model

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preliminarily.

b. Sequence the transcriptome of the patient-derived mouse xenograft models

at two different time points from castration to neuroendocrine

differentiation.

c. Detect overlapped genes differentially expressed between two platforms

(microarray on LNCaP model and RNA-seq on xenograft model) and

investigate these genes for pathway and function enrichment analysis.

d. Experimentally validate the highest priority targets, using quantitative

RT-PCR.

e. Estimate the clinical significance of the REST targets using public

datasets.

It is expected that sequencing-based approaches to measuring gene expression levels

have more advantages than microarray. This ultra-high-throughput sequencing

techniques enable thousands of megabases of DNA/RNA to be sequenced in a matter

of day. Furthermore, RNA-Seq has much higher resolution, better dynamic range of

detection and lower technical variation than microarray. Nevertheless, microarray

represents a well-established and widely used technology through last decades. Thus,

compared between these two platforms will provide us with more accurate

information.

The unique patient-derived xenograft model of PCa we used here is based on

sub-renal capsule xenografting of patient tumor tissue in NOC/SCID mice. The

xenograft retains key biological properties of the original malignancies, including

histopathological, genomic and transcriptomic characteristics, tumor heterogeneity

and metastatic ability. Therefore, this high fidelity model is more clinical research

relevant.

We believe all the results could contribute to elucidate the mechanism that drives

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adenocarcinoma to escape AR blockade through NED to androgen independent and

aggressive NEPC. Furthermore, it could provide a new prognosis factor for PCa and

also provide a new target for the PCa treatment.

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4 Materials and methods

4.1 Cell lines

LNCaP cells were kindly provided by Dr. Leland WK Chung (1992, University of

Texas MD Anderson Cancer Center, Houston, TX, USA). LNCaP cells used for

sequencing were provided by Pfizer (La Jolla, CA, USA). LNCaP cell line was

authenticated using aCGH, expression microarrays, and whole-genome and

whole-transcriptome sequencing on the Illumina Genome Analyzer IIx platform

(Illumina, San Diego, CA, USA) at the time of data collection for the current study.

4.2 Prostate tissue samples

All patients signed a consent form approved by the Ethics Board (University of

British Columbia UBC Ethics Board No: H09-01 628; VCHRI Nos: V09-0320 and

V07-0058). Surgical samples were collected and snap-frozen (FF) at the Vancouver

General Hospital (VGH). The remaining tissue was fixed in formalin and used for

histopathological evaluation by three independent pathologists from the VGH

pathology department and Vancouver Prostate Centre (VPC). Snap-frozen and

formalin-fixed blocks are stored at the VPC Tissue Bank. [56].

4.3 Mouse xenograft model

LTL331 xenograft of human prostate cancer was developed by grafting tumor biopsy

specimen (tumor 927) under the renal capsules of nonobese diabetic/severe combined

immunodeficiency (NOD/SCID) mice [57]. The tumor line development protocol and

use of human specimen use was approved by the Clinical Research Ethics Board of

the UBC, in accordance with the Laboratory Animal Guidelines of the Institute of

Experimental Animal Sciences. To collect PCa tissue after castration, fresh LTL-331

tumor tissues from the 5th generation of grafting were cut into 3mm×3mm×1mm

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pieces and re-grafted into the subrenal capsules of male NOD/SCID mice.

4.4 Public dataset

Study data is deposited in NCBI GEO under accession GSE21032. The analyzed data

can also be accessed and explored through the Memorial Sloan-Kettering Cancer

Center (MSKCC) Prostate Cancer Genomics Data Portal:

http://cbio.mskcc.org/prostate-portal/ [58]. This database contains a total of 218 tumor

samples and 149 matched normal samples that were obtained from patients treated by

radical prostatectomy at MSKCC. All patients provided informed consent and

samples were procured and the study was conducted under MSKCC Institutional

Review Board approval. Clinical and pathologic data were entered and maintained in

our prospective prostate cancer database. Following radical prostatectomy, patients

were followed with history, physical exam, and serum PSA testing every 3 months for

the first year, 6 months for the second year, and annually thereafter. For all analyses

described here, biochemical recurrence (BCR) was defined as increase in serum PSA

concentration by ≥ 0.2 ng/ml on two occasions. At the time of data analysis, patient

follow-up was completed through December 2008 [58].

4.5 siRNA and shRNA experiments

siRNAs and shRNAs corresponding to the antisense sequence of human REST gene

were purchased from Santa Cruz Biotechnology, Inc. REST siRNA (ID: sc-38129) is

a pool of 3 target-specific 19-25 nucleotides siRNAs designed to knock down gene

expression. REST shRNA Plasmid (ID: sc-38129-SH) is a pool of 3 target-specific

lentiviral vector plasmids each encoding 19-25 nt (plus hairpin) shRNAs designed to

knock down gene expression. Each plasmid contains a puromycin resistance gene for

the selection of cells stably expressing shRNA. In addition, negative controls were

purchased from the same company: control siRNA-A (sc-37007) and control shRNA

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plasmid-A (sc-108060). The REST siRNA was transfected into LNCaP cells and

allowed to grow for 48 h before harvest with Trizol. The mRNA from this experiment

was prepared for gene expression microarray. The REST shRNA knock-down

experiment was also transfected into LNCaP cells for analysis by RT-PCR.

4.6 Microarray

Total RNA was isolated from the LNCaP cells treated with 2 mg REST siRNA or 2

mg control siRNA for 48 h in duplicate using the Trizol reagent according to the

manufacturer’s instructions. All conditions were done in duplicate. Then RNA quality

was assessed with the Agilent 2100 bioanalyzer prior to microarray analysis. Samples

with a RNA Integrity Number (RIN) value of greater than or equal to 8.0 were

deemed to be acceptable for microarray analysis. Total RNA samples were prepared

following Agilent One-Color Microarray-Based Exon Analysis Low Input Quick Amp

WT Labeling Protocol Version 1.0. An input of 100ng of total RNA was used to

generate Cyanine-3 labeled cRNA and samples were hybridized on Agilent SurePrint

G3 Human 4x180K Microarrays (Design ID 028679). Arrays were scanned with the

Agilent DNA Microarray Scanner at a 3um scan resolution, and data was processed

with Agilent Feature Extraction 10.10 and analyzed by Agilent GeneSpring 11

software. Statistical analysis was performed using unpaired t-test considering

Benjamini–Hochberg corrected p-value of 0.05. Differentially expressed genes with

over log2 fold change (FC) of 1.5 greater than or equal to 1.5 (up or down) were

considered for the present study (supplementary 4.1).

4.7 Transcriptome sequencing

Transcriptome sequencing of the samples was performed at British Columbia Cancer

Agency Michael Smith Genome Sciences Centre (Vancouver, BC, Canada) using the

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the Illumina Genome Analyser II according to established protocols described in [59].

Junction aware alignment tool (Tophat [60]) was employed to map RNA-seq reads

onto transcriptome and genome. Read counts for each gene, transcript, exon or

junction were calculated according to gene model annotation (ENSEBML database, v.

62). Raw read counts were normalized by R-package DESeq [61], which is able to

remove experimental variability for RNA-seq data across different samples. Since

there are only two time points, we simply calculated fold change for each gene. And

differentially expressed genes with log2 fold change (FC) greater than or equal to 1.5

(up or down) were considered for the present study.

4.8 Statistics Analysis

Results were reported as means ± standard deviations. To detect genes differentially

expressed between subsets of tumor samples, a two-tailed Student's t-test was applied

with equal variances with a P value level cut-off of 0.05. Survival curves were

calculated using the Kaplan–Meier method, where the significance was determined by

log-rank test.

4.9 Data analysis

Pathway and functional enrichment analysis was performed using Ingenuity (IPA)

Knowledge Base 9 (Ingenuity®Systems, www.ingenuity.com).

4.10 Experimental RT-PCR validation

Total RNA was extracted from cultured cells after 48 hours of treatment using Trizol

reagent (Invitrogen). Two micrograms of total RNA was reverse transcribed using the

Transcriptor First Strand cDNA Synthesis Kit (Roche Applied Science). Real-time

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monitoring of PCR amplification of cDNA was performed using DNA primers (Table

4.1 ) on ABI PRISM 7900 HT Sequence Detection System (Applied Biosystems) with

SYBR PCR Master Mix (Applied Biosystems). Target gene expression was

normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) levels in

respective samples as an internal standard, and the comparative cycle threshold (Ct)

method was used to calculate relative levels of target mRNAs. Each assay was

performed in triplicate.

Table 4.1 RT-PCR primer sequence

Gene Oligonucleotide primer sequence (5’—3’)

REST-F TGCGTACTCATTCAGGTGAGA

REST-R TCTTGCATGGCGGGTTACTT

Bex1-F GATAGGCCCAGGAGTAATGGAG

Bex1-R CTTGGTTGGCATTTTCCATG

Srrm3-F CATCCTTTTGAGCTGGCTGC

Srrm3-R CCAAGAGTGAGGTGCCTAGC

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5 Results

5.1 Specific and efficient knock-down of REST mRNA in

LNCaP cells

The emergence and development of RNA interference (RNAi) as a biological

mechanism to silence gene expression has enabled mammalian cells to lose function

in a potentially genome-wide manner [38]. The applications of RNAi can be mediated

through two types of molecules; the chemically synthesized double-stranded small

interfering RNA (siRNA) or vector based short hairpin RNA (shRNA). We have

utilized both siRNA and shRNA to identify genes regulated by REST in the human

LNCaP cell model. The efficiency of the RNAi strategy for REST was evaluated by

semi-quantitative real-time PCR in LNCaP cells treated with a REST siRNA

compared with that treated with controls treated with a scrambled siRNA. As shown

in Fig. 5.1, RT-PCR gel shows down-regulation of REST and concomitant

up-regulation of NE differentiation marker SYP, whereas beta-actin was not changed.

Figure 5.1 siRNA knockdown of REST in LNCaP cells. The RT-PCR gel showed down-regulation of REST and concomitant up-regulation of NE differentiation marker SYP, whereas beta-actin (reference gene) was not changed.

Compared to siRNA, small hairpin RNA (shRNA) is an advantageous mediator of

RNAi in that it has a relatively low rate of degradation and turnover. They are

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synthesized in the nucleus of cells, further processed and transported to the cytoplasm,

and then incorporated into the RNA induced silencing complex (RISC) for activity

[62]. The experimental protocols between siRNA and shRNA share similarity except

that shRNA needs puromycin dihydrochloride selection. Thus we selected a batch of

puromycin-resistant clones (a, b, e, f, g, h, j) which may have varying levels of

shRNA expression due to the random integration of the plasmid into the genome of

the cell. As shown in Fig. 5.2, the RT-PCR result indicated that all expression levels of

the shRNA knockdown samples were lower than the three controls (right side),

whereas sample e showed the best silencing efficiency. Thus we continued to use e for

the further studies.

Figure 5.2 shRNA knockdown of REST in LNCaP cells.

Sh(-a to -j) are the experimental groups which are LNCaP cells treated by REST shRNA while control-a, control-c and control-d are LNCaP cells treated by scrambled control RNA. Expression level is calculated from relative Ct value from experimental group and beta-actin (reference gene).

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5.2 High fidelity of mouse xenograft model for

neuroendocrine transdifferentiation

LTL331 xenograft of human prostate cancer was developed by grafting tumor biopsy

specimen (tumor 927) under the renal capsules of SCID mice [57]. The

patient-derived LTL331 xenograft retains the histological characteristics of the patient

tumor and is strongly PSA positive and AR positive (Fig. 5.3 A, B). Androgen

deprivation by castration of LTL 331 mice resulted decreased tumor volume and

serum PSA. In less than 4 months, tumors recurs, accompany with PSA and AR

negative (Fig. 5.3 C, D). This recurrent tumor model is re-named “LTL 331R” and

expresses a range of neuroendocrine markers including most common one

synaptophysin (SYP), as shown E. Judging from this pathological evidence, we

hypothesized our treatment of the LTL 331 model had evolved into the

neuroendocrine differentiated LTL 331R model in response to castration.

Figure 5.3 The LTL-331 xenograft model of neuroendocrine transdifferentiation.

A-E: Immunohistochemical stains before and after NE transdifferentiation.

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5.3 Microarray and RNA-seq analysis for identification of

differential expressed genes

Total RNA of LNCaP cells treated with REST siRNA or scramble control for 48 h

was isolated for comparative expression analysis using human cDNA microarrays.

Overall, 63 genes were found to be up-regulated and 59 genes were down-regulated

over log2- fold change of 1.5 (P< 0.05) after knocking down REST in LNCaP cells

(Figure 5.4 and Supplementary 4.1). The up-regulated genes including SCG3, BEX1,

CHRNB2, SYP, UNC13A, RAB39A, VGF, GDAP1, KCNC1, SYT4, are mainly

involved in cell-to-cell signaling and interaction, molecular transport, small molecular

biochemistry and drug metabolism.

Figure 5.4 Summary of microarray data on LNCaP model. 63 genes were found to be up-regulated and 59 genes were down-regulated over log2- fold change of 1.5 (P< 0.05) after knocking down REST in LNCaP cells As discussed previously, sequencing-based approaches to measuring gene expression

levels have more advantages than hybridization-based approaches such as microarray.

In principle, RNA-seq has much higher resolution, better dynamic range of detection

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and lower technical variation. Moreover, massively parallel sequencing allows

investigation of far more genome and transcriptome features (i.e. alternative splicing,

fusion transcript etc.) than expression microarray which may be used better for the

preliminary characterization. Therefore, we performed trancriptome sequencing on

patient-derived mouse xenograft models at two different time points: before castration

and after neuroendocrine differentiation. We identified 59.7% (18195/30485) genes as

differentially expressed (10381 is up-regulated and 7814 is down-regulated) over log2

fold change of 1.5, providing strong evidence that the majority of genes identified by

sequence data are genuinely differentially expressed (Fig. 5.5).

Figure 5.5 Summary of RNA-seq data on xenograft model. 10381 genes were found to be up-regulated and 7814 genes were down-regulated over log2 fold change of 1.5 after castration on mouse xenograft model.

Interestingly, there was a significant overlap between the genes differentially

expressed in the siRNA knockdown of REST in LNCaP cells and those in LTL-331R.

Overall, we identified 34 genes up-regulated and 12 genes down-regulated

overlapping between these two platforms (Fig. 5.6 Tab 5.1). This enrichment

(p=0.035<0.05; Fisher’s exact test) is statistical significant to the different model

systems.

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Figure 5.6 The overlap between genes differentially expressed from microarray data and from RNA-seq data. The Venn diagram shows the number of up/down-regulated genes shared (red) and platform-specific genes (blue).

Table 5.1 Profiling of overlap genes differentially expressed

Gene Entrez Gene Name Microarray Fold change

RNA-seq Fold change

BEX1 brain expressed, X-linked 1 44.72 10.41 SYT4 synaptotagmin IV 3.37 8.91

KCNH6 potassium voltage-gated channel, subfamily H, member 6 1.86 6.12

GDAP1 ganglioside induced differentiation associated protein 1 6.34 5.94

CHRNB2 cholinergic receptor, nicotinic, beta 2 17.10 5.88

KCNC1 potassium voltage-gated channel, Shaw-related subfamily, member 1 4.19 5.61

UNC13A unc-13 homolog A 13.01 5.61 FGFBP3 fibroblast growth factor binding protein 3 1.57 5.52

CELSR3 cadherin, EGF LAG seven-pass G-type receptor 3 2.40 5.28

SCG3 secretogranin III 143.70 5.17 SYP synaptophysin 16.17 5.10 OLFM1 olfactomedin 1 2.43 5.04 SRRM3 serine/arginine repetitive matrix 3 2.40 5.01 BSN bassoon (presynaptic cytomatrix protein) 2.05 4.48 STMN3 stathmin-like 3 2.80 4.12 RAB39 RAB39A, member RAS oncogene family 8.24 3.99

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RIC3 resistance to inhibitors of cholinesterase 3 homolog 1.74 3.50

GEMIN5 gem (nuclear organelle) associated protein 5 1.72 3.03

AP3B2 adaptor-related protein complex 3, beta 2 subunit 2.35 2.99

TMEM198 transmembrane protein 198 3.13 2.87 CCDC64 coiled-coil domain containing 64 1.86 2.81 SYN1 synapsin I 1.72 2.72

SCN8A sodium channel, voltage gated, type VIII, alpha subunit 2.21 2.61

ECSIT ECSIT homolog 1.59 2.56 SECISBP2 SECIS binding protein 2 1.68 2.41 VGF VGF nerve growth factor inducible 6.71 2.06 KLHL2 kelch-like 2, Mayven 1.51 1.93

MMP24 matrix metallopeptidase 24 2.66 1.83

SLC2A14 solute carrier family 2 (facilitated glucose transporter), member 14 1.57 1.81

TMEM180 transmembrane protein 180 1.92 1.77

PTPRN2 protein tyrosine phosphatase, receptor type, N polypeptide 2 1.86 1.76

NOLC1 nucleolar and coiled-body phosphoprotein 1 1.69 1.69 MANEAL mannosidase, endo-alpha-like 1.87 1.67 DPYSL4 dihydropyrimidinase-like 4 2.09 1.55 PLAGL2 pleiomorphic adenoma gene-like 2 -1.70 -1.52 SP9 Sp9 transcription factor homolog -1.51 -1.87

CMPK2 cytidine monophosphate (UMP-CMP) kinase 2, mitochondrial -2.02 -2.07

C2orf50 chromosome 2 open reading frame 50 -2.04 -2.36 DAPK1 death-associated protein kinase 1 -1.66 -3.00

TMEFF2 transmembrane protein with EGF-like and two follistatin-like domains 2 -1.56 -3.20

SLITRK3 SLIT and NTRK-like family, member 3 -2.39 -3.33 FZD2 frizzled family receptor 2 -1.50 -3.59 IL28A interleukin 28A -2.14 -3.83 GBP4 guanylate binding protein 4 -1.76 -3.89 ARG1 arginase, liver -1.96 -4.43 CAV2 caveolin 2 -2.20 -4.51 REST RE1-silencing transcription factor -4.95 -5.38

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5.4 Functional categorization analysis

Ingenuity pathway analysis (IPA) functional analysis for pathways and diseases

revealed many pathways represented in our overlapping dataset, including cell-to-cell

signaling and interaction, molecular transport, small molecule biochemistry, and

cellular assembly and organization (Fig. 5.7). We overlapped our results with

functional pathways or disease categories defined by IPA, the data in Fig.5.7 represent

the most significant sub-level function in each category. Cell-to-cell signaling and

interaction, molecular transport, small molecule biochemistry and neurological

disease were the most significantly represented in our dataset. People found that

cell-to-cell signaling influences the fate of prostate cancer stem cells and accelerates

them into more aggressive tumors [63].

Figure 5.7 Functional categorization analysis of most significant pathways and diseases.

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A functional categorization analysis of the most significant pathways and diseases represented in the microarray-and-RNA-seq-generated list of significantly different genes was generated using Ingenuity software. The enrichment significance for each function is provided as a Benjamini–Hochberg adjusted p-value. The Benjamini-Hochberg procedure aims to calculate the False Discovery Rate (FDR) for each of the p-values. Each bar represents the highest level of function for each category, each of which includes many sub-level functions. The bar was determined by the lowest p-value among sub-level functions for each category. Threshold indicates p-value adjusted with Benjamini-Hochberg cutoff of 0.05.

5.5 Network analysis

To investigate the interaction of these differentially regulated genes with other gene

pathways and biological processes, IPA was used to form molecular networks based

on functional relationship between gene products referenced in peer-reviewed

literature. Other groups of genes, which were biologically relevant to the pathway but

not identified in our experiment, were also included. The top 4 networks among the

significant genes up/down regulated in our overlap dataset represent many pathways

suspected to be involved in cellular assembly and organization, nervous system

development and function, neurological disease, cellular compromise, cell cycle,

hereditary disorder, and drug metabolism, lipid metabolism, molecular transport and

so on. Each network was identified based on a numerical rank score according to the

degree of relevance of the network to the molecules in the significant genes list and

based on the hypergeometric distribution calculated as –log (p-value). In these

network illustrations, genes or gene products are represented as nodes, and the

biological relationship between two nodes is represented as an edge (line). All edges

are supported by at least one published reference.

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The score is a numerical rank of the degree of relevance of the network to the molecules in the significant genes list and is based on the hypergeometric distribution calculated as –log (Fisher’s exact test result). The bold genes were in our overlapping dataset with up-regulated genes in red arrow and down-regulated genes in green arrow.

Network 1 (Fig. 5. 8) received the highest score (44) and was assembled 18 focus

genes that were differentially regulated in REST knockdown condition. In particular,

Network 1 contains gene and molecules known to be involved in nervous system

development and function (SYN1, UNC13A), neurological disease (BSN, CHRNB2,

KCNC1, SYN1), small molecule biochemistry and cell-to-cell signaling and

interaction (CHRNB2, SYN1, SYT4, UNC13A). REST acts as one of the hubs in this

network, directly interacting with 6 genes (SYT4, SCG3, CHRNB2, KCNH6, SYP, SYN)

which significantly up-regulated in our datasets. This network also reveals that REST

acts on other 9 genes (PCLO, PHKB, TRPM2, SCN1A, B3GAT1, FGF12, NCAM2,

Table 5.2 IPA-generated molecular networks

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PHF21A, RCOR2) which were not shown in our over-expressed dataset.

Of the genes involved in the top ranked network which were not altered in our

experiments, death-associated protein kinase 1 (DAPK1), which is directly regulated

by ERK1/2 in the network, plays a crucial role in metastasis of carcinoma cell lines

and delay in growth of tumor. The top canonical pathways derived from this network

were bladder cancer signaling (DAPK1, ERK1/2, FGF12, MMP24), Huntington’s

disease signaling (Creb, ERK1/2, RCOR2, REST), protein kinase A signaling

(Calmodulin, Creb, ERK1/2, PHKB), ERK/MAPK signaling (Creb, ERK1/2, estrogen

receptor) which is involved in the development of many cancers, and androgen

signaling (Calmodulin, Creb, ERK1/2) which plays a critical role in the development,

function and homeostasis of the prostate.

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Figure 5.8 Ingenuity pathway analysis Network 1.

Ingenuity pathway analysis was used to assemble a network based upon 18 differentially expressed focus genes. Genes are represented as nodes, with node shape representing the functional class of the gene product (seen in legend). Node color depicts degree of overrepresentation in our experimental data; uncolored nodes are depicted based upon evidence in the IPA Knowledge Base indicating a strong biological relevance to the network. Corresponding fold change values are listed beneath each gene label.

Furthermore, in order to view all molecular interactions through a more

comprehensive pathway network, we merged top 3 IPA-generated molecular networks:

Network 1, Network 2 and Network 3 described in Table 5.2. This merge network

(Fig. 5.9) covered 39 focus genes (18 in Network 1, 12 in Network 2 and 9 in

Network 3) , in total 103 genes and molecules which were associated with cell-to-cell

signaling and interaction, nervous system development and function, neurological

disease, cellular function and maintenance and molecular transport. Specifically, this

comprehensive network contains several cancer-related biomarkers: EGFR, NFkB,

TNF, TH1 cytokine, FGF2.

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Figure 5.9 Combination of Network 1, Network 2 and Network 3.

Ingenuity pathway analysis was used to assemble a merge network based upon Network 1, Network 2 and Network 3 (Table 5.2), covering 39 differentially expressed focus genes. Corresponding fold change values are listed beneath each gene label. All 103 gene names are defined in Supplementary 5.1.

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5.6 Experimental RT-PCR validation

Representative genes BEX1 and SRRM3 were selected from overlap datasets of

differentially up-regulated genes for validation via semi-quantitative RT-PCR, using

the same LNCaP cell line used in the microarray. In the further research, we utilized

REST knockdown LNCaP cell line via shRNA instead of siRNA. And as shown in Fig

5.2, among the batch of puromycin-resistant clones (a, b, e, f, g, h, j), sample e group

shows the best silencing efficiency.

BEX1 is signaling adapter involved in p75 neurotrophin receptor/ Nerve Growth

Factor Receptor (p75NTR/NGFR) signaling, which plays a role in cell cycle

progression and neuronal differentiation. BEX1 increased by 44.7 fold change in our

microarray platform while increasing by 10.4 fold change in the RNA-Seq platform.

Interestingly, the semi-quantitative RT-PCR result (Fig. 5.10) showed that BEX1

significantly over-expressed in e which has better REST silencing efficiency than

other groups. Thus, the RT-PCR result of BEX1 is consistent with both microarray

and RNA-seq data.

Figure 5.10 BEX1 expression level in REST shRNA knockdown LNCaP cells.

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Serine/arginine repetitive matrix protein 3 (SRRM3), belongs to the complexed with

Cef1 protein 21 (CWC21) family. The biological function of SRRM is still unknown.

However, we found SRRM3 might activate an alternative splicing on PHD finger

protein 21A (PHF21A), a REST associated genes from our preliminary data. SRRM3

increased by 2.4 fold change in microarray platform while increasing by 5.0 fold

change in the RNA-Seq platform. In the RT-PCR result, e group showed the

significant increase of SRRM3 expression level (Fig. 5.11).

Figure 5.11 SRRM3 expression level in REST shRNA knockdown LNCaP cells.

ShRNA_REST are the experimental groups which are LNCaP cells treated by REST

shRNA while controla and controlc are LNCaP cells without any treatment.

Expression level is calculated from relative Ct value from experimental group and

beta-actin (reference gene).

5.7 Kaplan Meier survival analysis

In order to estimate their clinical significance of the preliminary set of potential REST

targets we identified, we utilized the Kaplan–Meier analysis of the survival which

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measures the fraction of patients living for a certain amount of time after treatment.

However, prostate tumors show tremendous biological heterogeneity, coupled with its

high prevalence. Genomic-based classification is needed for more informed clinical

decision-making. Therefore we extended our research to an external public dataset

which contains transcriptomes and copy-number alterations (CNA) in 218 prostate

samples (181 primaries, 37 metastases) and 12 prostate cancer cell lines and

xenografts through Memorial Sloan-Kettering Cancer Center (MSKCC) Prostate

Cancer Genomics Data Portal: http://cbio.mskcc.org/prostate-portal/ [58].

After obtaining a general view of all gene alterations in a cohort of 216 tumors, we

selected a small high priority groups of genes identified by microarray and RNA-seq

based on their percentage of alteration and alteration trend. Three genes BEX1, SCG3,

and UNC13A were found to be up-regulated in both LNCaP and LTL331 experimental

systems. Interestingly, all three of these candidate genes show shortened disease free

survival (Fig. 5.12).

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Figure 5.12 Kaplan–Meier survival plots on 3 selective genes in a public dataset

prostate tumors (p<0.01).

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6 Discussion

Protate cancer is a clinically and biologically heterogeneous disease. Neuroendocrine

prostate cancer is an aggressive subtype of prostate cancer that most commonly

evolves from preexisting prostate adenocarcinoma. Remarkably, growing evidence

indicates that neuroendocrine differentiation occurs 30-100% of prostate cancer

recurrence especially after androgen deprivation treatment.

The unique patient-derived xenograft model of PCa we used here is based on

sub-renal capsule xenografting of patient tumor tissue in NOC/SCID mice. The

xenograft retains key biological properties of the original malignancies, including

histopathological, genomic and transcriptomic characteristics, tumor heterogeneity

and metastatic ability. Therefore, this high fidelity model is more clinical research

relevant.

It must be stressed that we still need a more comprehensive system to acquire a more

detailed understanding of neuroendocrine differentiated prostate cancer. We are still

far away from identifying all the regulated genes or molecules involved in this

phenotype; it is possible to do so through microarray and RNA-seq, as microarrays

give a broad overview whereas RNA-seq detects subtle changes in gene expression

and detects alternative aplicing, which occurs quite often in cancer progression.

RE1 silencing transcription factor REST is expressed throughout the body and it was

recently shown to be absent in various types of cancer including prostate cancer.

REST transcriptional complex acts as a master repressor of the neuronal phenotype.

Blockade of REST function in the human LNCaP cells is found to cause NED,

indicating REST may be one of the important determinants of the neuroendocrine

phenotype in PCa.

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In our study, an integrated data analysis of RNA-Seq on mouse xenograft model and

expression microarray with RNA interference (RNAi) on human LNCaP cells was

performed to identify potential REST targets: 34 genes were up-regulated and 12

genes were down-regulated. Afterwards, a functional categorization analysis of the

most significant pathways and diseases represented by these significantly different

genes was generated using Ingenuity software. To investigate the interaction of these

differentially regulated genes with other gene pathways and biological processes,

molecular networks were formed based on functional relationships between gene

products described in peer-reviewed literature. We found that REST acts as one of the

hubs in the most significant network, directly interacting with 6 genes (SYT4, SCG3,

CHRNB2, KCNH6, SYP, SYN) significantly up-regulated in our datasets, as well as

other 9 genes (PCLO, PHKB, TRPM2, SCN1A B3GAT1, FGF12, NCAM2, PHF21A

RCOR2) which were not shown to be over-expressed in our dataset. We then extended

our research to an external public dataset which contains transcriptomes and

copy-number alterations (CNA) in 218 prostate samples (181 primaries, 37 metastases)

and 12 prostate cancer cell lines and xenografts. In addition, in order to estimate their

clinical significance, we utilized the Kaplan–Meier analysis of survival which

measures the fraction of patients living for a certain amount of time after treatment,

correlated to the expression of particular genes.

This preliminary study investigated the potential role of REST in prostate cancer

progression. It is expected that during the neuroendocrine differentiation, REST may

help to repress the expression of several genes not yet required by the differentiation

program. And, REST may function different ways at different stages of cell

differentiation as changing the expression of associated genes. Thus, further studies

are needed to understand these problems.

We identified high consistency between microarray and RNA-seq platforms, thus

encouraging the continual use of this method as a tool for differential gene expression

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analysis. In conclusion, our study contributes to the elucidation of the basic biological

mechanism that drives adenocarcinoma to escape AR blockade through NED to

androgen independent and aggressive NEPC. Furthermore, REST may serve as a

potential target for the treatment of prostate cancer and a novel prognosis factor for

prostate cancer.

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