Date

2018-07-01

Author

Begik, Oguzhan
Ercan, Melda
Cingoz, Harun
Can, Tolga
ÖYKEN, MERVE
Erson Bensan, Ayşe Elif

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

URI

https://hdl.handle.net/11511/41615

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Department of Computer Engineering, Conference / Seminar