TY - JOUR
T1 - Comprehensive Characterization of Cancer Driver Genes and Mutations
AU - The MC3 Working Group
AU - The Cancer Genome Atlas Research Network
AU - Bailey, Matthew H.
AU - Tokheim, Collin
AU - Porta-Pardo, Eduard
AU - Sengupta, Sohini
AU - Bertrand, Denis
AU - Weerasinghe, Amila
AU - Colaprico, Antonio
AU - Wendl, Michael C.
AU - Kim, Jaegil
AU - Reardon, Brendan
AU - Ng, Patrick Kwok Shing
AU - Jeong, Kang Jin
AU - Cao, Song
AU - Wang, Zixing
AU - Gao, Jianjiong
AU - Gao, Qingsong
AU - Wang, Fang
AU - Liu, Eric Minwei
AU - Mularoni, Loris
AU - Rubio-Perez, Carlota
AU - Nagarajan, Niranjan
AU - Cortés-Ciriano, Isidro
AU - Zhou, Daniel Cui
AU - Liang, Wen Wei
AU - Hess, Julian M.
AU - Yellapantula, Venkata D.
AU - Tamborero, David
AU - Gonzalez-Perez, Abel
AU - Suphavilai, Chayaporn
AU - Ko, Jia Yu
AU - Khurana, Ekta
AU - Park, Peter J.
AU - Van Allen, Eliezer M.
AU - Liang, Han
AU - Caesar-Johnson, Samantha J.
AU - Demchok, John A.
AU - Felau, Ina
AU - Kasapi, Melpomeni
AU - Ferguson, Martin L.
AU - Hutter, Carolyn M.
AU - Sofia, Heidi J.
AU - Tarnuzzer, Roy
AU - Yang, Liming
AU - Zenklusen, Jean C.
AU - Zhang, Jiashan (Julia)
AU - Baylin, Stephen
AU - Cope, Leslie
AU - Danilova, Ludmila
AU - Torbenson, Michael
AU - Karchin, Rachel
N1 - Publisher Copyright:
© 2018
PY - 2018/4/5
Y1 - 2018/4/5
N2 - 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 a blueprint for future biological and clinical endeavors. A comprehensive analysis of oncogenic driver genes and mutations in >9,000 tumors across 33 cancer types highlights the prevalence of clinically actionable cancer driver events in TCGA tumor samples.
AB - 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 a blueprint for future biological and clinical endeavors. A comprehensive analysis of oncogenic driver genes and mutations in >9,000 tumors across 33 cancer types highlights the prevalence of clinically actionable cancer driver events in TCGA tumor samples.
KW - driver gene discovery
KW - mutations of clinical relevance
KW - oncology
KW - structure analysis
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U2 - 10.1016/j.cell.2018.02.060
DO - 10.1016/j.cell.2018.02.060
M3 - Article
C2 - 29625053
AN - SCOPUS:85044623848
SN - 0092-8674
VL - 173
SP - 371-385.e18
JO - Cell
JF - Cell
IS - 2
ER -