Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

Nature

Authors	Esther Rheinbay Morten Nielsen Federico Abascal Jeremiah Wala Ofer Shapira Grace Tiao Henrik Hornshøj Julian Hess Randi Juul Ziao Lin Lars Feuerbach Radhakrishnan Sabarinathan Tobias Madsen Jaegil Kim Loris Mularoni Shimin Shuai Andrés Lanzós Carl Herrmann Yosef Maruvka Ciyue Shen Samirkumar Amin Pratiti Bandopadhayay Johanna Bertl Keith Boroevich John Busanovich Joana Carlevaro-Fita Dimple Chakravarty Calvin Chan David Craft Priyanka Dhingra Klev Diamanti Nuno Fonseca Abel Gonzalez-Perez Qianyun Guo Mark Hamilton Nicholas Haradhvala Chen Hong Keren Isaev Todd Johnson Malene Juul Andre Kahles Abdullah Kahraman Youngwook Kim Jan Komorowski Kiran Kumar Sushant Kumar Donghoon Lee Kjong- Van Lehmann Yilong Li Eric Liu Lucas Lochovsky Keunchil Park Oriol Pich Nicola Roberts Gordon Saksena Steven Schumacher Nikos Sidiropoulos Lina Sieverling Nasa Sinnott-Armstrong Chip Stewart David Tamborero José Tubio Husen Umer Liis Uusküla-Reimand Claes Wadelius Lina Wadi Xiaotong Yao Cheng-Zhong Zhang Jing Zhang James Haber Asger Hobolth Marcin Imielinski Manolis Kellis Michael Lawrence Christian von Mering Hidewaki Nakagawa Benjamin Raphael Mark Rubin Chris Sander Lincoln Stein Joshua Stuart Tatsuhiko Tsunoda David Wheeler Rory Johnson Jüri Reimand Mark Gerstein Ekta Khurana Peter Campbell Nuria López-Bigas PCAWG Drivers and Functional Interpretation Working Group PCAWG Structural Variation Working Group Joachim Weischenfeldt Rameen Beroukhim Iñigo Martincorena Jakob Pedersen Gad Getz PCAWG Consortium
Abstract	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.
Year of Publication	2020
Journal	Nature
Volume	578
Issue	7793
Pages	102-111
Date Published	2020 02
ISSN	1476-4687
DOI	10.1038/s41586-020-1965-x
PubMed ID	32025015
PubMed Central ID	PMC7054214
Links	PubMed DOI Google Scholar
Grant list	R01 CA188228 / CA / NCI NIH HHS / United States U54 CA143798 / CA / NCI NIH HHS / United States R01 HG007069 / HG / NHGRI NIH HHS / United States U24 CA143845 / CA / NCI NIH HHS / United States R01 CA215489 / CA / NCI NIH HHS / United States U24 CA210999 / CA / NCI NIH HHS / United States R35 GM127029 / GM / NIGMS NIH HHS / United States U24 CA211000 / CA / NCI NIH HHS / United States