Abstract 282: Inferring transcriptional regulatory programs in gynecological cancers

Abstract

Abstract Cancer cells acquire genetic and epigenetic alterations that often lead to dysregulation of oncogenic signal transduction pathways, which in turn alter downstream transcriptional programs. Transcription factors (TFs) are the main link between signaling pathways and the transcriptional regulatory programs. The Cancer Genome Atlas (TCGA) has studied several of the most common and aggressive gynecologic tumors including high-grade serous ovarian carcinomas (HGSOC), uterine carcinosarcoma (UCS), and the serous-like subset of endometrial cancer (UCEC), together with basal breast cancer, which shares many genomic features with serous ovarian tumors. TFs impacts on gene regulation have not been well characterized in gynecological and basal breast cancers. The majority of these tumors lack accurate predictors of response and resistance and share an unmet need for adequate treatment of recurrent disease. We developed a multitask learning framework for integrating regulatory sequence from ATAC-mapped promoters and enhancers from cell line models with RNA-seq data from patient tumors in order to infer transcription factor (TF) regulatory activities and explore similarities and differences between uterine, ovarian, and basal breast tumors. We showed that our multitask learning framework enables us to selectively share the information across tumors and strongly improves the accuracy of gene expression prediction models for gynecological and basal breast tumors. Our analysis identified histologic type specific and common TF regulators of gene expression as well as predicted distinct dysregulated transcriptional regulators downstream of somatic alterations in these different cancers. Moreover, many of the identified TF regulators were significantly associated with survival outcome within the histological subtype. Computationally dissecting the role of TFs in these cancers may ultimately lead to new therapeutics tailored to groups of subtypes or individuals. Citation Format: Hatice U. Osmanbeyoglu, Petar Jelinic, Douglas Douglas, Christina Leslie. Inferring transcriptional regulatory programs in gynecological cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 282.