Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

Abstract

Short AbstractThe 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. Specific protein domains are enriched in DEEPEST calls, demonstrating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. DEEPEST also reveals a high enrichment for fusions involving known and novel oncogenes in diseases including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.\n\nSignificanceGene fusions are tumor-specific genomic aberrations and are among the most powerful biomarkers and drug targets in translational cancer biology. The advent of RNA-Seq technologies over the past decade has provided a unique opportunity for detecting novel fusions via deploying computational algorithms on public sequencing databases. Yet, precise fusion detection algorithms are still out of reach. We develop DEEPEST, a highly specific and efficient statistical pipeline specially designed for mining massive sequencing databases, and apply it to all 33 tumor types and 10,500 samples in The Cancer Genome Atlas database. We systematically profile the landscape of detected fusions via employing classic statistical models and identify several signatures of selection for fusions in tumors.\n\nSoftware availabilityDEEPEST-Fusion workflow with a detailed readme file is available as a Github repository: https://github.com/salzmanlab/DEEPEST-Fusion. In addition to the main workflow, which is based on CWL, example input and batch scripts (for job submission on local clusters), and codes for building the SBT files and SBT querying are provided in the repository. All custom scripts used for systematic analysis of fusions are also available in the same repository.