identification of rest regulated genes in prostate cancer via high-throughput technologies ·...
TRANSCRIPT
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
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.
Dedication For my parents and 天海祐希.
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
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
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
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
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
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.
V
...................................................................................................................... 44
Figure 5.12 Kaplan–Meier survival plots on 3 selective genes in a public dataset
prostate tumors (p<0.01). ............................................................................. 46
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
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.
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):
3
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
4
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
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).
6
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.
7
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,
8
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
9
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].
10
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).
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).
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).
12
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
13
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.
14
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].
15
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.
16
Figure 2.6 Mechanism of repression/activation via REST-RE1 interaction.
17
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.
18
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.
19
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.
20
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
21
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.
22
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
23
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
24
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.
25
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
26
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
27
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
28
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
29
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
30
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
31
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).
32
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.
33
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
34
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.
35
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
36
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
37
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.
38
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.
39
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
40
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.
41
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.
42
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.
43
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.
44
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
45
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).
46
Figure 5.12 Kaplan–Meier survival plots on 3 selective genes in a public dataset
prostate tumors (p<0.01).
47
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.
48
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
49
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.
50
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