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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Public Library of Science (PLoS)
2014
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oai:doaj.org-article:a29e5a4806f34d02a21d59875c6c8e592021-11-18T08:17:54ZIdentification of druggable cancer driver genes amplified across TCGA datasets.1932-620310.1371/journal.pone.0098293https://doaj.org/article/a29e5a4806f34d02a21d59875c6c8e592014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24874471/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The 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 16 cancer subtypes and identified 486 genes that were amplified in two or more datasets. The list was narrowed to 75 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 42 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 42 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 adapters GRB2 and 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.Ying ChenJeremy McGeeXianming ChenThompson N DomanXueqian GongYouyan ZhangNicole HammXiwen MaRichard E HiggsShripad V BhagwatSean BuchananSheng-Bin PengKirk A StaschkeVipin YadavYong YueHosein Kouros-MehrPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 5, p e98293 (2014) |
| institution |
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Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Ying Chen Jeremy McGee Xianming Chen Thompson N Doman Xueqian Gong Youyan Zhang Nicole Hamm Xiwen Ma Richard E Higgs Shripad V Bhagwat Sean Buchanan Sheng-Bin Peng Kirk A Staschke Vipin Yadav Yong Yue Hosein Kouros-Mehr Identification of druggable cancer driver genes amplified across TCGA datasets. |
| 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 16 cancer subtypes and identified 486 genes that were amplified in two or more datasets. The list was narrowed to 75 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 42 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 42 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 adapters GRB2 and 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. |
| format |
article |
| author |
Ying Chen Jeremy McGee Xianming Chen Thompson N Doman Xueqian Gong Youyan Zhang Nicole Hamm Xiwen Ma Richard E Higgs Shripad V Bhagwat Sean Buchanan Sheng-Bin Peng Kirk A Staschke Vipin Yadav Yong Yue Hosein Kouros-Mehr |
| author_facet |
Ying Chen Jeremy McGee Xianming Chen Thompson N Doman Xueqian Gong Youyan Zhang Nicole Hamm Xiwen Ma Richard E Higgs Shripad V Bhagwat Sean Buchanan Sheng-Bin Peng Kirk A Staschke Vipin Yadav Yong Yue Hosein Kouros-Mehr |
| author_sort |
Ying Chen |
| title |
Identification of druggable cancer driver genes amplified across TCGA datasets. |
| title_short |
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_sort |
identification of druggable cancer driver genes amplified across tcga datasets. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2014 |
| url |
https://doaj.org/article/a29e5a4806f34d02a21d59875c6c8e59 |
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