%0 Journal Article %J Nature %D 2023 %T Author Correction: Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. %A Rheinbay, Esther %A Nielsen, Morten Muhlig %A Abascal, Federico %A Wala, Jeremiah A %A Shapira, Ofer %A Tiao, Grace %A Hornshøj, Henrik %A Hess, Julian M %A Juul, Randi Istrup %A Lin, Ziao %A Feuerbach, Lars %A Sabarinathan, Radhakrishnan %A Madsen, Tobias %A Kim, Jaegil %A Mularoni, Loris %A Shuai, Shimin %A Lanzós, Andrés %A Herrmann, Carl %A Maruvka, Yosef E %A Shen, Ciyue %A Amin, Samirkumar B %A Bandopadhayay, Pratiti %A Bertl, Johanna %A Boroevich, Keith A %A Busanovich, John %A Carlevaro-Fita, Joana %A Chakravarty, Dimple %A Chan, Calvin Wing Yiu %A Craft, David %A Dhingra, Priyanka %A Diamanti, Klev %A Fonseca, Nuno A %A Gonzalez-Perez, Abel %A Guo, Qianyun %A Hamilton, Mark P %A Haradhvala, Nicholas J %A Hong, Chen %A Isaev, Keren %A Johnson, Todd A %A Juul, Malene %A Kahles, Andre %A Kahraman, Abdullah %A Kim, Youngwook %A Komorowski, Jan %A Kumar, Kiran %A Kumar, Sushant %A Lee, Donghoon %A Lehmann, Kjong-Van %A Li, Yilong %A Liu, Eric Minwei %A Lochovsky, Lucas %A Park, Keunchil %A Pich, Oriol %A Roberts, Nicola D %A Saksena, Gordon %A Schumacher, Steven E %A Sidiropoulos, Nikos %A Sieverling, Lina %A Sinnott-Armstrong, Nasa %A Stewart, Chip %A Tamborero, David %A Tubio, Jose M C %A Umer, Husen M %A Uusküla-Reimand, Liis %A Wadelius, Claes %A Wadi, Lina %A Yao, Xiaotong %A Zhang, Cheng-Zhong %A Zhang, Jing %A Haber, James E %A Hobolth, Asger %A Imielinski, Marcin %A Kellis, Manolis %A Lawrence, Michael S %A von Mering, Christian %A Nakagawa, Hidewaki %A Raphael, Benjamin J %A Rubin, Mark A %A Sander, Chris %A Stein, Lincoln D %A Stuart, Joshua M %A Tsunoda, Tatsuhiko %A David A Wheeler %A Johnson, Rory %A Reimand, Jüri %A Gerstein, Mark %A Khurana, Ekta %A Campbell, Peter J %A Lopez-Bigas, Nuria %A Weischenfeldt, Joachim %A Beroukhim, Rameen %A Martincorena, Iñigo %A Pedersen, Jakob Skou %A Getz, Gad %B Nature %V 614 %P E40 %8 2023 Feb %G eng %N 7948 %1 https://www.ncbi.nlm.nih.gov/pubmed/36697832?dopt=Abstract %R 10.1038/s41586-022-05599-9 %0 Journal Article %J Nature %D 2023 %T Author Correction: The repertoire of mutational signatures in human cancer. %A Alexandrov, Ludmil B %A Kim, Jaegil %A Haradhvala, Nicholas J %A Huang, Mi Ni %A Tian Ng, Alvin Wei %A Wu, Yang %A Boot, Arnoud %A Covington, Kyle R %A Gordenin, Dmitry A %A Bergstrom, Erik N %A Islam, S M Ashiqul %A Lopez-Bigas, Nuria %A Klimczak, Leszek J %A McPherson, John R %A Morganella, Sandro %A Sabarinathan, Radhakrishnan %A David A Wheeler %A Mustonen, Ville %A Getz, Gad %A Rozen, Steven G %A Stratton, Michael R %B Nature %V 614 %P E41 %8 2023 Feb %G eng %N 7948 %1 https://www.ncbi.nlm.nih.gov/pubmed/36697836?dopt=Abstract %R 10.1038/s41586-022-05600-5 %0 Journal Article %J Nature %D 2020 %T Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. %A Rheinbay, Esther %A Nielsen, Morten Muhlig %A Abascal, Federico %A Wala, Jeremiah A %A Shapira, Ofer %A Tiao, Grace %A Hornshøj, Henrik %A Hess, Julian M %A Juul, Randi Istrup %A Lin, Ziao %A Feuerbach, Lars %A Sabarinathan, Radhakrishnan %A Madsen, Tobias %A Kim, Jaegil %A Mularoni, Loris %A Shuai, Shimin %A Lanzós, Andrés %A Herrmann, Carl %A Maruvka, Yosef E %A Shen, Ciyue %A Amin, Samirkumar B %A Bandopadhayay, Pratiti %A Bertl, Johanna %A Boroevich, Keith A %A Busanovich, John %A Carlevaro-Fita, Joana %A Chakravarty, Dimple %A Chan, Calvin Wing Yiu %A Craft, David %A Dhingra, Priyanka %A Diamanti, Klev %A Fonseca, Nuno A %A Gonzalez-Perez, Abel %A Guo, Qianyun %A Hamilton, Mark P %A Haradhvala, Nicholas J %A Hong, Chen %A Isaev, Keren %A Johnson, Todd A %A Juul, Malene %A Kahles, Andre %A Kahraman, Abdullah %A Kim, Youngwook %A Komorowski, Jan %A Kumar, Kiran %A Kumar, Sushant %A Lee, Donghoon %A Lehmann, Kjong-Van %A Li, Yilong %A Liu, Eric Minwei %A Lochovsky, Lucas %A Park, Keunchil %A Pich, Oriol %A Roberts, Nicola D %A Saksena, Gordon %A Schumacher, Steven E %A Sidiropoulos, Nikos %A Sieverling, Lina %A Sinnott-Armstrong, Nasa %A Stewart, Chip %A Tamborero, David %A Tubio, Jose M C %A Umer, Husen M %A Uusküla-Reimand, Liis %A Wadelius, Claes %A Wadi, Lina %A Yao, Xiaotong %A Zhang, Cheng-Zhong %A Zhang, Jing %A Haber, James E %A Hobolth, Asger %A Imielinski, Marcin %A Kellis, Manolis %A Lawrence, Michael S %A von Mering, Christian %A Nakagawa, Hidewaki %A Raphael, Benjamin J %A Rubin, Mark A %A Sander, Chris %A Stein, Lincoln D %A Stuart, Joshua M %A Tsunoda, Tatsuhiko %A David A Wheeler %A Johnson, Rory %A Reimand, Jüri %A Gerstein, Mark %A Khurana, Ekta %A Campbell, Peter J %A Lopez-Bigas, Nuria %A Weischenfeldt, Joachim %A Beroukhim, Rameen %A Martincorena, Iñigo %A Pedersen, Jakob Skou %A Getz, Gad %K Databases, Genetic %K DNA Breaks %K Gene Expression Regulation, Neoplastic %K Genome, Human %K Genome-Wide Association Study %K Humans %K INDEL Mutation %K Mutation %K Neoplasms %X

The discovery of drivers of cancer has traditionally focused on protein-coding genes. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

%B Nature %V 578 %P 102-111 %8 2020 Feb %G eng %N 7793 %1 https://www.ncbi.nlm.nih.gov/pubmed/32025015?dopt=Abstract %R 10.1038/s41586-020-1965-x %0 Journal Article %J Nature %D 2020 %T The repertoire of mutational signatures in human cancer. %A Alexandrov, Ludmil B %A Kim, Jaegil %A Haradhvala, Nicholas J %A Huang, Mi Ni %A Tian Ng, Alvin Wei %A Wu, Yang %A Boot, Arnoud %A Covington, Kyle R %A Gordenin, Dmitry A %A Bergstrom, Erik N %A Islam, S M Ashiqul %A Lopez-Bigas, Nuria %A Klimczak, Leszek J %A McPherson, John R %A Morganella, Sandro %A Sabarinathan, Radhakrishnan %A David A Wheeler %A Mustonen, Ville %A Getz, Gad %A Rozen, Steven G %A Stratton, Michael R %K Age Factors %K Base Sequence %K Exome %K Genome, Human %K Humans %K Mutation %K Neoplasms %K Sequence Analysis, DNA %X

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

%B Nature %V 578 %P 94-101 %8 2020 Feb %G eng %N 7793 %1 https://www.ncbi.nlm.nih.gov/pubmed/32025018?dopt=Abstract %R 10.1038/s41586-020-1943-3 %0 Journal Article %J Cell %D 2018 %T Comprehensive Characterization of Cancer Driver Genes and Mutations. %A Bailey, Matthew H %A Tokheim, Collin %A Porta-Pardo, Eduard %A Sengupta, Sohini %A Bertrand, Denis %A Weerasinghe, Amila %A Colaprico, Antonio %A Wendl, Michael C %A Kim, Jaegil %A Reardon, Brendan %A Ng, Patrick Kwok-Shing %A Jeong, Kang Jin %A Cao, Song %A Wang, Zixing %A Gao, Jianjiong %A Gao, Qingsong %A Wang, Fang %A Liu, Eric Minwei %A Mularoni, Loris %A Rubio-Perez, Carlota %A Nagarajan, Niranjan %A Cortés-Ciriano, Isidro %A Zhou, Daniel Cui %A Liang, Wen-Wei %A Hess, Julian M %A Yellapantula, Venkata D %A Tamborero, David %A Gonzalez-Perez, Abel %A Suphavilai, Chayaporn %A Ko, Jia Yu %A Khurana, Ekta %A Park, Peter J %A Van Allen, Eliezer M %A Liang, Han %A Lawrence, Michael S %A Godzik, Adam %A Lopez-Bigas, Nuria %A Stuart, Josh %A Wheeler, David %A Getz, Gad %A Chen, Ken %A Lazar, Alexander J %A Mills, Gordon B %A Karchin, Rachel %A Ding, Li %K Algorithms %K B7-H1 Antigen %K Computational Biology %K Databases, Genetic %K Entropy %K Humans %K Microsatellite Instability %K Mutation %K Neoplasms %K Principal Component Analysis %K Programmed Cell Death 1 Receptor %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.

%B Cell %V 173 %P 371-385.e18 %8 2018 Apr 05 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/29625053?dopt=Abstract %R 10.1016/j.cell.2018.02.060 %0 Journal Article %J Cell %D 2018 %T Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. %A Ding, Li %A Bailey, Matthew H %A Porta-Pardo, Eduard %A Thorsson, Vesteinn %A Colaprico, Antonio %A Bertrand, Denis %A Gibbs, David L %A Weerasinghe, Amila %A Huang, Kuan-Lin %A Tokheim, Collin %A Cortés-Ciriano, Isidro %A Jayasinghe, Reyka %A Chen, Feng %A Yu, Lihua %A Sun, Sam %A Olsen, Catharina %A Kim, Jaegil %A Taylor, Alison M %A Cherniack, Andrew D %A Akbani, Rehan %A Suphavilai, Chayaporn %A Nagarajan, Niranjan %A Stuart, Joshua M %A Mills, Gordon B %A Wyczalkowski, Matthew A %A Vincent, Benjamin G %A Hutter, Carolyn M %A Zenklusen, Jean Claude %A Hoadley, Katherine A %A Wendl, Michael C %A Shmulevich, Llya %A Lazar, Alexander J %A Wheeler, David A %A Getz, Gad %K Carcinogenesis %K Databases, Genetic %K DNA Repair %K Genes, Neoplasm %K Genomics %K Humans %K Metabolic Networks and Pathways %K Microsatellite Instability %K Mutation %K Neoplasms %K Transcriptome %K Tumor Microenvironment %X

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

%B Cell %V 173 %P 305-320.e10 %8 2018 Apr 05 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/29625049?dopt=Abstract %R 10.1016/j.cell.2018.03.033