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.
Description
Comprehensive Characterization of Cancer Driver Genes and Mutations. - PubMed - NCBI
%0 Journal Article
%1 Bailey:2018:Cell:29625053
%A Bailey, M H
%A Tokheim, C
%A Porta-Pardo, E
%A Sengupta, S
%A Bertrand, D
%A Weerasinghe, A
%A Colaprico, A
%A Wendl, M C
%A Kim, J
%A Reardon, B
%A Ng, P K
%A Jeong, K J
%A Cao, S
%A Wang, Z
%A Gao, J
%A Gao, Q
%A Wang, F
%A Liu, E M
%A Mularoni, L
%A Rubio-Perez, C
%A Nagarajan, N
%A Cortés-Ciriano, I
%A Zhou, D C
%A Liang, W W
%A Hess, J M
%A Yellapantula, V D
%A Tamborero, D
%A Gonzalez-Perez, A
%A Suphavilai, C
%A Ko, J Y
%A Khurana, E
%A Park, P J
%A Van Allen, E M
%A Liang, H
%A MC3 Working Group,
%A Cancer Genome Atlas Research Network,
%A Lawrence, M S
%A Godzik, A
%A Lopez-Bigas, N
%A Stuart, J
%A Wheeler, D
%A Getz, G
%A Chen, K
%A Lazar, A J
%A Mills, G B
%A Karchin, R
%A Ding, L
%D 2018
%J Cell
%K cancer-research drivers fulltext gdc mustread pan-cancer tcga
%N 2
%P 371-385
%R 10.1016/j.cell.2018.02.060
%T Comprehensive Characterization of Cancer Driver Genes and Mutations
%U https://www.ncbi.nlm.nih.gov/pubmed/29625053
%V 173
%X 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.
@article{Bailey:2018:Cell:29625053,
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 a blueprint for future biological and clinical endeavors.},
added-at = {2019-05-04T15:15:24.000+0200},
author = {Bailey, M H and Tokheim, C and Porta-Pardo, E and Sengupta, S and Bertrand, D and Weerasinghe, A and Colaprico, A and Wendl, M C and Kim, J and Reardon, B and Ng, P K and Jeong, K J and Cao, S and Wang, Z and Gao, J and Gao, Q and Wang, F and Liu, E M and Mularoni, L and Rubio-Perez, C and Nagarajan, N and Cort{\'e}s-Ciriano, I and Zhou, D C and Liang, W W and Hess, J M and Yellapantula, V D and Tamborero, D and Gonzalez-Perez, A and Suphavilai, C and Ko, J Y and Khurana, E and Park, P J and Van Allen, E M and Liang, H and {MC3 Working Group} and {Cancer Genome Atlas Research Network} and Lawrence, M S and Godzik, A and Lopez-Bigas, N and Stuart, J and Wheeler, D and Getz, G and Chen, K and Lazar, A J and Mills, G B and Karchin, R and Ding, L},
biburl = {https://www.bibsonomy.org/bibtex/213d9fb0d15150dfad8ff7a683bdf8803/marcsaric},
description = {Comprehensive Characterization of Cancer Driver Genes and Mutations. - PubMed - NCBI},
doi = {10.1016/j.cell.2018.02.060},
interhash = {36ffca27dafef836b2ae21df4a9aac3d},
intrahash = {13d9fb0d15150dfad8ff7a683bdf8803},
journal = {Cell},
keywords = {cancer-research drivers fulltext gdc mustread pan-cancer tcga},
month = {04},
number = 2,
pages = {371-385},
pmid = {29625053},
timestamp = {2019-05-04T15:15:24.000+0200},
title = {Comprehensive Characterization of Cancer Driver Genes and Mutations},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29625053},
volume = 173,
year = 2018
}