%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 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 Nat Commun %D 2015 %T A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing. %A Alioto, Tyler S %A Buchhalter, Ivo %A Derdak, Sophia %A Hutter, Barbara %A Eldridge, Matthew D %A Hovig, Eivind %A Heisler, Lawrence E %A Beck, Timothy A %A Simpson, Jared T %A Tonon, Laurie %A Sertier, Anne-Sophie %A Patch, Ann-Marie %A Jäger, Natalie %A Ginsbach, Philip %A Drews, Ruben %A Paramasivam, Nagarajan %A Kabbe, Rolf %A Chotewutmontri, Sasithorn %A Diessl, Nicolle %A Previti, Christopher %A Schmidt, Sabine %A Brors, Benedikt %A Feuerbach, Lars %A Heinold, Michael %A Gröbner, Susanne %A Korshunov, Andrey %A Tarpey, Patrick S %A Butler, Adam P %A Hinton, Jonathan %A Jones, David %A Menzies, Andrew %A Raine, Keiran %A Shepherd, Rebecca %A Stebbings, Lucy %A Teague, Jon W %A Ribeca, Paolo %A Giner, Francesc Castro %A Beltran, Sergi %A Raineri, Emanuele %A Dabad, Marc %A Heath, Simon C %A Gut, Marta %A Denroche, Robert E %A Harding, Nicholas J %A Yamaguchi, Takafumi N %A Fujimoto, Akihiro %A Nakagawa, Hidewaki %A Quesada, Victor %A Valdés-Mas, Rafael %A Nakken, Sigve %A Vodák, Daniel %A Bower, Lawrence %A Lynch, Andrew G %A Anderson, Charlotte L %A Waddell, Nicola %A Pearson, John V %A Grimmond, Sean M %A Peto, Myron %A Spellman, Paul %A He, Minghui %A Kandoth, Cyriac %A Lee, Semin %A Zhang, John %A Létourneau, Louis %A Ma, Singer %A Seth, Sahil %A Torrents, David %A Xi, Liu %A Wheeler, David A %A López-Otín, Carlos %A Campo, Elías %A Campbell, Peter J %A Boutros, Paul C %A Puente, Xose S %A Gerhard, Daniela S %A Pfister, Stefan M %A McPherson, John D %A Hudson, Thomas J %A Schlesner, Matthias %A Lichter, Peter %A Eils, Roland %A Jones, David T W %A Gut, Ivo G %K Genome, Human %K High-Throughput Nucleotide Sequencing %K Humans %K Leukemia, Lymphoid %K Medulloblastoma %K Mutation %X

As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼ 100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.

%B Nat Commun %V 6 %P 10001 %8 2015 Dec 09 %G eng %1 https://www.ncbi.nlm.nih.gov/pubmed/26647970?dopt=Abstract %R 10.1038/ncomms10001