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HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

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 suff...

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Autores principales: Shrestha, Raunak, Hodzic, Ermin, Sauerwald, Thomas, Dao, Phuong, Wang, Kendric, Yeung, Jake, Anderson, Shawn, Vandin, Fabio, Haffari, Gholamreza, Collins, Colin C., Sahinalp, S. Cenk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580716/
https://www.ncbi.nlm.nih.gov/pubmed/28768687
http://dx.doi.org/10.1101/gr.221218.117
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author Shrestha, Raunak
Hodzic, Ermin
Sauerwald, Thomas
Dao, Phuong
Wang, Kendric
Yeung, Jake
Anderson, Shawn
Vandin, Fabio
Haffari, Gholamreza
Collins, Colin C.
Sahinalp, S. Cenk
author_facet Shrestha, Raunak
Hodzic, Ermin
Sauerwald, Thomas
Dao, Phuong
Wang, Kendric
Yeung, Jake
Anderson, Shawn
Vandin, Fabio
Haffari, Gholamreza
Collins, Colin C.
Sahinalp, S. Cenk
author_sort Shrestha, Raunak
collection PubMed
description 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.
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spelling pubmed-55807162018-03-01 HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology Shrestha, Raunak Hodzic, Ermin Sauerwald, Thomas Dao, Phuong Wang, Kendric Yeung, Jake Anderson, Shawn Vandin, Fabio Haffari, Gholamreza Collins, Colin C. Sahinalp, S. Cenk Genome Res Method 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. Cold Spring Harbor Laboratory Press 2017-09 /pmc/articles/PMC5580716/ /pubmed/28768687 http://dx.doi.org/10.1101/gr.221218.117 Text en © 2017 Shrestha et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ 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/.
spellingShingle Method
Shrestha, Raunak
Hodzic, Ermin
Sauerwald, Thomas
Dao, Phuong
Wang, Kendric
Yeung, Jake
Anderson, Shawn
Vandin, Fabio
Haffari, Gholamreza
Collins, Colin C.
Sahinalp, S. Cenk
HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
title HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
title_full HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
title_fullStr HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
title_full_unstemmed HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
title_short HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
title_sort hit'ndrive: patient-specific multidriver gene prioritization for precision oncology
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580716/
https://www.ncbi.nlm.nih.gov/pubmed/28768687
http://dx.doi.org/10.1101/gr.221218.117
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