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

PCAWG Drivers and Functional Interpretation Working Group; PCAWG Structural Variation Working Group; PCAWG Consortium

doi:10.1038/s41586-020-1965-x

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

PCAWG Drivers and Functional Interpretation Working Group, PCAWG Structural Variation Working Group, PCAWG Consortium

Research output: Contribution to journal › Article › peer-review

Abstract

The discovery of drivers of cancer has traditionally focused on protein-coding genes^1,2,3,4. 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) Consortium⁵ 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^6,7, 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.

Original language	English
Pages (from-to)	102-111
Number of pages	10
Journal	Nature
Volume	578
Issue number	7793
Early online date	5 Feb 2020
DOIs	https://doi.org/10.1038/s41586-020-1965-x
Publication status	Published - 6 Feb 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1038/s41586-020-1965-xLicence: CC BY

Rheinbay_2020_Nature_Non-coding_CC
Copyright © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Final published version, 14.3 MBLicence: CC BY

Cite this

@article{1ee80b96cf6043b19680426ab681752a,

title = "Analyses of non-coding somatic drivers in 2,658 cancer whole genomes",

abstract = "The discovery of drivers of cancer has traditionally focused on protein-coding genes1,2,3,4. 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) Consortium5 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 drivers6,7, 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.",

author = "{PCAWG Drivers and Functional Interpretation Working Group} and {PCAWG Structural Variation Working Group} and {PCAWG Consortium} and Esther Rheinbay and Nielsen, {Morten Muhlig} and Federico Abascal and Wala, {Jeremiah A.} and Ofer Shapira and Grace Tiao and Henrik Hornsh{\o}j and Hess, {Julian M.} and Juul, {Randi Istrup} and Ziao Lin and Lars Feuerbach and Radhakrishnan Sabarinathan and Tobias Madsen and Jaegil Kim and Loris Mularoni and Shimin Shuai and Andr{\'e}s Lanz{\'o}s and Carl Herrmann and Maruvka, {Yosef E.} and Ciyue Shen and Amin, {Samirkumar B.} and Pratiti Bandopadhayay and Johanna Bertl and Boroevich, {Keith A.} and John Busanovich and Joana Carlevaro-Fita and Dimple Chakravarty and Chan, {Calvin Wing Yiu} and David Craft and Priyanka Dhingra and Klev Diamanti and Fonseca, {Nuno A.} and Abel Gonzalez-Perez and Qianyun Guo and Hamilton, {Mark P.} and Haradhvala, {Nicholas J.} and Chen Hong and Keren Isaev and Johnson, {Todd A.} and Malene Juul and Andre Kahles and Abdullah Kahraman and Youngwook Kim and Jan Komorowski and Kiran Kumar and Sushant Kumar and Donghoon Lee and Kjong-Van Lehmann and Yilong Li and Liu, {Eric Minwei} and Lucas Lochovsky and Keunchil Park and Oriol Pich and Roberts, {Nicola D.} and Gordon Saksena and Steven Schumacher and Nikos Sidiropoulos and Lina Sieverling and Nasa Sinnott-Armstrong and Chip Stewart and David Tamborero and Tubio, {Jose M. C.} and Husen Umer and Liis Uusk{\"u}la-Reimand and Claes Wadelius and Lina Wadi and Xiaotong Yao and Cheng-Zhong Zhang and Jing Zhang and Haber, {James E} and Asger Hobolth and Marcin Imielinski and Manolis Kellis and Lawrence, {Michael S.} and {von Mering}, Christian and Hidewaki Nakagawa and Benjamin Raphael and Rubin, {Mark A} and Chris Sander and Lincoln Stein and Stuart, {Joshua M.} and Wheeler, {David A.} and Rory Johnson and Tatsuhiko Tsunoda and J{\"u}ri Reimand and Mark Gerstein and Ekta Khurana and Campbell, {Peter J.} and N{\'u}ria L{\'o}pez-Bigas and Joachim Weischenfeldt and Rameen Berouckhim and I{\~n}igo Martincorena and Pedersen, {Jakob Skou} and Gad Getz and Lynch, {Andy G.}",

year = "2020",

month = feb,

day = "6",

doi = "10.1038/s41586-020-1965-x",

language = "English",

volume = "578",

pages = "102--111",

journal = "Nature",

issn = "0028-0836",

publisher = "Nature publishing group",

number = "7793",

}

TY - JOUR

T1 - Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

AU - PCAWG Drivers and Functional Interpretation Working Group

AU - PCAWG Structural Variation Working Group

AU - PCAWG Consortium

AU - Rheinbay, Esther

AU - Nielsen, Morten Muhlig

AU - Abascal, Federico

AU - Wala, Jeremiah A.

AU - Shapira, Ofer

AU - Tiao, Grace

AU - Hornshøj, Henrik

AU - Hess, Julian M.

AU - Juul, Randi Istrup

AU - Lin, Ziao

AU - Feuerbach, Lars

AU - Sabarinathan, Radhakrishnan

AU - Madsen, Tobias

AU - Kim, Jaegil

AU - Mularoni, Loris

AU - Shuai, Shimin

AU - Lanzós, Andrés

AU - Herrmann, Carl

AU - Maruvka, Yosef E.

AU - Shen, Ciyue

AU - Amin, Samirkumar B.

AU - Bandopadhayay, Pratiti

AU - Bertl, Johanna

AU - Boroevich, Keith A.

AU - Busanovich, John

AU - Carlevaro-Fita, Joana

AU - Chakravarty, Dimple

AU - Chan, Calvin Wing Yiu

AU - Craft, David

AU - Dhingra, Priyanka

AU - Diamanti, Klev

AU - Fonseca, Nuno A.

AU - Gonzalez-Perez, Abel

AU - Guo, Qianyun

AU - Hamilton, Mark P.

AU - Haradhvala, Nicholas J.

AU - Hong, Chen

AU - Isaev, Keren

AU - Johnson, Todd A.

AU - Juul, Malene

AU - Kahles, Andre

AU - Kahraman, Abdullah

AU - Kim, Youngwook

AU - Komorowski, Jan

AU - Kumar, Kiran

AU - Kumar, Sushant

AU - Lee, Donghoon

AU - Lehmann, Kjong-Van

AU - Li, Yilong

AU - Liu, Eric Minwei

AU - Lochovsky, Lucas

AU - Park, Keunchil

AU - Pich, Oriol

AU - Roberts, Nicola D.

AU - Saksena, Gordon

AU - Schumacher, Steven

AU - Sidiropoulos, Nikos

AU - Sieverling, Lina

AU - Sinnott-Armstrong, Nasa

AU - Stewart, Chip

AU - Tamborero, David

AU - Tubio, Jose M. C.

AU - Umer, Husen

AU - Uusküla-Reimand, Liis

AU - Wadelius, Claes

AU - Wadi, Lina

AU - Yao, Xiaotong

AU - Zhang, Cheng-Zhong

AU - Zhang, Jing

AU - Haber, James E

AU - Hobolth, Asger

AU - Imielinski, Marcin

AU - Kellis, Manolis

AU - Lawrence, Michael S.

AU - von Mering, Christian

AU - Nakagawa, Hidewaki

AU - Raphael, Benjamin

AU - Rubin, Mark A

AU - Sander, Chris

AU - Stein, Lincoln

AU - Stuart, Joshua M.

AU - Wheeler, David A.

AU - Johnson, Rory

AU - Tsunoda, Tatsuhiko

AU - Reimand, Jüri

AU - Gerstein, Mark

AU - Khurana, Ekta

AU - Campbell, Peter J.

AU - López-Bigas, Núria

AU - Weischenfeldt, Joachim

AU - Berouckhim, Rameen

AU - Martincorena, Iñigo

AU - Pedersen, Jakob Skou

AU - Getz, Gad

AU - Lynch, Andy G.

PY - 2020/2/6

Y1 - 2020/2/6

N2 - The discovery of drivers of cancer has traditionally focused on protein-coding genes1,2,3,4. 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) Consortium5 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 drivers6,7, 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.

AB - The discovery of drivers of cancer has traditionally focused on protein-coding genes1,2,3,4. 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) Consortium5 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 drivers6,7, 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.

U2 - 10.1038/s41586-020-1965-x

DO - 10.1038/s41586-020-1965-x

M3 - Article

C2 - 32025015

AN - SCOPUS:85079047263

SN - 0028-0836

VL - 578

SP - 102

EP - 111

JO - Nature

JF - Nature

IS - 7793

ER -

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

Abstract

UN SDGs

Access to Document

Fingerprint

Cite this