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Comprehensive Characterization of Cancer Driver Genes and Mutations

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising... Full description

Journal Title: Cell 05 April 2018, Vol.173(2), pp.371-385.e18
Main Author: Bailey, Matthew H
Other Authors: Tokheim, Collin , Porta-Pardo, Eduard , Sengupta, Sohini , Bertrand, Denis , Weerasinghe, Amila , Colaprico, Antonio , Wendl, Michael C , Kim, Jaegil , Reardon, Brendan , Ng, Patrick Kwok-Shing , Jeong, Kang Jin , Cao, Song , Wang, Zixing , Gao, Jianjiong , Gao, Qingsong , Wang, Fang , Liu, Eric Minwei , Mularoni, Loris , Rubio-Perez, Carlota
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0092-8674 ; E-ISSN: 1097-4172 ; DOI: 10.1016/j.cell.2018.02.060
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recordid: elsevier_sdoi_10_1016_j_cell_2018_02_060
title: Comprehensive Characterization of Cancer Driver Genes and Mutations
format: Article
creator:
  • Bailey, Matthew H
  • Tokheim, Collin
  • Porta-Pardo, Eduard
  • Sengupta, Sohini
  • Bertrand, Denis
  • Weerasinghe, Amila
  • Colaprico, Antonio
  • Wendl, Michael C
  • Kim, Jaegil
  • Reardon, Brendan
  • Ng, Patrick Kwok-Shing
  • Jeong, Kang Jin
  • Cao, Song
  • Wang, Zixing
  • Gao, Jianjiong
  • Gao, Qingsong
  • Wang, Fang
  • Liu, Eric Minwei
  • Mularoni, Loris
  • Rubio-Perez, Carlota
subjects:
  • Oncology
  • Driver Gene Discovery
  • Structure Analysis
  • Mutations of Clinical Relevance
  • Biology
ispartof: Cell, 05 April 2018, Vol.173(2), pp.371-385.e18
description: Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as...
language: eng
source:
identifier: ISSN: 0092-8674 ; E-ISSN: 1097-4172 ; DOI: 10.1016/j.cell.2018.02.060
fulltext: fulltext
issn:
  • 0092-8674
  • 00928674
  • 1097-4172
  • 10974172
url: Link


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titleComprehensive Characterization of Cancer Driver Genes and Mutations
creatorBailey, Matthew H ; Tokheim, Collin ; Porta-Pardo, Eduard ; Sengupta, Sohini ; Bertrand, Denis ; Weerasinghe, Amila ; Colaprico, Antonio ; Wendl, Michael C ; Kim, Jaegil ; Reardon, Brendan ; Ng, Patrick Kwok-Shing ; Jeong, Kang Jin ; Cao, Song ; Wang, Zixing ; Gao, Jianjiong ; Gao, Qingsong ; Wang, Fang ; Liu, Eric Minwei ; Mularoni, Loris ; Rubio-Perez, Carlota
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subjectOncology ; Driver Gene Discovery ; Structure Analysis ; Mutations of Clinical Relevance ; Biology
descriptionIdentifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as...
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Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as...

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abstract

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as...

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doi10.1016/j.cell.2018.02.060
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date2018-04-05