RNA Sequencing of Carboplatin- and Paclitaxel-Resistant Endometrial Cancer Cells Reveals New Stratification Markers and Molecular Targets for Cancer Treatment

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.