Objectives
The purpose of this study was to determine the level of heterogeneity in high grade serous ovarian cancer (HGSOC) by analyzing RNA expression in single epithelial and cancer associated stromal cells. In addition, we explored the possibility of identifying subgroups based on pathway activation and pre-defined signatures from cancer stem cells and chemo-resistant cells.
Methods
A fresh, HGSOC tumor specimen derived from ovary was enzymatically digested and depleted of immune infiltrating cells. RNA sequencing was performed on 92 single cells and 66 of these single cell datasets passed quality control checks. Sequences were analyzed using multiple bioinformatics tools, including clustering, principle components analysis, and geneset enrichment analysis to identify subgroups and activated pathways. Immunohistochemistry for ovarian cancer, stem cell and stromal markers was performed on adjacent tumor sections.
Results
Analysis of the gene expression patterns identified two major subsets of cells characterized by epithelial and stromal gene expression patterns. The epithelial group was characterized by proliferative genes including genes associated with oxidative phosphorylation and MYC activity, while the stromal group was characterized by increased expression of extracellular matrix (ECM) genes and genes associated with epithelial-to-mesenchymal transition (EMT). Neither group expressed a signature correlating with published chemo-resistant gene signatures, but many cells, predominantly in the stromal subgroup, expressed markers associated with cancer stem cells.
Conclusions
Single cell sequencing provides a means of identifying subpopulations of cancer cells within a single patient. Single cell sequence analysis may prove to be critical for understanding the etiology, progression and drug resistance in ovarian cancer.
UNC-45A is a novel regulator of neuronal differentiation. UNC-45A localizes at the growth cone, binds to NMIIA and NMIIB, and is disposable for neuronal survival but is required for neurite initiation and extension via regulating NMII activation. Thus UNC-45A is a potential master regulator of a number of NMII-mediated cellular processes.
Despite advances in surgical technique and adjuvant treatment, endometrial cancer has recently seen an increase in incidence and mortality in the USA. The majority of endometrial cancers can be cured by surgery alone or in combination with adjuvant chemo- or radiotherapy; however, a subset of patients experience recurrence for reasons that remain unclear. Recurrence is associated with chemoresistance to carboplatin and paclitaxel and consequentially, high mortality. Understanding the pathways involved in endometrial cancer chemoresistance is paramount for the identification of biomarkers and novel molecular targets for this disease. Here, we generated the first matched pairs of carboplatin-sensitive/carboplatin-resistant and paclitaxel-sensitive/paclitaxel-resistant endometrial cancer cells and subjected them to bulk RNA sequencing analysis. We found that 45 genes are commonly upregulated in carboplatin- and paclitaxel-resistant cells as compared to controls. Of these, the leukemia inhibitory factor, (LIF), the protein tyrosine phosphatase type IVA, member 3 (PTP4A3), and the transforming growth factor beta 1 (TGFB1) showed a highly significant correlation between expression level and endometrial cancer overall survival (OS) and can stratify the 545 endometrial cancer patients in the TCGA cohort into a high-risk and low-risk-cohorts. Additionally, four genes within the 45 upregulated chemoresistance-associated genes are ADAMTS5, MICAL2, STAT5A, and PTP4A3 codes for proteins for which small-molecule inhibitors already exist. We identified these proteins as molecular targets for chemoresistant endometrial cancer and showed that treatment with their correspondent inhibitors effectively killed otherwise chemoresistant cells. Collectively, these findings underline the utility of matched pair of chemosensitive and chemoresistant cancer cells to identify markers for endometrial cancer risk stratification and to serve as a pharmacogenomics model for identification of alternative chemotherapy approaches for treatment of patients with recurrent disease.
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