Epigenetic Reprogramming Towards Mesenchymal-Epithelial Transition

bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Epigenetic reprogramming towards mesenchymal-epithelial transition in ovarian cancer- associated mesenchymal stem cells drives metastasis Fan, Huihui1*, Atiya, Huda2*, Wang, Yeh3, Pisanic, Thomas R4, Wang, Tza-Huei3, Shih, Ie-Ming3 Foy, Kelly K1, Frisbie, Leonard2, Chandler, Chelsea5, Shen, Hui1&, Coffman, Lan2,5&$ *co-first authorship &co-senior authorship, co-corresponding authorship: [email protected], [email protected] $lead contact author

1Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, USA. 2Division of Hematology/Oncology, Department of Medicine, Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA. 3Department of Pathology, Department of Gynecology and Obstetrics, Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD, 4Johns Hopkins Institute for NanoBiotechnology, Johns Hopkins University, Baltimore, MD, USA. 5Division of Gynecologic Oncology, Department of Obstetrics, Gynecology, and Reproductive Sciences, Magee Women’s Research Institute, University of Pittsburgh, Pittsburgh, PA

Summary: Ovarian cancer develops early intra-peritoneal metastasis establishing a supportive tumor microenvironment (TME) through reprogramming normal mesenchymal stem cells into carcinoma-associated mesenchymal stem cells (CA-MSCs). CA-MSCs are the stromal stem cell of the TME, supporting cancer growth, increasing desmoplasia, angiogenesis and chemotherapy resistance. We demonstrate epigenetic reprogramming drives CA-MSC formation via enhancer- enriched DNA hypermethylation, altered chromatin accessibility and differential histone modifications inducing a partial mesenchymal to epithelial transition (MET) increasing adhesion to tumor cells. Direct CA-MSC:tumor cell interactions, confirmed in patient ascites, facilitate ovarian cancer metastasis through co-migration. WT1, a developmental mediator of MET, and EZH2, mediate CA-MSC epigenetic reprogramming. WT1 overexpression induces CA-MSC conversion while WT1 knock-down, along with EZH2 inhibition, blocks CA-MSC formation. EZH2 inhibition subsequently decreases intra-abdominal metastasis.

Keywords: Mesenchymal stem cells, carcinoma-associated mesenchymal stem cells, ovarian cancer, tumor microenvironment, epigenetic reprogramming, EZH2, WT1, metastasis, mesenchymal to epithelial transition

Significance: This work presents a new paradigm of stromal reprogramming involving a partial mesenchymal to epithelial transition. Rather than a classic tumor cell epithelial to mesenchymal transition, metastasis relies on epigenetic rewiring of a CA-MSC allowing enhanced tumor cell binding and co-migration with tumor cells to form metastasis. Indeed, CA-MSCs in complex with tumor cells are abundant in patient ascites. Reversion of CA-MSCs to normal MSCs is observed in patients achieving complete response with neoadjuvant therapy. Identification of WT1 and EZH2 as mediators of the epigenetic reprogramming of CA-MSCs present potential targets to block the formation of CA-MSCs thus disrupting the TME and limiting ovarian cancer metastasis.

Introduction

Ovarian cancer, the deadliest gynecologic cancer, kills over 14,000 US women yearly and 70% of all women diagnosed with the disease. This high mortality is due to early, diffuse intra- abdominal metastatic spread. Ovarian cancer quickly colonizes the peritoneal cavity building a complex tumor microenvironment (TME) that supports ovarian cancer survival, growth, and spread. This TME has unique physical and chemical properties with a complex bionetwork of tumor cells, immune cells, and stromal cells interacting within a hypoxic, nutrient poor environment (Zhang et al. 2003; Tothill et al. 2008; Verhaak et al. 2013; Karlan et al. 2014; Konecny et al. 2014). Efforts to determine what drives the formation and function of the ovarian TME led to the identification of a critical stromal stem cell, the carcinoma-associated mesenchymal stem cell (CA- MSC) (McLean et al. 2011). CA-MSCs are benign stromal cells that meet all the criteria for mesenchymal stem cells/multipotent mesenchymal stromal cells (MSCs) as defined by the International Society for Cellular Therapy (ISCT) (Dominici et al. 2006). CA-MSCs are not tumor cells. Karyotype analysis, high resolution genotyping, and in vivo tumor initiation assays demonstrated that CA-MSCs have normal genomes, lack tumor-related mutations (such as p53 mutations), and isolated CA-MSCs lack malignant potential (McLean et al. 2011; Verardo et al. 2014). CA-MSCs are distinct from carcinoma associated fibroblasts (CAFs) and other more terminally differentiated stromal cells with long term proliferative capacity, lineage differentiation capacity and a unique gene expression pattern (Augsten 2014; Coffman et al. 2019). CA-MSCs strongly enhance ovarian cancer initiation, growth, chemotherapy resistance, increase the cancer stem cell-like (CSC) pool, and dramatically alter the stromal TME, increasing tumor-associated fibrosis and inducing angiogenesis (Spaeth et al. 2009; Cho et al. 2012; Coffman et al. 2016b). CA-MSCs have a unique gene expression profile characterized by altered extracellular matrix protein and cytokine expression (McLean et al. 2011; Coffman et al. 2016b; Coffman et al. 2019). CA-MSCs arise from normal tissue MSCs through cancer-mediated reprogramming (Coffman et al. 2019). Using a 6-gene mRNA expression classifier that accurately distinguishes normal MSCs from CA-MSCs, we demonstrated that ovarian cancer reprograms normal ovary and omental MSCs into ovarian cancer promoting CA-MSCs. Thus, ovarian cancer drives the formation of its own supportive TME (Coffman et al. 2019). The mechanism driving this reprogramming remains unknown however given the wide-spread, durable expression changes without gain of genomic mutations we hypothesized epigenetic reprogramming likely drives this conversion. Cancer-related epigenetic changes are well documented, although most studies focus on epigenetic modification of cancer cells rather than associated stroma (Sharma et al. 2010; Poli et al. 2018). Here we demonstrate that CA-MSCs have a unique epigenetic landscape compared to normal MSCs characterized by enhancer-enriched DNA hypermethylation, enrichment in repressive histone modifications and altered chromatin accessibility. We demonstrate DNA methylation changes are acquired during cancer-stimulated conversion of normal MSCs into CA-MSCs and are not altered by lineage differentiation into fibroblasts or adipocytes. However, MSCs derived from patients achieving a complete pathologic response to neoadjuvant chemotherapy revert to a normal MSC profile. Unexpectedly, the CA-MSC DNA methylation pattern partially resembles fallopian tube epithelium (FTE) while normal MSCs align more closely with fibroblasts. CA-MSCs appear to undergo a partial mesenchymal to epithelial transition with increase in cell contact pathways and functional demonstration of increased tumor cell adhesion. CA-MSCs increase ovarian cancer metastasis via direct tumor cell binding and co-migration in an orthotopic mouse model. CA-MSCs are also found in complex with tumor cells isolated from patient ascites. Further, we identify EZH2 and WT1 as potential mediators of CA-MSC epigenetic mesenchymal to

epithelial reprograming. Interrupting this reprogramming dramatically decreases the ability of ovarian cancer cells to form intra-abdominal metastasis.

Results:

CA-MSCs have a unique DNA methylation profile compared to normal tissue MSCs. We hypothesized that CA-MSCs undergo epigenetic alterations which drives their mitotically stable pro-tumorigenic phenotype. We used the Infinium MethylationEPIC (850K/EPIC/HM850) array to profile 13 CA-MSCs derived from high-grade serous ovarian cancer (HGSC), 20 omental/adipose/fallopian tube (FT) derived normal MSCs, and six patient tumor cell samples (sup table 1). We first performed QC on these samples, for both data quality (methods) and sample quality including purity determination and potential samples swaps. The promoter of MIR200C/141 is unmethylated in epithelial cells and methylated in mesenchymal cells (Vrba et al. 2010), and the methylation level (beta value) of MIR200C/141 directly corresponds to the fraction of non-epithelial cells (Cherniack et al. 2017; George et al. 2018). As expected, our MSCs (CA-MSCs and normal MSCs) had close to 100% methylation at the MIR200C/141 promoter site, consistent with a non-epithelial origin and high sample purity. In contrast, primary human tumor cell samples demonstrated lack of methylation at this locus (Fig1A). One CA-MSC sample had low MIR200C/141 promoter methylation consistent with tumor cell contamination and this sample also clustered with sorted tumor cells across the most variably methylated sites in the genome (SupFig1A) and was removed from all further analysis.

We downloaded and reprocessed external DNA methylation data on normal cell types from prior publications (Patch et al. 2015; Klinkebiel et al. 2016) (methods), including normal FTE and ovary stroma, profiled on the earlier generation of the Infinium platform, Infinium HumanMethylation450 (450K) array. The intersect between the two platforms (450,161 CpGs) were used in joint analysis with these external normal controls. Uniform Manifold Approximation and Projection (UMAP) reveals clustering coinciding with sample type (Fig1B), with a few exceptions: 1) One CA-MSC (CAMSC-OM08) grouped with the normal ovary stroma. After final pathology review, this sample was found to be derived from an ovarian carcinosarcoma, not HGSC. 2) One additional sample, CAMSC-OM01, grouped with CA-MSCs on the UMAP but clustered with the normal MSCs on the unsupervised hierarchical clustering (Fig1C) and overall demonstrated an intermediate profile between normal MSCs and CA-MSCs. This sample was derived from tumor adjacent omentum without clear malignant involvement and may represent a mixture of normal MSCs and CA-MSCs. 3) MSC-FT-BrcaR, derived from the right FT of a patient with a germline BRCA2 mutation undergoing risk reduction surgery grouped with CA-MSCs. In contrast, MSCs derived from the left FT of the same patient grouped with normal MSCs as expected. The expression-based CA- MSC classifier score for the left FT MSCs was 0.01 (consistent with a normal MSC) and for the right FT MSC was 0.95 (approaching the CA-MSC threshold of 0.97) in line with the methylation grouping and indicating certain stromal cells derived from a high risk patient more closely align with cancer associated stroma (Coffman et al. 2019). As expected, flow-sorted tumor cells were the closest to normal FTE, the presumed cell of origin for HGSC.

MSCs derived from patients with complete response to chemotherapy resemble normal MSCs Interestingly, MSCs derived from the omentum of patients treated with neoadjuvant chemotherapy who achieved a pathologic complete response clustered with the normal MSCs (n=4, MSC- OMneo1-4). Likewise, the expression classifier scores of these MSCs ranged from 0.01-0.15 (all within the normal MSC range). Radiographic evidence of omental involvement prior to neoadjuvant chemotherapy was documented in all cases.

CA-MSCs demonstrate epigenetic alteration characterized by hypermethylation at distal enhancers Top 10,000 most variably methylated probes across all MSC samples (CA-MSCs and normal MSCs) demonstrated a clear delineation between CA-MSCs and normal MSCs (Fig1C). CA- MSCs demonstrated distinct gains in DNA methylation vs normal MSCs (Fig1D, 1E). Greater than 128,000 differentially methylated cytosines (DMCs; 32% out of ~401,000 filtered probes; methods), mapping to 22,991 differentially methylated regions (DMRs), were identified based on a threshold of FDR < 0.05. The most significant DMRs (ranked P-value followed by mean beta value levels) are listed in supplemental table 2. Such difference is more pronounced at distal rather than proximal sites (Fig1F; SupFig S2A,B), with distal regions three times more likely to acquire hypermethylation of >0.2 beta value difference compared to proximal regions consistent with DNA methylation changes preferentially occurring in enhancer regions.

CA-MSC DNA methylation profile demonstrates similarity to that of epithelium We examined sites with absolute beta value difference of greater than 0.2 in CA-MSCs vs normal MSCs (n=36,606 in HM850 loci, n=17,904 in HM850/HM450 intersect). Unexpectedly, in this space, CA-MSCs closely resembled FTE (Fig1G), which are benign epithelial cells, while normal MSCs were more similar to fibroblasts (of dermal origin). Similarly, CA-MSCs bore more resemblance to sorted HGSC cells, which are also epithelial in origin.

Intrigued by this result, we further examined all sites (instead of just CA-MSC vs MSC differential sites) in the HM850/HM450 intersect. Globally CA-MSCs exhibit a similarity to FTE (SupFig S1B), but are clearly not identical. We continued to investigate sites shared between, or unique to MSCs, CA-MSCs and FTE (methods; Fig2A). Normal ovarian stroma, fibroblast, high-grade serous ovarian cancer (HGSC), Sarcoma (SARC) and Uterine carcinosarcoma (UCS) are plotted as representatives of various relevant cell types. This new analysis revealed that CA-MSCs had a DNA methylation profile that combined MSC and FTE features, with a limited set of CA-MSC- specific hyper- and hypo-methylated sites (Fig2A). Thus, CA-MSCs demonstrated a partial conversion from normal MSC towards FTE. CA-MSCs are also distinct from other epithelial and mesenchymal malignancies including uterine carcinosarcoma and soft tissue sarcomas.

Quantitative methylation specific PCR (qMSP) validates DMRs To confirm the methylation differences noted in the EPIC array, and to develop an economical assay for classification, we selected a panel of five of the top DMRs (CA-MSCs vs MSCs): two regions of hypermethylation (SASH1, C13orf45) and three regions of hypomethylation (ELN, GATA6, MARVELD2). Average beta values for each CpG as determined by the microarray are plotted in Figure 2B. Quantitative MSP (qMSP) assays were designed for each locus. qMSP was performed on an independent set of 6 additional normal MSCs and 9 CA-MSCs. Across the five loci tested, the pattern of methylation between CA-MSCs and normal MSCs was confirmed with significant methylation demonstrated in CA-MSCs at SASH1 and C13Orf45 and significant hypomethylation at ELN, GATA6 and MARVELD2 (Fig2C).

CA-MSC-associated DMRs persist throughout lineage differentiation and are acquired during cancer-mediated reprogramming of MSCs To determine if lineage differentiation alters methylation at the identified DMRs, we also used qMSP to characterize normal MSC and CA-MSC in vitro lineage directed differentiation into fibroblasts or adipocytes (Fig2D). Interestingly, these cells maintained their methylation status at these loci even after differentiation.

Given our previous work demonstrating that normal MSCs are induced by tumor cells to become CA-MSCs, we analyzed in vitro indirect cancer stimulation of normal MSCs (which yields a partial conversion to a CA-MSC) for the development of CA-MSC specific DMRs with the qMSP assays developed above. Three of the five loci tested demonstrated CA-MSC-like alterations, with acquisition of DNA methylation at SASH1 and loss of methylation at ELN and MARVELD2. C13orf45 and GATA6 loci remained unchanged (Fig2E). This finding suggests a partial conversion to a CA-MSC DNA methylation profile with in vitro cancer stimulation. This is consistent with a change in the CA-MSC classifier score from 0.1 in normal MSCs (consistent with a normal phenotype) to 0.62 after indirect cancer stimulation (threshold for CA-MSC is 0.97).

CA-MSCs have altered chromatin accessibility We next analyzed the chromatin accessibility of CA-MSCs vs normal MSCs using the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATACseq). As expected, clustering based on ATACseq peaks accurately separated normal MSCs from CA-MSCs, with two replicates from the same sample located right next to each other as expected for such controls (Fig3A). DESeq2 identified 5,129 statistically differential peaks with an adjusted p value lower than 0.05. Power was limited in this analysis, partly due to the small sample size after removing samples that failed QC. However, even in the majority of sites that did not reach statistical significance, CA-MSCs in general demonstrated a decrease in peak strength compared to MSCs after normalizing for library size (Fig3B), consistent with the observed widespread gain of DNA methylation in CAMSCs. The majority of the differential peaks between CA-MSCs and MSCs were found in distal intergenic regions, closely tracking the DNA methylation results (SupFig S2C,D). Overall, sites with reduced peak strength in CA-MSCs tended to have lower gene expression (Fig3C; R2=0.02; p value < 2.2e-16) and higher DNA methylation (Fig3D).

DNA methylation and chromatin accessibility correspond to transcriptomic differences in CA- MSCs vs MSCs. We incorporated previously reported RNA sequencing data (Coffman et al. 2019) with our DNA methylation and chromatin accessibility data (Fig3E). In general, genes with gained peaks and genes with lost peaks segregated based on gene expression. Genes exemplified by WT1, LRRN4, KRT8, KRT18 and COL4A6 were significantly overexpressed in CA-MSCs and demonstrated hypomethylation and open ATAC peaks. In contrast, genes such as COL15A1, EBF2, EDNRB, and COLEC12 which have decreased expression in CA-MSCs demonstrated hypermethylation and reduced ATAC peaks.

Gene expression pathway analysis confirms CA-MSCs undergo a partial mesenchymal to epithelial transition (MET) Pathway analysis of the most differentially expressed genes demonstrated an upregulation of multiple pathways associated with cell:cell and cell:matrix adhesion, a largely epithelial feature, and down-regulation of genes maintaining mesenchymal cellular identity (Fig4A-B). Genes downstream of the TGF-beta signaling pathway , a strong inducer for EMT (Xu et al. 2009), were significantly enriched in the downregulated set in CA-MSCs. We took top 25 epithelial (E-) and mesenchymal (M-) signature genes from a previous study (Creighton et al. 2013) ranked based on the Pearson’s correlations of gene expression with the EMT score calculated from the canonical EMT markers (Lee et al. 2006). CA-MSCs clearly exhibited higher expression for the E-genes and lower expression for the M-genes. A quantitative EMT score derived with the 16- gene signature from the same study also suggested that CA-MSCs indeed had a more “epithelial” signature while normal MSCs had a more “mesenchymal” signature (Fig4C). This observation

coalesces with the DNA methylation results, which also suggest a partial mesenchymal-epithelial transition in CA-MSCs.

To test if CA-MSCs exhibit an increase in epithelial phenotypic characteristics such as increased cell adhesion capacity compared to normal MSCs, we performed in vitro cell adhesion assays of tumor cells to normal MSCs or CA-MSCs. CA-MSCs had a 3-fold increase in tumor cell binding capacity after 30 mins of co-incubation. This pattern is true regardless of MSC or CA-MSC origin (fallopian tube or omentum) and is consistent across two ovarian cancer cell lines and one primary patient tumor cell line tested (Fig5A, SupFig3A). The increased tumor cell binding confirms the pathway analysis showing increased cell:cell adhesion in CA-MSCs.

CA-MSCs are found in direct contact with ovarian cancer cells in patient ascites To understand the physiologic relevance of a partial MET resulting in enhanced adhesion of CA- MSCs to tumor cells, we isolated cellular complexes from ovarian cancer patient ascites. Flow cytometry analysis of complexes demonstrated the presence of CA-MSCs in a roughly 1:10 ratio to tumor cells (Fig5D). CA-MSC identity was verified via flow cytometry analysis with cell surface markers (CD90, 73, 105+/ CD45, 34, 14, 19-) and differentiation capacity (adipocyte, osteocyte, chondrocyte) was confirmed to meet ISCT criteria for MSCs (Dominici et al. 2006). Cytospins of cellular complexes were analyzed with immunofluorescent confocal microscopy for Epcam (pacific blue) positive tumor cells and CD90 (FITC)/CD73 (RFP) positive CA-MSCs confirming physical interactions between CA-MSCs and tumor cell in ascites (Fig5D).

CA-MSCs enhance ovarian cancer metastasis As transcoelomic metastasis is thought to be a main mechanism of ovarian cancer metastasis resulting in diffuse peritoneal disease and malignant ascites coupled with the fact that CA-MSCs are found within cellular complexes in ascites, we next assessed the role of CA-MSCs in ovarian cancer metastasis. We tested if CA-MSCs support critical steps in ovarian cancer metastasis including migration/invasion and survival under non-adherent conditions. CA-MSCs allowed to directly interact with tumor cells (CAOV3, OVSAHO, OVCAR3) significantly enhanced tumor cell migration and invasion through a matrigel-coated transwell (Fig5B). CA-MSCs also enhanced the ability of tumor cells to grow under non-adherent conditions as spheres (Fig5C). Fluorescent microscopy of CA-MSCs (expressing mT) and tumor cells (expressing GFP) demonstrated CA- MSCs and tumor cells remained in physical contact during migration/invasion and sphere formation indicating co-migration and formation of heterocellular spheres (Fig5Ci). Increasing the ratio of tumor cells to CA-MSCs from 1:1 to 10:1 under non-adherent conditions resulted in each tumor cell sphere containing at least one CA-MSC even at low CA-MSC concentrations (Fig5Cii). This indicates a preferential direct binding between these cell types. This also mirrors the ratio of tumor cells to CA-MSCs (~10:1) found in patient ascites cellular complexes (Fig5E).

Direct tumor cell:CA-MSC interactions mediate enhancement of ovarian cancer metastasis To confirm the enhancement of metastatic steps is mediated through direct CA-MSC:TC interactions, we repeated the above experiments but prevented the direct interaction between CA-MSCs and tumor cells by encapsulating CA-MSCs in alginate or separating the cells by a permeable membrane (allowing paracrine signaling without direct contact). Luciferase-secreting CA-MSCs were used to verify the continued viability and effective protein secretion of encapsulated CA-MSCs (SupFig3B). Alginate encapsulated CA-MSCs failed to enhance tumor cell migration/invasion (Fig5B). Similarly, CA-MSCs separated from tumor cells by a permeable membrane did not enhance tumor cell sphere formation (Fig5C). Together, this implicates direct contact between CA-MSCs and TCs as critical to in vitro enhancement of metastasis.

We next assessed the impact of CA-MSCs in a murine model of metastasis. Ovarian cancer cells (OVCAR3) were implanted orthotopically in the ovarian bursa alone, with CA-MSCs or with alginate encapsulated CA-MSCs. Mice with tumor cells + CA-MSCs demonstrated significant increases in metastasis and systemic illness as measured by decreased weight over time compared to mice with tumor cells alone or encapsulated CA-MSCs (Fig5F). Mice with tumor cells allowed to directly contact CA-MSCs demonstrated significantly more intra-abdominal metastasis with co-localization of tumor cells and CA-MSCs at metastatic implants consistent with tumor cell:CA-MSC co-migration (Fig5I). Mice with tumor cells alone or with encapsulated CA-MSCs demonstrated large localized ovarian tumors but did not develop ascites, distant abdominal metastasis, lung or parenchymal liver metastasis (Fig5H). CA-MSCs within the alginate capsule remained viable and were re-isolated at the time of necropsy. Collectively, this indicates CA- MSCs significantly enhance ovarian cancer metastasis and the epigenetically driven partial MET allowing increased tumor cell adhesion is critical to the pro-metastatic phenotype of CA-MSCs.

WT1 is a driver of MET and mediates CA-MSC formation One of the most significantly differentially regulated genes with increased RNA expression, decreased DNA methylation and increased ATAC peak was WT1 (Fig3E; Fig6A). WT1 expression is often found in cells going through mesenchymal to epithelial transitions or cells with mixed epithelial and mesenchymal properties, such as podocytes (Moore et al. 1998). In addition, WT1 overexpression has been reported to induce MET and is a regulator of MET during development (Hohenstein and Hastie 2006; Essafi et al. 2011). While known as a tumor marker, WT1 is present in tumor-associated stroma (Hylander et al. 2006; He et al. 2008). Independent qRT-PCR validation of WT1 expression demonstrated ~100 fold increased expression in CA-MSCs vs normal MSCs with corresponding increase in protein level (though protein levels varied more substantially than RNA expression) (Fig6B,C). Further, during cancer stimulated reprogramming of MSCs into CA-MSCs, WT1 expression was induced (Fig6C).

Interestingly, while Figure 3E showed a decrease in WT1 promoter methylation associated with WT1 overexpression, upon close examination of the entire WT1 region (Fig6A), we discovered that the promoter DNA methylation differences were carried by only a subset of the normal MSC samples. However, two regions (highlighted by orange and cyan boxes in Figure 6A) in the gene body of WT1 were consistently methylated in normal MSCs and unmethylated in CA-MSCs. Further, each of these differentially methylated regions overlapped with a site with differential accessibility between CA-MSCs and MSCs as determined by ATAC-seq. These ATAC-seq differential peaks were statistically significant even with the small sample size. In particular, when we looked up ENCODE histone modification data, Region 2 (cyan box) clearly functions as an enhancer in K562 cells, a malignant leukemia cell line, but is not active in normal fibroblasts (NHLF), a cell type normal MSCs appear to resemble based on DNA methylation results. These results seem to suggest that hypomethylation at this cryptic enhancer, associated with enhanced accessibility, likely turned this region normally methylated in normal MSCs into an active enhancer, and greatly promotes WT1 expression (>1,000 fold by RNA-seq, >100 fold by RT- PCR). Given the global trend of hypermethylation in CA-MSCs, this specific hypomethylation is even more likely to be a driver event, instead of a passenger event associated with global gain of DNA methylation.

To determine if WT1 is important in the formation of CA-MSCs, we created WT1 overexpressing (OE) normal MSCs via lentiviral transduction. Compared to lenti-GFP control MSCs, WT1 expression increased to levels approximating CA-MSCs (Fig6C). Analysis of the CA-MSC

classifier score in WT1 OE MSCs vs control MSCs demonstrated increase in the classifier score from 0.01 (a normal MSC score) to 0.99 (a CA-MSC score). This indicates WT1 OE drives the conversion of a normal MSC to a CA-MSC. We also performed siRNA mediated knock down (KD) of WT1 in normal MSCs. WT1 KD MSCs underwent cancer stimulation via hypoxic direct co- culture with ovarian cancer cells which we previously demonstrated was the most effective in vitro condition to convert a normal MSC to a CA-MSC. Cancer-stimulated (CS) WT1 KD, compared to CS scrambled control MSCs, demonstrated a 23% decrease in the conversion into a CA-MSC (based on change in CA-MSC classifier score) (Fig6D).

CA-MSC expression pattern correlates with altered EZH2 binding sites. DNA methylation alterations at enhancer regions can serve as a readout of overall activity of transcription factors, where hypermethylation often indicates an inactive transcription factor binding site (TFBS), and hypomethylation likely marks an active state (Yao et al. 2015) . Enrichment analysis for annotated TFBS sites identified EZH2 binding sites as the most significantly enriched category with hypermethylation in CA-MSCs (Fig7A). Concordantly, binding sites of SUZ12, a member of the polycomb repressive complex 2 (PRC2) along with EZH2, were also enriched. Interestingly, EZH2/SUZ12 binding sites were also enriched in loci with hypomethylation (Fig7A), indicating a likely redistribution of EZH2/SUZ12. Consistent with this observation, gene set enrichment analysis (GSEA) demonstrated an enrichment in EZH2 targets in both up- and downregulated genes in CA-MSCs compared to normal MSCs (Fig7B).

CA-MSCs are enriched with PRC2-related histone marks. Given the role of EZH2 in histone modifications at H3K27, we investigated global changes in histone modifications between CA-MSCs and normal MSCs. We performed mass spectrometry on the most common 80 histone modifications in CA-MSCs and MSCs. Out of the 80 modifications tested, six histone marks were differentially represented in CA-MSCs vs MSCs (p value <0.1): H3K27ac, H3K27me3, H1K26me1, H3K79me1, H3K27me2, and H4K16ac (Multiple testing corrected p value shown on Fig7C). Differential histone modification was verified via western blot of independent MSCs and CA-MSCs (3 MSCs and 5 CAMSCs not used in the mass spec screen). All histone differences were confirmed with the exception of H3K79me1 (SupFigS5D,E). H3K27ac, H1K26me1 and H4K16ac are generally associated with transcriptional activation and were decreased in CA-MSCs. In contrast, H3K27me3 and H3K27me2 are associated with transcriptional repression and were increased in CA-MSCs. Three of the top six differential histone modifications are directly related to the PRC2: H3K27me2, H3K27me3 and H3K27ac. This supports a role for EZH2 differential targeting in CA-MSC formation.

Further supporting a role for differential EZH2 targeting in CA-MSCs, during cancer stimulated reprogramming of CA-MSCs, EZH2 RNA expression is increased (Fig7D). This induction occurs at early stages of reprogramming with paracrine tumor signaling during indirect tumor cell: MSC co-culture (Fig7D), conditions which we demonstrated above induced DNA methylation changes in three of the five CA-MSC DMRs assessed in Figure 2. This induction is also seen with in vivo cancer stimulation in a xenograft mouse model as previous described (Coffman et al. 2019). This implies a role for EZH2 during the initial stages of CA-MSC formation.

EZH2 inhibition in combination with WT1 KD blocks the formation of a CA-MSC. Given our findings indicating altered EZH2 genomic targeting and known interaction between WT1 and EZH2/PRC2 targeting (Xu et al. 2011), we tested if EZH2 and WT1 directly interact in CA-MSCs. Using co-immunoprecipitation with an anti-EZH2 antibody in CA-MSCs, we 8

demonstrated WT1 is pulled down with EZH2 (Fig7E). We then tested if EZH2 inhibition alone or in combination with WT1 KD would further decrease the development of a CA-MSC. To verify lack of toxicity, we performed viability studies with increasing dosage of an EZH2 inhibitor, GSK126. At doses of 20nM (Ki0.59nM, IC50 9.9 nM), there was no significant decrease in cell viability of tumor cells or MSCs (SupFig5A). Next, WT1 KD and scrambled control MSCs were treated with 20nM GSK126 during cancer stimulation. GSK126 treatment of scrambled control MSCs lead to a 38% decrease in CA-MSC conversion. However, GSK126 treatment of WT1 KD MSCs resulted in a 55% decrease in the conversion of WT1 KD MSCs into CA-MSCs (Fig7F). Similar results were seen using a different EZH2 inhibitor, Tazemetostat (SupFig S5B). Collectively, this demonstrated WT1 and EZH2 jointly mediate the formation of a CA-MSC.

EZH2 inhibition limits ovarian cancer metastasis As EZH2 inhibition (EZH2i) disrupted the in vitro formation of CA-MSCs and CA-MSCs significantly enhanced metastasis, we tested if EZH2i impacted ovarian cancer metastasis. We used a previously described tail vein injection model of ovarian cancer which yields robust intra- abdominal metastasis (Coffman et al. 2016a). Mice were treated with GSK126 or vehicle control for one week prior to tail vein injection of OVCAR3 tumor cells and continued for two weeks after injection. Mice with vehicle control initial treatment were given GSK126 starting two weeks post- injection. EZH2i does not impact already established CA-MSCs (classifier score remains >0.97) (supFig S5C). Therefore, pretreatment/early treatment vs delayed treatment of GSK126 permitted comparison of tumor-mediated stromal reprogramming with and without EZH2i but controlled for the potential impact of EZH2i on tumor cell growth within an established TME. Four weeks post injection, all mice were sacrificed and necropsy was performed to identify areas of gross metastatic colonization. Strikingly, EZH2i decreased the rate of ovary malignant colonization by 60% (95% colonization in control group vs 35% colonization in EZH2i treated group) (Fig7G). Additionally, EZH2i decreased the amount of abdominal metastasis including bowel and mesentery involvement and ascites formation by 90%, 70% and 50% respectively. Rates of liver metastasis were decreased by 50% in the EZH2i group. Interestingly, rates of lung metastasis were equivalent (90% vs 100% in control vs EZH2i group). Additionally, lung tumor burden as measured by quantitative human specific PCR in whole lung tissue demonstrated equivalent tumor burden arguing against a direct cytotoxic effect of EZH2i which is consistent with the lack of in vitro toxicity (Fig7H).

Discussion: Since described about a decade ago (Barrallo-Gimeno and Nieto 2005; Klymkowsky and Savagner 2009; Polyak and Weinberg 2009; Thiery et al. 2009; Yilmaz and Christofori 2009), partial transition to a mesenchymal phenotype in the epithelial compartment (EMT) in carcinomas has been well established as a hallmark of cancer with increased metastatic potential (Hanahan and Weinberg 2011). Here, we describe alterations in the other direction – mesenchymal to epithelial transition (MET) - in stromal progenitors of the TME in close contact with the carcinoma, that also contribute to cancer invasion and metastasis. As stromal progenitor cells, CA-MSCs have a unique capacity to influence the formation of the TME. We previously demonstrated that CA-MSCs arise from cancer-mediated reprogramming of normal tissue MSCs creating a strongly pro-tumorigenic phenotype. Here we demonstrate that CA-MSCs have a unique epigenetic landscape different from both tumor cells and normal MSCs, characterized by DNA hypermethylation, altered chromatin accessibility and gain of repressive histone marks. The acquisition of DNA methylation at top DMRs during cancer stimulated reprogramming of MSCs into CA-MSCs validates an altered epigenetic landscape as a mitotically stable registrant of cancer stimulation. The majority of such differential DNA

methylation occurs in enhancer regions. It has been previously shown that enhancers are the most dynamically utilized compartment (Calo and Wysocka 2013; Shlyueva et al. 2014; Sur and Taipale 2016) and closely related to cellular identity. Interestingly, at loci where CA-MSCs and MSC differed, the DNA methylation profile of CA-MSCs resembles benign FTE and sorted carcinoma while normal MSCs more closely resemble normal fibroblasts. Pathway analysis of differentially regulated genes demonstrated cell:cell adhesion pathways are upregulated in CA- MSCs. This is consistent with a more epithelial phenotype. While remaining mesenchymal in lineage, a partial MET appears to occur during CA-MSC formation. In vitro adhesion assays demonstrated CA-MSCs have a 3-fold increased capacity to bind tumor cells compared to normal MSCs also consistent with a MET phenotype. Importantly, this increase in tumor cell binding is critical for the pro-tumorigenic functions of CA-MSCs. CA-MSCs strikingly enhance ovarian cancer metastasis through a process of co-migration driven by direct CA-MSC:tumor cell contact. Indeed, CA-MSCs are abundant in ovarian cancer patient ascites as part of heterocellular clusters with tumor cells. This process of cancer cell and stromal cell co-migration is akin to the cancer “seed” traveling with its “soil” to enhance survival during metastasis and aid in distant colonization where the CA-MSC, as a stromal progenitor cell, may play a critical role in establishing the metastatic TME through stromal lineage differentiation. Indeed, MET in CA-MSCs may be associated with increased differentiation capacity. De- differentiation of mesenchymal cells such as fibroblasts into induced pluripotent stem cells (iPSCs) requires MET (Li et al. 2010; Shu and Pei 2014). During embryogenesis, stem cells often transition from EMT to MET stages, and MET in stem cell reprogramming is associated with epigenetic changes, consistent with our findings (Wu et al. 2016). We previously reported that CA-MSCs compared to normal MSCs have enhanced “stemness” with increased colony formation, increased ALDH expression and greater differentiation potential consistent with greater pluripotency(McLean et al. 2011). WT1, one of the most significantly upregulated genes in CA-MSCs, is an important mediator of MET during development and thought to be important in maintaining the potential to transition between EMT and MET (Miller-Hodges and Hohenstein 2012). Interestingly, WT1 has been shown to impact epigenetic regulation through directly binding the polycomb protein EZH2 and the DNA methyltransferases DNMT1 and DNMT3A (Xu et al. 2011; Szemes et al. 2013). Given the differential histone modifications (particularly related to PRC2 targets including H3K27me3) and DNA methylation changes in CA-MSCs, WT1 may be an important mediator of these epigenetic modifications. Indeed, co-IP confirmed WT1/EZH2 interaction in CA-MSCs, and knock down of WT1 in combination with EZH2 inhibition during cancer stimulation blocked the formation of a CA-MSC. Pharmacologic inhibition of EZH2, while not impacting tumor cell growth within the lung, significantly decreased the ability of ovarian cancer cells to form intra-abdominal metastasis. As metastasis is dependent on the colonization of a receptive TME, this supports the conclusion that EZH2 is important in the cancer stimulated epigenetic reprogramming of normal intra- abdominal MSCs to CA-MSCs. Further, we demonstrated that the CA-MSC DNA methylation pattern persists throughout CA- MSC differentiation into other stromal elements including fibroblasts and adipocytes yet is not acquired during normal MSC differentiation. This points to the potential to develop a CA-MSC epigenetic signature which may be amplified throughout the stroma due to CA-MSC differentiation akin to generating a “field effect”. However, there may be opportunities to alter this stromal “field effect”. Omental MSCs derived from patients with a pathologic complete response to neoadjuvant chemotherapy fit a normal MSC profile indicating that either the CA-MSC phenotype is reversible or normal MSCs replace CA-MSCs after tumor death. Alternatively, these MSCs may be resistant to developing a CA-MSC phenotype and may be important in driving the excellent response to chemotherapy given the previously reported role of CA-MSCs in enhancement of chemotherapy

resistance (Coffman et al. 2016b) . Work currently under review in collaboration with the Buckanovich group demonstrated that CA-MSCs derived from metformin treated ovarian cancer patients have a methylation profile resembling normal MSCs supporting the ability to alter the development of a CA-MSC phenotype. Thus, understanding the mechanistic steps leading to CA- MSC development and the ability to reverse, replace or create MSCs resistant to reprogramming is essential to effective therapeutic targeting of the TME and represents a novel and powerful approach to ovarian cancer treatment.

Acknowledgements : We would like to thank Marie Adams and the Genomics Core at Van Andel Institute (VAI) for the DNA methylation microarray service, and the VAI High Performance Computing Cluster supported by Zack Ramjan. We would like to also thank Dr. R. Buckanovich for providing cell lines. LGC is supported by NIH/NCI 7K08CA211362-02, Tina’s Wish Rising Star Grant, The Mary Kay Foundation Cancer Research Grant and DoD Ovarian Cancer, Omics Consortium Development Award. H. Shen is supported by NIH/NCI 1R37CA230748 and Ovarian Cancer Research Alliance’s Liz Tilberis Award (373933).

Author contributions: Conceptualization, L.G.C and H.S.; Methodology, L.G.C, H.S, H.F, L.F, C.C, T.P, Y.W; Formal Analysis, L.G.C, H.S, H.F; Investigation L.G.C, L.F, C.C, H.F, H.A; Writing—Original Draft, L.G.C, H.S; Writing-Review & Editing, L.G.C, H.S, H.F, T.P, H.A; Funding Acquisition, L.G.C, H.S, I.S, T.W; Resources, L.G.C, H.S, T.P, I.S, T.W; Supervision, L.G.C, H.S, I.S, T.W

Declaration of conflicts: The authors have no relevant conflicts of interest to disclose.

Figure titles and legends: Figure 1. CA-MSCs have unique DNA methylation profiles compared to normal tissue MSCs A. DNA methylation at the MIR141/MIR200C promoter separates samples of epithelial and mesenchymal nature. Each row of the heatmap shows a probe from the MIR141/MIR200C promoter region. Each column shows one of the CA-MSC, normal MSC (referred to as “MSC”) or tumor samples, with accompanying column-side color bars indicating the sample group (‘Group’), or isolation site (‘Source’, if MSC and CA-MSC) for each sample. A blue-to-red gradient indicates a beta value of 0-1 (DNA methylation level of 0% to 100%) in all heatmaps. B. Samples aggregate by cell type by DNA methylation. Normal fallopian tube epithelium and ovarian stroma samples are included as controls. Samples are shown as dots on scatterplots in the first two dimensions (x axis – Dimension 1; y axis – Dimension 2) from UMAP of the intersect set of 438,064 CpG loci present on both the EPIC and HM450 platform. Each cell type cluster is indicated by dotted circle with label. C. CA-MSCs have differential DNA methylation vs MSCs. Top variable CpG probes (N=10,000, filter with standard deviation) among 13 CA-MSC and 19 MSC samples, are shown as rows and samples are shown as columns, with annotations similar to Panel A. MIR141/200C promoter methylation (average of the three probes shown in A) for each sample is used to track the percentage of mesenchymal component proportion in each sample.

11 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

D. Overall distribution (as smoothed probability density distribution) per sample of DNA methylation beta values (x axis) for CA-MSCs (purple lines) and MSCs (green lines). E. Histogram showing the distribution of DNA methylation (beta value) group differences between CA-MSCs and normal MSCs. Red represents sites with methylation difference > 0 (hypermethylation in CAMSCs), and black represents sites with methylation difference <0 (hypomethylation in CAMSCs). F. Distal regions show more frequent DNA methylation gain in CA-MSCs compared to MSCs. Both density and histogram plots are shown for proximal and distal CpG probe set (vertical in order). Proximal probes are defined as probes overlapping with transcript promoters (+/-500 bp around the TSS), while distal probes are defined as probes located near transcription factor binding sites (+/-100bp) but not within +/-2 kb away from the TSS. Distal elements are almost three times more likely to acquire a DNA methylation gain greater than 0.2 in CA-MSCs. G. DNA methylation heatmap showing differential methylation cytosine sites (DMC; N=17,883) between CA-MSCs and MSCs. Other tissue types are included for comparison. Visual similarities can be appreciated between MSCs and fibroblasts, and CA-MSCs to tissues of epithelial origin (ovarian tumor cells and fallopian tube epithelium/FTEs) respectively. Figure 2. Verification of differential DNA methylation loci demonstrating methylation differences persist with MSC differentiation and are acquired during CA-MSC reprogramming. A. DNA methylation heatmap showing six different categories of CpG probes hyper- or hypo- methylated in different combinations of CA-MSC, FTE and MSC. Probe categories and the number of probes included are labelled on the left side of the heatmap. A blue-to-red gradient indicates a beta value of 0-1 (DNA methylation level of 0% to 100%) in all heatmaps. Corresponding cell types or cancer types are labelled on top of each heatmap panel. B. Top DMRs within five different genes chosen for quantitative methylation specific PCR (qMSP) validation. Mean beta values for CA-MSCs and MSCs are plotted on the y-axis against probe locations as plotted on the x-axis. Within-group variability is indicated by standard error bars for each group at each probe locus. C. qMSP validation of differential methylation between groups of CA-MSCs and normal MSCs shown in B, using independent CA-MSC and MSC samples. D. Both CA-MSCs and MSCs that undergo lineage differentiation (adipose and fibroblast differentiation) do not demonstrate significant DNA methylation changes at the selected loci as shown in C. Mean & SEM of 3 independent samples are represented. E. Cancer stimulation of normal MSCs in vitro induces a partial CA-MSC methylation pattern. MSCs that underwent partial conversion to a CA-MSC phenotype via hypoxic co-culture with ovarian cancer cells acquire CA-MSC-related methylation changes in three (SASH1, ELN, MARVELD2) out of the five loci tested. Mean & SEM of 3 independent samples are represented.

Figure 3. CA-MSCs have altered chromatin accessibility compared to normal MSCs which correspond to DNA methylation and RNA expression changes.

A. Principal component analysis (PCA) on the merged peak regions from Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) separates CA-MSCs from MSCs. B. Heatscatter plot showing the correlations between ATAC-seq peak signal difference (x axis) and their corresponding gene expression fold changes (y axis, log2-transformed), with color indicating local smoothed density. A LOWESS (Locally Weighted Scatterplot Smoothing) regression line is shown in black. C. ATAC-seq heatmaps centered at the summits of all the merged peaks, grouped into three categories: peaks (statistically) significantly gained (G1), lost (G2), and not with statistical significance (G3) in CA-MSCs comparing to MSCs. Heatmap is colored based on 10 bp- binned ATAC-seq signal, normalized against sequencing library size per sample. D. DNA methylation patterns around the summits for peak group G1, G2, and G3 shown in C. Line plot is centered on the summits of all the peaks within each group plotted as x- axis, while average of the overlapping methylation difference is shown as y-axis. E. Integration of DNA methylation, gene expression, and ATAC-seq calls. Probe-level DNA methylation difference (CA-MSC - MSC) for promoter probes (defined as +/- 1.5 kb away from TSS) is plotted as x-axis. Expression fold changes (log2-scaled) for the corresponding genes are plotted as y-axis. Dots are colored based on gene-overlapping ATAC-seq peak orientations, where red represents open, blue closed, and black unaltered peaks in comparing CA-MSCs to MSCs. Gene labels are colored the same way. Top candidate genes with largest gene expression alterations (50 each way based on the gene fold change rank), and combined methylation and gene expression differences (absolute beta value difference > 0.5 and fold change > 2) are labelled with gene names. Exemplified up and down genes are highlighted as bold and italic.

Figure 4. CA-MSCs undergo a partial mesenchymal to epithelial transition (MET). A. Gene ontology (GO) enrichment analysis (biological concept of Biological Process; GOBP). The numbers of up- and down-regulated genes comparing CA-MSCs to MSCs are labeled underneath the plot. Each dot represents a GOBP term, with dot size indicating enrichment score (ES), and dot color representing significance level. Two cell adhesion related processes are highlighted for genes upregulated in CA-MSCs. B. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. Two pathways relevant to cell adhesion are highlighted for upregulated genes, and two pathways relevant to EMT (epithelial-mesenchymal transition), the reverse process of MET, are highlighted for downregulated genes. C. Heatmap of epithelial- and mesenchymal-signature genes indicates a partial MET in CA- MSCs. EMTness score based on a 16-gene scoring system (higher and positive score indicate a more mesenchymal phenotype) is shown as column annotation, together with sample group information. D. Gene Set Enrichment Analysis (GSEA) results are in line with a potential MET in CA- MSCs. Genes are ranked based on fold changes comparing CA-MSCs to MSCs. Relevant GSEA terms are plotted as bars, with significance levels indicated by red stars. Normalized enrichment scores are plotted as y-axis.

Figure 5. CA-MSCs have enhanced tumor cell binding and increase ovarian cancer metastasis through direct tumor cell:CA-MSC interactions and co-migration. A. CA-MSCs demonstrate greater cancer cell adhesion compared to normal MSCs. Quantification of the number of adherent cancer cells (OVSAHO or primary patient cancer cells pt412, GFP labeled) to plastic, normal MSCs derived from the omentum or fallopian tube (FT) or CA-MSCs derived from omental tumor or fallopian tube tumor via fluorescent microscopy, cell counts per low power field (LPF). OVCAR3 binding presented in supplemental Figure 3A. B. CA-MSCs enhance the invasion of OVSAHO tumor cells (TCs) through Matrigel coated transwells. Results were quantified via fluorescent microscopic counting of TCs which invaded to the underside of the transwell per LPF; TCs are GFP labeled, CA-MSCs are mT labeled. Alginate encapsulation (enc.) prevents enhanced invasion demonstrating the necessity of direct CA-MSC:TC contact. C. CA-MSCs enhance OVSAHO TC sphere growth under non-adherent condition through direct CA-MSC:TC interactions. GFP-TC and mT-CA-MSCs grown in low attachment plates were allowed to directly interact or were separated by a porous membrane. Spheres containing GFP-TCs were quantified via fluorescent microscopy per LPF and compared to TCs alone and TCs allowed to directly or indirectly interact with CA-MSCs. (i) representative picture of GFP-TCs and mT-CA-MSCs within heterocellular spheres. (ii) Quantification of the percent of spheres containing at least one CA-MSC per TC sphere at 1:1 and 10:1 TC:CA-MSC ratio demonstrating preferential formation of heterocellular spheres even at low CA-MSC concentrations. For all in vitro experiments, mean & SEM from 3 independent experiments are represented. D. Cellular complexes isolated from high grade serous ovarian cancer patient ascites. Confocal microscopy demonstrates TCs (as identified by Epcam+) in contact with CA- MSCs (identified with CD90+/CD73+/Epcam-). E. Quantification of the average ratio of TC and CA-MSCs within patient ascites cellular complexes. Cellular complexes were isolated from ascites, dissociated into single cells and TCs and CA-MSCs quantified by flow cytometry. Ratios from four independent patient ascites are presented. F-G. Orthotopic mouse model demonstrating increased metastasis with CA-MSCs in physical contact with TCs. Ovarian cancer cells were injected into the ovarian bursa alone (TC only), injected with CA-MSCs (TC + CA-MSC), or injected with encapsulated CA-MSCs (TC + enc. CA- MSC) and monitored for disease growth. F. Mouse weight over time across experimental groups demonstrates the TC + CA-MSC group had the most weight loss associated with systemic illness and disease burden (*, ** indicate significant decrease in weight between time points). G. Quantification of organs involved with gross metastatic disease at necropsy demonstrating increased lung, liver and intra-abdominal metastasis in the TC + CA-MSC group. H. Representative images of metastatic sites, including lung, liver, ovary, abdomen metastasis. I. CA-MSCs co-localize with TCs at the primary and metastatic tumor and metastatic tumor sites have an increased proportion of CA-MSCs. Quantification of the TC to CA-MSC ratio as measured via flow cytometry in the TC + CA-MSC group.

Figure 6. WT1 is associated with a permissive epigenetic state in CA-MSCs and participates in the formation of a CA-MSC. A. Integrated view of epigenetic profiles for WT1. Normalized read count (10-bp bin) for ATAC-seq is plotted on top according to the genomic location they map, each row representing one sample. Significantly gained peaks in CA-MSCs are indicated by black boxes below the ATAC-seq signal plots. WT1 transcripts are plotted under the chromosome ideogram, with arrows indicating the direction of the transcript. Layered H3K4me1 (enhancer mark), H3K4me3 (promoter mark), H3K27ac (active chromatin mark) ChIP-seq tracks from ENCODE for two cell types, K562 (a bone marrow derived myeologenic leukemia cell line, purple) and NHLF (normal human lung fibroblast, pink) are plotted below. WT1 transcripts, ATAC-seq peaks, and ENCODE histone ChIP-seq tracks are drawn to proportion on the hg19 genome with scale indicated on this panel. DNA methylation level for each CpG (columns of heatmap) in this region is plotted below as a heatmap. As the DNA methylation probes are not evenly distributed, this heatmap is not directly aligned with genomic locations for tracks above, but with lines connecting each CpG to the corresponding genomic location. Each row represents a sample as labelled on the right. Differentially methylated regions (DMRs) called by DMRcate between MSCs and CA-MSCs are indicated below with blue boxes (blue = DMR probe), followed by CpG island (CGI) designation for each probe (green=CGI probe). Three regions of interest discussed in the main text are highlighted with orange (Region 1), cyan (Region 2) and green (Region 3) shaded boxes. The locations for these regions are indicated again for the heatmap below with the same color. Region 1 and Region 2 are consistently methylated in normal MSCs and unmethylated in CAMSCs, while Region 3 overlapping the promoter region is only methylated in a subset of MSCs although a statistically significant DMR. B. Western blot of WT1 from MSCs (3 independent samples) and CA-MSCs (5 independent samples) demonstrating higher protein levels of WT1 in CA-MSCs, Actin is the loading control. C. Expression fold change of WT1 vs normal fallopian tube MSC, CA-MSCs, CS MSCs, WT1 overexpression (OE) MSCs and WT1 siRNA knock down (KD) MSCs. D. Quantification of conversion to a CA-MSC in CS siRNA scrambled control MSCs, WT1 OE MSCs alone, and CS WT1 KD MSCs. CS was performed with hypoxic direct cancer cell co-culture. Values are normalized to the conversion of scrambled control MSCs based on the CA-MSC classifier score. Mean & SEM of 3 independent experiments is represented. *,** represents marked group vs. scrambled control without CS; & represents marked group vs. scrambled control with CS

Figure 7. EZH2, along with WT1, impacts CA-MSC reprograming and ovarian cancer metastasis. A. Transcription factor binding sites (TFBS) enrichment for distal DMCs hypermethylated (top) and hypomethylated (bottom) in CA-MSCs. Negative log10-transformed FDRs are plotted as x-axis. Bolded gene names are referenced in text. B. GSEA plot shows enrichment of the Polycomb group (PcG) target genes on both ends of the ranked gene list (ordered by gene fold changes in comparing CA-MSCs to MSCs).

C. Box plots of top histone modifications differentially represented in CA-MSCs vs. MSCs identified by mass spectrometry. D. EZH2 expression is increased during in vitro (indirect) and in vivo cancer stimulation with ovarian cancer cells. Fold change compared to normal MSC values. Mean & SEM of 3 independent experiments is represented. E. EZH2 directly interacts with WT1. Co-immunoprecipitation of EZH2 pulls down WT1. F. WT1 knock down (KD) in combination with EZH2 inhibition (EZH2i) partially blocks the formation of a CA-MSC. Quantification of the percent of conversion to CA-MSC in WT1 KD MSCs or scrambled control after cancer stimulation (CS) with hypoxic direct cancer cell co-culture with, and without GSK126. Values are normalized to scrambled control conversion based on CA-MSC classifier score. Mean & SEM of 3 independent experiments is represented.*, ** represent comparison to scrambled control; & represents comparison to CS scrambled control without GSK126 G. EZH2 inhibition with GSK126 decreases the formation of intra-abdominal metastasis. The percent of mice developing metastasis at each organ site is represented, N=10 mice per group. H. EZH2 inhibition did not impact tumor cell growth in the lung as demonstrated by quantitative human vs. mouse specific PCR from extracted lungs.

Methods: Tissue harvesting, culture Patients samples were obtained in accordance with protocols approved by the University of Michigan’s IRB (HUM0009149) and University of Pittsburgh’s IRB (PRO17080326). Tissue was processed for DNA, RNA and protein isolation as previously described(Coffman et al. 2016b; Coffman et al. 2019) . MSCs were isolated as previously described (McLean et al. 2011). Briefly, CA-MSCs were derived from surgical resection of human ovarian cancer involving the FT, ovary and/or omental metastatic deposits. Normal FT and omental MSCs were derived from surgical samples of women undergoing surgery for benign indications or risk reduction surgery. Cells were plated in supplemented MEBM, MSCs were selected for plastic adherence and cell surface marker expression (CD105, CD90, CD73 positive; CD45, CD34, CD14, CD19 negative). Adipocyte, osteocyte, and chondrocyte differentiation capacity was verified (following guidelines presented by the ISCT on the minimal criteria for defining multipotent mesenchymal stem cells (Dominici et al. 2006)) (SupFig6). To ensure lack of fibroblast contamination, cells were screened for fibroblast markers fibroblast surface protein (FSP) via flow cytometry and alpha smooth muscle actin via immunohistochemistry (SupFig6). Any cell line with a population >5% differing from above criteria were FACs purified. Fibroblast differentiation was performed as previously described by growing MSCs with connective tissue growth factor (100 ng/ml) and ascorbic acid (50 μg/ml) for 2 weeks (Coffman et al. 2019). MSCs were maintained in culture as previously described and used at passage 5 or below (McLean et al. 2011). All cell lines were tested and verified negative for mycoplasma (last test 9/2019).

Cell Culture Ovarian cancer cell lines CAOV3 and OVCAR3 were purchased through ATCC. OVSAHO and the primary patient cell line, PT412, were kind gifts from Dr. R. Buckanovich. All cells were grown

in DMEM, 10% serum, 1% pen/strep. All cell lines were tested and verified negative for mycoplasma (last test 9/2019).

DNA methylation array DNA was isolated from CA-MSCs derived from omental metastasis in 6 patients with high grade serous ovarian cancer (HGSOC) and 1 patient with ovarian carcinosarcoma; 1 patient with high grade serous carcinoma involving the FT, 3 patients with high grade serous carcinoma involving the ovary. Samples from 4 patients with omental with initial radiographic involvement of HGSOC treated with neoadjuvant chemotherapy and pathologic complete response in the omentum, 5 benign patient FT samples, 1 set of bilateral FTs from a BRCA2 germline patient , 4 benign patient normal omental MSCs and 2 adipose MSCs purchased through ATCC. All normal MSCs were derived from patients undergoing surgery for benign indications. “A” and “B” indicate samples from the same patient from 2 distinct anatomic locations. A list of all samples with anatomic and histologic information is provided in supplemental table 1. All MSCs used for DNA methylation analysis were at passage 5 or below. DNA underwent bisulphite conversion followed by analysis on the Illumina MethylationEPIC Beadchip microarray through the Genomics core at the University of Michigan and Van Andel Research Institute.

Assay for Transposase Accessible Chromatin with sequencing (ATACseq) Nuclei were isolated from 4 CA-MSCs derived from omental metastasis of patients with high grade serous ovarian cancer, 2 normal omental MSCs and 2 adipose MSCs purchased from ATCC. All MSCs used for ATACseq analysis were at passage 5 or below. ATACseq was performed through Active Motif.

Histone mass spectrometry CA-MSCs and MSCs used for ATACseq were also used for mass spectrometry to measure the relative abundance of 80 histone modifications through Active Motif. Histones were extracted from frozen cell pellets and digested with trypsin. Samples were analyzed on a triple quadrupole (QdQ) mass spectrometer directly coupled with an UltiMate 3000 Dionex nan-liquid chromatography system. Three separate mass spec runs for each sample. Each modified and unmodified form of the monitored amino acid residue was quantified as the percentage of the total pool of modifications with mean and standard deviation reported.

Quantitative real-time PCR RNA was isolated with the RNeasy Mini Kit (Qiagen, Hilden, Germany) and on-column DNase treatment (Qiagen, Hilden, Germany). RNA concentration was determined with NanoDrop ND- 1000 Spectrophotometer. cDNA was synthesized with the SuperScript III First-Strand Synthesis System for RT-PCR (Invitrogen, Grand Island, NY) as previously described [21]. SYBR green- based RT-PCR was performed using the 7900HT Sequence Detection System (Applied Biosystems, Foster City, California) and respective primers. The comparative Ct method was used for data analysis with GAPDH as the comparator gene.

Immunoblotting Cell pellets were homogenized in RIPA buffer (Pierce, Rockford, IL) with complete protease inhibitor (Roche, Basel, Switzerland). Insoluble material was removed by centrifugation at 16,000g at 4oC for 15mins. Protein concentrations were determined using the Bradford Protein Assay Kit (Bio-Rad, Hercules,CA). Equal amounts of protein were separated

on 4-12% NuPAGE SDS gel (Invitrogen, Grand Island, NY) and transferred onto a PVDF membrane. Anti-H3K27me3, anti-H3K27me2, anti-H4K16ac, anti-H3K27ac, anti-H3K79me1, anti-H3, anti-H4, anti-WT1 and anti-EZH2 (1:1000 dilution, Active Motif) and anti-B-actin (1:10,000 dilution, Sigma-Aldrich, St. Louis, MO) were used. Bands were visualized using the ECL Kit (Pierce, Rockford, IL).

Quantitative Methylation Specific PCR (qMSP) Bisulfite Conversion and DNA Yield Bisulfite conversion of extracted cellular or tissue DNA was performed using the DNA Methylation-Lightning™ Kit (Zymo Research) according to manufacturer’s instructions and eluted into 40 µl DNA Elution buffer (Zymo Research). Post- bisulfite treatment DNA yields were quantified by MethyLight assay for the methylation- independent bisulfite converted (BSC) consensus ALU sequence, as previously described (Weisenberger et al. 2005), using primer and probe sequences: forward primer, 5’- GGT TAG GTA TAG TGG TTT ATA TTT GTA ATT TTA GTA -3’; reverse primer, 5’- ATT AAC TAA ACT AAT CTT AAA CTC CTA ACC TCA -3’, spanning a 98-bp locus, and 100nM probe, 5’- \56-FAM\ CCT ACC TTA ACC TCC C -3’ with Minor Groove Binder (MGB; ThermoFisher Scientific). PCR was performed using 10X Master Mix to yield a final volume of 25 µl and final working concentrations of 16.6mM (NH4)2SO4, 67mM Tris pH 8.8, 6.7mM MgCl2 10mM - mercaptoethanol, 200µM of each deoxynucleotide triphosphate (dNTP) and 0.04 U/µl of Platinum Taq polymerase (ThermoFisher Scientific). Cycling conditions were 95˚C for 5 minutes, followed𝛽𝛽 by 50 cycles of (95˚C for 5 seconds, 60˚C for 30 seconds and 72˚C for 30 seconds). Standards for quantification were created by serial dilution of pre-quantified BSC human male DNA (Promega). Amplification reactions were performed in duplicate using 96 well-plates using a CFX96 Touch Real-time PCR Detection System (Bio-Rad). Locus-specific methylation analysis qMSP assays (Lo et al. 1999) were developed and analytically-validated for five select loci: SASH1, c13orf45, ELN, GATA6, and MARVELD2, for which primer sequences and assay specifications are detailed in Supplementary Table 1. A standard curve was created using serial dilutions of BSC Epitect unmethylated control DNA (Qiagen) mixed with BSC CpG-Methylated HeLa Genomic DNA (New England BioLabs) (Lo et al. 1999). Data Analysis MSP results were analyzed using the CFX Manager v3.1 (Bio-Rad) using regression to obtain the mean quantification cycle (Cq) for each respective sample. Percent methylation was calculated for each sample using the BSC-ALU and BSC Control DNA standard curves, according to the formula % methylation = (# locus copies methylated in sample)/(# ALU- calculated genomic copies in sample).

CA-MSC encapsulation CA-MSCs were encapsulated into 3% alginate as previously described (Schmitt et al. 2015). Briefly, 3% w/v sodium alginate was dissolved in PBS, filtered for sterility with a 0.2um filter. Cells were mixed with the alginate solution and added dropwise into a 5mM solution of calcium chloride to allow for gelation. Alginate beads were washed 2x with PBS prior to use in assays. Cell viability over time was verified by transection of the alginate capsule after 2 weeks incubation in standard culture media (DMEM, 10% FBS, 1% pen/strep) and re-isolation and growth of previously encapsulated CA-MSCs. Also, CA-MSCs lentivirally transduced with a secreted luciferase construct were encapsulated in alginate and the media was sampled for secreted luciferase overtime using the Ready-to-glow Secreted luciferase reporter system (Takara). Sphere assays

CA-MSCs transduced with lentiviral mT construct were mixed with lentiviral labeled GFP tumor cells (CAOV3, OVSAHO, OVCAR3) (as previously described (Coffman et al. 2019)) in a 1:1 ratio in ultra-low attachment 6 well plates grown in serum free supplemented MEBM as previously described (Coffman et al. 2019). Alternatively, CA-MSCs were added to the top of a 0.4um transwell system and tumor cells seeded in the bottom to prevent direct contact. After 5 days, the number of GFP+ spheres were counted. Experiments were repeated independently 3 times per tumor cell type. Migration/Invasion assays 8um transwells were coated with growth factor reduced Matrigel and allowed to solidify. GFP- tumor cells (CAOV3, OVSAHO, OVCAR3) with or without mT-CA-MSCs were added to the top of the transwell. Supplemented DMEM was added to the bottom of the transwell and cell migration/invasion through the coated membrane was quantified after 24hrs via fluorescent microscopy. Alginate encapsulated CA-MSCs were added in place of control CA-MSCs to determine the impact of preventing CA-MSC:tumor cell direct interaction. Experiments were repeated independently 3 times per tumor cell type. Adhesion assay: 5x10^4 MSCs or CA-MSCs were cultured overnight in 12 well-plate to form a mono-layer. 5x10^4 fluorescently labeled tumor cells (OVSAHO, OVCAR3 or pt412) were then added. After 30 minutes, cells were washed twice with PBS and the attached tumor cells were counted using a fluorescence microscope and quantified as the number of adherent cells per low power field (10x). Experiments were repeated independently 3 times per tumor cell type.

Cancer stimulation, cancer cell:MSC co-culture MSCs were grown in a 1:1 ratio with ovarian tumor cells allowing for direct interaction or with use of a transwell system(0.4um) to separate the two cell types to allow only indirect co-culture as previously described (Coffman et al. 2019). Cells were grown in 1:1 mixture of CA-MSC media (supplemented MEBM) and tumor cell media (supplemented DMEM) for 5 days under hypoxic conditions (1% O2). Cells were isolated via FACs separation with fluorochrome labeled MSCs or tumor cells or removed from their respective transwell chamber.

CA-MSC classifier As previously described (Coffman et al. 2019), we created a regression model based on the expression of 6 genes which accurately distinguishes normal MSCs from CA-MSCs: Annexin A8-like protein 2 (ANXA8L2), Collagen Type XV Alpha 1 Chain (COL15A1), Cytokine Receptor Like Factor 1 (CRLF1), GATA Binding Protein 4 (GATA4), Iroquois Homeobox 2 (IRX2), and TGF-β2. Expression values are the delta CT values compared to GAPDH levels. The regression equation is: 1/(1+LOGIT((((B1*ANAX8L)+(B2*COL15)+(B3*CRLF1)+(B4*GATA4)+(B5*IRX2)+(B6*TGFb))+- 7.62691)))); B1 = −0.00622, B2 = 0.175026, B3 = 0.886027, B4 = −0.34594, B5 = 0.416952, B6 = −0.00824

Flow cytometric analysis: Cellular complexes were isolated from ovarian cancer patients’ ascites through centrifugation and serial washing steps with 2% FBS in PBS. The isolated complexes were enzymatically dissociated into a single cell suspension. Cells were then stained with anti-CD90, anti-CD73, anti-CD105 (Stem-cell Technology), and anti-EpCAM (Novus) antibodies for 20 minutes at RT. The

percentage of CAMSC (CD90, 73, and 105+) and tumor cells (EpCAM+) were calculated on live cells gating. Immunofluorescence staining: Cellular complexes were isolated from ovarian cancer patients’ ascites as above and immobilized on slides using the cytospin centrifuge as previously described (Koh 2013). Then, the cellular complexes were fixed using methanol for 10 minutes. Immunofluorescence conjugated antibodies (anti-CD90, anti-CD73, anti-CD105 (Stem-cell Technology), and anti-EpCAM (Novus)) were added after permeabilize the cellular complexes with 0.1% Triton 100X. The stained cellular complexes were examined using Nikon A1 confocal microscope.

Orthotopic ovarian cancer mouse model 5x10^5 OVCAR3 cells were injected alone (n=5) or with control CA-MSCs (5x10^5 cells) (n=5) or encapsulated CA-MSCs (5X10^5 cells) (n=4) into the ovarian bursa of NSG mice. Mouse weight and health were monitored over time and mice were sacrificed when the first group of mice met endpoint criteria of >10% weight loss and necropsy was performed. Tail vein injection model 5x10^5 OVCAR3 cells were injected into NSG mice (n=10 per group). Mice in the EZH2 inhibition group were treated with 300mg/kg of GSK126 (selleckchem) 3 x weekly for 1 week prior to injection and then 150mg/kg 3 x weekly for 2 weeks immediately after injection. Mice in the control group underwent parallel treatment with vehicle control and were treated with 150mg/kg 3x weekly for 2 weeks starting 2 weeks after treatment. Mice were euthanized at 4 weeks post tumor cell injection and necropsy performed. Quantitative human specific PCR was performed on whole lung isolates as previously described (Alcoser et al. 2011). Transcriptome data re-processing and analysis Raw mRNA sequencing data (FASTQ format) of 4 normal omental MSCs and 10 ovarian CA- MSCs, using Illumina TruSeq RNA Sample Preparation V2 kit (Illumina San Diego, CA, USA) and Illumina HiSeq2000 instrument 100 bp PE sequencing, were download through NCBI’s Gene Expression Omnibus (GEO) with GEO Series accession number GSE118624 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118624) (Coffman et al. 2019). Each raw sequencing file was aligned to the human reference genome (GRCh37) using STAR version 2.7 (Dobin et al. 2013), with default settings. Estimation of gene-level abundance was carried out using RSEM version 1.3.1 (Li and Dewey 2011). Raw reads count from RSEM were further normalized using R package edgeR (function cpm), and log-transformed for downstream analysis, with one CA-MSC sample (GSM3335697) removed due to potential tumor cell contamination. Differential gene expression analysis was carried out using R package Limma (Ritchie et al. 2015). Specifically, differentially expressed genes were identified based on a p-value less than 0.05, and an absolute log-transformed fold change greater than 1.5. Followed by functional enrichment analysis using R package clusterProfiler (Yu et al. 2012), with FDR cutoff at 0.05 for KEGG pathway, Gene Ontology (Biological Process), and Gene Set Enrichment Analysis (GSEA). In particular for GSEA, genes were ranked based on log-transformed fold changes by comparing sample group of CA-MSC to MSC. DNA methylation data processing and QC

Raw IDATs files were processed using R package SeSAMe (Zhou et al. 2018) with noob background correction, non-linear dye bias correction, and non-detection masking (any data point not significantly different from background was replaced with NA). Probes with design issues are also masked (Zhou et al. 2017). DNA methylation beta values, ranging from 0 to 1 (with “0” indicating fully unmethylated and “1” fully methylated), were calculated as quantitative percentage of methylated signals over both methylated and unmethylated signals. All IDATs and processed beta values have been uploaded and made available through Gene Expression Omnibus (GEO) under the accession number GSE138072. SNP probes (‘rs’ probes) were used to examine potential sample swaps which can occur in genomic studies. No such swap was identified. DNA Methylation beta values for three MIR141/200C promoter probes ("cg12161331", "cg18185189", "cg19794481") were examined to track mesenchymal content within each sample. MIR141/200C is master regulator for epithelial/mesenchymal phenotype transition, and this process is controlled by its promoter methylation state (Vrba et al. 2010). Methylation level at these three probes used highly correlated with mesenchymal content in flow sorting results (George et al. 2018). Two samples had a failure rate of >40% with our stringent background masking (Zhou et al. 2018) likely due to DNA degradation, but were kept in the study as the remaining data points were passed the very stringent filter and represented high quality data.

Unsupervised and supervised DNA Methylation analysis Unsupervised hierarchical clustering was performed on top variable CpG probes (N=10,000, filtered by standard deviations) across all CA-MSC and MSC samples measured on the EPIC array with the R function hclust(). Uniform Manifold Approximation and Projection (UMAP) was performed with the R package uwot.. For global comparison of CAMSC/FTE/MSCs, different probe categories are defined as such: 1) CA-MSC and FTE shared hyper-methylated probes: mean methylation difference > 0.4 in both CA-MSC to MSC, and FTE to MSC comparisons; 2) CA-MSC-specific hyper-methylated probes: mean methylation difference > 0.4 when comparing CA-MSC to MSC, but absolute mean methylation difference < 0.1 when comparing FTE to MSC; 3) FTE-specific hyper-methylated probes: mean methylation difference > 0.4 when comparing FTE to MSC, but absolute methylation difference < 0.1 when comparing CA-MSC to MSC; 4) CA-MSC and FTE shared hypo-methylated probes: mean methylation difference < -0.4 in both CA-MSC to MSC, and FTE to MSC comparisons; 5) CA-MSC-specific hyo-methylated probes: mean methylation difference < -0.4 when comparing CA-MSC to MSC, but absolute methylation difference < 0.1 when comparing FTE to MSC; 6) FTE-specific hypo-methylated probes: mean methylation difference < -0.4 when comparing FTE to MSC, and absolute mean methylation difference < 0.1 when comparing CA-MSC to MSC. Differentially methylated cytosines (DMCs) were calculated using R package DMRcate (Peters et al. 2015), by comparing sample group of CA-MSC to MSC, based on its default FDR cutoff of 0.05. DMRcate does not tolerate missing values (NA), and therefore we removed probes with any NA in any of the samples, leaving a total of ~401,000 probes for any analyses involving DMR/DMC calling. DMCs were split into hypermethylated (hyper-) and hypomethylated (hypo-) DMCs, by comparing group of CA-MSC to MSC. Enrichement or depletion of various genomic features (annotated with R package annotatr for each probe) in hyper- and hypo DMCs were measured 21

as odds ratios, together with significance level calculated using Chi-square test. DMCs were mapped to genes captured by mRNA sequencing, if they were located within gene promoter regions defined as +/- 1.5 kb away from transcription start sites (TSSs). Annotation of CpG probes against transcription factor binding sites (TFBSs) were adopted from our previous study (Zhou et al. 2017). We then performed enrichment analysis of TFBSs over differentially methylated probes at distal regulatory elements (i.e. potential enhancers). Distal probes were defined as probes located within TFBSs, but not overlapping with +/- 2 kb flanking regions surrounding TSSs. For each set of hyper- or hypo-DMCs, hypergeometric test was applied to calculate the enrichment of binding sites for each TF within the set of DMCs within all distal probes. Significance cutoff was made at p=1e-3 after false discovery rate (FDR) correction. ATACseq data processing and analysis Quality control was carried out using the PEPATAC pipeline adopted by the TCGA ATAC-seq project (Corces et al. 2018). We followed current ENCODE QC standards and excluded four samples due to insufficient transcription start site (TSS) enrichment value (less than 6), therefore were removed from further analysis. Next, raw sequencing reads were mapped to the human reference genome (GRCh37) using Burrows-Wheeler Alignment tool (BWA) with default settings (Li and Durbin 2009), followed by peak calling with software MACS2 (Zhang et al. 2008). Specifically, parameters of --nomodel and -f BAMPE were specified in calling open chromatin regions, for pileup of the whole fragments. Sample-based open peaks were then merged across all the samples, and fed into R package DESeq2 (Love et al. 2014) to generate a normalized reads count matrix against effective library sizes. Log-transformed reads count were used for further differential peak analysis, with reads counts of replicates from the same sample averaged. Principal component analysis (PCA) was performed, based on the normalized reads count matrix. Peak summit centered heatmap per sample was generated with deepTools using normalized 10 base pair bin-based ATACseq signal (Ramírez et al. 2016).

Public available data resources DNA methylation data (Illumina HM450 array) of ovarian stroma samples (N=4; samples annotated as ovary and pathologist evaluation of available slides confirms ovarian stroma histology) were downloaded from the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov), together with that of Uterine Carcinosarcoma (UCS; N=57) and Sarcoma (SARC; N=261). Pure, uncultured fallopian tube epithelium samples (FTE; N=4) were downloaded through GEO database, with one sample GSM2146822 taken from Series GSE81224 (Klinkebiel et al. 2016), and the other three samples (GSM1606969, GSM1606970, and GSM1606973) from Series GSE65820 (Patch et al. 2015). Other FTE samples in the same studies either had insufficient epithelial purity or had undergone in vitro culturing and therefore not included. All DNA methylation microarray data were reprocessed from raw IDATs in the same way as described above for the MSCs. Gapped H3K27me3 chromatin peak files of MSC-derived from adipose, bone marrow, chondrocyte, and H1 stem cell were downloaded through the NIH Roadmap Epigenomics projects (http://www.roadmapepigenomics.org/data/). EZH2 target genes were then defined as genes overlapping with H3K27me3 marked regions shared by all four different cell types.

Supplemental information title and legend: Supp Figure S1.

A. DNA methylation heatmap of top differentially methylated loci (FDR<0.05, and absolute group beta value difference > 0.4) between tumor samples and CA-MSCs. Probes are plotted as rows, with samples plotted as columns. Sample group of tumor, MSC, and CA- MSC is plotted as column annotation, together with sample isolation source if samples are MSCs or CA-MSCs. Sample CA-MSC_OM10 with heavy epithelial contamination is highlighted. B. Dot plot of probe-level similarity between CA-MSC and fallopian tube epithelium (FTE) samples, stratified by their methylation level in MSCs. Linear regression lines are in blue, with corresponding coefficient indicated on top of each dot plot. A blue-to-red gradient indicates a beta value of 0-1 (DNA methylation level of 0% to 100%). Supp Figure S2. A. Enrichment of CA-MSC hyper-methylated loci against different genomic features of CpG and genic annotations. CpG annotations include open sea (cpg_inter), CpG shores (cpg_shores), CpG shelves (cpg_shelves), CpG islands (cpg_islands). Genic annotations contain 1-5Kb upstream of the TSS (genes_1to5kb), the promoter (< 1Kb upstream of the TSS; genes_promoters), 5’UTR (genes_5UTRs), first exons (genes_firstexons), exons (genes_exons), introns (genes_introns), CDS (genes_cds), 3’UTR (genes_3UTRs), and intergenic regions (the intergenic regions exclude the previous list of annotations; genes_intergenic). Supp Figure S3. A. Adhesion of a third cell line in addition to what was shown in Figure 5, OVCAR3 cells to plastic, normal omentum (OM) MSCs, CA-MSCs derived from the OM and ovary (OV) demonstrates increased tumor cell adhesion to CA-MSCs. Results quantified by counting fluorescent tumor cells per high power field (HPF). Mean & SEM of 3 independent experiments is represented. B. Luciferase secretion of encapsulated (enc.) CA-MSCs. CA-MSCs transduced with a secreted luciferase construct were encapsulated into alginate and the amount of secreted luciferase after 5 days was compared. Alginate encapsulation did not alter CA-MSC protein secretion. Supp Figure S4. Venn diagram showing the H3K27me3 peak overlaps among MSCs derived from different sources of adipose, chondrocytes, bone marrow, and H1 cell line. The common set of 1241 peak regions are applied to map the EZH2 targets (as in red), if they are located within their transcriptional regions plus 2 kb upstream flanking regions of their TSSs. Supp Figure S5. A. Viability of tumor cells PT412 and OVCAR3 and normal MSCs after 5 days treatment with 20nM GSK126 compared to vehicle control. B. Quantification of the percent of conversation to CA-MSC in WT1 KD MSCs or scrambled control after cancer stimulation (CS) with hypoxic direct cancer cell co-culture with and without a different EZH2 inhibitor, Tazemetostat. Values are normalized to scrambled

control conversion based on CA-MSC classifier score. *, ** = comparison to treatment group scrambled control; $ = comparison to CS scrambled control without Tazemetostat C. Measurement of the CA-MSC classifier score in normal MSCs, CA-MSCs and CA-MSCs treated with EZH2i (GSK126 or Tazemetostat) demonstrating EZH2i does not impact the classifier score of already established CA-MSCs. For all in vitro experiments, mean & SEM of 3 independent experiments is represented. D. Western blot verification of differential histone modifications identified by mass spec. E. Densitometry quantification of histone modification western blots, compared to actin control. Supp Figure S6. A. Immunohistochemistry of undifferentiated and differentiated MSCs (adipocyte: oil red o; osteocyte: alizarin red; chondrocyte: alcian blue). B. Immunofluorescence of alpha-smooth muscle actin (aSMA) in undifferentiated and fibroblast differentiated MSCs. C. Representative flow cytometry plots characterizing MSCs based on CD105,73,90 positivity and CD34,45,11b negativity and negativity for the fibroblast marker, fibroblast surface protein (FSP). Supp. Table 1. Sample list summary Supp. Table 2. Most significant differentially methylated regions (DMRs) between CA-MSCs and normal MSCs (ranked P-value followed by mean beta value levels).

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Figure 1 A MIR141/200C Promoter Methylation B Global DNA Methylation C UMAP

MIR200C Group

1.5 Ovarian ﬆroma Group Source Source

cg19794481 1.0 MSC cg12161331 0.5 cg18185189 MSC_OM MSC_AD

0.0 MSC_FT MSC_OMneo UMAP 2 CA-MSC FTE (N=10, 000)

CAMSC_OV03 CAMSC_FT01 CAMSC_OM10 ctrlTumor6 ctrlTumor1 ctrlTumor3 ctrlTumor5 ctrlTumor4 ctrlTumor2 MSC_OMneo02 MSC−FT−BrcaL CAMSC_OV02 MSC_FT07 MSC−FT−BrcaR MSC_FT02 CAMSC_OV01 MSC_FT01 MSC_OM07 MSC_OM01d CAMSC_OM06b MSC_OM01a MSC_OM05 CAMSC_OM05 MSC_OM01b MSC_OM01c CAMSC_OM03a CAMSC_OM06a CAMSC_OM03b MSC_AD02 MSC_OMneo03 CAMSC_OM01 MSC_FT03 MSC_FT04 MSC_AD01 CAMSC_OM02 MSC_OMneo01 CAMSC_OM09 CAMSC_OM08 MSC_OMneo04 Ovarian ﬆroma CA-MSC_FT Group MSC Source CA-MSC Source Methylation CA-MSC_OMmets 100% Moﬆ Variable Probes CA-MSC MSC_OM CA-MSC_FT CA-MSC_OV Epithelium Tumor

MSC MSC_AD CA-MSC_OMmets −1.5 −1.0 −0.5 Tumor MSC_FT CA-MSC_OV −4 −2 0 2 4 6 8 10 MSC_OMneo 0% UMAP 1 CAMSC_OM06b CAMSC_OM06a CAMSC_OM03a CAMSC_OM03b CAMSC_OM09 CAMSC_OM02 CAMSC_OM05 CAMSC_FT01 CAMSC_OV02 CAMSC_OV01 CAMSC_OV03 MSC−FT−BrcaR CAMSC_OM08 CAMSC_OM01 MSC_FT02 MSC_OM07 MSC_OM05 MSC−FT−BrcaL MSC_FT07 MSC_FT04 MSC_FT01 MSC_FT03 MSC_AD01 MSC_OMneo04 MSC_AD02 MSC_OM01c MSC_OM01a MSC_OM01d MSC_OM01b MSC_OMneo01 MSC_OMneo03 MSC_OMneo02

D F G Proximal Probes Diﬆal Probes MSC CA-MSC FTE Fibroblaﬆ HGSC E (N=209,109) (N=198,086) CA-MSC CA-MSC Source MSC MSC MIR200C 6 7 5 Density Density 0 1 2 3 4 0 1 2 3 4

0 2 4 6 8 10 0.0 0.5 1.0 0.0 0.5 1.0 −0.5 0.0 0.5 Beta Value Beta Value

Density Methylation Diﬀerence (CA-MSC − MSC) 15 10

ΔBeta>0.2 ΔBeta>0.2

Density N=7,995 N=20,496 5 DMCs with group difference 0.2 (Intersect with HM450; N=17,833) 0 1 2 3 4 0 2 4 6 8 0 0.0 0.2 0.4 0.6 0.8 1.0 −0.5 0.0 0.5 −0.5 0.0 0.5 Methylation Difference Methylation Difference Beta Value CA-MSC − MSC CA-MSC − MSC N=19 N=13 N=4 N=62 N=6 Figure 2 A NORMAL SAMPLES CANCER SAMPLES

MSC FTE Fibroblaﬆ HGSC UCS SARC CA-MSC Ovary

bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made CA-MSC/FTE available under aCC-BY-NC-ND 4.0 International license. HyperM. Loci N=2,169

CA-MSC HyperM. N=213

FTE HyperM N=3,264

CA-MSC/FTE HypoM. Loci N=873 CA-MSC HypoM. N=221

FTE HypoM N=3,464

B SASH1 C13orf45 ELN GATA6 MARVELD2

CAMSC MSC

Mean Methylation 0.0 0.5 1.0

chr7:73474232 chr7:73474235 chr5:68710325chr5:68710808chr5:68710813chr5:68710818chr5:68710831chr5:68710907chr5:68711028chr5:68711277chr5:68711519chr5:68711640 chr6:148593324 chr6:148593434chr13:76444798chr13:76444823chr13:76444831chr13:76444850chr13:76445015chr13:76445065chr13:76445108chr13:76445372 chr18:19757468chr18:19757890chr18:19758221chr18:19761168chr18:19761485 C

D E Control Adipose Diﬀerentiation Fibroblaﬆ Diﬀerentiation

Normal MSC (CS=0.1)

Cancer-ﬆimulated MSC (CS=0.62)

CA-MSC (CS=0.99)

Normal MSC differentation CA-MSC differentation Figure 3 MSC CA-MSC A PCA plot using Merged ATAC-seq Peaks B MSC c2a CAMSC_OM03a CA-MSC a3a MSC_AD01 MSC_OM01 CA-MSC a2 CA-MSC a4 100 30 MSCbioRxiv c2a preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint20 (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made10 CAMSC_OM03aavailable under aCC-BY-NC-ND 4.0 International license. 0 A Group 1 16 50 CA−MSC a3a N=4,114 Group 2 14 MSC_AD01 N=1,015 12 0 CA−MSC a2 10 8 CA−MSC a4 Group 3

PC2 : 27% variance N=123,601 −50 6 MSC_OM01b CA-MSC 4 MSC_OM01a MSC 2 −100 −50 0 50 100

Summit Summit Summit Summit Summit Summit Summit Number of reads per bin / scaling factor for 1x average coverag e PC1: 33% variance -2.0kb 2.0kb -2.0kb 2.0kb -2.0kb 2.0kb -2.0kb 2.0kb -2.0kb 2.0kb -2.0kb 2.0kb -2.0kb 2.0kb 0 C D 0.6 4

0.4 2

0.2

0.0 Gene Log2−FoldChange −0.2 −4 −2 0 Over Corresponding Locations

−0.4 −4 −2 0 2 4 +2 kb Summit -2 kb Average Methylation Difference (CA-MSC - MSC) ATACseq Signal Difference Significantly Gained (Group1) / Loﬆ (Group2) Accessibility in CA-MSCs Non-significantly Altered (Group3) Accessibility in CA-MSCs E WT1

10 AP1M2 NPPB PKP2 GPR143 AADAC GATA4 SOX17 OLR1 SLITRK5 FAM101A IGFBP2 CLDN1 GPRC5A COL4A6 KCTD8 ZPLD1 CHRM2 EVPL MYCT1 CHMP4C GPR115 SNCA CXADR LRRN4 RASGRF2 IL1RL1 SELP DSC3 CACNG8 CACNG6 TSPAN8 ST6GALNAC5 PDZK1IP1 GPR37 CA2 MUM1L1 VCAM1 CPA4 KRT8 SLPI PCDH10 CSF2RB MSLN UPK1B CSF2 SYT1 SERPINA9 SPOCK3 DSC2 CLDN16 SDPR SH3RF2 RARB CCRL2 5 KRT18 WT1−AS B3GAT1 PDZK1 ANXA3 REC8 TSPAN13 KLK11 MMEL1 F11 IL33 TSTD1 SAMD12 SULT1E1 GCNT2 ABCG2 TM4SF1 PTPN6 PARD6B SLC24A3 RAB38 RAB11FIP1 HKDC1 FAM163A HTR1D CSF3 GRIK2 SEZ6 MARVELD2 GMPR CA9 IL18R1 CTSK MXRA8 0 LAMA2 PIK3R6 ITGBL1 COL16A1 CLEC14A NTRK1 ADAMTSL1 KAZALD1 CD109 MSX1 ECM2 COL6A3 KCNE4 ACVRL1 NPR3 FN1 IGF1 NINJ2 CD248 ABCA9 MX2 A2M HMCN1 PTGIR FNDC1 APCDD1 KCNK2 ISLR PDE1A BAALC GPC4 PCDH18 −5 COL5A3 MASP1 MFAP5 ZIC4 TBX5 OLFML2A LSP1 COL15A1 ENPP2 COLEC12 EDNRB EBF2 Expression Fold Change (Log2-Scaled) SIM1 IRX1 EN1 CRLF1 NRN1 MEOX2 EBF3 DPT IRX2 C5orf38 H19 MMP3 WISP2 TBX15 ZIC1 ELN SFRP2 Significantly Gained Accessibility TMEM119 −10 Significantly Loﬆ Accessibility COMP Non-significantly Altered Accessibility

−0.5 0.0 0.5 Methylation Difference (CA-MSC − MSC) bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Figure 4 A GOBP B KEGG

regulation of cell−cell adhesion Small cell lung cancer regulation of blood coagulation Complement and coagulation cascades regulation of hemoﬆasis AGE−RAGE signaling pathway in diabetic complications regulation of coagulation MAPK signaling pathway negative regulation of blood coagulation Amoebiasis negative regulation of hemoﬆasis Cell adhesion molecules (CAMs) ECM−receptor interaction positive regulation of cell adhesion Hypertrophic cardiomyopathy (HCM) negative regulation of coagulation TNF signaling pathway regulation of response to wounding Arrhythmogenic right ventricular cardiomyopathy (ARVC) collagen−activated signaling pathway PI3K−Akt signaling pathway extracellular matrix organization African trypanosomiasis extracellular ﬆructure organization ES Focal adhesion urogenital syﬆem development 4 Axon guidance renal syﬆem development 8 Leukocyte transendothelial migration 12 skeletal syﬆem morphogenesis Tight junction 16 connective tissue development Cushing syndrome cartilage development Protein digeﬆion and absorption p.adjuﬆ pattern speciﬁcation process DNA replication embryonic skeletal syﬆem development 0.01 Melanogenesis bone development 0.02 Wnt signaling pathway 0.03 Proteoglycans in cancer UP DOWN 0.04 Dilated cardiomyopathy (DCM) ES (582) (886) Basal cell carcinoma 2 Rap1 signaling pathway 3 C Proﬆate cancer 4 Cortisol synthesis and secretion 5 Transcriptional misregulation in cancer Group Human papillomavirus infection p.adjuﬆ cGMP−PKG signaling pathway EMTness TGF−beta signaling pathway 0.01 KRT18 Malaria 0.02 DDR1 Breaﬆ cancer 0.03 FLJ20273 0.04 AP1M2 Ras signaling pathway TOM1L1 MAP7 UP DOWN PAIP1 10 (280) (395) TOB1 GOLPH3L APBA2BP LASS2 D GSEA Enrichment LRBA ERBB3 FAAH MYO5C TMEM41B −10 DCI

Ephithelial Phenotype DCXR APITD1 TUBB6 3 THY1 ** GFPT2 1.5 ** LAMB1 EMILIN1 GLT8D2 1.0 FAP SNAI2 COL5A2 0.5

COL3A1 Expression-3 EMTness THBS2 COL5A1 0.0 NID2 MMP2 JECHLINGER_EPITHELIAL_TO_ MESENCHYMAL_TRANSITION_UP SARRIO_EPITHELIAL_MESENCHYMAL_ TRANSITION_UP GO_REGULATION_OF_EPITHELIAL_ CELL_PROLIFERATION HALLMARK_EPITHELIAL_ MESENCHYMAL_TRANSITION DACT1 −0.5 ANGPTL2 CA-MSC TWIST1 −1.0 CTSK FBLN2 COL6A3 −1.5

COL6A2 MSC Mesenchymal Phenotype PCOLCE ** ** SERPINF1 −2.0 Group ** GAS1 GO_EPITHELIAL_CELL_ DIFFERENTIATION GO_EPITHELIAL_CELL_ DEVELOPMENT **

OLFML2B Normalized Enrichement Score for CA-MSC-MSC Diﬀerentially Expressed Genes D B A C #Spheres cellular complexesfromascites Figure 5 10 15 20 25 30 35 40 # Adherent Cancer Cells per LPF

0 5 # of invading tumor cells (which wasnotcertifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmade CaoeTC+CA-MSC TC alone bioRxiv preprint TC alone direct doi: CA-MSC green=CD90 blue=Epcam TC + https://doi.org/10.1101/2020.02.25.964197 red=CD73 CA-MSC TC indirect TC +CA-MSC TC +enc. red=CA-MSC CA-MSC green=TC available undera E CC-BY-NC-ND 4.0Internationallicense ; G this versionpostedFebruary26,2020. direct tumor diﬆant abd extension TC only TC +enc.CA-MSC TC +CA-MSC mets F abd mets H I diﬆant mets liver met lung TC +CA-MSC TC only TC +enc.CA-MSC . The copyrightholderforthispreprint direct tumor extension ovary wall abd bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Figure 6 A Chromosome 11 B hg19 10 kb WT1 WT1 RefSeq Transcripts actin 25 _

0_ MSCs CA-MSCs 25 _ MSC_AD01 0_ MSC c2a 25 _ C 0_ MSC_OM01 25 _ 0_ CAMSC_OM03a 25 _

0_ CA-MSC a2 25 _ 0_ CA-MSC a3a 25 _ ATAC-seq Reads 0_ CA-MSC a4 Sig. ATAC-seq Peaks 50 _ K562 NHLF 0 _ Layered H3K4Me1 150 _ 0 _ Layered H3K4Me3 100 _ 0 _ Layered H3K27Ac ENCODE 1 2 3 Hiﬆone ChIP MSC_OMneo01 MSC_OMneo02 MSC_OMneo03 MSC_OMneo04 MSC_FT01 D MSC_FT02 MSC_FT03 MSC_FT04 MSC−FT−BrcaL MSC−FT−BrcaR MSC_FT07 MSC_AD01 MSC_AD02 MSC_OM01a MSC_OM01b MSC_OM01c MSC_OM01d MSC_OM05 MSC_OM07 CAMSC_OM01 CAMSC_OM02 CAMSC_OM03a CAMSC_OM03b

DNA Methylation CAMSC_OM05 CAMSC_OM06a CAMSC_OM06b 100% CAMSC_OM09 CAMSC_FT01 CAMSC_OV01 CAMSC_OV02 CAMSC_OV03 Sig. DMRs 0% CpG Islands bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Figure 7 A B F EZH2 GSEA Analysis of the Polycomb Target Genes CTCF EBF1 USF1 0.4 ZNF263 SUZ12 ESRRA 0.2 E2F6 HNF4A 0 10 20 30 0.0

TFAP2C MYC Enrichment Score −0.2

Over−represented TFs TFAP2A ESR1 0 4000 8000 12000

(Hyper−DMCs n=25,688) POLR2A TCF7L2 Position in the Ranked Liﬆ of Genes E2F1 NR3C1 RCOR1 C TEAD4 GATA1 SMARCB1 G SMARCC1 TAL1 MAZ CHD2 MAX EGR1 GATA2 FOS RBBP5 CTBP2 CA-MSC CA-MSC CA-MSC MXI1 GATA3 GTF2F1 HDAC2 EP300 EZH2 ZNF217 TAF1 STAT3 GABPA TBP NR2F2 HMGN3 STAT1 CA-MSC CA-MSC CA-MSC TCF12 SUZ12 CCNT2 D E H Over−represented TFs (Hypo−DMCs n=6,617) ZEB1 ZKSCAN1 JUND REST E2F4 IgG whole cell ETS1 α-EZH2 STAT2 IP control lysate CEBPD BHLHE40 HDAC8 EZH2 SMARCC2 HDAC1 ELK4 ZNF263 WT1 ZBTB7A SIN3A ZBTB33 SP1 ELF1 UBTF SMARCA4 CREB1 0 20 40 60 Negative Log(FDR, 10) bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

SupFigure 1 A

Group Source Diﬀerentially Methylated Sites |Δ Beta Value| > 0.4 (n=6,278) CAMSC_OM02 CAMSC_OM06b CAMSC_OM06a CAMSC_OM03a CAMSC_OM03b CAMSC_OM09 CAMSC_OM05 CAMSC_OV02 CAMSC_OV01 CAMSC_OV03 CAMSC_FT01 MSC−FT−BrcaR MSC−FT−BrcaL CAMSC_OM08 CAMSC_OM01 MSC_OM07 MSC_FT02 MSC_OM05 MSC_FT07 MSC_FT04 MSC_FT01 MSC_FT03 MSC_AD01 MSC_OM01c MSC_OM01a MSC_OM01d MSC_OM01b MSC_OMneo03 MSC_OMneo01 MSC_OMneo04 MSC_AD02 MSC_OMneo02 ctrlTumor4 ctrlTumor6 ctrlTumor5 ctrlTumor2 ctrlTumor3 ctrlTumor1 CAMSC_OM10 Group CA-MSC MSC Methylation 0% 100% MSC Source MSC_OM MSC_AD MSC_FT MSC_OMneo CA-MSC Source CA-MSC_FT CA-MSC_OMmets CA-MSC_OV

Coefficient=0.54 Coefficient=0.76 Coefficient=0.85 Coefficient=0.93 Coefficient=0.99

MSC [0~0.2] (0.2~0.4] (0.4~0.6] (0.6~0.8] (0.8~1] 1.00

0.75

0.50 CA-MSC

0.25

0.00

0.00 0.50 1.00 0.00 0.50 1.00 0.00 0.50 1.00 0.00 0.50 1.00 0.00 0.50 1.00 FTE bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

SupFigure 2 A CA−MSC Hyper−methylated Probes B CA−MSC Hypo−methylated Probes

cpg_inter (pval=0.00e+00) cpg_inter (pval=0.00e+00) genes_intergenic (pval=0.00e+00) genes_introns (pval=3.37e−102) cpg_shelves (pval=0.00e+00) genes_intergenic (pval=3.03e−44) genes_3UTRs (pval=1.76e−23) cpg_shores (pval=2.69e−25) genes_introns (pval=5.45e−46) cpg_shelves (pval=8.11e−05) cpg_shores (pval=2.27e−05) genes_3UTRs (pval=6.74e−01) genes_1to5kb (pval=2.26e−65) genes_1to5kb (pval=6.74e−01) genes_cds (pval=3.34e−69) genes_cds (pval=1.47e−25) genes_exons (pval=0.00e+00) genes_intronexonboundaries (pval=1.46e−109) genes_intronexonboundaries (pval=0.00e+00) genes_exons (pval=1.02e−118) genes_exonintronboundaries (pval=0.00e+00) genes_exonintronboundaries (pval=2.47e−122)

Genomic Features (hg19) genes_promoters (pval=0.00e+00) genes_promoters (pval=1.44e−233) genes_ﬁrﬆexons (pval=0.00e+00) genes_ﬁrﬆexons (pval=3.69e−163) genes_5UTRs (pval=0.00e+00) genes_5UTRs (pval=2.00e−115) cpg_islands (pval=0.00e+00) cpg_islands (pval=0.00e+00) 11 2 0.5 1.0 1.5 Odds Ratio & 95% CI Odds Ratio & 95% CI C CA−MSC−Closed ATACseq Peaks D CA−MSC−Open ATACseq Peaks

cpg_shelves (pval=2.59e−07) cpg_inter (pval=2.18e−79) cpg_inter (pval=1.52e−10) genes_intergenic (pval=5.36e−36) genes_introns (pval=1.00e+00) cpg_shelves (pval=3.71e−02) genes_intergenic (pval=1.00e+00) genes_introns (pval=1.83e−23) genes_1to5kb (pval=1.00e+00) genes_1to5kb (pval=1.87e−11) genes_3UTRs (pval=1.00e+00) genes_intronexonboundaries (pval=2.48e−40) genes_intronexonboundaries (pval=1.17e−03) genes_exons (pval=1.11e−70) genes_exons (pval=2.38e−07) genes_3UTRs (pval=2.52e−08) genes_exonintronboundaries (pval=2.38e−07) genes_promoters (pval=1.01e−56) genes_cds (pval=7.17e−05) cpg_shores (pval=4.30e−69) cpg_shores (pval=1.13e−09) genes_exonintronboundaries (pval=2.74e−71)

Genomic Features (hg19) genes_promoters (pval=2.27e−08) genes_cds (pval=9.52e−47) genes_ﬁrﬆexons (pval=2.54e−09) genes_ﬁrﬆexons (pval=6.91e−64) genes_5UTRs (pval=1.52e−09) cpg_islands (pval=1.51e−80) cpg_islands (pval=1.74e−24) genes_5UTRs (pval=9.10e−68) 0.51.0 1.5 2.0 11 2 Odds Ratio & 95% CI Odds Ratio & 95% CI bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

SupFigure 3 A B bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

SupFigure 4

Chondrocytes Bone Marrow

Adipose H1 2019 702

2741 131 19

455 1243 435 227

1241

61 48 15 16

34 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

SupFigure 5 A B C

D E bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

SupFigure 6

A) undifferentiated adipocyte C) FITC APC PE 0.1% 0.2% 0.08%

200um 200um

isotype isotype isotype

0.5% 0.4% 0.6%

200um 200um osteocyte chondrocyte

CD34 CD45 CD11b B) undifferentiated αSMA+ 98.5% 97.8% 98.1% MSC fibroblast

CD105 CD73 CD90 0.9% 96.8% gated on CD90+ C D 1 0 5

FSP CD73 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

SupTable 1

SampleName SampleID PatientID Sample SampleEPIC Source SourceCode Group CAMSC_OM01 493_cams s493 CAMSC 201557520003 CA-MSCs derived from the omental CAMSC- c _R03C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM02 549_cams s549 CAMSC 201557520040 CA-MSCs derived from the omental CAMSC- c _R03C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM03 CAMSC1 s0009 CAMSC 200516380137 CA-MSCs derived from the omental CAMSC- a _R01C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM03 CAMSC2 s0009 CAMSC 200516380137 CA-MSCs derived from the omental CAMSC- b _R02C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM05 CAMSC3 s0003 CAMSC 200516380137 CA-MSCs derived from the omental CAMSC- _R03C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM06 CAMSC6 s0006 CAMSC 200516380137 CA-MSCs derived from the omental CAMSC- a _R06C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM06 CAMSC7 s0006 CAMSC 200516380137 CA-MSCs derived from the omental CAMSC- b _R07C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM08 CAMSC8 s0008 CAMSC 200516380137 CA-MSCs derived from the omental CAMSC- _R08C01 metastasis of patients with ovarian OMmets carcinosarcoma CAMSC_OM09 CAMSC9 s0009 CAMSC 200526210119 CA-MSCs derived from the omental CAMSC- _R01C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_OM10 406_cams s406 CAMSC 201557520040 CA-MSCs derived from the omental CAMSC- c _R02C01 metastasis of patients with high grade OMmets serous ovarian cancer CAMSC_FT01 19-175 s19-175 CAMSC 203020780110 CAMSCs derived from fallopian tube CAMSC-FT FT.camsc _R04C01 invovled with high grade serous ovarian cancer bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

CAMSC_OV01 19-90 s19-90 CAMSC 203020780110 CAMSCSs derived from ovary involved with CAMSC-OV ov.camsc _R05C01 high grade serous ovarian cancer CAMSC_OV02 19-95 s19-95 CAMSC 203020780110 CAMSCSs derived from ovary involved with CAMSC-OV ov.camsc _R06C01 high grade serous ovarian cancer CAMSC_OV03 19-263 s19-263 CAMSC 203038250154 CAMSCSs derived from ovary involved with CAMSC-OV ovcamsc _R06C01 ovarian cancer MSC_OMneo01 18-0030 s18-0030 MSC 203020780110 MSCs derived from omentum that was MSC-OMneo neo n.om _R08C01 initially involved with high grade serous ovarian cancer but had a complete pathologic response to neoadjuvant chemotherapy MSC_OMneo02 18-0054 s18-0054 MSC 203013220061 MSCs derived from omentum that was MSC-OMneo neo n.om _R01C01 initially involved with high grade serous ovarian cancer but had a complete pathologic response to neoadjuvant chemotherapy MSC_OMneo03 18-0048 s18-0048 MSC 203013220061 MSCs derived from omentum that was MSC-OMneo neo n.om _R02C01 initially involved with high grade serous ovarian cancer but had a complete pathologic response to neoadjuvant chemotherapy MSC_OMneo04 19-107 s19-107 MSC 203013220061 MSCs derived from omentum that was MSC-OMneo n.om _R03C01 never involved with cancer (stage II ovarian (neo) cancer) but patient had neoadjuvant chemotherapy MSC_FT01 19-120 s19-120 MSC 203013220061 MSCs derived from normal fallopian tube MSC-FT n.FT _R04C01 MSC_FT02 19-197 s19-197 MSC 203013220061 MSCs derived from normal fallopian tube MSC-FT n.FT _R05C01 MSC_FT03 19-128 s19-128 MSC 203013220061 MSCs derived from normal fallopian tube MSC-FT n.FT _R06C01 but with hydrosalpinx MSC_FT04 AL n.FT s0007 MSC 203013220061 MSCs derived from normal fallopian tube MSC-FT _R07C01 MSC-FT-BrcaL 19-257 s19-257 MSC 203038250154 MSCs derived from left fallopian tube with MSC-FT LFT _R02C01 BRCA2 mutation BRCA2 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.25.964197; this version posted February 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

MSC-FT-BrcaR 19-257 s19-257 MSC 203038250154 MSCs derived from right fallopian tube with MSC-FT RFT _R03C01 BRCA2 mutation BRCA2 MSC_FT07 19-223 #2 s19-223 MSC 203038250154 MSCs derived from normal fallopian tube MSC-FT nFT _R04C01 MSC_AD01 amsc_112 s1125 MSC 201557520040 MSCs derived from adipose purchased MSC-AD 5 _R04C01 through ATCC MSC_AD02 amsc_211 s2118 MSC 201557520003 MSCs derived from adipose purchased MSC-AD 8 _R05C01 through ATCC MSC_OM01a MSC1 s0001 MSC 200526210119 MSCs derived from normal omental tissue MSC-OM _R02C01 undergoing surgery for benign indications MSC_OM01b MSC2 s0001 MSC 200526210119 MSCs derived from normal omental tissue MSC-OM _R03C01 undergoing surgery for benign indications MSC_OM01c MSC5 s0001 MSC 200526210119 MSCs derived from normal omental tissue MSC-OM _R06C01 undergoing surgery for benign indications MSC_OM01d MSC6 s0001 MSC 200526210119 MSCs derived from normal omental tissue MSC-OM _R07C01 undergoing surgery for benign indications MSC_OM05 18-691 s18-691 MSC 203013220061 MSCs derived from normal omentum from MSC-OM n.om CC _R08C01 patient with a stage II clear cell endometrial cancer MSC_OM05b 18-691 s18-691 MSC 203038250154 MSCs derived from normal omentum from MSC-OM n.om CC _R01C01 patient with a stage II clear cell endometrial cancer MSC_OM07 19-319 s19-319 MSC 203038250154 MSCs derived from normal omentum MSC-OM n.om _R05C01

SupTable 2

seqnames start end width strand no.cpgs minfdr Stouffer maxbetafc meanbetafc overlapping.promoters Case Control CaseRegionMean ControalRegionMean DMRdirection maxProbe MaxProbeDMRdirection MaxProbemaxProbe MaxProbeseqnames LogFC chr19 18759877 18761792 1916 * 11 7.36E-288 88.98567 0.7293782 0.519803 NA CAMSC MSC 0.687622495 0.128560609 HyperDMR cg17126026 chr19 18761288 18761289 NA chr19 10928172 10928696 525 * 9 1.63955398014343e-316 83.96939 0.7927038 0.6335535 MIR199A1-201 CAMSC MSC 0.785149616 0.064766301 HyperDMR cg27648270 chr19 10928172 10928173 NA chr1 172113756 172114068 313 * 7 3.01E-236 65.67276 0.7670376 0.7023941 MIR199A2-201, DNM3OS-001 CAMSC MSC 0.882012164 0.07677775 HyperDMR cg25214966 chr1 172113756 172113757 NA chr17 1960199 1962627 2429 * 9 6.31E-117 57.08689 0.700195 0.5456916 HIC1-005, HIC1-006, HIC1-004 CAMSC MSC 0.77330094 0.08891489 HyperDMR cg13389502 chr17 1961440 1961441 HIC1 (logFC = -1.374) chr16 85319859 85320882 1024 * 7 6.60E-139 52.54841 0.7816523 0.5704594 NA CAMSC MSC 0.810589474 0.183728119 HyperDMR cg06984848 chr16 85320722 85320723 NA chr16 10652706 10653180 475 * 5 1.60E-172 48.24152 0.8134456 0.5852531 EMP2-003 CAMSC MSC 0.748956491 0.076741072 HyperDMR cg27178401 chr16 10652711 10652712 EMP2 (logFC = -2.102) chr13 110992996 110993558 563 * 4 1.21E-145 37.06349 0.7582463 0.5848581 NA CAMSC MSC 0.96105512 0.332125574 HyperDMR cg16872841 chr13 110993558 110993559 NA chr2 127933484 127934558 1075 * 5 4.60E-113 35.48019 0.7249007 0.5478429 NA CAMSC MSC 0.834758889 0.226152625 HyperDMR cg13858900 chr2 127933617 127933618 NA chr19 18770246 18771327 1082 * 5 4.50E-99 34.58856 0.6449692 0.5243662 NA CAMSC MSC 0.753523359 0.169511807 HyperDMR cg05295841 chr19 18771126 18771127 NA chr19 55598443 55599029 587 * 4 2.58E-111 33.87648 0.699117 0.5019409 EPS8L1-014, EPS8L1-019, EPS8L1-018 CAMSC MSC 0.642303524 0.087551852 HyperDMR cg10016788 chr19 55599029 55599030 EPS8L1 (logFC = 1.297) chr17 71361857 71361901 45 * 3 4.57E-144 32.51407 0.6413124 0.5668295 NA CAMSC MSC 0.715439618 0.06927801 HyperDMR cg16050897 chr17 71361894 71361895 NA chr10 126704108 126705323 1216 * 3 3.74E-122 31.55661 0.7250566 0.5886836 NA CAMSC MSC 0.907442426 0.250888337 HyperDMR cg09127448 chr10 126704108 126704109 NA chr14 81916570 81916796 227 * 3 4.99E-119 30.78225 0.6039515 0.5787561 RP11-299L17.3-001 CAMSC MSC 0.920944713 0.325831342 HyperDMR cg11287675 chr14 81916570 81916571 NA chr6 149803087 149803547 461 * 3 8.22E-134 30.21463 0.6156467 0.5187553 ZC3H12D-002, ZC3H12D-004 CAMSC MSC 0.770573703 0.19371894 HyperDMR cg00695799 chr6 149803087 149803088 NA chr3 140813919 140814133 215 * 3 2.78E-110 28.83858 0.7627201 0.7116277 NA CAMSC MSC 0.903026251 0.102988244 HyperDMR cg23339720 chr3 140814055 140814056 NA chr2 169652805 169653384 580 * 4 4.35E-92 28.65252 0.6245311 0.500596 NOSTRIN-201 CAMSC MSC 0.904632017 0.355569505 HyperDMR cg10864298 chr2 169653226 169653227 NA chr17 76876040 76876995 956 * 4 4.53E-94 28.57398 0.7840838 0.5194597 NA CAMSC MSC 0.792991615 0.206555851 HyperDMR cg00863893 chr17 76876239 76876240 NA chr4 2069669 2069750 82 * 3 5.66E-91 27.77406 0.6939412 0.6665106 NA CAMSC MSC 0.906921022 0.165483514 HyperDMR cg26179133 chr4 2069669 2069670 NA chr18 74770431 74770815 385 * 3 1.41E-89 27.01509 0.7234693 0.6636352 NA CAMSC MSC 0.933050494 0.149090144 HyperDMR cg07807210 chr18 74770431 74770432 NA chr17 78808060 78808570 511 * 3 6.37E-85 26.84628 0.719898 0.6656556 NA CAMSC MSC 0.921647134 0.244825167 HyperDMR cg11637695 chr17 78808060 78808061 NA chr1 3514921 3514990 70 * 3 1.40E-84 26.81498 0.7744937 0.6748508 NA CAMSC MSC 0.85653759 0.120669171 HyperDMR cg09187007 chr1 3514990 3514991 NA chr21 47404109 47404268 160 * 4 3.90E-64 26.3765 0.5884378 0.53861 NA CAMSC MSC 0.819431051 0.204529037 HyperDMR cg11401293 chr21 47404268 47404269 NA chr7 75592134 75592838 705 * 3 7.32E-72 24.88143 0.6670547 0.5659473 NA CAMSC MSC 0.781307974 0.148719294 HyperDMR cg16182457 chr7 75592134 75592135 NA chr1 9211836 9211843 8 * 2 2.91E-124 24.00732 0.5450763 0.5177195 MIR34A-201 CAMSC MSC 0.929293265 0.366827538 HyperDMR cg10640389 chr1 9211843 9211844 NA chr12 56329296 56329641 346 * 3 1.42E-83 23.39366 0.8046711 0.5024244 DGKA-025, DGKA-006, DGKA-023, DGKA-008, DGKA-051 CAMSC MSC 0.799196415 0.32195769 HyperDMR cg07679948 chr12 56329641 56329642 DGKA (logFC = 0.378) chr12 105096609 105096644 36 * 2 1.31E-112 22.74617 0.8105228 0.7785906 NA CAMSC MSC 0.914861002 0.074741188 HyperDMR cg07911905 chr12 105096644 105096645 NA chr11 120056113 120056948 836 * 2 1.40E-91 22.4105 0.6504854 0.6316515 TRIM29-010, TRIM29-011 CAMSC MSC 0.871830701 0.197739015 HyperDMR cg01260325 chr11 120056113 120056114 TRIM29 (logFC = 0.588) chr3 129315176 129315717 542 * 2 2.31E-95 22.06255 0.7817968 0.7245812 NA CAMSC MSC 0.863975821 0.062870426 HyperDMR cg12175038 chr3 129315717 129315718 NA chr1 19752812 19753627 816 * 2 3.03E-69 21.92314 0.6709574 0.6126393 RN7SL277P-201 CAMSC MSC 0.918388945 0.249146745 HyperDMR cg09875257 chr1 19753627 19753628 NA chr19 5282046 5282167 122 * 2 4.58E-88 21.87158 0.5535625 0.548446 NA CAMSC MSC 0.83123952 0.266265566 HyperDMR cg10941356 chr19 5282046 5282047 NA chr1 226825435 226825606 172 * 3 1.44E-63 21.79817 0.6731972 0.5566437 NA CAMSC MSC 0.708546004 0.188048554 HyperDMR cg26917828 chr1 226825448 226825449 NA chr15 68900624 68900864 241 * 2 6.04E-91 21.53263 0.6898485 0.5523716 NA CAMSC MSC 0.822242187 0.225420759 HyperDMR cg02939263 chr15 68900864 68900865 NA chr15 67228722 67228986 265 * 3 2.67E-54 21.02615 0.7019303 0.6382555 NA CAMSC MSC 0.783647213 0.033370667 HyperDMR cg20560283 chr15 67228949 67228950 NA chr11 353762 353919 158 * 2 6.27E-82 20.85779 0.538012 0.5275708 NA CAMSC MSC 0.814549662 0.277917294 HyperDMR cg18813159 chr11 353762 353763 NA chr13 111013877 111014385 509 * 2 4.24E-81 20.77643 0.5806996 0.5259811 NA CAMSC MSC 0.945335048 0.384337371 HyperDMR cg16717572 chr13 111013877 111013878 NA chr12 67072769 67073062 294 * 4 7.14E-44 20.73827 0.6067005 0.5344072 GRIP1-201, GRIP1-202, GRIP1-001, GRIP1-002, GRIP1-010 CAMSC MSC 0.920700838 0.410403737 HyperDMR cg24103040 chr12 67072988 67072989 GRIP1 (logFC = 3.396) chr22 22297376 22297505 130 * 2 5.97E-75 20.58104 0.6221081 0.5844712 NA CAMSC MSC 0.780724542 0.173785473 HyperDMR cg18377381 chr22 22297505 22297506 NA chr3 127506038 127506604 567 * 2 1.01E-70 20.41777 0.5533273 0.5194511 NA CAMSC MSC 0.858730516 0.322218796 HyperDMR cg14750757 chr3 127506604 127506605 NA chr16 69516113 69516705 593 * 2 8.61E-69 20.40873 0.6291306 0.6188372 NA CAMSC MSC 0.927639638 0.299900768 HyperDMR cg05265936 chr16 69516113 69516114 NA chr15 69087480 69087809 330 * 2 1.59E-71 20.11587 0.7742118 0.7364407 NA CAMSC MSC 0.95181593 0.126769191 HyperDMR cg10205045 chr15 69087809 69087810 NA chr17 75473667 75473895 229 * 2 1.14E-108 19.93238 0.601304 0.5561776 NA CAMSC MSC 0.800571206 0.180896421 HyperDMR cg05626616 chr17 75473895 75473896 NA chr22 46455099 46455182 84 * 2 2.34E-89 19.88783 0.5911889 0.5069458 RP6-109B7.2-001 CAMSC MSC 0.941656253 0.43514096 HyperDMR cg23147996 chr22 46455099 46455100 NA chr1 62803739 62804294 556 * 2 2.43E-63 19.76321 0.6272146 0.5891263 NA CAMSC MSC 0.859986574 0.21008336 HyperDMR cg05766570 chr1 62804294 62804295 NA chr1 109373104 109373336 233 * 2 4.26E-67 19.59839 0.6157859 0.5648802 AKNAD1-009 CAMSC MSC 0.675451075 0.074804326 HyperDMR cg03884543 chr1 109373104 109373105 NA chr5 126187516 126188498 983 * 2 8.20E-77 18.91595 0.7852167 0.5094314 NA CAMSC MSC 0.934540818 0.398690715 HyperDMR cg16656749 chr5 126187516 126187517 NA chr22 45034765 45035105 341 * 2 1.14E-58 18.51877 0.568949 0.5612343 NA CAMSC MSC 0.755353278 0.144848674 HyperDMR cg18817678 chr22 45035105 45035106 NA chr19 7224513 7224713 201 * 2 7.31E-58 17.88105 0.5313415 0.5209221 NA CAMSC MSC 0.692458039 0.187571478 HyperDMR cg09779027 chr19 7224513 7224514 NA chr6 109274429 109274464 36 * 2 1.95E-53 17.60955 0.7795448 0.733557 NA CAMSC MSC 0.917999672 0.065293514 HyperDMR cg18575863 chr6 109274464 109274465 NA chr7 46643490 46643498 9 * 2 1.50E-52 17.4793 0.6014706 0.5649745 NA CAMSC MSC 0.807742713 0.141746546 HyperDMR cg05183712 chr7 46643490 46643491 NA chr6 148593324 148593434 111 * 2 3.58E-52 17.3474 0.6997531 0.5905241 SASH1-002 CAMSC MSC 0.708663048 0.059025236 HyperDMR cg08663446 chr6 148593434 148593435 SASH1 (logFC = -1.536) chr1 7351630 7352249 620 * 2 2.73E-84 17.18158 0.6288318 0.5353237 NA CAMSC MSC 0.656320306 0.098305935 HyperDMR cg17178218 chr1 7352249 7352250 NA chr2 201342582 201342647 66 * 2 3.94E-55 17.17293 0.6345867 0.6094616 NA CAMSC MSC 0.969046856 0.357017007 HyperDMR cg24746666 chr2 201342647 201342648 NA chr4 87374237 87374491 255 * 2 1.43E-48 16.82162 0.5877528 0.5144803 MAPK10-003, MAPK10-005, MAPK10-020, MAPK10-021 CAMSC MSC 0.815133465 0.25878576 HyperDMR cg17452384 chr4 87374237 87374238 MAPK10 (logFC = 1.358) chr7 155049476 155049620 145 * 2 4.89E-47 16.71513 0.6225911 0.6221257 NA CAMSC MSC 0.815681733 0.06873652 HyperDMR cg03251454 chr7 155049476 155049477 NA chr8 49494374 49494724 351 * 2 1.73E-64 16.70857 0.7603265 0.6310578 NA CAMSC MSC 0.778164234 0.069005997 HyperDMR cg10549944 chr8 49494374 49494375 NA chr5 55471934 55472069 136 * 2 1.61E-63 16.59393 0.6221734 0.5030313 NA CAMSC MSC 0.742653808 0.240311034 HyperDMR cg06528416 chr5 55471934 55471935 NA chr9 97906115 97906431 317 * 2 2.25E-46 16.59132 0.6191259 0.5252725 RNA5SP288-201 CAMSC MSC 0.897694648 0.322519331 HyperDMR cg12698381 chr9 97906431 97906432 NA chr1 21643774 21643843 70 * 2 7.41E-46 16.51459 0.6038258 0.5786058 NA CAMSC MSC 0.91346717 0.231855248 HyperDMR cg22501992 chr1 21643843 21643844 NA chr9 137418069 137418962 894 * 2 1.49E-43 16.21145 0.5160562 0.5081476 RP11-473E2.4-001 CAMSC MSC 0.686358337 0.132436681 HyperDMR cg08059303 chr9 137418962 137418963 NA chr21 47405667 47405786 120 * 2 6.87E-40 14.01083 0.5495562 0.5462036 COL6A1-004 CAMSC MSC 0.899028713 0.278170072 HyperDMR cg05194808 chr21 47405786 47405787 COL6A1 (logFC = -3.096) chr20 62451390 62451399 10 * 2 1.04E-33 13.93462 0.5928195 0.5651166 NA CAMSC MSC 0.884493093 0.251135051 HyperDMR cg08319194 chr20 62451390 62451391 NA chr3 197081654 197081990 337 * 2 3.18E-32 13.7531 0.6827561 0.5732264 NA CAMSC MSC 0.910836545 0.207252983 HyperDMR cg14875327 chr3 197081654 197081655 NA chr15 101226468 101226769 302 * 2 1.90E-38 13.64315 0.5770879 0.5286367 NA CAMSC MSC 0.769648266 0.138897084 HyperDMR cg10701586 chr15 101226769 101226770 NA chr16 81520178 81520589 412 * 2 2.09E-30 13.40629 0.5914666 0.510169 NA CAMSC MSC 0.949615793 0.383746496 HyperDMR cg05705335 chr16 81520589 81520590 NA chr8 125685871 125685873 3 * 2 1.37E-29 13.0275 0.5938969 0.5448879 MTSS1-018 CAMSC MSC 0.727549296 0.110622804 HyperDMR cg09822636 chr8 125685871 125685872 MTSS1 (logFC = 1.298) chr8 144935784 144935835 52 * 2 2.05E-26 12.17046 0.6540525 0.5611613 NA CAMSC MSC 0.878816173 0.221705269 HyperDMR cg00566369 chr8 144935784 144935785 NA chr8 86262603 86262948 346 * 2 1.99E-26 12.1434 0.5547777 0.5219954 CA1-201, CA1-202 CAMSC MSC 0.864420148 0.254614932 HyperDMR cg23898477 chr8 86262948 86262949 NA chr11 61521905 61523045 1141 * 11 3.21E-297 -92.3201 -0.7260057 -0.6175189 MYRF-002, MYRF-001, MYRF-005 CAMSC MSC 0.152561612 0.841890167 HypoDMR cg05881566 chr11 61522765 61522766 NA chr5 68710808 68711640 833 * 9 1.06E-241 -74.7455 -0.7196715 -0.5853283 MARVELD2-006, MARVELD2-002, MARVELD2-004, MARVELD2-0CAMSC MSC 0.070594431 0.741679478 HypoDMR cg02505827 chr5 68711277 68711278 MARVELD2 (logFC = 2.063) chr12 95010017 95010321 305 * 5 9.12E-174 -44.1495 -0.7259935 -0.5633941 TMCC3-003, TMCC3-002, TMCC3-004 CAMSC MSC 0.098683629 0.70441589 HypoDMR cg06247422 chr12 95010311 95010312 TMCC3 (logFC = 4.076) chr19 49935823 49936274 452 * 4 2.69E-92 -30.862 -0.7632541 -0.5955081 SLC17A7-005 CAMSC MSC 0.120985949 0.832782777 HypoDMR cg22231400 chr19 49935823 49935824 SLC17A7 (logFC = -4.303) chr18 21572622 21572656 35 * 3 5.26E-117 -30.7299 -0.6070941 -0.5546158 TTC39C-002 CAMSC MSC 0.388062492 0.91989887 HypoDMR cg12639429 chr18 21572622 21572623 TTC39C (logFC = -0.701) chr1 33225975 33227555 1581 * 4 5.69E-66 -30.0744 -0.6865147 -0.503127 NA CAMSC MSC 0.276292499 0.772145834 HypoDMR cg13254240 chr1 33225975 33225976 NA chr14 100001136 100001737 602 * 3 5.61E-79 -25.891 -0.6592057 -0.5866462 CCDC85C-006 CAMSC MSC 0.185561945 0.809590509 HypoDMR cg08645119 chr14 100001737 100001738 CCDC85C (logFC = 2.148) chr14 101157950 101158251 302 * 3 1.41E-75 -25.4082 -0.7201844 -0.6463908 NA CAMSC MSC 0.239572714 0.946878571 HypoDMR cg11806439 chr14 101157950 101157951 NA chr17 750241 750541 301 * 3 3.56E-68 -23.1029 -0.676623 -0.5714064 NA CAMSC MSC 0.152544751 0.73028422 HypoDMR cg10805039 chr17 750241 750242 NA chr1 60221114 60221182 69 * 2 5.56E-101 -22.8171 -0.6900691 -0.6725451 FGGY-016 CAMSC MSC 0.201136378 0.918540646 HypoDMR cg04387953 chr1 60221114 60221115 FGGY (logFC = -0.538) chr13 110995625 110995800 176 * 2 3.92E-97 -22.653 -0.6040793 -0.5362278 NA CAMSC MSC 0.35538153 0.925955781 HypoDMR cg19389286 chr13 110995800 110995801 NA chr3 149955247 149955310 64 * 2 1.23E-101 -22.6002 -0.7545429 -0.7175064 RP11-167H9.6-002, RP11-167H9.6-001 CAMSC MSC 0.150081539 0.905585776 HypoDMR cg24685025 chr3 149955310 149955311 NA chr3 134100875 134100881 7 * 2 4.84E-84 -21.3978 -0.7050311 -0.6325435 NA CAMSC MSC 0.244174496 0.915928121 HypoDMR cg10758667 chr3 134100875 134100876 NA chr14 51955716 51956044 329 * 2 6.69E-84 -21.3671 -0.6132337 -0.5423608 FRMD6-003, FRMD6-007 CAMSC MSC 0.358116973 0.876825616 HypoDMR cg18894781 chr14 51955716 51955717 FRMD6 (logFC = -0.844) chr1 208714871 208714976 106 * 2 5.03E-84 -21.1172 -0.5868984 -0.5639301 NA CAMSC MSC 0.064827978 0.639968999 HypoDMR cg09238969 chr1 208714871 208714872 NA chr6 13408505 13408527 23 * 2 1.17E-78 -20.9715 -0.5575968 -0.5320567 GFOD1-002 CAMSC MSC 0.415562865 0.947813358 HypoDMR cg14180777 chr6 13408505 13408506 GFOD1 (logFC = 1.592) chr22 23536136 23536228 93 * 2 3.53E-76 -20.7127 -0.6394917 -0.584938 NA CAMSC MSC 0.260744464 0.901094127 HypoDMR cg17703135 chr22 23536228 23536229 NA chr1 169078711 169079632 922 * 2 2.33E-52 -19.6241 -0.6428509 -0.6248461 ATP1B1-201, ATP1B1-003, ATP1B1-202 CAMSC MSC 0.160294585 0.843479735 HypoDMR cg24304617 chr1 169079632 169079633 ATP1B1 (logFC = 0.581) chr19 5250376 5250569 194 * 3 1.15E-45 -19.021 -0.5821768 -0.5145338 NA CAMSC MSC 0.233944597 0.795470754 HypoDMR cg04253465 chr19 5250495 5250496 NA chr7 131223393 131223417 25 * 2 8.81E-60 -18.4801 -0.6450332 -0.6390916 NA CAMSC MSC 0.199458013 0.887236895 HypoDMR cg08737189 chr7 131223417 131223418 NA chr2 100496760 100497020 261 * 2 5.61E-57 -18.2287 -0.6166537 -0.545724 NA CAMSC MSC 0.218560473 0.807008692 HypoDMR cg26438422 chr2 100497020 100497021 NA chr11 44730975 44731693 719 * 2 5.09E-50 -17.9873 -0.5811461 -0.5271572 NA CAMSC MSC 0.205768823 0.737601962 HypoDMR cg20587151 chr11 44730975 44730976 NA chr6 37441235 37441431 197 * 2 4.34E-59 -17.9033 -0.568491 -0.5320231 CMTR1-007, CMTR1-004 CAMSC MSC 0.171532039 0.752911545 HypoDMR cg09699430 chr6 37441431 37441432 NA chr16 85368682 85368854 173 * 2 1.10E-55 -17.8474 -0.6027988 -0.6026623 NA CAMSC MSC 0.08189794 0.778323057 HypoDMR cg02016305 chr16 85368682 85368683 NA chr10 14614164 14614385 222 * 2 1.48E-53 -17.7984 -0.5554052 -0.5440512 FAM107B-005, FAM107B-008, FAM107B-011, FAM107B-010, FA CAMSC MSC 0.248834306 0.870764493 HypoDMR cg00734838 chr10 14614164 14614165 FAM107B (logFC = 1.038) chr17 79334588 79334595 8 * 2 1.01E-54 -17.7922 -0.5694264 -0.5322722 RP11-1055B8.2-001 CAMSC MSC 0.294335063 0.858830124 HypoDMR cg22281702 chr17 79334595 79334596 NA chr1 228131966 228131977 12 * 2 1.32E-55 -17.7865 -0.6298123 -0.5716879 NA CAMSC MSC 0.175141965 0.843043552 HypoDMR cg02295872 chr1 228131977 228131978 NA chr2 220970010 220970226 217 * 2 6.32E-52 -17.5504 -0.5921263 -0.5084812 AC114765.1-001 CAMSC MSC 0.128708482 0.718234054 HypoDMR cg27160991 chr2 220970226 220970227 NA chr7 73474232 73474235 4 * 2 4.35E-48 -16.3286 -0.5861471 -0.5029551 NA CAMSC MSC 0.382683748 0.906641733 HypoDMR cg15983182 chr7 73474232 73474233 NA chr15 64305915 64306301 387 * 2 1.15E-41 -15.7423 -0.5377098 -0.5077809 NA CAMSC MSC 0.187790449 0.676509717 HypoDMR cg26987281 chr15 64305915 64305916 NA chr9 137279180 137279412 233 * 2 2.02E-38 -15.0231 -0.5043227 -0.5013535 NA CAMSC MSC 0.292583779 0.832856832 HypoDMR cg13595495 chr9 137279412 137279413 NA chr15 58782685 58782688 4 * 2 2.16E-37 -14.8588 -0.5928414 -0.5767543 NA CAMSC MSC 0.144128851 0.783038615 HypoDMR cg00870269 chr15 58782688 58782689 NA chr14 103844451 103844660 210 * 2 1.27E-37 -14.2034 -0.5381988 -0.5012978 NA CAMSC MSC 0.124842404 0.684373297 HypoDMR cg15940592 chr14 103844451 103844452 NA chr6 75794881 75794995 115 * 2 8.24E-36 -13.8342 -0.5293027 -0.5047749 NA CAMSC MSC 0.124488384 0.635024828 HypoDMR cg11353250 chr6 75794995 75794996 NA chr12 3371890 3371908 19 * 2 6.43E-38 -13.6881 -0.6169284 -0.508962 NA CAMSC MSC 0.220720216 0.757678113 HypoDMR cg17517613 chr12 3371908 3371909 NA chr5 53751493 53751919 427 * 2 3.70E-37 -13.1754 -0.6258279 -0.5231693 HSPB3-001