Precise, pan-cancer discovery of gene fusions reveals a signature of selection in primary tumors

Abstract

The extent to which gene fusions function as drivers of cancer remains a critical open question. In principle, transcriptome sequencing provided by The Cancer Genome Atlas (TCGA) enables unbiased discovery of gene fusions and post-analysis that informs the answer to this question. To date, such an analysis has been impossible because of performance limitations in fusion detection algorithms. By engineering a new, more precise statistical approach to analyzing fusions in TCGA data, we report new recurrent gene fusions, including those that could be druggable; new candidate pan-cancer oncogenes based on their profiles in fusions; and prevalent, previously overlooked, candidate oncogenic gene fusions in ovarian cancer, a disease with minimal treatment advances in recent decades. The novel and reproducible statistical algorithms and, more importantly, the biological conclusions open the door for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.