bioRxiv preprint doi: https://doi.org/10.1101/178061; this version posted August 18, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Precise, pan-cancer discovery of gene fusions reveals a signature of selection in primary 2 tumors 3 4 Donald Eric Freeman1,3,Gillian Lee Hsieh1, Jonathan Michael Howard1, Erik Lehnert2, Julia 5 Salzman1,3,4* 6 7 Author affiliation 8 1Stanford University Department of Biochemistry, 279 Campus Drive, Stanford, CA 94305 9 2Seven Bridges Genomics, 1 Main Street, Suite 500, Cambridge MA 02142 10 3Stanford University Department of Biomedical Data Science, Stanford, CA 94305-5456 11 4Stanford Cancer Institute, Stanford, CA 94305 12 13 *Corresponding author
[email protected] 14 15 Short Abstract: 16 The eXtent to which gene fusions function as drivers of cancer remains a critical open question 17 in cancer biology. In principle, transcriptome sequencing provided by The Cancer Genome 18 Atlas (TCGA) enables unbiased discovery of gene fusions and post-analysis that informs the 19 answer to this question. To date, such an analysis has been impossible because of 20 performance limitations in fusion detection algorithms. By engineering a new, more precise, 21 algorithm and statistical approaches to post-analysis of fusions called in TCGA data, we report 22 new recurrent gene fusions, including those that could be druggable; new candidate pan-cancer 23 oncogenes based on their profiles in fusions; and prevalent, previously overlooked, candidate 24 oncogenic gene fusions in ovarian cancer, a disease with minimal treatment advances in recent 25 decades.