Downloaded from genome.cshlp.org on October 3, 2021 - Published by Cold Spring Harbor Laboratory Press HIT’nDRIVE: Patient-Specific Multi-Driver Gene Prioritization for Precision Oncology Raunak Shrestha1,2,∗, Ermin Hodzic3,∗, Thomas Sauerwald4, Phuong Dao5, Kendric Wang2, Jake Yeung2, Shawn Anderson2, Fabio Vandin6, Gholamreza Haffari7, Colin C. Collins2,8, and S. Cenk Sahinalp2,3,9,† 1Bioinformatics Training Program, University of British Columbia, Vancouver, BC, Canada. 2Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, BC, Canada. 3School of Computing Science, Simon Fraser University, Burnaby, BC, Canada. 4Computer Laboratory, University of Cambridge, Cambridge, UK. 5National Center for Biotechnology Information, NLM, NIH, Bethesda, MD, USA. 6Department of Information Engineering, University of Padova, Padova, Italy. 7Faculty of Information Technology, Monash University, Melbourne, Australia. 8Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada. 9School of Informatics and Computing, Indiana University, Bloomington, IN, USA. *Co-first authors †Correspondence:
[email protected] Running Title: Computational prioritization of cancer driver genes Keywords: Driver gene prediction, random walk facility location, multi-hitting time, driver based phenotype inference, driver module, precision oncology Corresponding Author: S. Cenk Sahinalp School of Computing Science, Simon Fraser University Burnaby, BC V5A 1S6, Canada Email:
[email protected] School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA Email:
[email protected] Downloaded from genome.cshlp.org on October 3, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT’nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts.