Alternative Polyadenylation patterns for novel gene discovery and classification in cancer

2017-06-03
Beğik, Oğuzhan
Öyken, Merve
Can, Tolga
Erson Bensan, Ayşe Elif
Certain aspects of diagnosis, prognosis and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose 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 analyzed expression data for 1,045 cancer patients and found a significant shift in usage of poly(A) signals in cancers. Using machine-learning techniques, we further defined subsets of APA events to classify cancer types. Furthermore, detected APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data. Overall, our study offers a computational approach for the use of APA in novel gene discovery and classification in cancers, with important implications in basic research, biomarker discovery and precision medicine approaches.