Chemotherapy-Induced Distal Enhancers Drive Transcriptional Programs to Maintain the Chemoresistant State in Ovarian Cancer Stephen Shang1, Jiekun Yang1, Amir A

Published OnlineFirst July 29, 2019; DOI: 10.1158/0008-5472.CAN-19-0215 Cancer Genome and Epigenome Research

Chemotherapy-Induced Distal Enhancers Drive Transcriptional Programs to Maintain the Chemoresistant State in Ovarian Cancer 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, and Mazhar Adli1

Abstract

Chemoresistance is driven by unique regulatory net- tance, our findings identified SOX9 as a critical SE-regulated works in the genome that are distinct from those necessary transcription factor that plays a critical role in acquiring for cancer development. Here, we investigate the contri- and maintaining the chemoresistant state in ovarian cancer. bution of enhancer elements to cisplatin resistance in The approach and findings presented here suggest that ovarian cancers. Epigenome profiling of multiple cellular integrative analysis of epigenome and transcriptional pro- models of chemoresistance identified unique sets of distal grams could identify targetable key drivers of chemoresis- enhancers, super-enhancers (SE), and their gene targets tance in cancers. that coordinate and maintain the transcriptional program of the platinum-resistant state in ovarian cancer. Pharma- Significance: Integrative genome-wide epigenomic and cologic inhibition of distal enhancers through small- transcriptomic analyses of platinum-sensitive and -resistant molecule epigenetic inhibitors suppressed the expression ovarian lines identify key distal regulatory regions and of their target genes and restored cisplatin sensitivity in vitro associated master regulator transcription factors that can be and in vivo. In addition to known drivers of chemoresis- targeted by small-molecule epigenetic inhibitors.

Introduction epithelial ovarian cancer are high-grade serous ovarian cancer (HGSOC; ref. 7) that are more challenging to effectively treat. The The American Cancer Society estimates 22,240 new cases of first-line therapy for ovarian cancer involves the combination of ovarian cancer in 2018 (1). Unfortunately, the five-year survival cytoreductive surgery followed by platinum and taxane-based rate of ovarian cancer remains less than 50%. Thus, nearly 14,000 chemotherapy. Platinum-based compounds such as cisplatin women in the United States and 160,000 worldwide die of induce increased DNA damage through interstrand cross-links ovarian cancer each year (2). Epithelial ovarian cancers, which and cell death in proliferative cancerous cells (7, 8). Despite the account for nearly 90% of all ovarian cancer diagnoses, are high rate of initial response to therapy, the duration of response associated with worse prognosis (3). They originate mainly from declines over time and a vast majority of patients succumb to the epithelial cells of fallopian tubes (4, 5) and areas of endo- chemotherapy-resistant ovarian cancer (9–12). metriosis (6), among others. Critically, 75% of the patients with Recent genomic approaches have shed significant light on the genetic risk factors of ovarian cancer. Low-grade ovarian tumors

1 often harbor BRAF, KRAS, BRCA1/2, and PTEN mutations, where- Department of Biochemistry and Molecular Genetics, University of Virginia TP53 School of Medicine, Charlottesville, Virginia. 2Department of Gynecologic Oncol- as high-grade tumors are uniformly characterized by muta- ogy and Reproductive Medicine, The University of Texas MD Anderson Cancer tions (13, 14). Apart from the antiangiogenic agent bevacizumab, Center, Houston, Texas. 3Department of Radiation Oncology, University of and partially effective PARP inhibitors for patients with BRCA1/2 Virginia, Charlottesville, Virginia. 4Lilly Research Laboratories, Eli Lilly and mutations (15), targeted therapies are lacking for ovarian cancer. 5 Company, Indianapolis, Indiana. Department of Obstetrics and Gynecology, Although speciﬁc genetic alterations such as reversion of germline University of Virginia School of Medicine, Charlottesville, Virginia. 6Department BRCA1/2 mutations and inactivating mutations in tumor sup- of Genetics and Genome Sciences, Case Comprehensive Cancer Center, Case RB1, NF1, RAD51B, PTEN Western Reserve University, Cleveland, Ohio. pressor and genes were noted in some chemoresistant patients (16), the molecular network that drives Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). and maintains the chemoresistant state in ovarian cancer is largely unknown. In addition to genetic alterations, epigenetic regulation Corresponding Author: Mazhar Adli, Department of Biochemistry and Molecular of proximal promoters and distal enhancers are critical determi- Genetics, University of Virginia School of Medicine, 1340 JPA, Pinn Hall, Rm 6240, Charlottesville, VA 22902. E-mail: [email protected] nants of cellular identities. Alterations in the chromatin landscape are increasingly recognized as hallmarks of malignant cellular Cancer Res 2019;79:1–13 states (17–19). Because of the technical limitations, previous doi: 10.1158/0008-5472.CAN-19-0215 ovarian cancer epigenetic studies primarily focused on targeted 2019 American Association for Cancer Research. DNA methylation at individual gene promoters. Although these

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studies implicate differential methylation at multiple genes, such on the viability levels. Once the cells gain resistance, either the as MLH1 (20), SFRP1 (21), BRCA1 (16), MAL (22), FANCF (23) dose was increased or cells were periodically treated with with chemoresistance, there have been limited attempts to com- cisplatin to maintain the chemoresistant state. Resistant prehensively map differentially regulated gene promoters and SKOV3 cells reached a maximum concentration of 20 mmol/L distal enhancers in ovarian cancer. cisplatin tolerance, OV81 cells reached a maximum concentra- In this study, we aimed to identify molecular drivers of che- tion of 40 mmol/L, OVCAR3 cells reached a maximum periodic moresistance in ovarian cancer through unbiased epigenomic and dose of 18 mmol/L cisplatin, and OVCAR4 cells' largest sus- transcriptional profiling across multiple cellular models of ovar- tainable periodic dose of cisplatin was 12 mmol/L. ian cancer. We aimed to map differentially regulated proximal promoters and distal enhancers in multiple cellular models MTT and crystal violet cell proliferation and viability assay of ovarian cancer. By integrating genome-wide maps of a well- Each cell line was seeded at a density of 4–6 102 cells/well in characterized epigenetic mark of active regulatory genomic flat-bottomed 96-well culture plate in 100 mL of the culture elements with gene expression profiles, we aimed to identify medium. Stock solutions of cisplatin were subjected to serial differentially regulated proximal promoters and distal regulatory dilutions to give final concentrations ranging from 0.1 mmol/L elements that are specifically associated with chemoresistance to 250 mmol/L. JQ1 stock suspended in DMSO was first diluted to across multiple ovarian cancer cell lines. To this end, we generated 10 of the final concentration in DMSO, then further diluted multiple isogenic cellular models of cisplatin resistance and per- in PBS or complete media to concentrations of 0.25 to 2 mmol/L. formed chromatin immunoprecipitation sequencing (ChIP-seq) The dilutions were added to equal volumes of cell culture in analysis of the histone H3, lysine 27 acetylation (H3K27ac) triplicate wells and then the cells were left to incubate. After epigenetic mark, which is deposited to active enhancers and 24 hours, the media were aspirated and then washed once with promoters. By integrating ChIP-seq maps with RNA-seq gene 1 volume of PBS, then replaced with 1 volume of the cell's expression profiles across na€ve and chemoresistant cellular coun- respective complete media and left to incubate. After 48 hours, terparts, we found that the chemoresistant state is associated with 10% well volume of stock MTT diluted to 5 mg/mL was added largely cell-type–specific sets of distal enhancer elements. Criti- to 100–150 mL of fresh complete media, then added to each cally, we found significant upregulation of distal enhancer clusters well after aspirating the previous media. After 3–4 hours of known as super-enhancers (SE) in resistant cells. Small-molecule incubation, 1 equal volume of MTT solubilization media epigenetic drugs that target enhancers and SEs result in significant (10% SDS, 0.1% Tris HCl) was added to each well, then covered, decrease in the expression of their target genes and an increase in and stored at room temperature or in the incubator for 7þ hours. cisplatin sensitivity in chemoresistant HGSOC cells. Our findings The plate was read on a plate reader that shakes the plate, then identified, in addition to known drivers of chemoresistance, reads absorbance at 590 nmol/L. Background absorbance was SOX9 as a critical SE-regulated transcription factor (TF) that plays taken to be the readings of control wells with no cells. These a critical role in chemoresistance across multiple ovarian cancer treatments were carried out to determine IC50 values, that is, drug cell lines. concentrations required for 50% cell kill, as well as synergism between drugs. For crystal violet assays, fresh media were added every 3–5 days Materials and Methods after initial treatment. After 10–14 days, the wells were stained for Cell culture 30 minutes with crystal violet solution (0.4% crystal violet, 10% Human ovarian cancer OVCAR4, CAOV3, OV81, and COV362 formaldehyde, 80% methanol). After staining, the crystal violet cell lines were cultured in complete medium consisting of solution was removed, and then the stained cells washed once RPMI1640, 20% heat-inactivated FBS, 1% penicillin/streptomycin. with PBS and 3þ times with water. The plate was inverted SKOV3 cells were cultured in complete medium consisting of overnight and covered to dry the well for imaging with a custom McCoy 5A, 10% heat-inactivated FBS (Sigma Aldrich), 1% peni- 3D printed insert on an Epson tabletop scanner. cillin/streptomycin [100 U/mL penicillin, 100 mg/mL streptomycin (PAA Laboratories GmbH)]. Cells were cultured incubator at 37C Chromatin immunoprecipitation experiments in a humidified atmosphere consisting of 5% CO2 and 95% air. The SKOV3, OVCAR4, CAOV3, or COV362-na€ve, -resistant, or cells were originally obtained from ATCC and monitored period- -resensitized cells were grown to 80% confluency in 15-cm plates. ically for Mycoplasma contamination. The cells were validated using A total of 2 107 cells were cross-linked in 1% formaldehyde in FTA Sample Collection Kits for Human Cell Authentication Service complete media, or trypsinized, spun down, then resuspended in (ATCC). complete media containing 1% formaldehyde. After 15 minutes, the samples were quenched with glycine to a final concentration Creating cisplatin-resistant and resensitized cell lines of 0.125 mol/L glycine. Cells were then scraped off with a cell Cells were grown in their respective culture media and scraper and then collected with the mixed quenched media to passaged for at least two generations after thawing to ensure 50 mL Falcon tubes, where they were spun down and then proper viability. When the cells reached 80% confluency, they resuspended in SDS lysis buffer (1% SDS, 10 mmol/L EDTA, were split into two 6-cm plates with 40% confluency. Cells were 50 mmol/L Tris-HCl, pH 8.1) at a ratio of 1 mL per 2 107 cells. treated with an initial dose of 1 mmol/L cisplatin in 3 mL Pulse sonications were performed for 9 minutes at 40% ampli- complete media. After 4 hours, the media for both control and tude with 30% on/70% off on a Brandon Digital Sonifier (model treated cells were aspirated and replaced washed with an equal 250) with a total of 1 mL with maximum 50% SDS Lysis Buffer volume of PBS twice before replacing with drug-free complete solution diluted with ChIP Dilution Buffer (0.01% SDS, 1.1% media. Cells were allowed to recover for two passages, and Triton X-100, 1.2 mmol/L EDTA, 16.7 mmol/L Tris-HCl, pH 8.1, treated with the same or increasing dose of cisplatin depending 167 mmol/L NaCl). ChIPs were performed with antibodies for

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K27ac (Abcam, 4729, Lot GR286678-1) and mouse anti-SOX9 Hudson Alpha. Reads were aligned using bowtie or bowtie2, (AB5535, Millipore, Lot 2847051). Pulldowns were performed duplicates removed by samtools. Peaks were called using with 50%/50% mixed Dynabeads Protein A (1002D, Lot MACS2 (24) or MACS1.4. SEs were defined using K27ac intensity 00326545) & G (1004D, Lot 00342019). DNA quantities were versus rank as published in Whyte and colleagues, 2013. State- measured by Qubit 2.0 (Promega QuantiFluor dsDNA System) specific enhancers for the SKOV3 system were defined as having and Bioanalyzer. an intensity of 5-fold change higher over the other chemoresistant state. Normalization and differential peak analysis were per- DNA and RNA isolation formed using a custom R script utilizing edgeR or DESeq2 as RNA was isolated using Qiagen TRIzol (#15596108). DNA was referenced in DiffBind R package. The normalized read counts isolated using phenol chloroform extraction. Quantity was mea- (affinity scores) were used to generate plots through the DiffBind, sured using the Nanodrop 2000 Spectrophotometer at 260 nm. pheatmap, and ggplot2 packages. Clustering was performed using kmeans or hclust. Gene association was performed through bed- PCRs and qPCRs tools þ DAVID or GREAT analysis defined as proximally associ- PCR experiments were performed on an Eppendorf Nexus ated if within 12.5-kb inclusive window, or the most proximal Gradient equipment. Real-time quantitative PCR was performed upstream and downstream genes. SE-associated genes are defined on a StepOnePlus Applied Biosystems instrument with SYBR as proximally associated genes whose gene expression goes up or Green or TaqMan polymerase. down in the same direction as the SE, and whose expression in the resensitized cells falls in between the na€ve and resistant ChIP-seq and RNA-seq library preparation expression values. SOX9-binding site annotation was performed ChIP-seq libraries were prepared using the Illumina TruSeq through HOMER. ChIP Library Preparation Kit. RNA-seq libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit for Western blots Illumina. Libraries were prepared according to the manufacturer's Cells lysates were quantitated with a standard Bradford instruction. Qubit and bioanalyzer measurements were used to assay using Bio-Rad Protein Assay Dye Concentrate (catalog determine the library quality. no. 500-0006) and BSA as a control. After running the gel, a dry transfer system was used (Life Technologies, IB1001; 7 minutes Apoptosis assay at 20 volts). Membranes were blocked in 5% nonfat dried Cells were seeded into three 6-well plates per sample at 30% milk (Bio-Rad #170-6404). The SOX9 antibody was diluted confluency and then allowed to settle overnight. Each well was to 1:500 (AB5535, Millipore, Lot 2847051) a-tubulin (1:2,000; treated with DMSO control, JQ1 (1 mmol/L), cisplatin (3 mmol/L Invitrogen #62204). or 6 mmol/L), or both premixed in RPMI complete media. Twenty- four hours later, the wells were washed once with PBS and CRISPR/Cas9-mediated genetic knockout experiments refreshed with new complete media. Forty-eight hours later, the Cas9 and sgRNA-expressing plasmid (AddGene 1000000048) cells were checked under a microscope, then trypsinized, collect- was used in all CRISPR experiments. The BsmBI sites were intro- ed, and washed twice with PBS. All steps further are performed on duced from another plasmid. Twenty nucleotide sgRNAs were ice. Unstained and single stained controls are aliquoted and spun designed using the UCSC genome browser and/or GECKO down. Controls are stained with either Annexin V binding buffer library and checked for off-targets using CROP-IT. Overhangs of (eBioscience 00-0055-43) or binding buffer with DAPI or 50-CACC-30 and 50-AAAC-30 were added to the 50 of the Annexin-FITC, and the samples are stained with both. Data was forward and reverse complementary oligos, respectively. Forward collected inside the UVA Flow Core facility on a BD Biosciences and reverse sgRNA oligos were mixed in annealing buffer FACScalibur instrument with 30,000 collected events per sample. (10 mmol/L Tris pH 7.5–8, 50 mmol/L NaCl, 1 mmol/L EDTA), then heated to 95C, annealed in a stepwise thermal decrease and RNA-seq data analysis finally ligated with a BsmBI cut p413 plasmid before transforma- Paired-end reads were acquired using HiSeq 2500 (50 bp) or tion. Positive sequences confirmed through colony PCR with NextSeq 500 (75 bp) system on high-throughput mode from UVA the U6 promoter primer þ reverse sgRNA oligo primer through sequencing core. Reads were aligned to the hg19 genome using Sanger sequencing. Vector controls include a nontargeting bowtie2 or hisat2. Read abundance was estimated using either 20 nucleotide control guide sequences. tophat2 or stringtie, depending on if bowtie2 or hisat2 was the HEK293T cells were transfected with PsPAX2 and Pmd2G with aligner, respectively. The counts were then normalized and com- FuGene6 (Promega E2691) in a 4:1:5 ratio inside OptiMem pared for differential expression as per the DESeq2 R package. media (Gibco #31985070). Media were exchanged to fresh com- Custom R Scripts were used to perform further normalization and plete DMEM (10% FBS, 1% penicillin/streptomycin) after quality control. Genes with significant variance between each 4 hours, and then the media collected and replaced after 24 and replicate and with 0 read counts in any of the replicates were 48 hours. The collected media were then syringe filtered through removed. Downstream plots used the pheatmap, heatmap.3, and 0.22-mm filters, and then stored at 4C for immediate use or ggplot2 packages. Clustering was performed using kmeans or 80C for long-term storage up to 6 months. hclust packages. Downstream pathway enrichment analysis was Ovarian cancer cells were seeded at 40%–60% confluency and performed through preranked GSEA and DAVID gene ontology. allowed to attach for 8þ hours. Cells were then infected with the lentiviral mix with 10 mg/mL polybrene or a control media and ChIP-seq data analysis after 16 hours overnight, and subject to puromycin selection for Single-end reads were acquired using MiSeq, HiSeq 2500 (50 72 hours or until all the control cells died. For all cloning bp), or NextSeq 500 (75 bp) from UVA sequencing core or purposes, competent DH5a cells were used.

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Mouse xenograft experiments and in vivo drug treatment lowed by high-throughput sequencing (ChIP-seq) against the The mouse studies were conducted in accordance with the H3K27ac mark in na€ve and chemoresistant counterparts of guidelines established by the University of Virginia Institutional OVCAR4, OVCAR3, OV81, and SKOV3 cells. H3K27ac marks Animal Care and Use Committee (IACUC). A total of 5 106 cells active promoters as well as enhancer elements for a given cell diluted in 200-mL PBS were subcutaneously injected into each type (27). Comparative analysis of H3K27ac chromatin state hind quarter of 32 mice (Jackson Laboratories). Tumors were maps across a panel of na€ve-matched cisplatin-resistant ovarian allowed to form (25 mm3 or larger), then separated into 4 groups cancer cells demonstrates substantial chromatin remodeling in averaging 80 mm3 per tumor per group with 8–10 tumors cisplatin resistant cells. To better interpret the epigenetic altera- distributed between 5 and 6 mice. Mice were treated with DMSO tions and associate the changes with proximal gene expression, we control or with JQ1 at 250 mL per mouse (50 mg/kg; Selleckchem also acquired transcriptomic profiles from two different cellular S7110 lot 07), cisplatin at 100 mL per mouse (3 mg/kg; TEVA, models by acquiring RNA-seq gene expression profiles. NDC 0703-5748-11) or with both JQ1 and cisplatin. Except for To comparatively analyze the epigenome and transcriptome the first week's Wednesday treatment, JQ1 treatment was applied profiles across samples, we identified genomic regions that are on Tuesdays. Cisplatin was applied on Fridays of each week. significantly enriched (q < 0.01) for H3K27ac signal using the Tumor volumes were measured twice a week using calipers along established MACS2 ChIP-seq peak calling algorithm (24). The two axes with the formula: tumor volume ¼ longest diameter significantly enriched overlapping regions in different samples (shortest diameter)2. After the conclusion of the experiment, mice were then merged and their enrichment scores were calculated. were euthanized according to IACUC guidelines and policies. The peaks with 30 or fewer reads per region were filtered out. This analysis identifi ed a total of 36,388 distal and proximal regulatory elements across the four cellular models. To stratify the peaks Results differentially regulated upon platinum resistance, we quantified Generating cisplatin resistance in ovarian cancer cellular the relative fold changes of H3K27ac coverage per element sep- models arately for each matched na€ve/chemoresistant pair. Importantly, To gain molecular insights into cisplatin-induced epigenetic we observed that 6,568 (18%) of the regulatory genomic regions and transcriptional changes, we first generated multiple cellular undergo significant epigenetic reprogramming during cisplatin models of cisplatin resistance. We derived cisplatin-resistant resistance. Interestingly, a large fraction (88%) of the differentially ovarian cancer cells from their sensitive counterparts by period- regulated genomic elements were cell-type specific[(n ¼ ically exposing OVCAR4, OVCAR3, OV81, and SKOV3 ovarian 5,792; Fig. 1D and E]. Integrative analysis of gene expression cancer cell lines to increasing doses of cisplatin treatment. Here, profiles with the differentially regulated enhancer elements indi- we use the term na€ve to indicate relative sensitivity regardless of cates that the cell-type–specific epigenetic changes associate with whether the cell line was originated from a na€ve or treated corresponding transcriptional alterations in the relevant cell primary patient tumor. The treatments were performed as a 24- types. For example, among the 778 genes that are proximally hour pulse of cisplatin exposure followed by two cisplatin-free associated with the active enhancers specific to the resistant passages (see Materials and Methods; Fig. 1A). Notably, the cell OVCAR4 cells, 577 (74%) of them have increased expression in lines used in this study are widely used in ovarian cancer research the OVCAR4-resistant cells. In contrast, the same set of genes does as model systems for HGSOC. However, recent molecular pro- not show any expression changes in the SKOV3 cells. In the same filing efforts suggested that the origin of some of these cell lines cells, 916 (63%) of 1,459 genes proximal to the na€ve specific such as SKOV3 is likely nonserous epithelial ovarian can- enhancers reduce their expression during chemoresistance cer (25, 26). Nevertheless, these in vitro systems together provide (Fig. 1E). a tractable model to study the molecular dynamics of chemore- Notably, as expected, the genes that are proximal cell-type– sistance in ovarian cancer. The process was repeated until each cell specific clusters are enriched with gene ontology terms related to line developed significant resistance to cisplatin. The acquired cell-specific regulatory networks (Supplementary Fig. S3). On the chemoresistance was assessed through both short-term MTT cell other hand, when genes that are proximal to all enhancers that are viability assays (3 days) as well as longer-term (2 weeks) crystal up or downregulated in one or more cell types are comprehen- violet assays (Fig. 1B–D; Supplementary Fig. S1). In addition, we sively analyzed, we observed gene ontology terms related to also developed "resensitized" cellular counterparts of the SKOV3 cellular phenotypes and signaling pathways that are more rele- system by culturing cisplatin-resistant cells in the absence of vant to drug resistance (Fig. 1F). For example, genes that are cisplatin for an extended period (>6 months). We observed proximal to the resistant-specific enhancers are significantly significant restoration of cisplatin sensitivity in these previously enriched for epithelial-to-mesenchymal transition (EMT), and resistant cells (Fig. 1B, last panel; Supplementary Fig. S2). Rea- TGFb and WNT signaling pathways, which are known drivers of soning that these partially resensitized cells could provide addi- EMT (28). Furthermore, DNA damage response and drug- tional layers of information about the chemoresistance process, resistant genes are also spatially associated with the resistant we also profiled epigenomic and transcriptomic signatures of specific enhancers (Fig. 1F). In contrast, genes near enhancers these cells to further filter and identify chemoresistance- that are decommissioned in resistant cells are implicated in associated aberrantly regulated proximal and distal regulatory cellular migration, chemosensitivity, and apoptosis. In line with elements. previous reports (29, 30), these findings suggest that cisplatin resistance is associated with upregulation of mesenchymal and Identifying chemoresistance-mediated chromatin state stemness-related genes, and downregulation of migratory and alterations at regulatory genomic regions somatic cell phenotypes. We next investigated whether these To identify differentially regulated genomic elements and their cisplatin-resistant cells are generally resistant to other chemother- gene targets, we performed chromatin immunoprecipitation fol- apeutic and drugs. To this end, we tested multiple drugs and

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.. Naive Resistant Resensitized A B 100 100 100 100 80 80 ** 80 80 * Cisplatin *** ** ** treatment 60 60 60 60 ** * * ~6 mo 40 40 40 40 ** *** ** ** ** .. 20 20 20 *** 20 Naive Resistant Resensitized ** Percent viability 0 0 0 0 2824 51020 51040 61320

Cisplatin (μmol/L) Cisplatin (μmol/L) Cisplatin (μmol/L) Cisplatin (μmol/L) .. OVCAR 4 OVCAR 3 OV81 SKOV 3 C Naive (SKOV3) 100 50 kb 25 kb 100 kb 50 kb (IC50 = 0.435 μmol/L) .. 87 60 60 100 D Naive Resistant (SKOV3) 1 1 1 1 50 87 60 60 100 (IC = 1.603 μmol/L) Resistant 50 1 1 1 1 OVCAR4 .. 40 94 80 80 Percent Viability 0 Naive 1 1 1 1 -1 0 1 2 40 94 80 80 Log [Cisplatin] (μmol/L) 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 Resistant 1 1 1 1 0 μmol/L 0.24 μmol/L 0.49 μmol/L 0.97 μmol/L 1.95 μmol/L OVCAR3

H3K27ac ChIP-Seq RNA-Seq DNMBP ALDH1A1 TRPA1 SUZ12 EF Targets of upregulated enhancers n = 254 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 Gefitinib resistance n = 2,541 Enhancers Epithelial to mesenchymal transition Downregulated during invasion n = 543 DNA Damage response

n = 522 0251510 025 −Log (q-value) 10 Targets of downregulated enhancers Metabolic process n = 916 ESR1 Bound genes Migration Catabolic process n = 249 Programmed cell death Immune response Adipogenesis n = 3,251 Enhancers Chemosensitivity Hypoxia response Apoptosis Focal adhesion n = 305 R1 R2 R1 R2 R1 R2 R1 R2 0251015 025

.. .. − Res. Res. Log (q-value) 10 OV81 Naive Naive SKOV3 OVCAR3 OVCAR4 OVCAR4 SKOV3 0<−5 >5 −1 0 1

Figure 1. Cisplatin-resistant ovarian cancer cells display unique transcriptomes and epigenomes. A, Schematics show the strategy to generate cisplatin resistance in na€ve ovarian cancer cells. B, Bar graphs show percent viability of na€ve and resistant cellular counterparts for various ovarian cancer cell lines treated with increasing doses of cisplatin as measured by MTT assay. Error bars, SEM. , P < 0.05; , P < 0.01; , P < 0.001, as determined by Student t test. C, Line graph shows the quantitative measurement of crystal violet assays (bottom) for na€ve 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 na€ve-resistant pairs for selected genes. E, The heatmap depicts differentially regulated enhancers and promoters across multiple cisplatin-resistant cell models. Scale denotes z-scores of resistant over na€ve read coverage fold changes for the H3K27ac heatmap, or z-scores of normalized RNA expression in the RNA-seq heatmap. F, Bar graph shows the signiﬁcance of genes and pathways proximally associated with all up -or downregulated enhancer elements in E.

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chemicals (taxane, JQ1, Et-OH); however, we did not see stronger significantly downregulated in the resistant cells for both the resistance of these cells to these chemicals (Supplementary OVCAR4 and SKOV3 systems (Fig. 3A). Critically, the pathway- Fig. S4). In addition to these phenotypic assessments, compara- level analysis indicates that the genes whose expression is tive transcriptomic analysis revealed that unique sets of genes are significantly enhanced in the resistant cells are implicated with differentially up- and downregulated during cisplatin and taxane increased cell signaling. We observed that gene targets of a resistance (Supplementary Fig. S5). These results suggest that number of major signaling pathways are enriched and unique epigenetic and transcriptomic reprograming takes place expressed at higher levels in the resistant cells (Fig. 3B). For during cellular resistance to cisplatin and taxane resistance. example, gene targets of TNF-mediated NFkB signaling, IL2/STAT5, and TGFb, and WNT signaling pathways are all Mapping resistant-specific SEs in HGSOC cells among the genes that are significantly enriched in the genes Recent epigenomic analysis of enhancers shows that certain upregulated in the resistant state across the two cellular models. genomic regions contain clusters of enhancers that have been Furthermore, in line with the genes near the upregulated named as SEs (31–33). Importantly, cell-type–specific TFs, master cis-regulatory elements (Fig. 1F), genes involved in EMT and regulators of disease states, cell fates and oncogenesis tend to DNA repair pathways are enriched in upregulated genes. In be regulated by SEs (31–33). SEs can be computationally defined contrast, the analysis of downregulated genes suggests that by the presence of clustered genomic regions marked by intense metabolic pathways are generally downregulated in the che- H3K27ac ChIP-seq signals (31). We therefore aimed to identify moresistant cells (Fig. 3B). Specifically, genes involved in SEs and their targets genes in each of our cellular models. In catabolic processes and major metabolic pathways such as total, we identified 304 and 311 SEs in the OVCAR4- and oxidative phosphorylation, fatty acid metabolism, and TCA SKOV3-resistant cells, respectively. Importantly, a large fraction cycle are significantly enriched among the downregulated genes of these SEs only exists in the chemoresistant state. For example, in the resistant cells. Encouraged by these findings, we per- 88% (n ¼ 266) of the SEs identified in the OVCAR4-resistant cells formed Seahorse-mediated metabolic profiling.Inthegeneset are resistant-specific. These enhancers are either absent or not enrichment analysis, we observed that cisplatin SKOV3 cells intense enough to be classified as SEs in the na€ve cells (Fig. 2A have decreased glycolysis and oxidative phosphorylation as and B). Interestingly, we observed substantially more (>2.5 fold) measured by overall extracellular acidification rate and oxygen resistant-specific SEs than the na€ve-specific SEs in the SKOV3 consumption rate (Supplementary Fig. S6). In addition to system (Fig. 2B). However, we observed a comparable number metabolic genes, genes implicated in apoptosis and p53-related of na€ve- and resistant-specific SEs in the OVCAR4 system genes are among the significantly downregulated genes in (Fig. 2B). Notably, in the SKOV3 model, we also acquired chemoresistant cells (Fig. 3B). H3K27ac ChIP-seq epigenomic maps in the cells that lost their Reasoning that the transcriptional program of the chemoresis- cisplatin resistance due to prolonged culture in cisplatin-free tant state is governed by a unique set of TFs that are upregulated media (resensitized cells). Critically, as shown in Fig. 2C for during chemoresistance, we set out to investigate the major TFs the SIRPA locus, resistant-specific SEs lose nearly half of the involved in this process. To this end, we integrated epigenomic H3K27ac signal intensity in the resensitized cells. Globally, and transcriptomic analyses to identify those TFs that are poten- around 47% of all resistant-specific SEs return to the na€ve tially regulated by resistant-specific TFs and whose expression is state in the resensitized cells, indicating that cisplatin-resistant induced during chemoresistance. Given the known role of SEs in state is associated with dynamic gain and loss of enhancers and driving the expression of key cell-type-specific master regulators, SEs (Fig. 2D). Importantly, the genes that are proximally associ- we further prioritized the list of TFs by focusing on resistant- ated with the SEs (within 12.5 kb or the nearest gene) are specific SE-regulated TFs. Notably, among the significantly upre- expressed at higher levels compared with the genes near typical gulated and SE-associated TFs were multiple previously known enhancers (Fig. 2E). In line with this, a set of genes that are players of chemoresistance including ZEB2, E2F7, MYC (31, 33), associated with state-specific SEs are on average expressed more KLF6, and ELK3 (Fig. 3A; refs. 34, 35). In addition to known TFs, robustly in cells when these SEs are active (Fig. 2E). Specifically, we also identified other SE-driven genes previously implicated in the genes near the resistant-specific SEs are expressed at higher chemoresistance including ALDH1A1 (36), AKAP12 (37), levels in resistant cells. Similarly, the genes near the na€ve-specific FN1 (38–40), RAD18 (41), VEGFC (42), DNAJB12 (43), and SEs are expressed significantly higher in na€ve cells. Notably, a PARK7 (44). Critically, the analysis also identified several TFs highly comparable trend is observed in the SKOV3 system, where such as SOX9, HLX, MYBL1, ZNF430, and ZNF502 that have not we also have the resensitized counterparts. For example, genes previously been implicated in platinum resistance in ovarian near the resistant-specific SEs are expressed at higher levels in cancers (Fig. 3A). Notably, the analysis of gene expression and the resistant SKOV3 cells compared with the na€ve or resensitized patient survival in TCGA data shows that higher expression of a cells (Fig. 2E). number of these TFs such as ELK3, HIC1, ZEB2, and ZNF430, are significantly (P < 0.05) associated with poor patient survival Platinum resistance is associated with increased cell signaling (Fig. 3C; ref. 45). These observations suggest that the identified but decreased cell metabolism pathways resistance-associated TFs play significant roles in clinical settings Cell-type–specific transcriptional programs are tightly regu- and overall patient survival. lated by state-specific TFs. To identify differentially expressed To investigate whether the expression of these TFs is dynam- genes and potentially their driver factors, we integrated the ically induced by cisplatin treatment across different HGSOC cell state-specific epigenomic data with transcriptome analysis lines, we generated cisplatin-induced chemoresistance in addi- across the na€ve versus cisplatin-resistant cellular model sys- tional cell lines and assessed the expression of the nine TFs that are tems (Fig. 3A). Notably, we observed that approximately consistently upregulated in the OVCAR4 and SKOV3 cellular 1,000 genes were significantly upregulated and 816 genes systems. To this end, we periodically treated CAOV3 and COV362

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A ) ) OVCAR4 SKOV3 4 4 100 (×10

(×10 n = 304 80 Resistant-Specific n = 311 Resistant-Specific 88% 75 57% n = 266 60 n = 191 Shared RNF170 FRYL 50 40 12% YPEL2 43% C9orf84 AC023590.1 SOX9 HGSNAT Shared 25 CCDC121 20 MYOF MALT1 Figure 2. ROCK1 SOX9 LMCD1 Integrative analysis of enhancers 0 0 0 5,000 10,000 15,000 H3K27ac Read coverage and SEs that are associated with the H3K27ac Read coverage 0 5,000 10,000 15,000 gene expression program of Ranked enhancers Ranked enhancers cisplatin-resistant state. A, Dot plots show H3K27ac read coverage B OVCAR4 SKOV3 of distal enhancers in indicated 5.0 n = 266 resistant cells. Pie charts indicate 5.0 n = 191 Resistant Spec. SE percentages of resistance-speciﬁc 2.5 2.5 or shared SEs. Genes proximal to Naïve Spec. SE 0.0 the top SEs are highlighted. B, MA 0.0

plots show the fold change of Fold change Common SE

− Fold change 2 2.5 H3K27ac signal intensity at all 2 −2.5 Log promoters and enhancers for − Log 5.0 n = 307 − n = 76 Enhancer € 5.0 resistant versus na ve cells. Dotted 2 4 6 8 10 12 14 16 0 2 4 6 8 101214 line represents 1.5-fold change. C, Log mean H3K27ac intensity 2 Log2 mean H3K27ac intensity Normalized UCSC genome tracks are shown for RNA-seq and C D H3K27ac ChIP-seq at the SIRPA 100 kb 1,500 Super enhancer Enhancer € Fate of resistant-specific locus in na ve, resistant, and 0 resensitized SKOV3 cells. D, Pie 1,500 Naïve SE in resensitized cells chart depicts whether the intensity 0 of resistant-speciﬁc SEs identiﬁed in 1,500 Resistant RNA-Seq

Expression Amplified B are stable, further amplified, or 0 reduced more than 1.5 fold in the 55 Resensitized 11.0% resensitized state. E, Box plots 0 Reduced 55 Naïve depict relative expression of genes 47.1% Stable proximally associated with state- 0 55 Resistant 41.9% specific SEs. P values were H3 K27ac ChIP-Seq 0 determined by the Mann–Whitney Resensitized test. The midline represents the median, the boxes represent the SIRPA PDYN STK35 second and third quartiles, while the whiskers represent the minimum E OVCAR4 SKOV3 and maximum values of the gene -5 1.8×10-7 7.8×10-12 <2.2×10 -16 0.02 9.8×10 -13 expression levels in each sample. 80 4.5×10 60 60 Naïve

40 40 Resistant 20 20 Resensitized 0 0 Gene expression (FPKM) Gene expression (FPKM) Naïve SE Common Naïve SE CommonEnhancer Enhancer ResistantCommon SE SE ResistantCommon SE SE cells with increasing doses of cisplatin, harvested cells, and Targeting cisplatin resistance with the small-molecule measured the expression changes for these 9 TFs in cells that are epigenetic inhibitor resistant to different doses of cisplatin. Notably, as shown with the Targeting TFs with small molecules has been a formidable representative bar graph for SOX9 mRNA and the corresponding challenge. However, as SEs are differentially enriched for bromo- heatmaps for the other TFs, our targeted qRT-PCR results dem- and extra-terminal (BET) domain chromatin regulators and Medi- onstrate that these TFs are consistently upregulated as HGSOC ator complexes (32, 33), bromodomain inhibitors such as the cells become resistant to higher doses of cisplatin (Fig. 3D). small-molecule inhibitor JQ1 have been shown to differentially Collectively, our results suggest that TFs that are associated with target SE target genes (33). Therefore, small-molecule epigenetic the chemoresistant state are dynamically induced by cisplatin drugs that interfere with the regulatory programs of SEs provide an treatment and may function as critical drivers and master regu- exciting therapeutic opportunity to selectively target critical TFs lators of platinum resistance in ovarian cancer. that are regulated by SEs (31, 33). We thus tested whether JQ1 will

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OVCAR4 −Log (FDR q value) D 12 8 ABSKOV3 TF 10 10 COV362 7 CAOV3 0 5 10 15 20 6 8 P resis Ribonucleoprotein complex biogenesis 5 a 6 4

E2F7 thw Intracellular signal transduction ELK3 4 3 t Negative regulation of gene expression ant 2 FOXL1 a 2

y Positive regulation of biosynthetic process 1

HIC1 s c fold enrichment RNA Processing 0 fold enrichment 0 up

HLX ells 0 6912 069121518 Relative SOX9 mRNA μ Relative SOX9 mRNA n = 1,000 KLF6 Positive regulation of catalysis Cisplatin ( mol/L) Cisplatin (μmol/L) r eg TNFA Signaling via NFKB MYBL1 u MYC Targets MYC la 3

t KRAS Signaling e ZEB2 1.00 3.75 6.97 29.73 ZEB2 1.00 1.18 0.80 0.97 din 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 DNA Repair ZNF502 1.00 4.35 4.38 10.32 ZNF502 1.00 44.55 31.05 5.31 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 r P es

a Catabolic process t i

hw 1.00 2.74 3.51 8.81 1.00 3.87 5.59 4.62 s SOX9 SOX9

20 Oxidative phosphorylation ID4 tan

a Adipogensis n = 816 ys t TCA Cycle KLF6 1.00 2.37 5.01 7.22 KLF6 1.00 2.07 2.39 2.60 r ells

downregula 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 te 1.00 1.26 1.51 2.10 1.00 1.00 1.15 1.37 R1 R2 R1 R2 R1 R2 R1 R2 d Apoptosis ELK3 ELK3

in P53 Pathway Naïve Res. Naïve 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) Log-rank test Log-rank test Log-rank test − − Log-rank 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 Survival (months) Survival (months) Survival (months) Survival (months)

Figure 3. Cisplatin treatment results in the induction of SEs and target TFs that are associated with poor prognosis. A, The heatmap shows relative mRNA expression levels of 1,000 consistently upregulated and 816 consistently downregulated genes in resistance. Resistant and na€ve SE-associated TFs are red and blue, respectively. B, Pathway-level analysis for differentially regulated genes is shown. C, Kaplan–Meier 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 heatmaps show qRT-PCR–measured mRNA expression levels for SOX9 (bar graph) and other genes (heatmaps) relative to GAPDH in COV362 (left) and CAOV3 (right) cells at various stages of cisplatin resistance.

selectively target the expression of the selected SE target genes drug dose combinations in both cell lines, we observe much lower identified from our RNA- and ChIP-seq analyses. As a control, we CI values (stronger synergism) in the cisplatin-resistant cells at also selected gene targets of a typical enhancers that are not higher dose combinations (Supplementary Fig. S7). Critically, the associated with SE, but have similar basal- and resistance- combinatorial JQ1 and cisplatin treatment resulted in greater mediated gene expression fold change to the SE-target genes. rates of apoptosis than individual treatments in multiple cell Notably, although we observe that JQ1 reduced the expression lines (Fig. 4E and F; Supplementary Fig. S8), suggesting that the of non-SE target genes as well, the SE target genes were impacted combination results in reduced overall cell proliferation and significantly more (P ¼ 6.67e-05) after a 3-day JQ1 treatment increased cytotoxicity. (Fig. 4A). We next tested whether JQ1 treatment will result in Next, we assessed the clinical relevance of JQ1 and cisplatin synergistic cytotoxicity in the resistant cells when combined with combination in vivo on cisplatin-resistant cells in a subcutaneous cisplatin. The results indicate that JQ1 and cisplatin cotreatment xenograft model. At the beginning of the treatment, mice were resulted in a significant and more profound reduction in cellular randomly separated into four groups. Notably, single-agent treat- viability in the resistant cells compared with the na€ve cells ment of JQ1 (50 mg/kg/wk) or cisplatin (3 mg/kg/wk) did not (Fig. 4B). In line with the short-term (3 days) MTT assay, lon- have a significant effect on the relative tumor volume (Fig. 4G). In ger-term (14 days) crystal violet viability assay also demonstrated contrast, cisplatin and JQ1 combination treatment resulted in significant synergy between cisplatin and JQ1 treatment in the significantly smaller tumors when compared with single-drug resistant OVCAR4 cells (Fig. 4C). To further corroborate these treatments or the controls (Fig. 4G). These results are in line with findings, we tested JQ1 and cisplatin combination on four addi- the in vitro data and suggest that small-molecule epigenetic tional HGSOC cell lines. These results show that JQ1 treatment inhibitors that interfere with SE function can synergistically significantly increases the growth-inhibitory activity of cisplatin increase the therapeutic efficacy of platinum-based compounds across multiple ovarian cancer cell lines (Fig. 4D). To further in cisplatin-resistant ovarian cancer cells in vivo. characterize the synergism between JQ1 and cisplatin, we tested various dose combinations on both na€ve and cisplatin-resistant SOX9 is required for acquiring and maintaining the cells and calculated the combination index (CI), a well- chemoresistant phenotype established model to study drug synergism (46), where CI values Our results so far suggest that the chemoresistance process is, at less than one indicate synergism. Although we observe synergistic least in part, regulated by epigenomic reprogramming and

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200 P = 6.67e-05 B 100 180 100 Non-SE associated genes 90 160 80 80 SE associated genes 140 70 120 60 60 100 50 SKOV3-R 40 80 40 30 60 * 20 40 20 10 SKOV3-R+JQ1 20 Relative viability (%)

0 (JQ1 vs. Control treatment) 0 (JQ1 vs. Control treatment) Percent gene expression changes 0 Percent gene expression changes HLX

HIC1 012915

BST1 Super ELK3 ZEB2 B7H6 SOX9 Non SE CCNO STK38 FOXL1 enhancer NEGR1 FRMD6 enhancer ZNF430 ZUFSP1 MRPL46 CDC165 Cisplatin (µmol/L) TMEM200A C D Control Cisp. JQ1 Cisp.+JQ1 OVCAR4 SKOV3 COV362 100 100 100 * ** * * 80 80 ** 80 60 60 ** 60 40 *** 40 40 *** 20 20 *** 20

Relative growth (%) 0 0 0 JQ1 JQ1 JQ1 Cisp. Cisp. Cisp. DMSO DMSO DMSO Cisp.+JQ1 Cisp.+JQ1 OVCAR4 Cisp.+JQ1 EFControl Cisplatin G 6 1.12 5.26 6 2.63 9.48 1,000 5 5 P = 0.0144 DMSO (n = 9) 7 900 4 4 JQ1 (n = 9) ) 800 6 3 3 3 P = 0.0266 Cisplatin (n = 10) 0 0 700 89.5 4.16 76.8 11.1 5 JQ1 + Cisplatin (n = 10) 0 345 6 0 345 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 (mm 200 3

3 Relative rate of apoptosis DAPI 0 100 0 83.1 7.19 0 65 17.4 30 4 5 6 0 345 6 JQ1 0 DMSO 0 3 6 9 12 15 18 21 24 27 30 33 Annexin V Staining Cisplatin Cisp + JQ1 Time (days)

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 SE target genes in cisplatin- resistant SKOV3 cells. B, Graphs show percent viability (MTT assay) of SKOV3-resistant cells after 24-hour treatment with the indicated doses of cisplatin and constant treatment with JQ1 (1 mmol/L) or control DMSO for an additional 48 hours. C, Crystal violet assay shows relative growth of OVCAR4-resistant cells treated with 1 mmol/L of cisplatin and/or JQ1 for 24 hours, 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, 1 mmol/L JQ1, or both. Error bars, 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 (3 mmol/L), JQ1 (1 mmol/L), or both. F, Bar plot shows the relative percentages of the top right quartile in E. Error bars, SEM 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, SEM. , P < 0.05; , P < 0.01; , P < 0.001, as determined by Student t test for boxplots or ANOVA for line graphs.

licensing of a speciﬁc set of distal enhancers, SEs, and differential likely established by the combinatorial actions of a network of expression of their target genes. Notably, targeting these distal TFs (47), we focused our efforts to assess the contribution of SOX9 enhancers through small-molecule epigenetic inhibitors results in in platinum resistance. We chose to study SOX9 for two reasons. signiﬁcant reductions in cisplatin resistance and in the expression First, the TF had never been implicated in chemoresistance in of SE-associated target genes. Although cellular states are most ovarian cancer. SOX9 is a known player of chondrogenesis (48)

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H3K27ac ChIP-Seq RNA-Seq 100 kb Super enhancer 5 kb B A 300 30 0 R1 SKOV3 Resistant Resensitized OVCAR4 Naïve 0 300 10 R2 100 PDX N1 0 Resistant 0 300 OVCAR4 SOX9 R3 WT clone 10 0 sg1 Naïve PDX N2 100 KO-1

0 Naïve R1 80 50 0 Tubulin KO-2 SKOV3 0 100 sg2 0 R2 KO-3 100 60 R3 KO-4 SKOV3 0 sg2sg1 300 30 R1 40 OVCAR4 0 0 10 300 KO-4 KO-1 KO-2 KO-3 R2 Control WT Clone PDX T1 0 0 Percent viability 300 20 10 OVCAR4 0 R3 PDX T2 0 SOX9 100 Resistant 50 0 R1 0 SKOV3 Resistant β 0 100 -Actin 0 30 0 R2 4816 COV362 SKOV3 0 100 μ 0 R3 Cisplatin ( mol/L) 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 Naïve 1.0 Naïve 1 1 60 60 1.0 Res. 0.5 Res. 1 H3K27ac ChIP-Seq SKOV3 1 350 80 OVCAR4 Naïve 0.0 0.5

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

SKOV3 1 SKOV3 −3,000 −1,500 Center 1,500 3,000 −3,000 −1,500 Center 1,500 3,000 RNA-Seq BMP2 WNT5A Distance from SOX9 Peak Distance from SOX9 Peak

0.08 1.1× 10-4 E F − G 60 H SKOV3 OVCAR4 I Log10 (FDR q value) SOX9-Binding sites 05 10 -7 -8 100 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 (WT vs. SOX9 KO) n = 216

Promoter-TSS 20 400 0 Relative mRNA level Cancer stem cell genes Gene expression (FPKM) 0 Gene expression (FPKM)

0 MAF

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

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-na€ve, resistant, and resensitized cells. Line plots depict MTT cell viability of cisplatin-treated resistant WT and SOX9 knockout cells. Error bars, 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 na€ve 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 na€ve versus 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 na€ve and resistant cells. P values were determined by the Mann–Whitney test. H, Box plots depict relative expression levels of genes proximal to sites with varying degrees 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 qRT-PCR–measured mRNA expression levels of resistance-associated TF genes relative to GAPDH in WT and SOX9 KO SKOV3-R cells. Error bars, SEM from at least three independent experiments. , P < 0.05; , P < 0.001, as determined by Student t test for I.

and stemness of neural progenitors (49) and hair follicles (50). mRNA levels go down, further indicating that high SOX9 expres- Notably, it is overexpressed in several different cancers (51) and sion and protein levels are restricted to the cisplatin-resistant cells believed to be involved in epithelial and mesenchymal transi- (Fig. 5B). tions. Second, SOX9 is one of the few TFs whose both expression To interrogate the functional role of SOX9 in chemoresis- level and putative enhancer elements are consistently upregulated tance, we used CRISPR to knock out (KO) SOX9 in both in all the chemoresistance models that we studied (Fig. 5A). na€ve and cisplatin-resistant SKOV3 cells. We performed both Notably, we also observe substantial activation of a SOX9 SE in population-level KO studies as well as clonal analyses of single two na€ve-matched cisplatin-resistant patient-derived xenograft cells. Notably, genetic depletion of SOX9 did not result in tumors (Fig. 5A). In line with the gene expression and distal signiﬁcant proliferation defects neither in resistant cells nor in enhancer activation of the SOX9 locus, we observe a substantial na€ve cells (Supplementary Figs. S9–S11), suggesting that SOX9 protein levels in resistant cells (Fig. 5B). Critically, in the maintenance of transcriptional programs downstream of resensitized cells, SOX9 protein levels, enhancer activity and SOX9 is not critical for cell proliferation. However, depletion

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of SOX9 in the cisplatin-resistant SKOV3 or OVCAR4 cells confers significant sensitivity to cisplatin when compared Discussion to the SOX9 expressing controls (Fig. 5B; Supplementary Chemoresistance is a major therapeutic obstacle in the treat- Fig. S10). On the other hand, CRISPR-mediated SOX9 deple- ment of ovarian cancer. Although specific genetic alterations tion does not confer additional sensitivity in na€ve cells (Sup- such as reversion of germline BRCA1/2 mutations and inactivat- plementary Figs. S11 and S12). Interestingly, when WT and ing mutations in tumor suppressor RB1, NF1, RAD51B, and SOX9 KO-na€ve cells are challenged with lower cisplatin PTEN (16) have been associated with chemoresistance, the molec- doses over a period of 14 days, significantly less number of ular network that drives and maintains the chemoresistant state at survivor colonies were observed in SOX9-depleted cells (Sup- the chromatin and transcriptional levels is poorly understood. In plementary Fig. S12). These findings collectively suggest this study, we integrate epigenome and transcriptome profiling in that SOX9 is not only necessary for the maintenance of cis- multiple chemoresistance models to investigate potential drivers platin-resistant state, it also critical for the acquisition of the of resistance in ovarian cancer. Our findings indicate that che- cisplatin-resistant state in ovarian cancer cells. moresistance is associated with the licensing of a specific set of To gain further insights into the mechanism of SOX9- distal enhancers. Notably, among these genes that are especially mediated acquisition and maintenance of chemoresistance, upregulated in the cisplatin-resistant cellular state are multiple we used ChIP-Seq to map SOX9-binding sites across the TFs, whose expression is controlled by unique enhancer clusters genome in cisplatin-resistant cells (Fig. 5C). As shown for known as SEs, which can be potentially targeted by small- BMP2 and WNT5A loci, SOX9 targets are expressed at higher molecule epigenetic inhibitors. The data presented here suggest levels in resistant cells (Fig. 5C). It is notable that WNT5A is a that JQ1 can effectively reduce the expression of such TFs and target of a distal SE in the chemoresistant SKOV3 cells. Our increase cisplatin sensitivity in the chemoresistant cells. In line enhancer and gene expression analysis highlighted upregu- with this, there is significant synergy between JQ1 and cisplatin lated WNT/b-catenin pathway during chemoresistance in mul- both in vitro and in vivo, suggesting a great translational potential tiple cellular models. It is notable that WNT5A is overex- to target chemoresistance in ovarian cancer with chromatin tar- pressed in malignant epithelial ovarian cancers (52), impli- geting small molecules. cated in cancer stem cells (53), and associated with poor Our findings provide key insights into the epigenetic bases of prognosis (54–56). In support of our findings, recent pub- chemoresistance. Data presented in this article indicate that lications also highlight significant involvement of WNT chemoresistance is associated with large-scale reprogramming pathway in ovarian cancer chemoresistance (57–60). These and redistribution of H3K27ac histone modifications across the results collectively highlight that targeting the WNT pathway genome. As expected, the reprogrammed enhancer elements are may have a strong potential to prevent and overcome che- highly enriched near the genes that are associated with DNA moresistance in ovarian cancer (61). damage response, cell signaling, EMT, as well as drug response To understand whether SOX9 is associated with activating or are aberrantly upregulated in chemoresistant cells. On the other repressive activity at the target genomic regions, we analyzed the hand, genes implicated in apoptosis signaling, catabolism, and epigenomic and transcriptional alterations at SOX9 targets in the cellular adhesion pathways are downregulated during chemore- na€ve and resistant counterparts. Notably, the genomic regions sistance. Notably, we also observed substantial cell-type–specific that are bound by SOX9 have increased accumulation of H3K27ac epigenetic and transcriptional programs, suggesting a great degree marks, indicating higher activity in resistant cells relative to the of heterogeneity in the epigenetic state of resistant cells. Critically, na€ve counterparts (Fig. 5D). When we analyzed the genomic despite the epigenetic and transcriptional heterogeneity, our data distribution of SOX9-binding sites, we observed strong enrich- suggest that small-molecule epigenetic drugs targeting distal ment of gene promoters (2 kb of TSS; Fig. 5E), which is in line enhancers and SEs can be used to alter the aberrant regulation with the previous SOX9 ChIP-seq studies (62, 63). The pathway and confer cisplatin sensitivity. BET inhibitors like JQ1 have been analysis of SOX9 targets indicates that these genes are implicated shown to release Mediator from cis-regulatory elements at regu- in invasion, drug sensitivity, cancer stem cell genes, and DNA latory elements and alter the transcription of target genes. Thus, replication (Fig. 5F). In line with our expectation, SOX9 target theoretically, JQ1 can affect both enhancers and SEs. However, genes are expressed at higher levels in the resistant cells compared because SEs could contain significantly higher levels of H3K27ac with the na€ve counterparts (Fig. 5G). Critically, when the SOX9- and BRD4, they are more sensitive to JQ1 treatment (33). Our binding intensity is correlated with the gene expression within the experimental analysis supports this notion and explains, in part, resistant cells, we observed significant positive correlation the particular effectiveness of JQ1 in our chemoresistant models. between SOX9-binding intensity and target gene expression Our findings highlight SOX9 as a critical determinant of the (Fig. 5H). These findings indicate that SOX9 is acting as a cisplatin resistance. We show that SOX9 is a target of a SE that is transcriptional activator while driving and maintaining the che- strongly activated in resistant cells. Genetic targeting efforts fur- moresistant state. To further test this hypothesis, we performed ther support the hypothesis that SOX9 is one of the key TFs that targeted qPCR mRNA expression analysis on select genes in WT maintain the gene expression programs of cisplatin-resistant and SOX9 KO cells. Critically, SOX9 deletion results in significant states. SOX9 depletion results in the downregulation of multiple depletion of multiple other TFs that are significantly upregulated TFs associated with chemoresistance and increased sensitivity to in the resistant cells (Fig. 5I). These results indicate that SOX9 is a cisplatin in multiple ovarian cancer lines. It remains to be shown critical player of gene expression programs of the cisplatin- whether the expression SOX9 alone is sufficient to confer cisplatin resistant cellular states. Furthermore, our data suggest that SOX9 resistance. It is notable that higher expression of SOX9 is asso- may mediate acquisition and maintenance of cisplatin chemore- ciated with worse prognosis in multiple solid tumors (64). Fur- sistance by coordinating the expression of multiple other TFs and thermore, SOX9 is also a critical marker of clinically aggressive thus controlling their gene targets. disease in metastatic high-grade serous carcinoma (65).

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Disclosure of Potential Conflicts of Interest Administrative, technical, or material support (i.e., reporting or organizing R.M. Campbell has ownership interest (including stock, patents, etc.) in Eli data, constructing databases): S. Shang, A.A. Jazaeri, A.J. Duval, P.J. Ebert, M. Adli Lilly & Company. P.J. Ebert has ownership interest (including stock, patents, Study supervision: M. Adli etc.) in Eli Lilly and Company. No potential conflicts of interest were disclosed Other (acquisition of preliminary data): N.L. Fischer by the other authors. Acknowledgments The study was supported by the NIH/NCI 1R01 CA211648-01 and Authors' Contributions UVA Cancer Center pilot award (NCI CCSG P30 CA44579) award (to Conception and design: S. Shang, A.A. Jazaeri, R.M. Campbell, T. Abbas, M. Adli). In addition, the work was supported by the NIH/NCI R01 C.N. Landen, M. Adli CA160356 and NIH/NCI R01 CA193677 awards (to P.C. Scacheri). S. Shang Development of methodology: S. Shang, A.A. Jazaeri, A.J. Duval, F. Guessous, was supported in part by the Cancer Training grant T32 CA009109. T. Abbas, C.N. Landen, M. Adli Acquisition of data (provided animals, acquired and managed patients, The costs of publication of this article were defrayed in part by the provided facilities, etc.): S. Shang, A.J. Duval, M. Benamar, F. Guessous, payment of page charges. This article must therefore be hereby marked I. Lee, P.J. Ebert, T. Abbas, C.N. Landen, A. DiFeo, P.C. Scacheri advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate Analysis and interpretation of data (e.g., statistical analysis, biostatistics, this fact. computational analysis): S. Shang, J. Yang, A.J. Duval, M. Benamar, I. Lee, R.M. Campbell, P.J. Ebert, C.N. Landen, P.C. Scacheri, M. Adli Writing, review, and/or revision of the manuscript: S. Shang, J. Yang, A.A. Jazaeri, Received January 22, 2019; revised June 5, 2019; accepted July 23, 2019; A.J. Duval, T. Tufan, R.M. Campbell, C.N. Landen, P.C. Scacheri, M. Adli published first July 29, 2019.

References 1. American Cancer Society. Cancer facts & figures 2018. Atlanta, GA: 19. Berdasco M, Esteller M. Aberrant epigenetic landscape in cancer: how American Cancer Society; 2018. cellular identity goes awry. Dev Cell 2010;19:698–711. 2. Disease GBD, Injury I, Prevalence C. Global, regional, and national 20. Zeller C, Dai W, Steele NL, Siddiq A, Walley AJ, Wilhelm-Benartzi CS, et al. incidence, prevalence, and years lived with disability for 310 diseases and Candidate DNA methylation drivers of acquired cisplatin resistance in injuries, 1990–2015: a systematic analysis for the Global Burden of Disease ovarian cancer identified by methylome and expression profiling. Onco- Study 2015. Lancet 2016;388:1545–602. gene 2012;31:4567–76. 3. Reid BM, Permuth JB, Sellers TA. Epidemiology of ovarian cancer: a review. 21. Cheng YY, Mok E, Tan S, Leygo C, McLaughlin C, George AM, et al. SFRP Cancer Biol Med 2017;14:9–32. tumour suppressor genes are potential plasma-based epigenetic biomar- 4. Jazaeri AA, Bryant JL, Park H, Li H, Dahiya N, Stoler MH, et al. Molecular kers for malignant pleural mesothelioma. Dis Markers 2017;2017: requirements for transformation of fallopian tube epithelial cells into 2536187. serous carcinoma. Neoplasia 2011;13:899–911. 22. Lee PS, Teaberry VS, Bland AE, Huang Z, Whitaker RS, Baba T, et al. Elevated 5. Karst AM, Levanon K, Drapkin R. Modeling high-grade serous ovarian MAL expression is accompanied by promoter hypomethylation and plat- carcinogenesis from the fallopian tube. Proc Natl Acad Sci U S A 2011;108: inum resistance in epithelial ovarian cancer. Int J Cancer 2010;126: 7547–52. 1378–89. 6. Perets R, Wyant GA, Muto KW, Bijron JG, Poole BB, Chin KT, et al. 23. Taniguchi T, Tischkowitz M, Ameziane N, Hodgson SV, Mathew CG, Joenje Transformation of the fallopian tube secretory epithelium leads to high- H, et al. Disruption of the Fanconi anemia-BRCA pathway in cisplatin- grade serous ovarian cancer in Brca;Tp53;Pten models. Cancer Cell 2013; sensitive ovarian tumors. Nat Med 2003;9:568–74. 24:751–65. 24. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Identifying ChIP-seq enrichment 7. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin using MACS. Nat Protoc 2012;7:1728–40. 2013;63:11–30. 25. Beaufort CM, Helmijr JC, Piskorz AM, Hoogstraat M, Ruigrok-Ritstier K, 8. Balch C, Fang F, Matei DE, Huang TH, Nephew KP. Minireview: epigenetic Besselink N, et al. Ovarian cancer cell line panel (OCCP): clinical impor- changes in ovarian cancer. Endocrinology 2009;150:4003–11. tance of in vitro morphological subtypes. PLoS One 2014;9:e103988. 9. Giaccone G. Clinical perspectives on platinum resistance. Drugs 2000; 26. Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as 59Suppl 4:9–17. tumour models by comparison of genomic profiles. Nat Commun 2013;4: 10. Ozols RF. Ovarian cancer: new clinical approaches. Cancer Treat Rev 1991; 2126. 18Suppl A:77–83. 27. Zhu J, Adli M, Zou JY, Verstappen G, Coyne M, Zhang X, et al. Genome- 11. Koberle B, Tomicic MT, Usanova S, Kaina B. Cisplatin resistance: preclinical wide chromatin state transitions associated with developmental and findings and clinical implications. Biochim Biophys Acta 2010;1806: environmental cues. Cell 2013;152:642–54. 172–82. 28. Moustakas A, Heldin CH. Mechanisms of TGFbeta-induced epithelial- 12. Galluzzi L, Senovilla L, Vitale I, Michels J, Martins I, Kepp O, et al. Molecular mesenchymal transition. J Clin Med 2016;5:pii: E63. mechanisms of cisplatin resistance. Oncogene 2012;31:1869–83. 29. Shah MM, Landen CN. Ovarian cancer stem cells: are they real and why are 13. Takenaka M, Saito M, Iwakawa R, Yanaihara N, Saito M, Kato M, et al. they important? Gynecol Oncol 2014;132:483–9. Profiling of actionable gene alterations in ovarian cancer by targeted deep 30. Steg AD, Bevis KS, Katre AA, Ziebarth A, Dobbin ZC, Alvarez RD, et al. Stem sequencing. Int J Oncol 2015;46:2389–98. cell pathways contribute to clinical chemoresistance in ovarian cancer. 14. Cole AJ, Dwight T, Gill AJ, Dickson KA, Zhu Y, Clarkson A, et al. Assessing Clin Cancer Res 2012;18:869–81. mutant p53 in primary high-grade serous ovarian cancer using immuno- 31. Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-Andre V, Sigova AA, et al. Super- histochemistry and massively parallel sequencing. Sci Rep 2016;6:26191. enhancers in the control of cell identity and disease. Cell 2013;155: 15. Kim JY, Cho CH, Song HS. Targeted therapy of ovarian cancer including 934–47. immune check point inhibitor. Korean J Intern Med 2017;32:798–804. 32. Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, et al. 16. Patch AM, Christie EL, Etemadmoghadam D, Garsed DW, George J, Fere- Master transcription factors and mediator establish super-enhancers at key day S, et al. Whole-genome characterization of chemoresistant ovarian cell identity genes. Cell 2013;153:307–19. cancer. Nature 2015;521:489–94. 33. Loven J, Hoke HA, Lin CY, Lau A, Orlando DA, Vakoc CR, et al. Selective 17. Dawson MA. The cancer epigenome: concepts, challenges, and therapeutic inhibition of tumor oncogenes by disruption of super-enhancers. Cell opportunities. Science 2017;355:1147–52. 2013;153:320–34. 18. Baylin SB, Jones PA. A decade of exploring the cancer epigenome - 34. Kong SY, Kim KS, Kim J, Kim MK, Lee KH, Lee JY, et al. The ELK3-GATA3 biological and translational implications. Nat Rev Cancer 2011;11: axis orchestrates invasion and metastasis of breast cancer cells in vitro and 726–34. in vivo. Oncotarget 2016;7:65137–46.

OF12 Cancer Res; 79(18) September 15, 2019 Cancer Research

Regulatory Elements Drive Chemoresistance in Ovarian Cancer

35. Buchwalter G, Gross C, Wasylyk B. The ternary complex factor Net regulates 50. Kadaja M, Keyes BE, Lin M, Pasolli HA, Genander M, Polak L, et al. SOX9: a cell migration through inhibition of PAI-1 expression. Mol Cell Biol 2005; stem cell transcriptional regulator of secreted niche signaling factors. 25:10853–62. Genes Dev 2014;28:328–41. 36. Yokoyama Y, Zhu H, Lee JH, Kossenkov AV, Wu SY, Wickramasinghe JM, 51. Matheu A, Collado M, Wise C, Manterola L, Cekaite L, Tye AJ, et al. et al. BET inhibitors suppress ALDH activity by targeting ALDH1A1 super- Oncogenicity of the developmental transcription factor Sox9. enhancer in ovarian cancer. Cancer Res 2016;76:6320–30. Cancer Res 2012;72:1301–15. 37. Chappell NP, Teng PN, Hood BL, Wang G, Darcy KM, Hamilton CA, et al. 52. Badiglian Filho L, Oshima CT, De Oliveira Lima F, De Oliveira Costa H, De Mitochondrial proteomic analysis of cisplatin resistance in ovarian cancer. Sousa Damiao R, Gomes TS, et al. Canonical and noncanonical Wnt J Proteome Res 2012;11:4605–14. pathway: a comparison among normal ovary, benign ovarian tumor and 38. Wu W, Wang Q, Yin F, Yang Z, Zhang W, Gabra H, et al. Identification of ovarian cancer. Oncol Rep 2009;21:313–20. proteomic and metabolic signatures associated with chemoresistance of 53. Zhou Y, Kipps TJ, Zhang S. Wnt5a signaling in normal and cancer stem human epithelial ovarian cancer. Int J Oncol 2016;49:1651–65. cells. Stem Cells Int 2017;2017:5295286. 39. Jinawath N, Vasoontara C, Jinawath A, Fang X, Zhao K, Yap KL, et al. 54. Qi H, Sun B, Zhao X, Du J, Gu Q, Liu Y, et al. Wnt5a promotes vasculogenic Oncoproteomic analysis reveals co-upregulation of RELA and STAT5 in mimicry and epithelial-mesenchymal transition via protein kinase Calpha carboplatin resistant ovarian carcinoma. PLoS One 2010;5:e11198. in epithelial ovarian cancer. Oncol Rep 2014;32:771–9. 40. Helleman J, Jansen MP, Span PN, van Staveren IL, Massuger LF, Meijer-van 55. Peng C, Zhang X, Yu H, Wu D, Zheng J. Wnt5a as a predictor in poor clinical Gelder ME, et al. Molecular profiling of platinum resistant ovarian cancer. outcome of patients and a mediator in chemoresistance of ovarian cancer. Int J Cancer 2006;118:1963–71. Int J Gynecol Cancer 2011;21:280–8. 41. Haynes B, Saadat N, Myung B, Shekhar MP. Crosstalk between 56. Bitler BG, Nicodemus JP, Li H, Cai Q, Wu H, Hua X, et al. Wnt5a suppresses translesion synthesis, Fanconi anemia network, and homologous epithelial ovarian cancer by promoting cellular senescence. Cancer Res recombination repair pathways in interstrand DNA crosslink repair 2011;71:6184–94. and development of chemoresistance. Mutat Res Rev Mutat Res 57. Niiro E, Morioka S, Iwai K, Yamada Y, Ogawa K, Kawahara N, et al. 2015;763:258–66. Potential signaling pathways as therapeutic targets for overcoming che- 42. Scartozzi M, Giampieri R, Loretelli C, Bittoni A, Mandolesi A, Faloppi L, moresistance in mucinous ovarian cancer. Biomed Rep 2018;8:215–23. et al. Tumor angiogenesis genotyping and efficacy of first-line chemo- 58. Huang L, Jin Y, Feng S, Zou Y, Xu S, Qiu S, et al. Role of Wnt/beta-catenin, therapy in metastatic gastric cancer patients. Pharmacogenomics 2013; Wnt/c-Jun N-terminal kinase and Wnt/Ca(2þ) pathways in cisplatin- 14:1991–8. induced chemoresistance in ovarian cancer. Exp Ther Med 2016;12: 43. Sopha P, Ren HY, Grove DE, Cyr DM. Endoplasmic reticulum stress- 3851–8. induced degradation of DNAJB12 stimulates BOK accumulation 59. Zhang C, Zhang Z, Zhang S, Wang W, Hu P. Targeting of Wnt/beta-catenin and primes cancer cells for apoptosis. J Biol Chem 2017;292: by anthelmintic drug pyrvinium enhances sensitivity of ovarian cancer 11792–803. cells to chemotherapy. Med Sci Monit 2017;23:266–75. 44. Trivedi R, Dihazi GH, Eltoweissy M, Mishra DP, Mueller GA, Dihazi H. The 60. Li J, Yang S, Su N, Wang Y, Yu J, Qiu H, et al. Overexpression of long non- antioxidant protein PARK7 plays an important role in cell resistance to coding RNA HOTAIR leads to chemoresistance by activating the Wnt/beta- cisplatin-induced apoptosis in case of clear cell renal cell carcinoma. Eur J catenin pathway in human ovarian cancer. Tumour Biol 2016;37: Pharmacol 2016;784:99–110. 2057–65. 45. Cancer Genome Atlas Research Network. Integrated genomic analyses of 61. Nagaraj AB, Joseph P, Kovalenko O, Singh S, Armstrong A, Redline R, et al. ovarian carcinoma. Nature 2011;474:609–15. Critical role of Wnt/beta-catenin signaling in driving epithelial ovarian 46. Chou TC, Talaly P. A simple generalized equation for the analysis of cancer platinum resistance. Oncotarget 2015;6:23720–34. multiple inhibitions of Michaelis-Menten kinetic systems. J Biol Chem 62. Liu CF, Lefebvre V. The transcription factors SOX9 and SOX5/SOX6 1977;252:6438–42. cooperate genome-wide through super-enhancers to drive chondrogen- 47. Han H, Shim H, Shin D, Shim JE, Ko Y, Shin J, et al. TRRUST: a reference esis. Nucleic Acids Res 2015;43:8183–203. database of human transcriptional regulatory interactions. Sci Rep 2015;5: 63. Shi Z, Chiang CI, Labhart P, Zhao Y, Yang J, Mistretta TA, et al. Context- 11432. specific role of SOX9 in NF-Y mediated gene regulation in colorectal cancer 48. Leung VY, Gao B, Leung KK, Melhado IG, Wynn SL, Au TY, et al. cells. Nucleic Acids Res 2015;43:6257–69. SOX9 governs differentiation stage-specific gene expression in growth 64. Ruan H, Hu S, Zhang H, Du G, Li X, Li X, et al. Upregulated SOX9 expression plate chondrocytes via direct concomitant transactivation and repression. indicates worse prognosis in solid tumors: a systematic review and meta- PLoS Genet 2011;7:e1002356. analysis. Oncotarget 2017;8:113163–73. 49. Martini S, Bernoth K, Main H, Ortega GD, Lendahl U, Just U, et al. A critical 65. Sherman-Samis M, Onallah H, Holth A, Reich R, Davidson B. SOX2 and role for Sox9 in notch-induced astrogliogenesis and stem cell maintenance. SOX9 are markers of clinically aggressive disease in metastatic high-grade Stem Cells 2013;31:741–51. serous carcinoma. Gynecol Oncol 2019;153:651–60.

www.aacrjournals.org Cancer Res; 79(18) September 15, 2019 OF13

Chemotherapy-Induced Distal Enhancers Drive Transcriptional Programs 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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