Data/hora: 13/06/2024 07:59:58

Provedor de dados: 	17	
País: 	United Kingdom	
Título: 	In silico Genetic Network Models for &amp;#xb;Pre-clinical Drug Prioritization	
Autores: 	Jianghui Xiong; Juan Liu; Simon Rayner; Ze Tian; Yinghui Li; Shanguang Chen.	
Data: 	2010-11-30	
Ano: 	2010	
Palavras-chave: 	Biotechnology; Cancer; Genetics &amp; Genomics; Molecular Cell Biology; Pharmacology; Bioinformatics.	
Resumo: 	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 &amp;#x2018;synergistic outcome determination&amp;#x2019; (SOD), a concept similar to &amp;#x2018;Synthetic Lethality&amp;#x2019;. SOD is defined as the synergy of a gene pair with respect to cancer patients&amp;#x27; 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.&amp;#xd;&amp;#xa;	
Tipo: 	Presentation	
Identificador: 	http://precedings.nature.com/documents/5343/version/1

oai:nature.com:10.1038/npre.2010.5343.1

http://dx.doi.org/10.1038/npre.2010.5343.1	
Fonte: 	Nature Precedings	
Fonte: 	Creative Commons Attribution 3.0 License