Epigenetic Signatures for Undifferentiated Prostate Cancer Cells
1. BACKGROUND
Undifferentiated prostate cancer cells are a critical cellular source of castration-resistant
prostate cancer (CRPC).
Prostate cancer (PCa) is a heterogeneous disease that contains both differentiated and
undifferentiated tumor cells. The undifferentiated PCa cells are pluripotent prostate
cancer stem cells (PCSCs) that can generate differentiated PCa cells through asymmetric
cell division (ACD) (Qin 2012). PCSCs may reside in quiescent state with minimal
cellular activities and insensitive to the mainstay androgen-deprivation therapy (ADT) as
well as standard-of-care chemotherapy, therefore represent a critical cellular source of
aggressive CRPC. CRPC typically develops following one to three years of ADT, and
effective treatments are limited (Hotte 2010). With intrinsic resistance to ADT, PCSCs
are unique candidates to study pathological mechanisms of CRPC and discover potential
therapeutic targets. To fully understand the molecular mechanisms of CRPC, it is
important to systematically detect aberrations in PCSCs and investigate how these
aberrations are causing tumor genesis properties and castration resistances.
Histone modifications are key epigenetic marks to characterize PCSC.
PCSC differentiation is an epigenetic process that normally does not involve genomic
sequence alterations, thus epigenetic signatures can be used to effectively distinguish
PCSCs from bulk tumors. Although the cancer stem cell hypothesis has been intensively
explored in various cancer models, global epigenetic landscape and the epigenetic
mechanisms underlying PCSC remain poorly understood. Post-translational
modifications of histone proteins are a family of critical epigenetic marks that regulate
gene transcription, chromatin remodeling and other fundamental cellular processes
(Sawan 2010). Aberrant histone modifications at gene promoter/enhancer regions may
lead to androgen mediated silencing of tumor suppressor genes or activation of protooncogenes (Chen 2010). Global histone modification levels have been reported to have
significant statistical correlation with prostate cancer status, and can be used as predictor
of clinical–pathological parameters, including relapse-free survival rate, preoperative
prostate specific antigen (PSA), Gleason score and metastasis status (Seligson 2009;
Bianco-Miotto 2010; Ellinger 2010). Individual studies have also linked H3K9me to the
repression of PSA (Yamane 2006; Wissmann 2007). These results suggest that histone
modification mediated epigenetic mechanisms are actively involved in prostate cancer
development and progression.
Histone modification mark extended promoter/enhancer domains in cancer cell
subpopulations.
One major epigenetic function of histone modifications is to mark active genomic regions
for transcription factor binding, i.e. promoters and enhancers. The diverse activities of
transcription factor bindings in active promoters/enhancers control cell-type-specific gene
expressions, therefore are particularly useful in characterizing cancer cell subpopulations.
“Super enhancers” are a special category of genomic regions with extended sizes to
harbor clusters of enhancers, which are occupied by master transcription factors that
regulate stem cell differentiation (Whyte 2013). Super-enhancers are reported to be
associated with critical oncogenic drivers in cancer cells (Loven 2013). Similarly,
extended size promoters, marked by elongated H3K4me3 peak signals, were also
observed in public ChIP-seq samples in our pilot study (Figure 3A). This “super
promoter” pattern resembles “super enhancers” in size, transcription factor density, and
the ability to distinguish cancer stem cell from bulk tumor cells. Specific histone
modification patterns associated with “super promoters” and “super enhancers” present a
unique angle to explore key transcription factor regulations in PCSC differentiation, and
provide critical insight to the epigenetic mechanisms of CRPC.
Our preliminary studies show PCSC specific histone modification patterns.
Our previous study (Qin 2012) showed that PCSCs are enriched in prostate cancer cells
with little or no prostate specific antigen (PSA) expression (i.e. PSA-/lo cells), whereas
PCa cells with high PSA expression (i.e. PSA+ cells) are differentiated PCa cells and are
more sensitive to ADT. PCSCs can then be purified through fluorescence-activated cell
sorting of PCa cells infected with PSAP-GFP lentiviral reporters.
Figure 1
H3K4me3 ChIP-seq in
LNCaP PCSC (red) /
non-PCSC (blue).
A)
ChIP-seq
peak
overlap Venn diagram.
B) Heatmap for specific
H3K4me3 peaks in
LNCaP
PCSC/nonPCSC.
C) Example of PCSC
specific
H3K4me3
peaks in the promoter
of short isoform of
SOCS3.
To evaluate whether specific histone modifications can effectively distinguish PCSCs
from bulk tumors, we carried out a pilot study to profile active promoter mark H3K4me3
on PCSCs and non-PCSCs derived from LNCaP cells, through whole-genome Chromatin
Immunoprecipitation sequencing (ChIP-Seq). Although the majority of H3K4me3 peaks
in these two cell populations show good overlap and reproducibility (Figure 1A), a subset
of H3K4me3 peak (Figure 1B) is significantly different between PCSCs and non-PCSCs.
Consistent with the biological properties of PCSCs and non-PCSCs, the genes
specifically associating with H3K4me3-occupied promoters in PCSCs are developmentrelated genes (e.g., SOX11, DACH1, FOXD3, CXCL12) and SC markers (e.g., CD24,
ALDH5A1) whereas genes in non-PCSCs are enriched for functions related to cell
metabolism and AR signaling (e.g., KLK2, PSA, FKBP5). Strikingly, several
neuronal/neural development-related genes are also preferentially occupied by H3K4me3
in PSA-/lo PCa cells (e.g., NRXN1, BRSK1). SOCS3, a key inflammatory signal inhibitor
that has been reported to correlate with aggressive prostate cancer progression (Puhr
2010, Pierconti 2011), also has specific H3K4me3 peak in PCSCs on the promoter of its
shorter isoform (Figure 1C). To fully understand the underlying mechanisms of the
histone modification specificity in PCSC, we propose to define epigenetic signatures for
PCSCs and test their functional relevance in CRPC by extending the preliminary study to
a comprehensive investigation of multiple key histone modifications.
2. HYPOTHESIS / OBJECTIVE
We hypothesize that alterations in histone modification patterns contribute to the unique
regulatory mechanisms of PCSCs, resulting in aggressive tumor genesis and propagation,
and leading to castration resistance. The objective is to identify epigenetic signatures
specific to PCSCs, and ultimately discover core signaling pathways, biomarkers and
potential drug targets for CRPC.
3. SPECIFIC AIMS
3.1 Specific Aim 1: To discover PCSC specific combinatorial histone modification
patterns .
We plan to profile 5 key histone modifications of PCSCs derived from LNCaP cells and
LAPC9 xenograft, including H3K4m1, H3K4me3, H3K9me3, H3K27me3 and
H3K36me3. We aim to identify differential patterns for each histone modification and
combine them to discover PCSC specific combinatorial histone patterns, and explore
underlying mechanisms in CRPC.
3.2 Specific Aim 2: To discover PCSC specific super promoters (elongated
H3K4me3 peak patterns)
We plan to explore a novel elongated peak patterns for H3K4me3 (i.e. “super
promoters”) that can distinguish PCSCs from non-PCSCs. This peak pattern is
conceptually similar to the “super enhancers” that as been shown to have master
regulatory functions in maintaining the differentiation statuses in stem cells (Whyte
2013). We aim to identify PCSC/non-PCSC specific “super promoters” and associated
gene sets for functional analyses.
4. RESEARCH STRATEGY
4.1 Specific Aim 1: To discover PCSC specific combinatorial histone modification
patterns
Rational: Histone modifications vary greatly in genomic distributions, peak patterns and
regulatory functions (Strahl 2000). Accumulating studies revealed highly coordinated
interactions between multiple types of histone modifications to accomplish different
regulatory functions in dynamic cellular environments (Wang 2008, Suganuma 2011,
Linghu 2013). A specific histone modification pattern, termed "bivalent domains",
consists of activate mark H3K4me3 and repressive mark H3K27me3 co-localized on
gene promoters (Bernstein 2006; Sanz 2008). Bivalent domains typically occur on
developmental genes that are silenced in stem cells but posed to be activated at
developmental stages, therefore are highly relevant in characterizing PCSCs
differentiation. More generally, the great diversity of histone modifications provides a
variety of possible combinatorial patterns that may be used to discover the unique
mechanism of CRPC tumor genesis and progression. However, a comprehensive
investigation of these combinatorial histone modification patterns in PCSCs has not been
done, and key epigenetic signatures remain undefined for PCSCs. We aim to bridge this
knowledge gap by applying novel bioinformatics methodologies as well as integrating
existing data processing pipelines where appropriate to systematically investigate
combinatorial histone modification patterns for PCSCs, including both known
combinatorial patterns, i.e. bivalent domains, and de novo combinatorial patterns. We
propose to carry out correlation studies of these epigenetic signatures in clinical samples
and explore their functional relevance in CRPC.
Experimental Design: We plan to perform ChIP-seq profiling for both LNCaP cells and
LAPC9 xenograpfts, in 5 key histone modifications, including H3K4me1, H3K4me3,
H3K9me3, H3K27me3 and H3K36me3, will be (Table 1). H3K4me3 and H3K27me3
are active/repressive promoter marks respectively, and co-localization of H3K4me3 and
H3K27me3 marks the bivalent domain promoters that could poise genes for activation
upon stem cell differentiation. H3K4me1 are active enhancer mark. Enhancer activity is
highly dynamic and transient during PCSC differentiation, and mark cell lineage specific
regulations. H3K9me3 are repressive marks for heterochromatin domain and have been
reported to the down regulation of PSA (Yamane 2006; Wissmann 2007). H3K36me3 is
typical gene body mark that positively correlate with actively transcribed genes. Together
these 5 histone modification cover active/repressive marks in the majority of regulatory
regions in the genome, and represent a collection of key histone modifications with
various regulatory functions. For each histone modification, we will perform ChIP-seq
profiling with 2 biological replicates for PCSCs and non-PCSCs respectively. Two
replicates of input controls will also be included.
Table 1 Proposed Histone Modification ChIP-seq.
Activation
Repression
Promoter
Mark
H3K4me3
H327me3
Enhancer
Mark
H3K4me1
Gene Body
Mark
H3K36me3
Heterochromatin
Mark
H3K9me3
Prostate cancer cells are infected with PSAP-GFP lentiviral reporter and incubated. For
LNCaP system, the infected cells will be incubated for 72 hours. For LAPC9 xenograft,
the infected cells were incubated for ~18hrs and injected into NOD/SCID mice to
establish reporter tumors. PSA-/lo and PSA+ isogenic subpopulations are purified through
fluorescence-activated cell sorting (FACS). The top 10% GFP-bright (i.e., PSA+) cells
and bottom 2-6% negative cells (i.e., PSA-/lo) were selected. We have tested and
validated one novel library preparation method, called ThruPLEX, to directly prepare
picogram amounts of DNA for Illumina next generation sequencing. Using this method,
we can determine histone modification patterns in very limited number of PCSCs (e.g.
5000).
Data Preprocessing: Raw ChIP-seq reads will be mapped to the reference genome using
short read mapping software BOWTIE (Langmead 2009), only uniquely mapped reads
will be retained. We will use MACS (Zhang 2008) to generate the whole genome ChIP-
seq signals. The ChIP-seq profiles will be normalized to a total read number of 10 million
per sample. We will build a UCSC track hub to integrate all histone modification ChIPseq datasets for flexible visualization. Processed ChIP-seq data will be compiled into
standard WIGGLE format for UCSC genome browser (http://genome.ucsc.edu/) (Kent
2002). Raw sequencing data and processed ChIP-seq WIGGLE files will be deposited in
NBCI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/).
Identification of Combinatorial Histone Modification Patterns: We will develop a
supervised machine learning approach to discriminate the histone modification profiles of
PCSCs and non-PCSCs, and identify significant differentially marked regions. A
classification model based on recursive feature elimination support vector machines
(RFE-SVM) will be built on the preprocessed histone modification ChIP-seq signals by
assigning different weights to signals to maximize separations between PCSCs and nonPCSCs. This model adjusts itself to fit the data by recursively removing the most
insignificant signal (feature) until the prediction error rate is lower than the termination
threshold. The remaining features will be sorted by the absolute weights to generate a
ranked list of differential peaks (Figure 1A).
Figure 2 Histone modification ChIP-seq differential analysis pipeline. A) ChIP-seq signal extraction
and differential analysis between PCSC/Non-PCSC. B) Combinatorial pattern discovery for
multiple histone modifications.
Different histone modification marks co-localize in different functional regions and may
not overlap with each other. We will associate peaks with their target genes and combine
the differential results for each individual histone mark at gene level. For each histone
mark, the differential status of a certain gene is represented by a differential score D,
which is defined as non-linear transformation of the differential p-value using the
following logistic function (Figure 2B):
1- p a
D=
1+ p a
where p is the signed p-value with positive value for enrichment and negative value for
depletion, and a represents the significant level in differential test, typically a = 0.01.
The differential score is bounded between -1 and +1, which represent PCSC specific and
non-PCSCs specific signals respectively, therefore allows comparisons of differential
results of multiple histone modification for combinatorial pattern discovery.
We will use ChromHMM software to identify common histone mark patterns (Kellis
2012). Genes with similar differential statuses will be grouped together and define
specific gene set signatures for PCSCs and non-PCSCs (Figure 2D). These gene sets
signatures will be tested for functional significance, including gene ontology (GO)
enrichment, pathway analysis and other correlations through gene set enrichment analysis
(GSEA) (Subramanian 2005). Our lab has access to commercial software applications
that provide extensive analysis based on large scale literature and database mining,
including Ingenuity Pathway Analysis (IPA) and Oncomine (Rhodes 2007), a canceroriented application. To explore the clinical relevance, the combinatorial histone
modification patterns and gene set signatures can also be integrated with proteomic data
and various tumor characteristics of the clinical samples in TCGA database (The Cancer
Genome Atlas, http://cancergenome.nih.gov/). We plan to perform meta-analyses to
combine these analysis results and generate a comprehensive framework connecting
molecular signatures to the underlying mechanisms and finally to the clinical outcomes.
4.1.5 Expected Outcome and Potential Problems
We expect to identify PCSC specific signatures for combinatorial histone modification
patterns, as well as PCSC specific signatures for each individual histone modifications.
We expect to generate a list of gene signatures defined by the specific histone
modification patterns for experimental validations with our bench collaborator. Through
correlation study with existing clinical data, we may further discover novel biomarkers
and drug-targets for PCSCs.
One potential technical problem is that the RFE-SVM approach in the histone
modification differential analysis is a highly computational intensive algorithm, and may
be a computational bottleneck in the analyses. To address this issue, we will use the
BlueBioU super computer in Rice University as extra computation resource. Although
our goal is to develop automatic data analysis pipeline for large scale whole genome
profile comparison, computer programs may not be optimal in handling certain biological
scenarios. Manual curations are necessary in some cases, especially in the stages of target
gene discovery and functional analyses. We expect to adjust our data processing pipeline
according to the feedback of bench collaborators and iteratively improve the analysis
quality.
4.2 Specific Aim 2: To discover PCSC specific super promoters (elongated
H3K4me3)
Rational: As one of the most widely studied histone mark, H3K4me3 is an active
promoter mark with peak intensities positively correlate with gene transcriptions
(Bernstein 2005, Heintzman 2007). In contrast to the typical sharp and narrow H3K4me3
peak pattern located in the proximal promoters, we observed an elongated H3K4me3
peak pattern that spreads into the gene body for several hundreds to thousands base pairs
(Figure 3). These unusual H3K4me3 peak patterns mark a significantly larger active
promoter regions that may be capable to recruit more transcription factors and more
complex transcription machinery for dynamic regulations. It’s conceptually similar to the
“super enhancers” that mark active enhancer domains for master transcription factors that
regulate embryonic stem cell differentiations (Whyte 2013), hence we termed them as
“super promoters”. We hypothesize that similar mechanisms in “super enhancer” also
exist in “super promoters” patterns marked by extensive H3K4me3 signal domains. The
master regulator of “super promoter” may possess critical functions in maintaining
differentiation status in PCSCs, therefore provide potential targets to interfere the CRPC
development.
“Super promoter” patterns
are commonly neglected in
existing
cancer
histone
modification studies that
focused on the heights of
H3K4me3 peaks. Through
large scale data mining in the
public ENCODE ChIP-seq
dataset
(Raney
2011;
Rosenbloom
2012),
we
observed a wide existence of
“super promoters” in a cell
population specific manner
(Figure 3A), suggesting
potential relationships with
cancer statuses. In our
preliminary H3K4me3 ChIPseq data for LNCaP cells, we
Figure 3 Enlongated H3K4me3 peak (“super promoter”) pattern also found evidences of
detected in A) cancer (red) v.s. normal (blue) in ENCODE
“super promoters” patterns
database. B) PCSCs (red) v.s. Non-PCSCs (blue) in LNCaP cells. distinguishing
PCSCs/nonPCSCs cells (Figure 3B).
TMEFF2, an androgen-regulated gene with anti-proliferative effects in prostate cancer
cells (Gery 2002), displays an elongated H3K4me3 peaks in non-PCSC cells, suggesting
such peak pattern may be highly relevant in revealing the connections between prostate
cancer stem cell differentiation /proliferation and androgen dependence.
Data Preprocessing: The reads mapping, whole genome profile generation and
visualization processes are same as Aim 1. To facilitate differential peak width detection,
we propose to use a modified approach developed for nucleosome positioning data
processing (Chen 2012) to extract histone mark signals, based on the rational that histone
modification signals follow the spatial occupancy of nucleosomes. We consider three
basic peak transition patterns in signal extraction (Figure 4A): 1) Peak intensity change,
which reflects the difference of histone modification levels within same genomic regions.
This is the most studied case in ChIP-seq data analysis. 2) Peak location shift, typically
caused by the nucleosome displacement. The significance of location shift of histone
modification signals depends on the extent of nucleosome displacement and need to be
processed accordingly in the signal extraction procedure. 3) Peak size change, which is
commonly observed in broad domain histone modification patterns, such as H3K36me3
and H3K9me3. In our case, it is also critical to accurately extract the peak width
information in H3K4me3 data for identification of “super prmoters” with extended peak
sizes. To allow effective comparisons across different transition patterns, we develop an
adaptive binning method based on the principle that converts complicated histone
modification peak patterns in the latter two cases to simple peak height changes. We
define data bins with respect to these three transitions patterns and extract ChIP-seq
intensities (Figure 4A):
1) For peak with occupancy change,
we will use the consensus peak
boundary to define the data bin;
2) For peaks with location change,
we will either assign peaks to
separate bins if they are far apart, or
allocate them in one data bin if they
are close, according to distance
threshold
measured
by
the
“fuzziness” of histone modification
peaks using a statistical model
derived to detect the nucleosome
position deviations (Jiang and Pugh
2009) (Chen 2012).
3) For peaks with size expansion, we
will split the wide peak into multiple
bins at the boundary of narrow peaks.
Figure 4 A) ChIP-seq signal extraction by adaptive
binning methods. B) “Super Promoter” discovery.
Identification of elongated H3K4me3 peaks: The preprocessed signals were extracted at
aligned boundaries for different samples and allow sensitive comparisons across samples.
Through this process we convert the “wide vs. narrow” or “still vs. moved” peak
comparisons into the classic “present vs. absent” comparisons. Peak width changes can
be detected as differential signals adjacent to non-differential signals from proximal
genomic regions. Base don this principle, we will develop a signal processing pipeline to
reconstruct wide peaks from divided signals with differential status and detect significant
peak width changes as “super promoters” (Figure 4B).
“Super promoters” represent a unique set of genes that may possess more sensitive and
flexible responses to cellular environments, including PCSC differentiation and androgen
receptor signaling. We plan to perform functional analyses similar to Aim 1 on super
promoter genes, including gene ontology (GO) enrichment, pathway analysis, gene set
enrichment analysis (GSEA) (Subramanian 2005), Ingenuity Pathway Analysis (IPA) and
Oncomine (Rhodes 2007).
Active promoters often harbor binding motifs which could identify upstream transcription
factors. We will perform sequence analyses to discover enriched motifs in the “super
promoters”, including both known motif and de novo motifs. Based on the detected
motifs, we may discover master upstream regulators to “super promoter” genes and
further reveal the regulatory network of “super promoters”.
We plan to perform meta-analysis to study the correlation of “super promoter” genes with
various tumor characteristics in TCGA clinical samples. We aim to combine these results
to generate a comprehensive network to of the two cell subpopulations and discover
novel molecular mechanisms in maintaining the differentiation status of PCSCs, as well
as better define CRPC aggressiveness.
Expected Outcome and Potential Problems: Using the customized differential peak width
pipeline, we expect to identify “super promoter” patterns in both PCSCs and non-PCSCs.
We will define associated gene signatures of the “super promoters” in PCSCs and nonPCSCs. We expect to discover regulatory mechanisms similar to the “super enhancer”
in “super promoter” genes, such as discovering master regulatory transcription factors in
PCSC differentiation. Through meta-analyses in clinical samples, we expect to establish
potential connections between “super promoter” genes and clinical classifications of
CRPC.
On potential problem is that elongated H3K4me3 peak patterns may be the results of
alternative promoters of multiple isoforms. To accurately distinguish alternative
promoters from single elongated H3K4me3 peak domain, we will collaborate with Dr.
Tang’s group to validate the detected “super promoters”. If the “super promoter” patterns
cover significant alternative promoters in gene annotation, we may either perform EST
sequencing or whole genome RNA sequencing to identify the transcripts of the interested
genes.
5. COLLABORATION
We are collaborating with Dr. Dean Tang from MD Anderson Cancer Center, who has
extensive experience in prostate cancer research and will provide suggestions from
biological and disease perspectives. Dr. Tang's lab has been routinely using both
hormone-naive and hormone-refractory prostate tumor samples. Dr. Tang's lab has
utilized 162 primary untreated patient prostate tumors ranging from Gleason Grade 6 to
9/10. These samples have been employed not only in efforts aimed to establish 'primary'
xenograft tumors but also in preparing single cell fractions for biological studies. Dr.
Tang's lab has been collaborating with Dr. Chris Logothetis's group at the GU Med
Oncology of M.D Anderson Cancer Center by using prostate cancer patients BM
aspirates to study the relationship between PCSCs and metastasis.
The Human Genome Sequncing Center (HGSC) of Baylor College of Medicine (BCM),
which is one of three large-scale sequencing centers funded by the National Institutes of
Health, will provide sequencing service for the histone modification ChIP-seq. The Dan
L. Duncan Cancer Center (DLDCC) at Baylor College of Medicine will provide high
performance computer cluster, data storage, and software maintenance.
6. OVERACRHING CHALLENGES AND FOCUS AREAS
This proposal aim to address the overarching challenge of develop effective treatment
and understanding mechanisms of resistance for men with high-risk or metastatic prostate
cancer, i.e. CRPC. With intrinsic capability of tumor genesis and insensitivity to ADT,
PCSCs are a driving force of CRPC development, therefore present a key therapeutic
target. However, PCSCs make up less than 1% of the prostate cancer cell population (Qin
2012), and its molecular signatures are greatly diluted in the tumor environment and have
not been subject to comprehensive characterization. A systematical investigation of key
epigenetic signatures in PCSCs will greatly increase current understanding of the
regulatory mechanisms of cancer stem cells in CRPC tumor progression and may lead to
identification of novel biomarker/drug targets for CRPC. In specific aim 1, we expect to
define combinatorial histone modification patterns in PCSCs and discover target genes as
well as core signaling pathways leading to androgen independence or alternative
mediations in CRPC. In specific aim 2, we will explore a specific novel H3K4me3
modification patterns that suggest potential extra transcription activities specific to cancer
status, which may reveal novel regulatory co-factors related to castration resistance.
We propose to carry out comprehensive bioinformatics studies in two focus areas of
prostate cancer research program (PCRP). 1) Biomarker discovery. We aim to identify
epigenetic signatures in PCSCs as biomarkers with better specificity to classify cancer
stem cells in CRPC. 2) Resistance Mechanisms. We aim to reveal the underlying
mechanisms of androgen independence or alternative mediation through PCSCS cell
proliferation and differentiation in CRPC.
This proposal emphasizes on the bioinformatics analyses of large-scale histone
modification ChIP-seq datasets. We propose to develop novel data processing
methodologies and integrate existing approaches where appropriate, and ultimately
provide comprehensive bioinformatics solutions for CRPC biomarker discovery through
epigenetic signatures. We plan to develop an integrated bioinformatics package for
combinatorial histone modification profiling, which can be used in not only in
classification of PCSCS/non-PCSCs, but also in more general comparative epigenetic
studies in other prostate cancer cell subpopulations. We expect to release this data
analysis package as an open source software to the research community, along with the
publication of the specific results on epigenetic signatures of CRPC from PCSCs.