Ovarian Carcinoma-Associated Mesenchymal Stem Cells Arise from Tissue-Specific Normal Stroma

Abstract

Carcinoma‐associated mesenchymal stem cells (CA‐MSCs) are critical stromal progenitor cells within the tumor microenvironment (TME). We previously demonstrated that CA‐MSCs differentially express bone morphogenetic protein family members, promote tumor cell growth, increase cancer “stemness,” and chemotherapy resistance. Here, we use RNA sequencing of normal omental MSCs and ovarian CA‐MSCs to demonstrate global changes in CA‐MSC gene expression. Using these expression profiles, we create a unique predictive algorithm to classify CA‐MSCs. Our classifier accurately distinguishes normal omental, ovary, and bone marrow MSCs from ovarian cancer CA‐MSCs. Suggesting broad applicability, the model correctly classifies pancreatic and endometrial cancer CA‐MSCs and distinguishes cancer associated fibroblasts from CA‐MSCs. Using this classifier, we definitively demonstrate ovarian CA‐MSCs arise from tumor mediated reprograming of local tissue MSCs. Although cancer cells alone cannot induce a CA‐MSC phenotype, the in vivo ovarian TME can reprogram omental or ovary MSCs to protumorigenic CA‐MSCs (classifier score of >0.96). In vitro studies suggest that both tumor secreted factors and hypoxia are critical to induce the CA‐MSC phenotype. Interestingly, although the breast cancer TME can reprogram bone marrow MSCs into CA‐MSCs, the ovarian TME cannot, demonstrating for the first time that tumor mediated CA‐MSC conversion is tissue and cancer type dependent. Together these findings (a) provide a critical tool to define CA‐MSCs and (b) highlight cancer cell influence on distinct normal tissues providing powerful insights into the mechanisms underlying cancer specific metastatic niche formation. S tem C ells 2019;37:257–269

Epigenetic reprogramming towards mesenchymal-epithelial transition in ovarian cancer-associated mesenchymal stem cells drives metastasis

“…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.…”

“…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.…”

“…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).…”

“…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).…”

“…Overall, sites with reduced peak strength in CA-MSCs tended to have lower gene expression (Fig3C; R 2 =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.…”