bioRxiv preprint doi: https://doi.org/10.1101/659078; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers Roozbeh Dehghannasiri1, Donald Eric Freeman1,2, Milos Jordanski3, Gillian L. Hsieh1, Ana Damljanovic4, Erik Lehnert4, Julia Salzman1,2,5* Author affiliation 1Department of Biochemistry, Stanford University, Stanford, CA 94305 2Department of Biomedical Data Science, Stanford University, Stanford, CA 94305 3Department of Computer Science, University of Belgrade, Belgrade, Serbia 4Seven Bridges Genomics, Cambridge, MA 02142 5Stanford Cancer Institute, Stanford, CA 94305 *Corresponding author
[email protected] Short Abstract: The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce a new algorithm, DEEPEST, that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling ten-fold fewer false-positive fusions in non-transformed human tissues. We leverage the increased precision of DEEPEST to discover new cancer biology. For example, 888 new candidate oncogenes are identified based on over-representation in DEEPEST-Fusion calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs partners, demonstrating a previously unappreciated prevalence and potential for function.