HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
- Raunak Shrestha1,2,10,
- Ermin Hodzic3,10,
- 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, British Columbia, Canada V6T 1Z4;
- 2Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6;
- 3School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6;
- 4Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, United Kingdom;
- 5National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA;
- 6Department of Information Engineering, University of Padova, 35131 Padova, Italy;
- 7Faculty of Information Technology, Monash University, Melbourne 3800, Australia;
- 8Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada V5Z 1M9;
- 9School of Informatics and Computing, Indiana University, Bloomington, Indiana 47408, USA
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↵10 These authors are co-first authors and contributed equally to this work.
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. HIT'nDRIVE aims to solve the “random walk facility location” (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: “multihitting time,” the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients’ survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.221218.117.
- Received January 31, 2017.
- Accepted July 6, 2017.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
