Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Title:

Chemotherapy-induced distal enhancers drive transcriptional programs to maintain the chemoresistant state in ovarian cancer.

Authors:

Stephen Shang1, Jiekun Yang1, Amir A. Jazaeri2, Alexander James Duval1, Turan Tufan1, Natasha Lopes Fischer1, Mouadh Benamar1,3, Fadila Guessous3, Inyoung Lee1, Robert M. Campbell4, Philip J. Ebert4, Tarek Abbas1,3, Charles N. Landen5, Analisa Difeo6, Peter C. Scacheri6, Mazhar Adli1#

1 Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, 1340 JPA, Pinn Hall, Charlottesville, VA 22908, USA

2 Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA

3 Department of Radiation Oncology, University of Virginia, Charlottesville, VA 22908, USA

4 Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285, USA

5 Department of Obstetrics and Gynecology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA

6 Department of Genetics and Genome Sciences, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH 44106, USA.

# Correspondence: Mazhar Adli, Ph.D. Email: [email protected] Address: Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, 1340 JPA, Pinn Hall, Rm: 640, Charlottesville, Virginia, 22902

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ABSTRACT

Chemoresistance is driven by unique regulatory networks in the genome that are distinct from those necessary for cancer development. Here, we investigate the contribution of enhancer elements to cisplatin resistance in ovarian cancers. Epigenome profiling of multiple cellular models of chemoresistance identified unique sets of distal enhancers, super-enhancers (SEs) and their targets that coordinate and maintain the transcriptional program of the platinum- resistant state in ovarian cancer. Pharmacologic inhibition of distal enhancers through small molecule epigenetic inhibitors suppressed the expression of their target and restored cisplatin sensitivity in vitro and in vivo. In addition to known drivers of chemoresistance, our findings identified SOX9 as a critical SE-regulated (TF) that plays a critical role in acquiring and maintaining the chemoresistant state in ovarian cancer. The approach and findings presented here suggest that integrative analysis of epigenome and transcriptional programs could identify targetable key drivers of chemoresistance in cancers.

INTRODUCTION

The American Cancer Society estimates 22,240 new cases of ovarian cancer (OC) in 2018 (1). Unfortunately, the five-year survival rate of OC remains less than fifty percent. Thus, nearly 14,000 women in the USA and 160,000 worldwide die of OC each year (2). Epithelial ovarian cancers, which account for nearly 90% of all OC diagnoses are associated with worse prognosis (3). They originate mainly from the epithelial cells of fallopian tubes (4, 5) and areas of endometriosis (6), among others. Critically, 75% of the patients with epithelial OC are high grade serous ovarian cancer (HGSOC) (7) that are more challenging to effectively treat. The frontline therapy for OC involves the combination of cytoreductive surgery followed by platinum and taxane-based chemotherapy. Platinum-based compounds such as cisplatin induce increased DNA damage through interstrand cross-links and cell death in proliferative cancerous cells (7, 8). Despite the high rate of initial response to therapy, the duration of response declines over time and a vast majority of patients succumb to chemotherapy- resistant ovarian cancer (9-12). Recent genomic approaches have shed significant light on the genetic risk factors of OC. Low- grade ovarian tumors often harbor BRAF, KRAS, BRCA1/2, and PTEN mutations whereas high-grade tumors are uniformly characterized by TP53 mutations (13, 14). Apart from the antiangiogenic agent bevacizumab, and partially effective PARP inhibitors for patients with BRCA1/2 mutations (15), targeted therapies are lacking for ovarian cancer. Although specific genetic alterations such as reversion of germline BRCA1/2 mutations and inactivating mutations in tumor suppressor RB1, NF1, RAD51B, and PTEN genes were noted in some chemoresistant patients (16), the molecular network that drives and

2 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. maintains the chemoresistant state in ovarian cancer is largely unknown. In addition to genetic alterations, epigenetic regulation of proximal promoters and distal enhancers are critical determinants of cellular identities. Alterations in the chromatin landscape are increasingly recognized as hallmarks of malignant cellular states (17-19). Due to technical limitations, previous ovarian cancer epigenetic studies primarily focused on targeted DNA methylation at individual gene promoters. Although these studies implicate differential methylation at multiple genes, such as MLH1 (20), SFRP1 (21), BRCA1 (16), MAL (22), FANCF (23) with chemoresistance, there have been limited attempts to comprehensively map differentially regulated gene promoters and distal enhancers in ovarian cancer. In this study, we aimed to identify molecular drivers of chemoresistance in ovarian cancer through unbiased epigenomic and transcriptional profiling across multiple cellular models of ovarian cancer. We aimed to map differentially regulated proximal promoters and distal enhancers in multiple cellular models of ovarian cancer. By integrating genome-wide maps of a well-characterized epigenetic mark of active regulatory genomic elements with profiles, we aimed to identify differentially regulated proximal promoters and distal regulatory elements that are specifically associated with chemoresistance across multiple OC cell lines. To this end, we generated multiple isogenic cellular models of cisplatin resistance and performed ChIP-Seq analysis of the Histone H3, Lysine 27 acetylation (H3K27ac) epigenetic mark, which is deposited to active enhancers and promoters. By integrating ChIP-Seq maps with RNA-Seq gene expression profiles across naïve and chemoresistant cellular counterparts, we found that the chemoresistant state is associated with largely cell type-specific sets of distal enhancer elements. Critically, we found significant upregulation of distal enhancer clusters known as super- enhancers (SE) in resistant cells. Small molecule epigenetic drugs that target enhancers and super- enhancers result in significant decrease in the expression of their target genes and an increase in cisplatin sensitivity in chemoresistant HGSOC cells. Our findings identified, in addition to known drivers of chemoresistance, SOX9 as a critical SE-regulated transcription factor (TF) that plays a critical role in chemoresistance across multiple ovarian cancer cell lines.

MATERIALS and METHODS:

Cell culture Human ovarian cancer OVCAR4, CAOV3, OV81 and COV362 cell lines were cultured in complete medium consisting of RPMI 1640, 20 % heat-inactivated FBS, 1% Pen/Strep. SKOV3 Cells were cultured in complete medium consisting of McCoy’s 5A, 10% heat-inactivated FBS (FBS, Sigma Aldrich), 1% Pen/Strep (100U/ml penicillin, 100μg/ml streptomycin (PAA Laboratories GmbH). Cells were cultured incubator at 37 °C in a humidified atmosphere consisting of 5 % CO2 and 95 % air. The cells were originally obtained from ATCC and monitored periodically for mycoplasma contamination. The cells were validated using FTA Sample Collection Kits for Human Cell Authentication Service (ATCC).

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Creating Cisplatin Resistant and Resensitized Cell Lines Cells were grown in their respective culture media and passaged for at least two generations after thawing to ensure proper viability. When the cells reached 80% confluency, they were split into two 6cm plates with 40% confluency. Cells were treated with an initial dose of 1 μM cisplatin in 3 mL complete media. After 4 hours, the media for both control and treated cells were aspirated and replaced washed with an equal volume of PBS twice before replacing with drug-free complete media. Cells were allowed to recover for two passages, and treated with the same or increasing dose of cisplatin depending on the viability levels. Once the cells gain resistance, either the dose was increased or cells were periodically treated with cisplatin to maintain the chemoresistant state. Resistant SKOV3, cells reached a maximum concentration of 20 μM cisplatin tolerance, OV81 cells reached a maximum concentration of 40 μM OVCAR3 cells reached a maximum periodic dose of 18μM cisplatin and OVCAR4 cells the largest sustainable periodic dose of cisplatin was 12 μM.

MTT and Crystal violet cell proliferation and viability assay Each cell line was seeded at a density of 4-6x102 cells/well in flat-bottomed 96 well culture plate in 100uL of the culture medium. Stock solutions of cisplatin were subjected to serial dilutions to give final concentrations ranging from 0.1μM to 250μM. JQ1 stock suspended in DMSO was first diluted to 10x of the final concentration in DMSO, then further diluted in PBS or complete media to concentrations of 0.25 to 2μM. The dilutions were added to equal volumes of cell culture in triplicate wells and then the cells were left to incubate. After 24 hours, the media was aspirated and then washed once with 1 volume of PBS, then replaced with 1 volume of the cell’s respective complete media and left to incubate. After 48 hours, 10% well volume of stock MTT diluted to 5mg/mL was added to 100-150uL of fresh complete media, then added to each well after aspirating the previous media. After 3-4 hours of incubation, 1 equal volume of MTT solubilization media (10% SDS, 0.1% tris HCl) was added to each well, then covered, and stored at room temperature or in the incubator for 7+ hours. The plate is read on a plate reader that shakes the plate, then reads absorbance at 590 nM. Background absorbance was taken to be the readings of control wells with no cells. These treatments were carried out to determine IC50 values i.e. drug concentrations required for 50 % cell kill, as well as synergism between drugs.

For crystal violet assays, fresh media was added every 3-5 days after initial treatment. After 10-14 days, the wells were stained for 30 minutes with crystal violet solution (0.4% crystal violet, 10% formaldehyde, 80% methanol). After staining, the crystal violet solution was removed, and then the stained cells washed once with PBS and 3+ times with water. The plate was inverted overnight and covered to dry the well for imaging with a custom 3D printed insert on an Epson tabletop scanner.

Chromatin Immunoprecipitation Experiments:

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SKOV3, OVCAR4, CAOV3, or COV362 naive, resistant, or resensitized cells were grown to 80% confluency in 15cm plates. 2x107 cells were cross-linked in 1% formaldehyde in complete media, or trypsinized, spun down, then resuspended in complete media containing 1% formaldehyde. After 15 minutes, the samples were quenched with glycine to a final concentration of 0.125M glycine. Cells were then scraped off with a cell scraper and then collected with the mixed quenched media to 50mL Falcon tubes, where they were spun down and then resuspended in SDS lysis buffer (1% SDS, 10mM EDTA, 50mM Tris-HCl, pH 8.1) at a ratio of 1mL per 2x107 cells. Pulse sonications were performed for 9 minutes at 40% amplitude with 30% on/70% off on a Brandon Digital Sonifier (Model 250) with a total of 1 mL with maximum 50% SDS Lysis Buffer solution diluted with ChIP Dilution Buffer (0.01%SDS, 1.1% Triton X-100, 1.2mM EDTA, 16.7mM Tris-HCl, pH8.1, 167mM NaCl). ChIPs were performed with antibodies for K27ac (Abcam, 4729, Lot GR286678-1) and mouse anti-SOX9 (AB5535, Millipore, Lot 2847051). Pulldowns were performed with 50%/50% mixed Dynabeads A (1002D, Lot 00326545) & G (1004D, Lot 00342019). DNA quantities were measured by Qubit 2.0 (Promega QuantiFluor dsDNA System) and Bioanalyzer.

DNA and RNA isolation RNA was isolated using Qiagen Trizol (#15596108). DNA was isolated using phenol chloroform extraction. Quantity was measured using the Nanodrop 2000 Spectrophotometer at 260nm.

PCRs and qPCRs PCR experiments were performed on an Eppendorf Nexus Gradient equipment. Real-time qPCR was performed on a StepOnePlus Applied Biosystems instrument with SYBR Green or TaqMan polymerase.

ChIP-seq and RNA-seq Library Preparation ChIP-seq libraries were prepared using the Illumina TruSeq ChIP Library Preparation Kit. RNA-Seq libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina. Libraries were prepared according to the company’s instruction. Qubit and bioanalyzer measurements were used to determine the library quality.

Apoptosis assay: Cells were seeded into 3 6-well plates per sample at 30% confluency, then allowed to settle over-night. Each well was treated with DMSO control, JQ1 (1μM), Cisplatin (3μM or 6μM), or both premixed in RPMI complete media. 24 hours later, the wells are washed once with PBS and refreshed with new complete media. 48 hours later, the cells are checked under a microscope, then trypsinized, collected, and washed

5 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. twice with PBS. All steps further are performed on ice. Unstained and single stained controls are aliquoted and spun down. Controls are stained with either Annexin V binding buffer (eBioscience 00- 0055-43) or binding buffer with DAPI or Annexin-FITC, and the samples are stained with both. Data was collected inside the UVA Flow Core facility on a BD Biosciences FACScalibur instrument with 30,000 collected events per sample.

RNA-Seq data analysis: Paired-end reads were acquired using HiSeq 2500 (50bp) or NextSeq 500 (75bp) system on high throughput mode from UVA sequencing core. Reads were aligned to the hg19 genome using bowtie2 or hisat2. Read abundance was estimated using either tophat2 or stringtie, depending on if bowtie2 or hisat2 was the aligner, respectively. The counts were then normalized and compared for differential expression as per the DESeq2 R package. Custom R Scripts were used to perform further normalization and quality control. Genes with significant variance between each replicate and with 0 read counts in any of the replicates were removed. Downstream plots used the pheatmap, heatmap.3, and ggplot2 packages. Clustering was performed using kmeans or hclust packages. Downstream pathway enrichment analysis was performed through pre-ranked GSEA and DAVID .

ChIP-Seq data analysis Single-end reads were acquired using MiSeq, HiSeq 2500 (50bp) or NextSeq 500 (75bp) from UVA sequencing core or Hudson Alpha. Reads were aligned using bowtie or bowtie2, duplicates removed by samtools. Peaks were called using MACS2 (24) or MACS1.4. Superenhancers were defined using K27ac intensity vs rank as published in Whyte et al., 2013. State-specific enhancers for the SKOV3 system was defined as having an intensity of 5 fold-change higher over the other chemoresistance state. Normalization and differential peak analysis were performed using a custom R script utilizing edgeR or DESeq2 as referenced in DiffBind R package. The normalized read counts (affinity scores) were used to generate the plots through the DiffBind, pheatmap, and ggplot2 packages. Clustering was performed using kmeans or hclust. Gene association was performed through bedtools + DAVID or GREAT analysis defined as proximally associated if within 12.5kb inclusive window, or the most proximal upstream and downstream genes. SE-associated genes are defined as proximally associated genes whose gene expression goes up or down in the same direction as the SE, and whose expression in the resensitized cells falls in between the naïve and resistant expression values. SOX9 binding site annotation was performed through HOMER.

Western Blots Cells lysates were quantitated with a standard Bradford assay using Bio-Rad Protein Assay Dye Concentrate (Cat. #500-0006) and BSA as a control. After running the gel, a dry transfer system was

6 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. used (Life Technologies, IB1001), (7 minutes at 20 volts). Membranes were blocked in 5% nonfat dried milk (BioRad #170-6404). The SOX9 antibody was diluted to 1:500 (AB5535, Millipore, Lot 2847051) alpha-Tubulin (1:2000 ; Invitrogen #62204).

CRISPR/Cas9 mediated genetic knockout experiments: Cas9 and sgRNA expressing plasmid (AddGene 1000000048) was used in all CRISPR experiments. The BsmBI sites were introduced from another plasmid. 20 nucleotide sgRNAs were designed using the UCSC genome browser and/or GECKO library and checked for off-targets using CROP-IT. Overhangs of 5’-CACC-3’ and 5’-AAAC-3’ were added to the 5’ of the forward and reverse complementary oligos, respectively. Forward and reverse sgRNA oligos were mixed in annealing buffer (10mM Tris pH 7.5-8, 50mM NaCl, 1mM EDTA), then heated to 95C, annealed in a stepwise thermal decrease and finally ligated with a BsmBI cut p413 plasmid before transformation. Positive sequences confirmed through colony PCR with the U6 promoter primer + reverse sgRNA oligo primer through Sanger sequencing. Vector controls include a non-targeting 20 nucleotide control guide sequences.

HEK293T Cells were transfected with PsPAX2 and Pmd2G with FuGene6 (Promega E2691) in a 4:1:5 ratio inside OptiMem media (Gibco #31985070). Media was exchanged to fresh complete DMEM media (10% FBS, 1% P/S) after 4 hours, and then the media collected and replaced after 24 and 48 hours. The collected media was then syringe filtered through 0.22μM filters, and then stored at 4°C for immediate use or -80°C for long term storage up to 6 months. Ovarian cancer cells were seeded at 40-60% confluency and allowed to attach for 8+ hours. Cells were then infected with the lentiviral mix with 10ug/mL polybrene or a control media and after 16 hours overnight, and subject to puromycin selection for 72 hours or until all the control cells died. For all cloning purposes competent DH5α cells were used.

Mouse xenograft experiments and in vivo drug treatment: The mouse studies were conducted in accordance with the guidelines established by the University of Virginia Animal Care and Use Committee (ACUC). 5 million cells diluted in 200uL PBS were subcutaneously injected into each hind quarter of 32 mice (Jackson Laboratories). Tumors were allowed to form (25mm3 or larger), then separated into 4 groups averaging 80 mm3 per tumor per group with 8-10 tumors distributed between 5-6 mice. Mice were treated with DMSO control or with JQ1 at 250uL per mouse (50mg/kg) (Selleckchem S7110 Lot. 07), cisplatin at 100uL per mouse (3mg/kg) (TEVA, NDC 0703-5748-11) or with both JQ1 and Cisplatin. Except for the first week’s Wednesday’s treatment, JQ1 treatment was applied on Tuesdays. Cisplatin was applied on Fridays of each week. Tumor volumes were measured twice a week using calipers along two axes with the formula: Tumor Volume = Long x

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Short2. After the conclusion of the experiment, mice were euthanized according to ACUC guidelines and policies.

RESULTS

Generating cisplatin-resistance in ovarian cancer cellular models. To gain molecular insights into cisplatin-induced epigenetic and transcriptional changes, we first generated multiple cellular models of cisplatin resistance. We derived cisplatin-resistant ovarian cancer cells from their sensitive counterparts by periodically exposing OVCAR4, OVCAR3, OV81, and SKOV3 ovarian cancer cell lines to increasing doses of cisplatin treatment. Here, we use the term naïve to indicate relative sensitivity regardless of whether the cell line was originated from a naïve or treated primary patient tumor. The treatments were performed as a 24h pulse of cisplatin exposure followed by two cisplatin-free passages (see Methods) (Figure 1a). Notably, the cell lines used in this study are widely used in OC research as model systems for HGSOC. However, recent molecular profiling efforts suggested that the origin of some of these cell lines such as SKOV3 is likely non-serous epithelial ovarian cancer (25, 26). Nevertheless, these in vitro systems together provide a tractable model to study the molecular dynamics of chemoresistance in ovarian cancer. The process was repeated until each cell line developed significant resistance to cisplatin. The acquired chemoresistance was assessed through both short-term MTT cell viability assays (3 days) as well as longer-term (2 weeks) crystal violet assays (Figure 1b, c, d, Supplementary Figure 1). Additionally, we also developed “re-sensitized” cellular counterparts of the SKOV3 system by culturing cisplatin-resistant cells in the absence of cisplatin for an extended period (>6 months). We observed significant restoration of cisplatin sensitivity in these previously resistant cells (Figure 1b (last panel), Supplementary Figure 2). Reasoning that these partially resensitized cells could provide additional layers of information about the chemoresistance process, we also profiled epigenomic and transcriptomic signatures of these cells to further filter and identify chemoresistance-associated aberrantly regulated proximal and distal regulatory elements.

Identifying chemoresistance-mediated chromatin state alterations at regulatory genomic regions. To identify differentially regulated genomic elements and their gene targets, we performed chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) against the H3K27ac mark in naïve and chemoresistant counterparts of OVCAR4, OVCAR3, OV81, and SKOV3 cells. H3K27ac marks active promoters as well as enhancer elements for a given cell type (27). Comparative analysis of H3K27ac chromatin state maps across a panel of naïve-matched cisplatin-resistant ovarian cancer cells demonstrates substantial chromatin remodeling in cisplatin resistant cells. To better interpret

8 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. the epigenetic alterations and associate the changes with proximal gene expression, we also acquired transcriptomic profiles from two different cellular models by acquiring RNA-Seq gene expression profiles. To comparatively analyze the epigenome and transcriptome profiles across samples, we identified genomic regions that are significantly enriched (q < 0.01) for H3K27ac signal using the established MACS2 ChIP-Seq peak calling algorithm (24). The significantly enriched overlapping regions in different samples were then merged and their enrichment scores were calculated. The peaks with 30 or fewer reads per region were filtered out. This analysis identified a total of 36,388 distal and proximal regulatory elements across the four cellular models. To stratify the peaks differentially regulated upon platinum resistance, we quantified the relative fold changes of H3K27ac coverage per element separately for each matched naïve/chemoresistant pair. Importantly, we observed that 6,568 (18%) of the regulatory genomic regions undergo significant epigenetic reprogramming during cisplatin resistance. Interestingly, a large fraction (88%) of the differentially regulated genomic elements were cell-type specific ([n=5,792], Figure 1d-e). Integrative analysis of gene expression profiles with the differentially regulated enhancer elements indicates that the cell-type specific epigenetic changes associate with corresponding transcriptional alterations in the relevant cell types. For example, among the 778 genes that are proximally associated with the active enhancers specific to the resistant OVCAR4 cells, 577 (74%) of them have increased expression in the OVCAR4 resistant cells. In contrast, the same set of genes does not show any expression changes in the SKOV3 cells. In the same cells, 916 (63%) of 1,459 genes proximal to the naïve specific enhancers reduce their expression during chemoresistance (Figure 1e). Notably, as expected, the genes that are proximal cell type specific clusters are enriched with gene ontology terms related to cell specific regulatory networks (Supplementary Figure 3). On the other hand, when genes that are proximal to all enhancers that are up or downregulated in one or more cell types are comprehensively analyzed, we observed gene ontology terms related to cellular phenotypes and signaling pathways that are more relevant to drug resistance (Figure 1f). For example, genes that are proximal to the resistant-specific enhancers are significantly enriched for epithelial to mesenchymal transition (EMT), and TGFβ and WNT signaling pathways, which are known drivers of EMT (28). Furthermore, DNA damage response and drug resistance genes are also spatially associated with the resistant specific enhancers (Figure 1f). In contrast, genes near enhancers that are decommissioned in resistant cells are implicated in cellular migration, chemosensitivity, and apoptosis. In line with previous reports (29, 30), these findings suggest that cisplatin resistance is associated with upregulation of mesenchymal and stemness-related genes, and downregulation of migratory and somatic cell phenotypes. We next investigated whether these cisplatin resistant cells are generally resistant to other chemotherapeutic and drugs. To this end, we tested multiple drugs and chemicals (taxane, JQ1, Et-OH), however we did not see stronger resistance of these cells to these chemicals (Supplementary Figure 4). In addition to these phenotypic assessments, comparative transcriptomic analysis revealed that

9 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. unique sets of genes are differentially up- and downregulated during cisplatin and taxane resistance (Supplementary Figure 5). These results suggest that unique epigenetic and transcriptomic reprograming takes place during cellular resistance to cisplatin and taxane resistance.

Mapping resistant-specific super-enhancers (SEs) in HGSOC cells. Recent epigenomic analysis of enhancers shows that certain genomic regions contain clusters of enhancers that have been named as “super-enhancers” (SEs)(31-33). Importantly, cell type-specific TFs, master regulators of disease states, cell fates and oncogenesis tend to be regulated by SEs (31-33). SEs can be computationally defined by the presence of clustered genomic regions marked by intense H3K27ac ChIP-Seq signals (31). We therefore aimed to identify SEs and their targets genes in each of our cellular models. In total, we identified 304 and 311 SEs in the OVCAR4 and SKOV3 resistant cells, respectively. Importantly, a large fraction of these SEs only exists in the chemoresistant state. For example, 88% (n=266) of the SEs identified in the OVCAR4 resistant cells are resistant-specific. These enhancers are either absent or not intense enough to be classified as SEs in the naïve cells (Figure 2a- b). Interestingly, we observed substantially more (> 2.5 fold) resistant-specific SEs than the naïve- specific SEs in the SKOV3 system (Figure 2b). However, we observed a comparable number of naïve- and resistant-specific SEs in the OVCAR4 system (Figure 2b). Notably, in the SKOV3 model, we also acquired H3K27ac ChIP-Seq epigenomic maps in the cells that lost their cisplatin resistance due to prolonged culture in cisplatin-free media (resensitized cells). Critically, as shown in Figure 2c for the SIRPA locus, resistant-specific SEs lose nearly half of the H3K27ac signal intensity in the resensitized cells. Globally, around 47% of all resistant-specific SEs return to the naïve state in the resensitized cells, indicating that cisplatin resistance state is associated with dynamic gain and loss of enhancers and SEs (Figure 2d). Importantly, the genes that are proximally associated with the SEs (within 12.5 kb or the nearest gene) are expressed at higher levels compared to the genes near typical enhancers (Figure 2e). In line with this, a set of genes that are associated with state-specific SEs are on average expressed more robustly in cells when these SEs are active (Figure 2e). Specifically, the genes near the resistant- specific SEs are expressed at higher levels in resistant cells. Similarly, the genes near the naïve-specific SEs are expressed significantly higher in naïve cells. Notably, a highly comparable trend is observed in the SKOV3 system, where we also have the resensitized counterparts. For example, genes near the resistant-specific SEs are expressed at higher levels in the resistant SKOV3 cells compared to the naïve or resensitized cells (Figure 2e).

Platinum resistance is associated with increased cell signaling but decreased cell metabolism pathways Cell type-specific transcriptional programs are tightly regulated by state-specific TFs. To identify differentially expressed genes and potentially their driver factors, we integrated the state-specific

10 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. epigenomic data with transcriptome analysis across the naïve vs cisplatin-resistant cellular model systems (Figure 3a). Notably, we observed that ~1,000 genes were significantly upregulated and 816 genes significantly downregulated in the resistant cells for both the OVCAR4 and SKOV3 systems (Figure 3a). Critically, the pathway-level analysis indicates that the genes whose expression is significantly enhanced in the resistant cells are implicated with increased cell signaling. We observed that gene targets of a number of major signaling pathways are enriched and expressed at higher levels in the resistant cells (Figure 3b). For example, gene targets of TNF mediated NF-κB signaling, IL2/STAT5, and TGFβ, and WNT signaling pathways are all among the genes that are significantly enriched in the genes upregulated in the resistant state across the two cellular models. Furthermore, in line with the genes near the upregulated cis-regulatory elements (Figure 1f), genes involved in EMT and DNA repair pathways are enriched in upregulated genes. In contrast, the analysis of downregulated genes suggests that metabolic pathways are generally downregulated in the chemoresistant cells (Figure 3b). Specifically, genes involved in catabolic processes and major metabolic pathways such as oxidative phosphorylation, fatty acid metabolism, and TCA cycle are significantly enriched among the downregulated genes in the resistant cells. Encouraged by these findings, we performed Seahorse-mediated metabolic profiling. In line the gene set enrichment analysis, we observed that cisplatin SKOV3 cells have decreased glycolysis and oxidative phosphorylation as measured by over all extracellular acidification rate and oxygen consumption rate (Supplementary Figure 6). In addition to metabolic genes, genes implicated in apoptosis and -related genes are among the significantly downregulated genes in chemoresistant cells (Figure 3b). Reasoning that the transcriptional program of the chemoresistant state is governed by a unique set of TFs that are upregulated during chemoresistance, we set out to investigate the major TFs involved in this process. To this end, we integrated epigenomic and transcriptomic analyses to identify those TFs that are potentially regulated by resistant-specific TFs and whose expression is induced during chemoresistance. Given the known role of SEs in driving the expression of key cell type-specific master regulators, we further prioritized the list of TFs by focusing on resistant-specific SE-regulated TFs. Notably, among the significantly upregulated and SE-associated TFs were multiple previously known players of chemoresistance including ZEB2, E2F7, (31, 33), KLF6 and ELK3(34, 35) (Figure 3a). In addition to known TFs, we also identified other SE-driven genes previously implicated in chemoresistance including ALDH1A1 (36), AKAP12 (37), FN1 (38-40), RAD18 (41), VEGFC (42), DNAJB12 (43), and PARK7 (44). Critically, the analysis also identified several TFs such as SOX9, HLX, MYBL1, ZNF430, and ZNF502 that have not previously been implicated in platinum resistance in ovarian cancers (Figure 3a). Notably, the analysis of gene expression and patient survival in TCGA data shows that higher expression of a number of these TFs such as ELK3, HIC1, ZEB2, and ZNF430, are significantly (p<0.05) associated with poor patient survival (Figure 3c) (45). These observations suggest

11 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. that the identified resistance-associated TFs play significant roles in clinical settings and overall patient survival. To investigate whether the expression of these TFs are dynamically induced by cisplatin treatment across different HGSOC cell lines, we generated cisplatin-induced chemoresistance in additional cell lines and assessed the expression of the nine TFs that are consistently upregulated in the OVCAR4 and SKOV3 cellular systems. To this end, we periodically treated CAOV3 and COV362 cells with increasing doses of cisplatin, harvest cells and measured the expression changes for these 9 TFs in cells that are resistant to different doses of cisplatin. Notably, as shown with the representative bar graph for SOX9 mRNA and the corresponding heatmaps for the other TFs, our targeted RT-qPCR results demonstrate that these TFs are consistently upregulated as HGSOC cells become resistant to higher doses of cisplatin (Figure 3d). Collectively, our results suggest that TFs that are associated with the chemoresistant state are dynamically induced by cisplatin treatment and may function as critical drivers and master regulators of platinum resistance in ovarian cancer.

Targeting cisplatin resistance with the small molecule epigenetic inhibitor. Targeting TFs with small molecules has been a formidable challenge. However, since SEs are differentially enriched for bromo- and extra-terminal (BET) domain chromatin regulators and Mediator complexes (32, 33), bromodomain inhibitors such as the small molecule inhibitor JQ1 have been shown to differentially target SE target genes (33). Therefore, small molecule epigenetic drugs that interfere with the regulatory programs of SEs provide an exciting therapeutic opportunity to selectively target critical TFs that are regulated by SEs (31, 33). We thus tested if JQ1 will selectively target the expression of the selected SE target genes identified from our RNA- and ChIP-Seq analyses. As a control, we also selected gene targets of a typical enhancers that are not associated with super enhancer but have similar basal and resistance mediated gene expression fold change to the SE-target genes. Notably, although we observe that JQ1 reduced the expression of non-super enhancer target genes as well, the super enhancer target genes were impacted significantly more (p=6.67e-05) after a 3-day JQ1 treatment (Figure 4a). We next tested whether JQ1 treatment will result in synergistic cytotoxicity in the resistant cells when combined with cisplatin. The results indicate that JQ1 and cisplatin co-treatment resulted in a significant and more profound reduction in cellular viability in the resistant cells compared to the naïve cells (Figure 4b). In line with the short-term (3 days) MTT assay, longer-term (14 days) crystal violet viability assay also demonstrated significant synergy between cisplatin and JQ1 treatment in the resistant OVCAR4 cells (Figure 4c). To further corroborate these findings, we tested JQ1 and cisplatin combination on four additional HGSOC cell lines. These results show that JQ1 treatment significantly increases the growth inhibitory activity of cisplatin across multiple OC cell lines (Figure 4d). To further characterize the synergism between JQ1 and cisplatin, we tested various dose combinations on both naïve and cisplatin resistant cells and calculated the combination index (CI), a well established model to

12 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. study drug synergism(46), where CI values less than one indicate synergism. Though we observe synergistic drug dose combinations in both cell lines, we observe much lower CI values (stronger synergism) in the cisplatin resistant cells at higher dose combinations (Supplemental Figure 7). Critically, the combinatorial JQ1 and Cisplatin treatment resulted in greater rates of apoptosis than individual treatments in multiple cell lines (Figure 4e-f, Supplemental Figure 8), suggesting that the combination results in reduced overall cell proliferation and increased cytotoxicity. Next, we assessed the clinical relevance of JQ1 and cisplatin combination in vivo on cisplatin- resistant cells in a subcutaneous xenograft model. At the beginning of the treatment, mice were randomly separated into four groups. Notably, single-agent treatment of JQ1 (50mg/kg/wk) or cisplatin (3mg/kg/wk) did not have a significant effect on the relative tumor volume (Figure 4g). In contrast, cisplatin and JQ1 combination treatment resulted in significantly smaller tumors when compared with single-drug treatments or the controls (Figure 4g). These results are in line with the in vitro data and suggest that small molecule epigenetic inhibitors that interfere with SE function can synergistically increase the therapeutic efficacy of platinum-based compounds in cisplatin-resistant OC cells in vivo.

SOX9 is required for acquiring and maintaining the chemoresistant phenotype.

Our results so far suggest that the chemoresistance process is, at least in part, regulated by epigenomic reprogramming and licensing of a specific set of distal enhancers, SEs and differential expression of their target genes. Notably, targeting these distal enhancers through small molecule epigenetic inhibitors results in significant reductions in cisplatin resistance and in the expression of SE- associated target genes. Although cellular states are most likely established by the combinatorial actions of a network of TFs (47), we focused our efforts to assess the contribution of SOX9 in platinum resistance. We chose to study SOX9 for two reasons. Firstly, the TF had never been implicated in chemoresistance in ovarian cancer. SOX9 is a known player of chondrogenesis (48) and stemness of neural progenitors (49) and hair follicles (50). Notably, it is overexpressed in several different cancers (51) and believed to be involved in epithelial and mesenchymal transitions. Secondly, SOX9 is one of the few TFs whose both expression level and putative enhancer elements are consistently upregulated in all the chemoresistance models that we studied (Figure 5a). Notably, we also observe substantial activation of a SOX9 super-enhancer in two naïve-matched cisplatin-resistant patient-derived xenograft tumors (Figure 5a). In line with the gene expression and distal enhancer activation of the SOX9 locus, we observe a substantial SOX9 protein levels in resistant cells (Figure 5b). Critically, in the resensitized cells, SOX9 protein levels, enhancer activity and mRNA levels go down, further indicating that high SOX9 expression and protein levels are restricted to the cisplatin resistant cells (Figure 5b). To interrogate the functional role of SOX9 in chemoresistance, we used CRISPR to knock out SOX9 in both naïve and cisplatin-resistant SKOV3 cells. We performed both population-level KO studies

13 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. as well as clonal analyses of single cells. Notably, genetic depletion of SOX9 did not result in significant proliferation defects neither in resistant cells nor in naïve cells (Supplemental Figure 9, 10, 11), suggesting that maintenance of transcriptional programs downstream of SOX9 is not critical for cell proliferation. However, depletion of SOX9 in the cisplatin-resistant SKOV3 or OVCAR4 cells confer significant sensitive to cisplatin compared (Figure 5b, Supplementary Figure 10). On the other hand, CRISPR mediated SOX9-depletion does not confer additional sensitivity in naïve cells (Supplementary Figure 11 & 12). Interestingly, when WT and SOX9 KO naive cells are challenged with lower cisplatin doses over a period of 14 days, significantly less number of survivor colonies were observed in SOX9 depleted cells (Supplementary Figure 12). These findings collectively suggest that SOX9 is not only necessary for the maintenance of cisplatin-resistant state, it also critical for the acquisition of the cisplatin resistant state in OC cells. To gain further insights into the mechanism of SOX9-mediated acquisition and maintenance of chemoresistance, we used ChIP-Seq to map SOX9 binding sites across the genome in cisplatin-resistant cells (Figure 5c). As shown for BMP2 and WNT5A loci, SOX9 targets are expressed at higher levels in resistant cells (Figure 5c). It is notable that WNT5A is a target of a distal SE in the chemoresistant SKOV3 cells. Our enhancer and gene expression analysis highlighted upregulated WNT/β-catenin pathway during chemoresistance in multiple cellular models. It is notable that WNT5A is overexpressed in malignant epithelial ovarian cancers (52), implicated in cancer stem cells (53) and associated with poor prognosis (54-56). In support of our findings, recent publications also highlight significant involvement of WNT pathway in ovarian cancer chemoresistance(57-60). These results collectively highlight that targeting the WNT pathway may have a strong potential to prevent and overcome chemoresistance in ovarian cancer (61). To understand whether SOX9 is associated with activating or repressive activity at the target genomic regions, we analyzed the epigenomic and transcriptional alterations at SOX9 targets in the naïve and resistant counterparts. Notably, the genomic regions that are bound by SOX9 have increased accumulation of H3K27ac marks, indicating higher activity in resistant cells relative to the naïve counterparts (Figure 5d). When we analyzed the genomic distribution of SOX9 binding sites, we observed strong enrichment of gene promoters (+/- 2kb of TSS) (Figure 5e), which is in line with the previous SOX9 ChIP-Seq studies (62, 63). The pathway analysis of SOX9 targets indicates that these genes are implicated in invasion, drug sensitivity, cancer stem cell genes and DNA replication (Figure 5f). In line with our expectation, SOX9 target genes are expressed at higher levels in the resistant cells compared to the naïve counterparts (Figure 5g). Critically, when the SOX9 binding intensity is correlated with the gene expression within the resistant cells, we observed significant positive correlation between SOX9 binding intensity and target gene expression (Figure 5h). These findings indicate that SOX9 is acting as a transcriptional activator while driving and maintaining the chemoresistant state. To further test this hypothesis, we performed targeted qPCR mRNA expression analysis on select genes in

14 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

WT and SOX9 KO cells. Critically, SOX9 deletion results in significant depletion of multiple other TFs that are significantly upregulated in the resistant cells (Figure 5i). These results indicate that SOX9 is a critical player of gene expression programs of the cisplatin-resistant cellular states. Furthermore, our data suggest that SOX9 may mediate acquisition and maintenance of cisplatin chemoresistance by coordinating the expression of multiple other TFs and thus controlling their gene targets.

DISCUSSION

Chemoresistance is a major therapeutic obstacle in the treatment of OC. Although specific genetic alterations such as reversion of germline BRCA1/2 mutations and inactivating mutations in tumor suppressor RB1, NF1, RAD51B, and PTEN (16) have been associated with chemoresistance, the molecular network that drives and maintains the chemoresistant state at the chromatin and transcriptional levels is poorly understood. In this study, we integrate epigenome and transcriptome profiling in multiple chemoresistance models to investigate potential drivers of resistance in OC. Our findings indicate that chemoresistance is associated with the licensing of a specific set of distal enhancers. Notably, among these genes that are especially upregulated in the cisplatin-resistant cellular state are multiple TFs, whose expression are controlled by unique enhancer clusters known as SEs, which can be potentially targeted by small molecule epigenetic inhibitors. The data presented here suggest that JQ1 can effectively reduce the expression of such TFs and increase cisplatin sensitivity in the chemoresistant cells. In line with this, there is significant synergy between JQ1 and cisplatin both in vitro and in vivo, suggesting a great translational potential to target chemoresistance in ovarian cancer with chromatin targeting small molecules.

Our findings provide key insights into the epigenetic bases of chemoresistance. Data presented in this manuscript indicate that chemoresistance is associated with large-scale reprogramming and redistribution of H3K27ac histone modifications across the genome. As expected, the reprogrammed enhancer elements are highly enriched near the genes that are associated with DNA damage response, cell signaling, EMT, as well as drug response are aberrantly upregulated in chemoresistant cells. On the other hand, genes implicated in apoptosis signaling, catabolism, and cellular adhesion pathways are downregulated during chemoresistance. Notably, we also observed substantial cell-type specific epigenetic and transcriptional programs, suggesting a great degree of heterogeneity in the epigenetic state of resistant cells. Critically, despite the epigenetic and transcriptional heterogeneity, our data suggest that small molecules epigenetic drugs targeting distal enhancers and super enhancers can be used to alter the aberrant regulation and confer cisplatin sensitivity. BET inhibitors like JQ1 have been shown to release Mediator from cis-regulatory elements at regulatory elements and alter the transcription of target genes. Thus, theoretically, JQ1 can affect both enhancers and SEs. However, since SEs could

15 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. contain significantly higher levels of H3K27ac and BRD4, they are more sensitive to JQ1 treatment (33). Our experimental analysis supports this notion and explains in part the particular effectiveness of JQ1 in our chemoresistant models.

Our findings highlight SOX9 as a critical determinant of the cisplatin resistance. We show that SOX9 is a target of a super-enhancer that is strongly activated in resistant cells. Genetic targeting efforts further support the hypothesis that SOX9 is one of the key TFs that maintain the gene expression programs of cisplatin-resistant states. SOX9 depletion results in the downregulation of multiple TFs associated with chemoresistance and increased sensitivity to cisplatin in multiple OC lines. It remains to be shown whether the expression SOX9 alone is sufficient to confer cisplatin resistance. It is notable that higher expression of SOX9 is associated with worse prognosis in multiple solid tumors(64). Furthermore, SOX9 is also a critical marker of clinically aggressive disease in metastatic high-grade serous carcinoma(65).

ACKNOWLEDGEMENTS The study was supported by the NIH/NCI 1R01 CA211648-01 and UVA Cancer Center pilot award (NCI CCSG P30 CA44579) award to MA. In addition, the work was supported by the NIH/NCI R01 CA160356 and NIH/NCI R01 CA193677 awards to PCS. SS was supported in part by the Cancer Training Grant T32 CA009109.

16 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

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FIGURE LEGENDS:

Figure 1: Cisplatin-resistant ovarian cancer cells display unique transcriptome and epigenomes. A) Schematics show the strategy to generate cisplatin resistance in naïve OC cells. B) Bar graphs show percent viability of naïve, resistant cellular counterparts for various OC cell lines treated increasing doses of cisplatin as measured by MTT assay. Error bars are standard error of the mean (SEM). *, **, or *** indicates p < 0.05, p<0.01, or p<0.001, as determined by Student’s t-test. C) Line graph shows the quantitative measurement of crystal violet assays (below) for naïve and resistant SKOV3 cells upon continuous exposure to the indicated doses of cisplatin for 14 days. D) Normalized H3K27ac ChIP-Seq chromatin tracks are shown for naïve-resistant pairs for selected genes. E) The heatmap depicts differentially regulated enhancers and promoters across multiple cisplatin-resistant cell models. z-scores shows read coverage fold changes of resistant over naïve. F) Bar graph shows the significance of genes and pathways proximally associated with all up or downregulated regulated enhancer elements in E.

Figure 2: Integrative analysis of enhancers and super-enhancers that are associated with the gene expression program of cisplatin-resistant state A) Dot plots show H3K27ac read coverage of distal enhancers in indicated resistant cells. Pie charts indicate percentages of resistance-specific or shared SEs. Genes proximal to the top SEs are highlighted. B) MA plots show the fold change of H3K27ac signal intensity at all promoters and enhancers for resistant vs naïve cells. Dotted line represents 1.5 fold change. C) Normalized UCSC genome tracks are shown for RNA-Seq and H3K27ac ChIP-seq at the SIRPA locus in naïve, resistant, and resensitized SKOV3 cells. D) Pie chart in the inset depicts whether the intensity of resistant-specific SEs identified in (B) are stable, further amplified or reduced more than 1.5 fold in the resensitized state. E) Box plots depict relative expression of genes proximally associated with state-specific SEs. p-values are determined by the Mann Whitney test. The midline represents the median, the boxes represent the second and third quartiles while the whiskers represent the minimum and maximum values of the gene expression levels in each sample.

Figure 3: Cisplatin treatment results in the induction of super-enhancers and target TFs that are associated with poor prognosis. A) The heatmap shows relative mRNA expression levels of 1000 consistently upregulated and 816 consistently downregulated in resistance. Resistant and Naïve SE- associated TFs are labeled with red and blue, respectively. B) Pathway-level analysis for differentially regulated genes is shown. C) Kaplan-Meyer survival plots are shown for indicated chemoresistant- specific genes. p-values indicate the log-rank p-value between the two curves. D) Bar graphs and heat maps show qRT-PCR measured (GAPDH normalized, mean of at least three independent experiment) relative SOX9 mRNA expression in naïve and COV362 (left) and CAOV3 (right) cells at various stages of cisplatin resistance.

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Figure 4: BET inhibitor JQ1 sensitizes cells to cisplatin in vitro and in vivo. A) Bar plot shows JQ1- mediated expression changes for typical and super-enhancer target genes in cisplatin-resistant SKOV3 cells. B) Graphs show percent viability (MTT assay) of SKOV3 resistant cells after 24h treatment with the indicated doses of cisplatin and constant treatment with JQ1 (1μM) or control DMSO for an additional 48 hours. C) Crystal violet assay shows relative growth of OVCAR4 resistant cells treated with 1μM of cisplatin and/or JQ1 for 24h, followed by 14 days of drug-free growth. D) Bar plots depict quantified crystal violet assays for cisplatin resistant OVCAR4, SKOV3, and COV362 cells treated with half the IC50 dose of cisplatin, 1μM JQ1, or both. Error bars represent SEM for at least three independent replicates. E) Flow cytometry results show Annexin V and DAPI levels in OVCAR4-R cells treated with control, cisplatin (3μM), JQ1 (1μM), or both. F) Bar plot shows the relative percentages of the upper right quartile in E). Error bars represent the standard error of the mean for three replicates. G) Line graphs showing average xenograft tumor volumes of cisplatin-resistant SKOV3 cells implanted subcutaneously in nude mice after indicated treatments. Error bars represent standard error of the mean.*, **, or *** indicates p < 0.05, p<0.01, or p<0.001, as determined by Student’s t-test for boxplots or ANOVA for line graphs.

Figure 5: SOX9 is critical for acquisition and maintenance platinum resistance in ovarian cells. A) Read-count normalized genome tracks are shown for H3K27ac ChIP-seq (left) and RNA-Seq (right) at SOX9 locus. B) Western blots show SOX9 and tubulin protein levels in SKOV3 naive, resistant and resensitized cells. Line plots depict MTT-cell viability of cisplatin treated resistant WT and SOX9 knockout cells. Error bars represent SEM of at least three independent experiments. C) Read-count normalized tracks show SOX9 ChIP-Seq (in SKOV3 resistant cells) data together with RNA-Seq or H3K27ac ChIP-Seq data for naïve and resistant OVCAR4 and SKOV3 cells at BMP2 and WNT5A loci. D) Global levels of H3K27ac ChIP-Seq signal intensity at SOX9 binding sites in naïve vs resistant SKOV3 and OVCAR3 cells. E) Pie chart depicts the distribution of SOX9 binding site in the genome. F) Bar plot displays FDR adjusted q-values of MSigDB and curated GSEA pathways associated with SOX9 binding sites G) Box plots show relative expression of SOX9-binding proximal genes in naïve and resistant cells. p-values are determined by the Mann Whitney test. H) Box plots depict relative expression levels of genes proximal to sites with varying degree of SOX9 binding intensity. The midline in boxplots represents the median, the boxes represent the second and third quartiles while the whiskers represent the minimum and maximum values of the gene expression levels in each sample. n represents the number of genes represented in the proximally associated bin. I) Bar graphs depict RT-qPCR measured relative mRNA expression levels of resistance associated transcription factor genes in WT and SOX9 KO SKOV3-R cells. Error bars represent standard error of the mean from at least three

23 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. independent experiments. *, **, and *** indicates p < 0.05, p<0.01, or p<0.001, as determined by ANOVA for (B) or Student’s t-test for (I).

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Naive Resistant Re-sensitized A B 100 100 100 100 80 80 ** 80 80 * Cisplatin *** ** 60 60 60 60 ** Treatment ** * * ~6 mo 40 40 40 40 ** *** ** Re- ** ** 20 20 20 *** 20 ** Naive Resistant sensitized Percent Viability 0 0 0 0 2 8 24 5 10 20 5 10 40 6 13 20

Cisplatin (μM) Cisplatin (μM) Cisplatin (μM) Cisplatin (μM) OVCAR 4 OVCAR 3 OV81 SKOV 3 Naive (SKOV3) 100 50 kb 25 kb 100 kb 50 kb C (IC50=0.435 μM) 87 60 60 100 Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215D Naive Author manuscripts have been peerResistant reviewed and (SKOV3)accepted for publication but have not yet been edited. 1 1 1 1 50 87 60 60 100 (IC50=1.603 μM) CAR 4 Resistant 1 1 1 1 OV 40 94 80 80

Percent Viability 0 Naive 1 1 1 1 -1 0 1 2 40 94 80 80 log[Cisplatin] (μM) Resistant

SKOV3 1 1 1 1 75 260 88 70 Naive 1 1 1 1 Naive 75 260 88 70

OV81 Resistant 1 1 1 1 48 30 30 50 Naive 1 1 1 1 Resistant H3K27ac Chromatin State 48 30 30 50 CAR 3 Resistant 1 1 1 1 0 μM 0.24 μM 0.49 μM 0.97 μM 1.95 μM OV

H3K27ac ChIP-Seq RNA-Seq DNMBP ALDH1A1 TRPA1 SUZ12 E F

n=254 Targets of Upregulated Enhancers n=577 TGFB Signaling Response Signal Transduction Cell Communication Chemical Stimulus Response Cell Proliferation Mesenchymal Gene Expression WNT Signaling Response n=201 TNFa Signaling Response Getinib Resistance n=2541 enhancers Epithelial to Mesenchymal Transition Downregulated During Invasion n=543 DNA Damage Response

n=522 0 5 1510 20 25 -log10(q-value) Targets of Downregulated En hancers Metabolic Process n=916 ESR1 Bound Genes Migration Catabolic Process n=249 Programmed Cell Death Immune Response Adipogenesis n=3251 enhancers Chemosensitivity Hypoxia Response Apoptosis Focal Adhesion n=305 R1 R2 R1 R2 R1 R2 R1 R2 0 5 10 15 20 25

Res. Res. -log10(q-value) OV81 Naive Naive SKOV3 OVCAR3 OVCAR4 OVCAR4 SKOV3

Downloaded from0<−5 cancerres.aacrjournals.org>5 −1 on September 0 1 29, 2021. © 2019 American Association for Cancer Research. Figure 2 A 4 Author ManuscriptOVCAR4 Published OnlineFirst4 on July 29, 2019; SKOV3DOI: 10.1158/0008-5472.CAN-19-0215 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. (×10 )

(×10 ) 100 n=304 80 Resistant-Speci c n=311 Resistant-Speci c 88% 75 57% n=266 60 n=191 Shared RNF170 FRYL 50 40 12% YPEL2 43% C9orf84 AC023590.1 SOX9 HGSNAT Shared 25 CCDC 121 20 MYOF ROCK1 MALT1 SOX9 LMCD1 0 0 H3K27ac Read Coverage H3K27ac Read Coverage 0 5,000 10,000 15,000 0 5,000 10,000 15,000 Ranked Enhancers Ranked Enhancers

B OVCAR4 SKOV3 5.0 n=266 5.0 n=191 Resistant Spec. SE 2.5 2.5 Naive Spec. SE 0.0 0.0 Common SE -2.5 -2.5 Log2 Fold Change

-5.0 n=307 Log2 Fold Change -5.0 n=76 Enhancer 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 Log2 Mean H3K27ac Intensity Log2 Mean H3K27ac Intensity

C D 100 kb 1500 super enhancer enhancer Fate of Resistant Speci c 0 1500 Naive SE in Resensitized Cells 0 1500 Resistant RNA-Seq Expression 0 Amplified 55 Resensitized 11.0% 0 Reduced 55 Naive 47.1% 0 Stable 55 Resistant 41.9% H3 K27ac ChIP-Seq 0 Resensitized

SIRPA PDYN STK35 E OVCAR4 SKOV3

-5 -7 -12 -16 0.02 9.8 ×10 1.8×10 7.8×10 <2.2×10 80 4.5 ×10 -13

60 60 Naive

40 40 Resistant 20 20 Resensitized 0 0 Gene Expression (FPKM) Gene Expression (FPKM) Naive SE Common Naive SE CommonEnhancer Enhancer Resistant SECommon SE Resistant SECommon SE

Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Figure Author3 manuscripts have been peer reviewed and accepted for publication but have not yet been edited. A B D OVCAR4 SKOV3 TF 12 8 −log10(FDR q value) 10 COV362 7 CAOV3 0 5 10 15 20 6 8 ssatCells C esistant R Pathways Upregulated in Upregulated Pathways Ribonucleoprotein Complex Biogenesis 5 E2F7 Intracellular Signal Transduction 6 4 ELK3 4 3 Negative Regulation of Gene Expression 2 FOXL1 2 HIC1 Positive Regulation of Biosynthetic Process 1

Fold Enrichment 0 0 HLX RNA Processing 0 6 9 12 Fold Enrichment 0 6 9 12 15 18 Relative SOX9 mRNA Relative SOX9 mRNA

n=1000 KLF6 Positive Regulation of Catalysis Cisplatin( μM) Cisplatin(μM) TNFA Signaling via NFKB MYBL1 MYC Targets MYC 3 KRAS Signaling ZEB2 1.00 3.75 6.97 29.73 ZEB2 1.00 1.18 0.80 0.97 Epithelial to Mesenchymal Transition WNT Beta Catenin Signaling 1.00 2.25 3.26 9.50 1.00 1.27 1.49 1.89 SOX9 TGF Beta Signaling HIC1 HIC1 ZEB2 Gastrin CREB signalling ZNF430 ZNF502 1.00 4.35 4.38 10.32 ZNF502 1.00 44.55 31.05 5.31

DNA Repair z-score of LFC

RNA-Seq ZNF502 −log10(FDR q value) 1.00 2.14 2.87 7.59 1.00 3.07 2.95 1.80 0 5 10 15 20 25 E2F7 E2F7 -3 ssatCells C esistant R Pathways Pathways Catabolic Process SOX9 1.00 2.74 3.51 8.81 SOX9 1.00 3.87 5.59 4.62

20 Oxidative Phosphorylation ID4

n=816 Adipogensis TCA Cycle KLF6 1.00 2.37 5.01 7.22 KLF6 1.00 2.07 2.39 2.60

egulated in Downr egulated Glycolysis Immune System C-MYC 1.00 1.46 2.91 3.19 C-MYC 1.00 2.65 2.20 6.68

z-scores Protein Secretion SNAI1 Hypoxia ZNF430 1.00 2.31 3.93 6.40 ZNF430 1.00 1.39 1.14 0.55 -2 Reactive Oxygen Species Response Fatty Acid Metabolism Apoptosis 1.00 1.26 1.51 2.10 1.00 1.00 1.15 1.37 R1 R2 R1 R2 R1 R2 R1 R2 ELK3 ELK3 P53 Pathway Naive Res. Naive Res. C ZNF430 ZEB2 ELK3 HIC1 100% 100% 100% Expr. Z > 3 (n=19) 100% Expr. Z > 0 (n=105) Expr. Z > 0 (n=95) Expr. Z > 0 (n=81) Expr. Z < 3 (n=284) Expr. Z < 0 (n=198) Expr. Z < 0 (n=208) Expr. Z < 0 (n=222) Logrank Test Logrank Test Logrank Test Logrank Test -3 -3 P-Value: 2.40 ×10 P-Value: 5.45 ×10 P-Value: 1.77 × 10 -4 P-Value: 0.05 50% 50% 50% 50% Surviving Surviving Surviving Surviving

0% 0% 0% 0% 0 180 0 180 0 180 0 180 Months Survival Months Survival Months Survival Months Survival

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Figu re 4

200 p = 6.67e-05 B 100 A 180 100 Non-SE associated genes 90 160 80 80 SE associated genes 140 70 120 on ssi on Changes 60 60 on ssi on Changes 100 50 40 80 40 SKOV3-R 30 60 * 20 40

ene Expre 20 10 Ge ne Expre SKOV3-R+JQ1 G 20

0 Relative Viability (%) cen t (JQ1 vs Control Traetment) 0 cen t (JQ1 vs Control Traetment) 0 Pe r Pe r HLX HIC1 BST1 ELK3

ZEB2 0 1 2 9 15

B7H6 Super SOX9 Non SE CCNO STK38 FOXL1 enhancer NEGR1 FRMD6

ZNF430 enhancer ZUFSP1 MRPL46 CDC165 Cisplatin (μM) TMEM200A

C Control Cisp. JQ1 Cisp.+JQ1 OVCAR4 D SKOV3 COV362 100 100 100 * ** * * 80 80 ** 80 60 60 ** 60 40 *** 40 40 *** 20 20 *** 20

R elative G (%) rowth 0 0 0 JQ1 JQ1 JQ1 Cisp. Cisp. Cisp. DMSO DMSO DMSO OVCAR4 Cisp.+JQ1 Cisp.+JQ1 Cisp.+JQ1 C ontrol C isplatin E 6 1.12 5.26 6 2.63 9.48 F G 1000 5 5 p=0.0144 DMSO (n=9) 7 900 4 4 JQ1 (n=9) 6 800 3 3 p=0.0266 Cisplatin (n=10) 0 0 700 89.5 4.16 76.8 11.1 5 JQ1 + Cisplatin (n=10) 0 3 4 5 6 0 3 4 5 6 4 600 500 JQ1 Cisp.+ JQ1 3 6 3.03 6.71 6 3.47 14.2 400 * 5 2 5 300 4 4 1 Tumor Volume (mm3) 200 3 3 DAPI 0

Rel ative rate of Apoptosis 100 0 83.1 7.19 0 65 17.4 30 4 5 6 0 3 4 5 6 JQ1 0 DMSO 0 3 6 9 12 15 18 21 24 27 30 33 Annexin V Staining Cisplatin Cisp + JQ1 Time (Days)

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H3K27ac ChIP-Seq RNA-Seq A 100 kb super enhancer 5 kb B 300 30 0 R1 SKOV3 OVCAR4 Naive Resistant Resensitized 0 300 10 0 R2 100 Resistant PDX N1 0

OVCAR4 300 SOX9 10 0 R3 WT clone Naive PDX N2 100

0 Naive KO-1 R1 80 50 0 Tubulin KO-2 SKOV3 0 100 sg2sg1 0 R2 60 KO-3 100 KO-4 SKOV3 0 R3 sg2sg1 300 30 R1 40 OVCAR4 0 0 10 300 KO-1 KO-2 KO-3 KO-4 Control

R2 WT Clone PDX T1 0 0 300 20

10 OVCAR4 R3 0 Percent Viability PDX T2 0 SOX9 100 Resistant 50 0 R1

SKOV3 Resistant 0 0 100 β-Actin 0 30 0 R2 4 8 16 COV362 SKOV3 0 100 0 R3 Cisplatin(µM) SOX9 SOX9

C 10 kb super D SKOV3 OVCAR3 200 kb 8 enhancer 2.0 16

1 1 1.5 SOX9 ChIP-Seq SOX9 ChIP-Seq 60 Resistant 1.5 Resistant 60 Naive 1.0 Naive 1 1 60 60 1.0 0.5 Res. Res. H3K27ac ChIP-Seq 1 SKOV3 1 350 80 OVCAR4 Naive 0.0 0.5

RNA-Seq Naive 1 1 Naive 80 350 Naive Res. Relative H3K27ac level Res. Relative H3K27ac level −0.5 0.0

SKOV3 1 SKOV3 −3000 −1500 Center 1500 3000 −3000 −1500 Center 1500 3000 RNA-Seq BMP2 WNT5A Distance from SOX9 Peak Distance from SOX9 Peak

0.08 1.1 × 10 -4 E F -log10 (FDR q value) G 60 H SKOV3 OVCAR4 I 100 SOX9 Binding Sites 0 5 10 -7 -8 50 80 1.60 ×10 2.97 ×10 *** Intergenic 80 Invasion 40 9.98 ×10 -4 5.88 ×10 -4 * 60 ** 0.05 0.11 * Proliferation 30 60 n=418 669 n=418 909 Gefitinib Sensitivity 20 40 40 n=369 n=369 n=354 10 n=354 20

Metastasis n=216 20 n=216 Promoter-TSS 400 0 (WT vs SOX9 KO) Cancer Stem Cell Genes Gene Expression (FPKM) 0 Relative mRNA Level

Gene Expression (FPKM) 0

DNA replication MAF Naive Naive E2F7 Intragenic KLF6 CMYC Resistant Resistant ZNF430 SKOV3 OVCAR4 SOX9 binding intensity (n=1,815) (n=1,815)

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Chemotherapy-induced distal enhancers drive transcriptional program to maintain the chemoresistant state in ovarian cancer.

Stephen Shang, Jiekun Yang, Amir A Jazaeri, et al.

Cancer Res Published OnlineFirst July 29, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-19-0215

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