RT Journal
A1 Shrestha, Raunak
A1 Hodzic, Ermin
A1 Sauerwald, Thomas
A1 Dao, Phuong
A1 Wang, Kendric
A1 Yeung, Jake
A1 Anderson, Shawn
A1 Vandin, Fabio
A1 Haffari, Gholamreza
A1 Collins, Colin C.
A1 Sahinalp, S. Cenk
T1 HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
JF Genome Research 
JO Genome Research 
YR 2017 
FD September 01 
VO 27 
IS 9 
SP 1573 
OP 1588 
DO 10.1101/gr.221218.117 
UL http://genome.cshlp.org/content/27/9/1573.abstract 
AB 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.