Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. We developed a pipeline to uncover patterns of alternative polyadenylation (APA), a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we found a significant shift in usage of poly(A) signals in six common tumor types compared to normal tissues. We further defined specific subsets of APA events to efficiently classify cancer types/subtypes. Triple negative breast cancers, for example, have specific 3'UTR length alterations where the significant majority are shortening events (70%, 113 of 165) of mostly proliferation-related transcripts compared with normal breast tissue. Such shortening events correlate with increased protein levels and relapse free survival of patients, suggesting functional significance of isoform variability. In line with this isoform diversity, we also detected deregulated expression of mRNA polyadenylation complex proteins in breast cancer cells. Of note, APA proteins are responsive to proliferative signals including estrogen and epidermal growth factor, suggesting a potential explanation to 3'-end isoform diversity in cancer cells. Overall, our study offers a computational and experimental approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

Citation Format: Oguzhan Begik, Melda Ercan, Harun Cingoz, Tolga Can, Merve Oyken, Ayse Elif Erson-Bensan. Deregulated APA and cancer specific APA isoforms [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 2360.