Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks asso...

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Main Authors: Jianghui Xiong, Juan Liu, Simon Rayner, Ze Tian, Yinghui Li, Shanguang Chen
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2010-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC2978107?pdf=render
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spelling doaj-ed51adb8dcd34a97913430d9ee67b6cd2020-11-25T00:12:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032010-01-01511e1393710.1371/journal.pone.0013937Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.Jianghui XiongJuan LiuSimon RaynerZe TianYinghui LiShanguang ChenThe high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call 'synergistic outcome determination' (SOD), a concept similar to 'Synthetic Lethality'. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.http://europepmc.org/articles/PMC2978107?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Jianghui Xiong
Juan Liu
Simon Rayner
Ze Tian
Yinghui Li
Shanguang Chen
spellingShingle Jianghui Xiong
Juan Liu
Simon Rayner
Ze Tian
Yinghui Li
Shanguang Chen
Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.
PLoS ONE
author_facet Jianghui Xiong
Juan Liu
Simon Rayner
Ze Tian
Yinghui Li
Shanguang Chen
author_sort Jianghui Xiong
title Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.
title_short Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.
title_full Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.
title_fullStr Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.
title_full_unstemmed Pre-clinical drug prioritization via prognosis-guided genetic interaction networks.
title_sort pre-clinical drug prioritization via prognosis-guided genetic interaction networks.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2010-01-01
description The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call 'synergistic outcome determination' (SOD), a concept similar to 'Synthetic Lethality'. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.
url http://europepmc.org/articles/PMC2978107?pdf=render
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