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Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplification...

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Autores principales: Chen, Ying, McGee, Jeremy, Chen, Xianming, Doman, Thompson N., Gong, Xueqian, Zhang, Youyan, Hamm, Nicole, Ma, Xiwen, Higgs, Richard E., Bhagwat, Shripad V., Buchanan, Sean, Peng, Sheng-Bin, Staschke, Kirk A., Yadav, Vipin, Yue, Yong, Kouros-Mehr, Hosein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038530/
https://www.ncbi.nlm.nih.gov/pubmed/24874471
http://dx.doi.org/10.1371/journal.pone.0098293
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author Chen, Ying
McGee, Jeremy
Chen, Xianming
Doman, Thompson N.
Gong, Xueqian
Zhang, Youyan
Hamm, Nicole
Ma, Xiwen
Higgs, Richard E.
Bhagwat, Shripad V.
Buchanan, Sean
Peng, Sheng-Bin
Staschke, Kirk A.
Yadav, Vipin
Yue, Yong
Kouros-Mehr, Hosein
author_facet Chen, Ying
McGee, Jeremy
Chen, Xianming
Doman, Thompson N.
Gong, Xueqian
Zhang, Youyan
Hamm, Nicole
Ma, Xiwen
Higgs, Richard E.
Bhagwat, Shripad V.
Buchanan, Sean
Peng, Sheng-Bin
Staschke, Kirk A.
Yadav, Vipin
Yue, Yong
Kouros-Mehr, Hosein
author_sort Chen, Ying
collection PubMed
description The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets. We carried out GISTIC2 analysis of TCGA datasets spanning 14 cancer subtypes and identified 461 genes that were amplified in two or more datasets. The list was narrowed to 73 cancer-associated genes with potential “druggable” properties. The majority of the genes were localized to 14 amplicons spread across the genome. To identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 40 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets. The amplified genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 40 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapter GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes. We discuss the patient tailoring implications for existing cancer drug targets and we further discuss potential novel opportunities for drug discovery efforts.
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spelling pubmed-40385302014-06-05 Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets Chen, Ying McGee, Jeremy Chen, Xianming Doman, Thompson N. Gong, Xueqian Zhang, Youyan Hamm, Nicole Ma, Xiwen Higgs, Richard E. Bhagwat, Shripad V. Buchanan, Sean Peng, Sheng-Bin Staschke, Kirk A. Yadav, Vipin Yue, Yong Kouros-Mehr, Hosein PLoS One Research Article The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets. We carried out GISTIC2 analysis of TCGA datasets spanning 14 cancer subtypes and identified 461 genes that were amplified in two or more datasets. The list was narrowed to 73 cancer-associated genes with potential “druggable” properties. The majority of the genes were localized to 14 amplicons spread across the genome. To identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 40 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets. The amplified genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 40 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapter GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes. We discuss the patient tailoring implications for existing cancer drug targets and we further discuss potential novel opportunities for drug discovery efforts. Public Library of Science 2014-05-29 /pmc/articles/PMC4038530/ /pubmed/24874471 http://dx.doi.org/10.1371/journal.pone.0098293 Text en © 2014 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Chen, Ying
McGee, Jeremy
Chen, Xianming
Doman, Thompson N.
Gong, Xueqian
Zhang, Youyan
Hamm, Nicole
Ma, Xiwen
Higgs, Richard E.
Bhagwat, Shripad V.
Buchanan, Sean
Peng, Sheng-Bin
Staschke, Kirk A.
Yadav, Vipin
Yue, Yong
Kouros-Mehr, Hosein
Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets
title Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets
title_full Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets
title_fullStr Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets
title_full_unstemmed Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets
title_short Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets
title_sort identification of druggable cancer driver genes amplified across tcga datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038530/
https://www.ncbi.nlm.nih.gov/pubmed/24874471
http://dx.doi.org/10.1371/journal.pone.0098293
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