
==== Front
Nature
Nature
Nature
0028-0836
1476-4687
Nature Publishing Group UK London

32025018
1943
10.1038/s41586-020-1943-3
Article
The repertoire of mutational signatures in human cancer
Alexandrov Ludmil B. 1
Kim Jaegil 2
Haradhvala Nicholas J. 23
Huang Mi Ni 45
Tian Ng Alvin Wei 45
Wu Yang 45
Boot Arnoud 45
Covington Kyle R. 67
Gordenin Dmitry A. 8
Bergstrom Erik N. 1
Islam S. M. Ashiqul 1
Lopez-Bigas Nuria 91011
Klimczak Leszek J. 12
McPherson John R. 45
Morganella Sandro 13
Sabarinathan Radhakrishnan 101415
Wheeler David A. 616
Mustonen Ville 171819
PCAWG Mutational Signatures Working GroupAlexandrov Ludmil B. 1
Bergstrom Erik N. 1
Boot Arnoud 45
Boutros Paul 25262728
Chan Kin 29
Covington Kyle R. 67
Fujimoto Akihiro 30
Getz Gad 232122
Gordenin Dmitry A. 8
Haradhvala Nicholas J. 23
Huang Mi Ni 45
Islam S. M. Ashiqul 1
Kazanov Marat 313233
Kim Jaegil 2
Klimczak Leszek J. 12
Lopez-Bigas Nuria 91011
Lawrence Michael 23435
Martincorena Iñigo 13
McPherson John R. 45
Morganella Sandro 13
Mustonen Ville 171819
Nakagawa Hidewaki 30
Tian Ng Alvin Wei 45
Polak Paz 2322
Prokopec Stephenie 27
Roberts Steven A. 3637
Rozen Steven G. 4523
Sabarinathan Radhakrishnan 101415
Saini Natalie 8
Shibata Tatsuhiro 3839
Shiraishi Yuichi 40
Stratton Michael R. 13
Teh Bin Tean 423414243
Vázquez-García Ignacio 13444546
Wheeler David A. 616
Wu Yang 45
Yousif Fouad 2
Yu Willie 45

Getz Gad 232122
Rozen Steven G. steverozen@gmail.com

4235
Stratton Michael R. mrs@sanger.ac.uk

13
PCAWG ConsortiumAaltonen Lauri A. 200
Abascal Federico 201
Abeshouse Adam 202
Aburatani Hiroyuki 203
Adams David J. 201
Agrawal Nishant 204
Ahn Keun Soo 205
Ahn Sung-Min 206
Aikata Hiroshi 207
Akbani Rehan 208
Akdemir Kadir C. 209
Al-Ahmadie Hikmat 202
Al-Sedairy Sultan T. 210
Al-Shahrour Fatima 211
Alawi Malik 212213
Albert Monique 214
Aldape Kenneth 215216
Alexandrov Ludmil B. 201217218
Ally Adrian 219
Alsop Kathryn 220
Alvarez Eva G. 221222223
Amary Fernanda 224
Amin Samirkumar B. 225226227
Aminou Brice 228
Ammerpohl Ole 229230
Anderson Matthew J. 231
Ang Yeng 232
Antonello Davide 233
Anur Pavana 234
Aparicio Samuel 235
Appelbaum Elizabeth L. 236237
Arai Yasuhito 238
Aretz Axel 239
Arihiro Koji 207
Ariizumi Shun-ichi 240
Armenia Joshua 241
Arnould Laurent 242
Asa Sylvia 243244
Assenov Yassen 245
Atwal Gurnit 246247248
Aukema Sietse 230249
Auman J. Todd 250
Aure Miriam R. R. 251
Awadalla Philip 246247
Aymerich Marta 252
Bader Gary D. 247
Baez-Ortega Adrian 253
Bailey Matthew H. 236254
Bailey Peter J. 255
Balasundaram Miruna 219
Balu Saianand 256
Bandopadhayay Pratiti 257258259
Banks Rosamonde E. 260
Barbi Stefano 261
Barbour Andrew P. 262263
Barenboim Jonathan 246
Barnholtz-Sloan Jill 264265
Barr Hugh 266
Barrera Elisabet 267
Bartlett John 214268
Bartolome Javier 269
Bassi Claudio 233
Bathe Oliver F. 270271
Baumhoer Daniel 272
Bavi Prashant 273
Baylin Stephen B. 274275
Bazant Wojciech 267
Beardsmore Duncan 276
Beck Timothy A. 277278
Behjati Sam 201
Behren Andreas 279
Niu Beifang 280
Bell Cindy 281
Beltran Sergi 282283
Benz Christopher 284
Berchuck Andrew 285
Bergmann Anke K. 286
Bergstrom Erik N. 217218
Berman Benjamin P. 287288289
Berney Daniel M. 290
Bernhart Stephan H. 291292293
Beroukhim Rameen 257294295296
Berrios Mario 297
Bersani Samantha 298
Bertl Johanna 299300
Betancourt Miguel 301
Bhandari Vinayak 246302
Bhosle Shriram G. 201
Biankin Andrew V. 255303304305
Bieg Matthias 306307
Bigner Darell 308
Binder Hans 291292
Birney Ewan 267
Birrer Michael 309
Biswas Nidhan K. 310
Bjerkehagen Bodil 272311
Bodenheimer Tom 256
Boice Lori 312
Bonizzato Giada 313
De Bono Johann S. 314
Boot Arnoud 315316
Bootwalla Moiz S. 297
Borg Ake 317
Borkhardt Arndt 318
Boroevich Keith A. 319320
Borozan Ivan 246
Borst Christoph 321
Bosenberg Marcus 322
Bosio Mattia 269283323
Boultwood Jacqueline 324
Bourque Guillaume 325326
Boutros Paul C. 246302327328
Bova G. Steven 329
Bowen David T. 201330
Bowlby Reanne 219
Bowtell David D. L. 220
Boyault Sandrine 331
Boyce Rich 267
Boyd Jeffrey 332
Brazma Alvis 267
Brennan Paul 333
Brewer Daniel S. 334335
Brinkman Arie B. 336
Bristow Robert G. 302337338339340
Broaddus Russell R. 215
Brock Jane E. 341
Brock Malcolm 342
Broeks Annegien 343
Brooks Angela N. 257294344345
Brooks Denise 219
Brors Benedikt 346347348
Brunak Søren 349350
Bruxner Timothy J. C. 231351
Bruzos Alicia L. 221222223
Buchanan Alex 352
Buchhalter Ivo 307353354
Buchholz Christiane 355
Bullman Susan 257294
Burke Hazel 356
Burkhardt Birgit 357
Burns Kathleen H. 358359
Busanovich John 257360
Bustamante Carlos D. 361362
Butler Adam P. 201
Butte Atul J. 363
Byrne Niall J. 228
Børresen-Dale Anne-Lise 251364
Caesar-Johnson Samantha J. 365
Cafferkey Andy 267
Cahill Declan 366
Calabrese Claudia 267367
Caldas Carlos 368369
Calvo Fabien 370
Camacho Niedzica 314
Campbell Peter J. 201371372
Campo Elias 373374
Cantù Cinzia 313
Cao Shaolong 208
Carey Thomas E. 375
Carlevaro-Fita Joana 376377378
Carlsen Rebecca 219
Cataldo Ivana 298313
Cazzola Mario 379
Cebon Jonathan 279
Cerfolio Robert 380
Chadwick Dianne E. 381
Chakravarty Dimple 382
Chalmers Don 383
Chan Calvin Wing Yiu 353384
Chan Kin 385
Chan-Seng-Yue Michelle 273
Chandan Vishal S. 386
Chang David K. 255303
Chanock Stephen J. 387
Chantrill Lorraine A. 303388
Chateigner Aurélien 228389
Chatterjee Nilanjan 274390
Chayama Kazuaki 207
Chen Hsiao-Wei 232241
Chen Jieming 363
Chen Ken 209
Chen Yiwen 208
Chen Zhaohong 391
Cherniack Andrew D. 257294
Chien Jeremy 392
Chiew Yoke-Eng 393394
Chin Suet-Feung 368369
Cho Juok 257
Cho Sunghoon 395
Choi Jung Kyoon 396
Choi Wan 397
Chomienne Christine 398
Chong Zechen 208399
Choo Su Pin 400
Chou Angela 303393
Christ Angelika N. 231
Christie Elizabeth L. 220
Chuah Eric 219
Cibulskis Carrie 257
Cibulskis Kristian 257
Cingarlini Sara 401
Clapham Peter 201
Claviez Alexander 402
Cleary Sean 273403
Cloonan Nicole 404
Cmero Marek 405406407
Collins Colin C. 408
Connor Ashton A. 403409
Cooke Susanna L. 255
Cooper Colin S. 314334335410
Cope Leslie 274
Corbo Vincenzo 261313
Cordes Matthew G. 236411
Cordner Stephen M. 412
Cortés-Ciriano Isidro 413414415
Covington Kyle 416
Cowin Prue A. 417
Craft Brian 345
Craft David 257418
Creighton Chad J. 419
Cun Yupeng 420
Curley Erin 421
Cutcutache Ioana 315316
Czajka Karolina 422
Czerniak Bogdan 215423
Dagg Rebecca A. 424
Danilova Ludmila 274
Davi Maria Vittoria 425
Davidson Natalie R. 426427428429430
Davies Helen 201431432
Davis Ian J. 433
Davis-Dusenbery Brandi N. 434
Dawson Kevin J. 201
De La Vega Francisco M. 361362435
De Paoli-Iseppi Ricardo 356
Defreitas Timothy 257
Tos Angelo P. Dei 436
Delaneau Olivier 437438439
Demchok John A. 365
Demeulemeester Jonas 440441
Demidov German M. 283323442
Demircioğlu Deniz 443444
Dennis Nening M. 366
Denroche Robert E. 273
Dentro Stefan C. 201440445
Desai Nikita 446447
Deshpande Vikram 309
Deshwar Amit G. 448
Desmedt Christine 449450
Deu-Pons Jordi 451452
Dhalla Noreen 219
Dhani Neesha C. 453
Dhingra Priyanka 454455
Dhir Rajiv 456
DiBiase Anthony 457
Diamanti Klev 458
Ding Li 236254459
Ding Shuai 460
Dinh Huy Q. 287
Dirix Luc 461
Doddapaneni HarshaVardhan 416
Donmez Nilgun 408462
Dow Michelle T. 391
Drapkin Ronny 463
Drechsel Oliver 283323
Drews Ruben M. 369
Serge Serge 201
Dudderidge Tim 275366
Dueso-Barroso Ana 269464
Dunford Andrew J. 257
Dunn Michael 465
Dursi Lewis Jonathan 246466
Duthie Fraser R. 255467
Dutton-Regester Ken 468
Eagles Jenna 422
Easton Douglas F. 469470
Edmonds Stuart 471
Edwards Paul A. 369472
Edwards Sandra E. 314
Eeles Rosalind A. 314366
Ehinger Anna 473
Eils Juergen 474475
Eils Roland 353354474475
El-Naggar Adel 215423
Eldridge Matthew 369
Ellrott Kyle 352
Erkek Serap 367
Escaramis Georgia 323476477
Espiritu Shadrielle M. G. 246
Estivill Xavier 323478
Etemadmoghadam Dariush 220
Eyfjord Jorunn E. 479
Faltas Bishoy M. 430
Fan Daiming 480
Fan Yu 208
Faquin William C. 309
Farcas Claudiu 391
Fassan Matteo 481
Fatima Aquila 295
Favero Francesco 482
Fayzullaev Nodirjon 228
Felau Ina 365
Fereday Sian 220
Ferguson Martin L. 483
Ferretti Vincent 228484
Feuerbach Lars 346
Field Matthew A. 485
Fink J. Lynn 231269
Finocchiaro Gaetano 486
Fisher Cyril 366
Fittall Matthew W. 440
Fitzgerald Anna 487
Fitzgerald Rebecca C. 432
Flanagan Adrienne M. 488
Fleshner Neil E. 489
Flicek Paul 267
Foekens John A. 490
Fong Kwun M. 491
Fonseca Nuno A. 267492
Foster Christopher S. 493494
Fox Natalie S. 246
Fraser Michael 246
Frazer Scott 257
Frenkel-Morgenstern Milana 495
Friedman William 496
Frigola Joan 451
Fronick Catrina C. 236411
Fujimoto Akihiro 320
Fujita Masashi 320
Fukayama Masashi 497
Fulton Lucinda A. 236
Fulton Robert S. 236254459
Furuta Mayuko 320
Futreal P. Andrew 225
Füllgrabe Anja 267
Gabriel Stacey B. 257
Gallinger Steven 273403409
Gambacorti-Passerini Carlo 498
Gao Jianjiong 241
Gao Shengjie 499
Garraway Levi 294
Garred Øystein 500
Garrison Erik 201
Garsed Dale W. 220
Gehlenborg Nils 257501
Gelpi Josep L. L. 269502
George Joshy 227
Gerhard Daniela S. 503
Gerhauser Clarissa 504
Gershenwald Jeffrey E. 505506
Gerstein Mark 507508509
Gerstung Moritz 267367
Getz Gad 257296510511
Ghori Mohammed 201
Ghossein Ronald 512
Giama Nasra H. 513
Gibbs Richard A. 416
Gibson Bob 277
Gill Anthony J. 303514
Gill Pelvender 515
Giri Dilip D. 512
Glodzik Dominik 201
Gnanapragasam Vincent J. 371516
Goebler Maria Elisabeth 517
Goldman Mary J. 345
Gomez Carmen 518
Gonzalez Santiago 267367
Gonzalez-Perez Abel 451452519
Gordenin Dmitry A. 520
Gossage James 521
Gotoh Kunihito 522
Govindan Ramaswamy 254
Grabau Dorthe 523
Graham Janet S. 255524
Grant Robert C. 273409
Green Anthony R. 472
Green Eric 525
Greger Liliana 267
Grehan Nicola 432
Grimaldi Sonia 313
Grimmond Sean M. 526
Grossman Robert L. 527
Grundhoff Adam 213528
Gundem Gunes 202
Guo Qianyun 529
Gupta Manaswi 257
Gupta Shailja 530
Gut Ivo G. 282283
Gut Marta 282283
Göke Jonathan 443531
Ha Gavin 257
Haake Andrea 229
Haan David 344
Haas Siegfried 321
Haase Kerstin 440
Haber James E. 532
Habermann Nina 367
Hach Faraz 408533
Haider Syed 246
Hama Natsuko 238
Hamdy Freddie C. 515
Hamilton Anne 417
Hamilton Mark P. 534
Han Leng 535
Hanna George B. 536
Hansmann Martin 537
Haradhvala Nicholas J. 257309
Harismendy Olivier 218538
Harliwong Ivon 231
Harmanci Arif O. 509539
Harrington Eoghan 540
Hasegawa Takanori 541
Haussler David 345542
Hawkins Steve 369
Hayami Shinya 543
Hayashi Shuto 541
Hayes D. Neil 256544545
Hayes Stephen J. 546547
Hayward Nicholas K. 356468
Hazell Steven 366
He Yao 548549
Heath Allison P. 550
Heath Simon C. 282283
Hedley David 453
Hegde Apurva M. 551
Heiman David I. 257
Heinold Michael C. 353354
Heins Zachary 202
Heisler Lawrence E. 277
Hellstrom-Lindberg Eva 552
Helmy Mohamed 553
Heo Seong Gu 554
Hepperla Austin J. 256
Heredia-Genestar José María 555
Herrmann Carl 353354556
Hersey Peter 356
Hess Julian M. 257557
Hilmarsdottir Holmfridur 479
Hinton Jonathan 201
Hirano Satoshi 558
Hiraoka Nobuyoshi 559
Hoadley Katherine A. 256560
Hobolth Asger 299529
Hodzic Ermin 462
Hoell Jessica I. 318
Hoffmann Steve 291292293561
Hofmann Oliver 562
Holbrook Andrea 297
Holik Aliaksei Z. 323
Hollingsworth Michael A. 563
Holmes Oliver 351468
Holt Robert A. 219
Hong Chen 346384
Hong Eun Pyo 554
Hong Jongwhi H. 564
Hooijer Gerrit K. 565
Hornshøj Henrik 300
Hosoda Fumie 238
Hou Yong 499566
Hovestadt Volker 567
Howat William 371
Hoyle Alan P. 256
Hruban Ralph H. 274
Hu Jianhong 416
Hu Taobo 568
Hua Xing 387
Huang Kuan-lin 236569
Huang Mei 312
Huang Mi Ni 315316
Huang Vincent 246
Huang Yi 570571
Huber Wolfgang 367
Hudson Thomas J. 422572
Hummel Michael 573
Hung Jillian A. 393394
Huntsman David 574
Hupp Ted R. 575
Huse Jason 202
Huska Matthew R. 576
Hutter Barbara 307347348
Hutter Carolyn M. 525
Hübschmann Daniel 354474577578579
Iacobuzio-Donahue Christine A. 512
Imbusch Charles David 346
Imielinski Marcin 580581
Imoto Seiya 541
Isaacs William B. 582
Isaev Keren 246302
Ishikawa Shumpei 583
Iskar Murat 567
Islam S. M. Ashiqul 391
Ittmann Michael 584585586
Ivkovic Sinisa 434
Izarzugaza Jose M. G. 587
Jacquemier Jocelyne 588
Jakrot Valerie 356
Jamieson Nigel B. 255305589
Jang Gun Ho 273
Jang Se Jin 590
Jayaseelan Joy C. 416
Jayasinghe Reyka 236
Jefferys Stuart R. 256
Jegalian Karine 591
Jennings Jennifer L. 592
Jeon Seung-Hyup 397
Jerman Lara 367593
Ji Yuan 594595
Jiao Wei 246
Johansson Peter A. 468
Johns Amber L. 303
Johns Jeremy 422
Johnson Rory 377596
Johnson Todd A. 319
Jolly Clemency 440
Joly Yann 597
Jonasson Jon G. 479
Jones Corbin D. 598
Jones David R. 201
Jones David T. W. 599600
Jones Nic 601
Jones Steven J. M. 219
Jonkers Jos 343
Ju Young Seok 201396
Juhl Hartmut 602
Jung Jongsun 603
Juul Malene 300
Juul Randi Istrup 300
Juul Sissel 540
Jäger Natalie 353
Kabbe Rolf 353
Kahles Andre 426427428429604
Kahraman Abdullah 605606607
Kaiser Vera B. 608
Kakavand Hojabr 356
Kalimuthu Sangeetha 273
von Kalle Christof 578
Kang Koo Jeong 205
Karaszi Katalin 515
Karlan Beth 609
Karlić Rosa 610
Karsch Dennis 611
Kasaian Katayoon 219
Kassahn Karin S. 231612
Katai Hitoshi 613
Kato Mamoru 614
Katoh Hiroto 583
Kawakami Yoshiiku 207
Kay Jonathan D. 237
Kazakoff Stephen H. 351468
Kazanov Marat D. 615616617
Keays Maria 267
Kebebew Electron 618619
Kefford Richard F. 620
Kellis Manolis 257621
Kench James G. 303514622
Kennedy Catherine J. 393394
Kerssemakers Jules N. A. 353
Khoo David 423
Khoo Vincent 366
Khuntikeo Narong 233623
Khurana Ekta 454455624625
Kilpinen Helena 237
Kim Hark Kyun 626
Kim Hyung-Lae 627
Kim Hyung-Yong 588
Kim Hyunghwan 397
Kim Jaegil 257
Kim Jihoon 628
Kim Jong K. 629
Kim Youngwook 630631
King Tari A. 632633634
Klapper Wolfram 249
Kleinheinz Kortine 353354
Klimczak Leszek J. 635
Knappskog Stian 201636
Kneba Michael 611
Knoppers Bartha M. 597
Koh Youngil 637638
Komorowski Jan 458639
Komura Daisuke 583
Komura Mitsuhiro 541
Kong Gu 588
Kool Marcel 599640
Korbel Jan O. 267367
Korchina Viktoriya 416
Korshunov Andrey 640
Koscher Michael 640
Koster Roelof 641
Kote-Jarai Zsofia 314
Koures Antonios 391
Kovacevic Milena 434
Kremeyer Barbara 201
Kretzmer Helene 292293
Kreuz Markus 642
Krishnamurthy Savitri 215643
Kube Dieter 644
Kumar Kiran 257
Kumar Pardeep 366
Kumar Sushant 508509
Kumar Yogesh 568
Kundra Ritika 232241
Kübler Kirsten 257296309
Küppers Ralf 645
Lagergren Jesper 552646
Lai Phillip H. 297
Laird Peter W. 647
Lakhani Sunil R. 648
Lalansingh Christopher M. 246
Lalonde Emilie 246
Lamaze Fabien C. 246
Lambert Adam 515
Lander Eric 257
Landgraf Pablo 649650
Landoni Luca 233
Langerød Anita 251
Lanzós Andrés 377378596
Larsimont Denis 651
Larsson Erik 652
Lathrop Mark 326
Lau Loretta M. S. 653
Lawerenz Chris 475
Lawlor Rita T. 313
Lawrence Michael S. 257309319
Lazar Alexander J. 215225
Lazic Ana Mijalkovic 434
Le Xuan 654
Lee Darlene 219
Lee Donghoon 509
Lee Eunjung Alice 655
Lee Hee Jin 590
Lee Jake June-Koo 413415
Lee Jeong-Yeon 656
Lee Juhee 657
Lee Ming Ta Michael 658
Lee-Six Henry 201
Lehmann Kjong-Van 426427428429604
Lehrach Hans 659
Lenze Dido 573
Leonard Conrad R. 351468
Leongamornlert Daniel A. 201314
Leshchiner Ignaty 257
Letourneau Louis 660
Letunic Ivica 661
Levine Douglas A. 202662
Lewis Lora 416
Ley Tim 663
Li Chang 499566
Li Constance H. 246302
Li Haiyan Irene 219
Li Jun 208
Li Lin 499
Li Shantao 509
Li Siliang 499566
Li Xiaobo 499566
Li Xiaotong 509
Li Xinyue 499
Li Yilong 201
Liang Han 208226664
Liang Sheng-Ben 381
Lichter Peter 347567
Lin Pei 257
Lin Ziao 257665
Linehan W. M. 666
Lingjærde Ole Christian 667
Liu Dongbing 499566
Liu Eric Minwei 202454455
Liu Fei-Fei Fei 340668
Liu Fenglin 669670
Liu Jia 671
Liu Xingmin 499566
Livingstone Julie 246
Livitz Dimitri 257
Livni Naomi 366
Lochovsky Lucas 227508509
Loeffler Markus 642
Long Georgina V. 356
Lopez-Guillermo Armando 672
Lou Shaoke 508509
Louis David N. 309
Lovat Laurence B. 237
Lu Yiling 551
Lu Yong-Jie 290673
Lu Youyong 674675676
Luchini Claudio 298
Lungu Ilinca 268273
Luo Xuemei 277
Luxton Hayley J. 237
Lynch Andy G. 369472677
Lype Lisa 678
López Cristina 229230
López-Otín Carlos 679
Ma Eric Z. 568
Ma Yussanne 219
MacGrogan Gaetan 680
MacRae Shona 681
Macintyre Geoff 369
Madsen Tobias 300
Maejima Kazuhiro 320
Mafficini Andrea 313
Maglinte Dennis T. 297682
Maitra Arindam 310
Majumder Partha P. 310
Malcovati Luca 379
Malikic Salem 408462
Malleo Giuseppe 233
Mann Graham J. 356393683
Mantovani-Löffler Luisa 684
Marchal Kathleen 685686
Marchegiani Giovanni 233
Mardis Elaine R. 236332687
Margolin Adam A. 688
Marin Maximillian G. 344
Markowetz Florian 369472
Markowski Julia 576
Marks Jeffrey 689
Marques-Bonet Tomas 282555690691
Marra Marco A. 219
Marsden Luke 515
Martens John W. M. 490
Martin Sancha 201692
Martin-Subero Jose I. 690693
Martincorena Iñigo 201
Martinez-Fundichely Alexander 454455625
Maruvka Yosef E. 257309557
Mashl R. Jay 236694
Massie Charlie E. 369
Matthew Thomas J. 344
Matthews Lucy 314
Mayer Erik 366695
Mayes Simon 696
Mayo Michael 219
Mbabaali Faridah 422
McCune Karen 697
McDermott Ultan 201
McGillivray Patrick D. 508
McLellan Michael D. 236254459
McPherson John D. 273422698
McPherson John R. 315316
McPherson Treasa A. 409
Meier Samuel R. 257
Meng Alice 699
Meng Shaowu 256
Menzies Andrew 201
Merrett Neil D. 233700
Merson Sue 314
Meyerson Matthew 257294296
Meyerson William 509701
Mieczkowski Piotr A. 702
Mihaiescu George L. 228
Mijalkovic Sanja 434
Mikkelsen Tom 703
Milella Michele 401
Mileshkin Linda 220
Miller Christopher A. 236
Miller David K. 231303
Miller Jessica K. 422
Mills Gordon B. 704
Milovanovic Ana 269
Minner Sarah 705
Miotto Marco 233
Arnau Gisela Mir 417
Mirabello Lisa 387
Mitchell Chris 220
Mitchell Thomas J. 201371472
Miyano Satoru 541
Miyoshi Naoki 541
Mizuno Shinichi 706
Molnár-Gábor Fruzsina 707
Moore Malcolm J. 453
Moore Richard A. 219
Morganella Sandro 201
Morris Quaid D. 248668
Morrison Carl 708709
Mose Lisle E. 256
Moser Catherine D. 513
Muiños Ferran 451452
Mularoni Loris 451452
Mungall Andrew J. 219
Mungall Karen 219
Musgrove Elizabeth A. 255
Mustonen Ville 710711712
Mutch David 713
Muyas Francesc 283323442
Muzny Donna M. 416
Muñoz Alfonso 267
Myers Jerome 714
Myklebost Ola 636
Möller Peter 715
Nagae Genta 203
Nagrial Adnan M. 303
Nahal-Bose Hardeep K. 228
Nakagama Hitoshi 716
Nakagawa Hidewaki 320
Nakamura Hiromi 238
Nakamura Toru 558
Nakano Kaoru 320
Nandi Tannistha 717
Nangalia Jyoti 201
Nastic Mia 434
Navarro Arcadi 282555690
Navarro Fabio C. P. 507508718
Neal David E. 369371
Nettekoven Gerd 719
Newell Felicity 351468
Newhouse Steven J. 267
Newton Yulia 344
Ng Alvin Wei Tian 720
Ng Anthony 721
Nicholson Jonathan 201
Nicol David 366
Nie Yongzhan 480722
Nielsen G. Petur 309
Nielsen Morten Muhlig 300
Nik-Zainal Serena 201431432723
Noble Michael S. 257
Nones Katia 351468
Northcott Paul A. 724
Notta Faiyaz 273725
O’Connor Brian D. 228726
O’Donnell Peter 727
O’Donovan Maria 432
O’Meara Sarah 201
O’Neill Brian Patrick 728
O’Neill J. Robert 729
Ocana David 267
Ochoa Angelica 202
Oesper Layla 730
Ogden Christopher 366
Ohdan Hideki 207
Ohi Kazuhiro 541
Ohno-Machado Lucila 391
Oien Karin A. 708731
Ojesina Akinyemi I. 732733734
Ojima Hidenori 735
Okusaka Takuji 736
Omberg Larsson 737
Ong Choon Kiat 738
Ossowski Stephan 283323442
Ott German 739
Ouellette B. F. Francis 228740
P’ng Christine 246
Paczkowska Marta 246
Paiella Salvatore 233
Pairojkul Chawalit 708
Pajic Marina 303
Pan-Hammarström Qiang 499741
Papaemmanuil Elli 201
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4 grid.428397.3 0000 0004 0385 0924 Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
5 grid.428397.3 0000 0004 0385 0924 Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
6 grid.39382.33 0000 0001 2160 926X Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX USA
7 grid.39382.33 0000 0001 2160 926X Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX USA
8 grid.280664.e 0000 0001 2110 5790 Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences (NIEHS), Durham, NC USA
9 grid.473715.3 0000 0004 6475 7299 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
10 grid.5612.0 0000 0001 2172 2676 Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
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15 grid.7722.0 0000 0001 1811 6966 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
16 grid.39382.33 0000 0001 2160 926X Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX USA
17 grid.7737.4 0000 0004 0410 2071 Department of Computer Science, University of Helsinki, Helsinki, Finland
18 grid.7737.4 0000 0004 0410 2071 Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
19 grid.7737.4 0000 0004 0410 2071 Institute of Biotechnology, University of Helsinki, Helsinki, Finland
21 grid.32224.35 0000 0004 0386 9924 Department of Pathology, Massachusetts General Hospital, Boston, MA USA
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29 grid.28046.38 0000 0001 2182 2255 Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario Canada
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32 grid.465331.6 Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
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34 grid.509459.4 0000 0004 0472 0267 Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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233 grid.411475.2 0000 0004 1756 948X Department of Surgery, Pancreas Institute, University and Hospital Trust of Verona, Verona, Italy
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235 grid.248762.d 0000 0001 0702 3000 Department of Molecular Oncology, BC Cancer Research Centre, Vancouver, BC Canada
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237 grid.83440.3b 0000000121901201 University College London, London, UK
238 grid.272242.3 0000 0001 2168 5385 Division of Cancer Genomics, National Cancer Center Research Institute, National Cancer Center, Tokyo, Japan
239 DLR Project Management Agency, Bonn, Germany
240 grid.410818.4 0000 0001 0720 6587 Tokyo Women’s Medical University, Tokyo, Japan
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242 grid.148313.c 0000 0004 0428 3079 Los Alamos National Laboratory, Los Alamos, NM USA
243 grid.417184.f 0000 0001 0661 1177 Department of Pathology, University Health Network, Toronto General Hospital, Toronto, ON Canada
244 grid.240404.6 0000 0001 0440 1889 Nottingham University Hospitals NHS Trust, Nottingham, UK
245 grid.7497.d 0000 0004 0492 0584 Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), Heidelberg, Germany
246 grid.419890.d 0000 0004 0626 690X Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON Canada
247 grid.17063.33 0000 0001 2157 2938 Department of Molecular Genetics, University of Toronto, Toronto, ON Canada
248 grid.494618.6 Vector Institute, Toronto, ON Canada
249 grid.9764.c 0000 0001 2153 9986 Hematopathology Section, Institute of Pathology, Christian-Albrechts-University, Kiel, Germany
250 grid.10698.36 0000000122483208 Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
251 grid.55325.34 0000 0004 0389 8485 Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
252 grid.5841.8 0000 0004 1937 0247 Pathology, Hospital Clinic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
253 grid.5335.0 0000000121885934 Department of Veterinary Medicine, Transmissible Cancer Group, University of Cambridge, Cambridge, UK
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256 grid.10698.36 0000000122483208 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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266 grid.413144.7 0000 0001 0489 6543 Gloucester Royal Hospital, Gloucester, UK
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268 grid.419890.d 0000 0004 0626 690X Diagnostic Development, Ontario Institute for Cancer Research, Toronto, ON Canada
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271 grid.22072.35 0000 0004 1936 7697 Departments of Surgery and Oncology, University of Calgary, Calgary, AB Canada
272 grid.55325.34 0000 0004 0389 8485 Department of Pathology, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
273 grid.419890.d 0000 0004 0626 690X PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON Canada
274 grid.21107.35 0000 0001 2171 9311 Department of Oncology, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD USA
275 grid.430506.4 0000 0004 0465 4079 University Hospital Southampton NHS Foundation Trust, Southampton, UK
276 grid.439344.d 0000 0004 0641 6760 Royal Stoke University Hospital, Stoke-on-Trent, UK
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278 grid.459583.6 0000 0004 4652 6825 Human Longevity Inc, San Diego, CA USA
279 grid.1018.8 0000 0001 2342 0938 Olivia Newton-John Cancer Research Institute, La Trobe University, Heidelberg, VIC Australia
280 grid.9227.e 0000000119573309 Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
281 grid.440163.4 0000 0001 0352 8618 Genome Canada, Ottawa, ON Canada
282 grid.473715.3 0000 0004 6475 7299 CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
283 grid.5612.0 0000 0001 2172 2676 Universitat Pompeu Fabra (UPF), Barcelona, Spain
284 grid.272799.0 0000 0000 8687 5377 Buck Institute for Research on Aging, Novato, CA USA
285 grid.189509.c 0000000100241216 Duke University Medical Center, Durham, NC USA
286 grid.10423.34 0000 0000 9529 9877 Department of Human Genetics, Hannover Medical School, Hannover, Germany
287 grid.50956.3f 0000 0001 2152 9905 Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA USA
288 grid.50956.3f 0000 0001 2152 9905 Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA USA
289 grid.9619.7 0000 0004 1937 0538 The Hebrew University Faculty of Medicine, Jerusalem, Israel
290 grid.4868.2 0000 0001 2171 1133 Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
291 grid.9647.c 0000 0004 7669 9786 Department of Computer Science, Bioinformatics Group, University of Leipzig, Leipzig, Germany
292 grid.9647.c 0000 0004 7669 9786 Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany
293 grid.9647.c 0000 0004 7669 9786 Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
294 grid.65499.37 0000 0001 2106 9910 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA USA
295 grid.65499.37 0000 0001 2106 9910 Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA USA
296 grid.38142.3c 000000041936754X Harvard Medical School, Boston, MA USA
297 grid.42505.36 0000 0001 2156 6853 USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA USA
298 grid.411475.2 0000 0004 1756 948X Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
299 grid.7048.b 0000 0001 1956 2722 Department of Mathematics, Aarhus University, Aarhus, Denmark
300 grid.154185.c 0000 0004 0512 597X Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus N, Denmark
301 Instituto Carlos Slim de la Salud, Mexico City, Mexico
302 grid.17063.33 0000 0001 2157 2938 Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
303 grid.1005.4 0000 0004 4902 0432 Cancer Division, Garvan Institute of Medical Research, Kinghorn Cancer Centre, University of New South Wales (UNSW Sydney), Sydney, NSW Australia
304 grid.1005.4 0000 0004 4902 0432 South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales (UNSW Sydney), Liverpool, NSW Australia
305 grid.411714.6 0000 0000 9825 7840 West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow, UK
306 grid.484013.a 0000 0004 6879 971X Center for Digital Health, Berlin Institute of Health and Charitè - Universitätsmedizin Berlin, Berlin, Germany
307 grid.7497.d 0000 0004 0492 0584 Heidelberg Center for Personalized Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany
308 grid.189509.c 0000000100241216 The Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, NC USA
309 grid.32224.35 0000 0004 0386 9924 Massachusetts General Hospital, Boston, MA USA
310 grid.410872.8 0000 0004 1774 5690 National Institute of Biomedical Genomics, Kalyani, West Bengal India
311 grid.5510.1 0000 0004 1936 8921 Institute of Clinical Medicine and Institute of Oral Biology, University of Oslo, Oslo, Norway
312 grid.10698.36 0000000122483208 University of North Carolina at Chapel Hill, Chapel Hill, NC USA
313 grid.411475.2 0000 0004 1756 948X ARC-Net Centre for Applied Research on Cancer, University and Hospital Trust of Verona, Verona, Italy
314 grid.18886.3f The Institute of Cancer Research, London, UK
315 grid.428397.3 0000 0004 0385 0924 Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
316 grid.428397.3 0000 0004 0385 0924 Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
317 grid.4514.4 0000 0001 0930 2361 Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
318 grid.411327.2 0000 0001 2176 9917 Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine-University, Düsseldorf, Germany
319 grid.509459.4 0000 0004 0472 0267 Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
320 grid.509459.4 0000 0004 0472 0267 RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
321 Department of Internal Medicine/Hematology, Friedrich-Ebert-Hospital, Neumünster, Germany
322 grid.47100.32 0000000419368710 Departments of Dermatology and Pathology, Yale University, New Haven, CT USA
323 grid.473715.3 0000 0004 6475 7299 Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
324 grid.4991.5 0000 0004 1936 8948 Radcliffe Department of Medicine, University of Oxford, Oxford, UK
325 grid.14709.3b 0000 0004 1936 8649 Canadian Center for Computational Genomics, McGill University, Montreal, QC Canada
326 grid.14709.3b 0000 0004 1936 8649 Department of Human Genetics, McGill University, Montreal, QC Canada
327 grid.19006.3e 0000 0000 9632 6718 Department of Human Genetics, University of California Los Angeles, Los Angeles, CA USA
328 grid.17063.33 0000 0001 2157 2938 Department of Pharmacology, University of Toronto, Toronto, ON Canada
329 grid.412330.7 0000 0004 0628 2985 Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere University Hospital, Tampere, Finland
330 grid.415967.8 0000 0000 9965 1030 Haematology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
331 grid.418116.b 0000 0001 0200 3174 Translational Research and Innovation, Centre Léon Bérard, Lyon, France
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333 grid.17703.32 0000000405980095 International Agency for Research on Cancer, World Health Organization, Lyon, France
334 grid.421605.4 0000 0004 0447 4123 Earlham Institute, Norwich, UK
335 grid.8273.e 0000 0001 1092 7967 Norwich Medical School, University of East Anglia, Norwich, UK
336 grid.5590.9 0000000122931605 Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, HB The Netherlands
337 CRUK Manchester Institute and Centre, Manchester, UK
338 grid.17063.33 0000 0001 2157 2938 Department of Radiation Oncology, University of Toronto, Toronto, ON Canada
339 grid.5379.8 0000000121662407 Division of Cancer Sciences, Manchester Cancer Research Centre, University of Manchester, Manchester, UK
340 grid.415224.4 0000 0001 2150 066X Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON Canada
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344 grid.205975.c 0000 0001 0740 6917 Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA USA
345 grid.205975.c 0000 0001 0740 6917 UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA USA
346 grid.7497.d 0000 0004 0492 0584 Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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352 grid.5288.7 0000 0000 9758 5690 Biomedical Engineering, Oregon Health and Science University, Portland, OR USA
353 grid.7497.d 0000 0004 0492 0584 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
354 grid.7700.0 0000 0001 2190 4373 Institute of Pharmacy and Molecular Biotechnology and BioQuant, Heidelberg University, Heidelberg, Germany
355 grid.5586.e 0000 0004 0639 2885 Federal Ministry of Education and Research, Berlin, Germany
356 grid.1013.3 0000 0004 1936 834X Melanoma Institute Australia, University of Sydney, Sydney, NSW Australia
357 grid.16149.3b 0000 0004 0551 4246 Pediatric Hematology and Oncology, University Hospital Muenster, Muenster, Germany
358 grid.21107.35 0000 0001 2171 9311 Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD USA
359 grid.21107.35 0000 0001 2171 9311 McKusick-Nathans Institute of Genetic Medicine, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD USA
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361 grid.168010.e 0000000419368956 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA USA
362 grid.168010.e 0000000419368956 Department of Genetics, Stanford University School of Medicine, Stanford, CA USA
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364 grid.5510.1 0000 0004 1936 8921 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
365 grid.94365.3d 0000 0001 2297 5165 National Cancer Institute, National Institutes of Health, Bethesda, MD USA
366 grid.5072.0 0000 0001 0304 893X Royal Marsden NHS Foundation Trust, London and Sutton, UK
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368 grid.5335.0 0000000121885934 Department of Oncology, University of Cambridge, Cambridge, UK
369 grid.5335.0 0000000121885934 Li Ka Shing Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
370 grid.14925.3b 0000 0001 2284 9388 Institut Gustave Roussy, Villejuif, France
371 grid.24029.3d 0000 0004 0383 8386 Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
372 grid.5335.0 0000000121885934 Department of Haematology, University of Cambridge, Cambridge, UK
373 grid.5841.8 0000 0004 1937 0247 Anatomia Patológica, Hospital Clinic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
374 grid.451322.3 0000 0004 1770 9462 Spanish Ministry of Science and Innovation, Madrid, Spain
375 grid.412590.b 0000 0000 9081 2336 University of Michigan Comprehensive Cancer Center, Ann Arbor, MI USA
376 grid.5734.5 0000 0001 0726 5157 Department for BioMedical Research, University of Bern, Bern, Switzerland
377 grid.5734.5 0000 0001 0726 5157 Department of Medical Oncology, Inselspital, University Hospital and University of Bern, Bern, Switzerland
378 grid.5734.5 0000 0001 0726 5157 Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
379 grid.8982.b 0000 0004 1762 5736 University of Pavia, Pavia, Italy
380 grid.265892.2 0000000106344187 University of Alabama at Birmingham, Birmingham, AL USA
381 grid.417184.f 0000 0001 0661 1177 UHN Program in BioSpecimen Sciences, Toronto General Hospital, Toronto, ON Canada
382 grid.59734.3c 0000 0001 0670 2351 Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY USA
383 grid.1009.8 0000 0004 1936 826X Centre for Law and Genetics, University of Tasmania, Sandy Bay Campus, Hobart, TAS Australia
384 grid.7700.0 0000 0001 2190 4373 Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
385 grid.28046.38 0000 0001 2182 2255 Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON Canada
386 grid.66875.3a 0000 0004 0459 167X Division of Anatomic Pathology, Mayo Clinic, Rochester, MN USA
387 grid.94365.3d 0000 0001 2297 5165 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
388 grid.417154.2 0000 0000 9781 7439 Illawarra Shoalhaven Local Health District L3 Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, NSW Australia
389 BioForA, French National Institute for Agriculture, Food, and Environment (INRAE), ONF, Orléans, France
390 grid.21107.35 0000 0001 2171 9311 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
391 grid.266100.3 0000 0001 2107 4242 University of California San Diego, San Diego, CA USA
392 grid.66875.3a 0000 0004 0459 167X Division of Experimental Pathology, Mayo Clinic, Rochester, MN USA
393 grid.1013.3 0000 0004 1936 834X Centre for Cancer Research, The Westmead Institute for Medical Research, University of Sydney, Sydney, NSW Australia
394 grid.413252.3 0000 0001 0180 6477 Department of Gynaecological Oncology, Westmead Hospital, Sydney, NSW Australia
395 PDXen Biosystems Inc, Seoul, South Korea
396 grid.37172.30 0000 0001 2292 0500 Korea Advanced Institute of Science and Technology, Daejeon, South Korea
397 grid.36303.35 0000 0000 9148 4899 Electronics and Telecommunications Research Institute, Daejeon, South Korea
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403 grid.231844.8 0000 0004 0474 0428 Hepatobiliary/Pancreatic Surgical Oncology Program, University Health Network, Toronto, ON Canada
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405 grid.1008.9 0000 0001 2179 088X Department of Surgery, University of Melbourne, Parkville, VIC Australia
406 grid.416107.5 0000 0004 0614 0346 The Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC Australia
407 grid.1042.7 0000 0004 0432 4889 Walter and Eliza Hall Institute, Parkville, VIC Australia
408 grid.412541.7 0000 0001 0684 7796 Vancouver Prostate Centre, Vancouver, Canada
409 grid.416166.2 0000 0004 0473 9881 Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON Canada
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411 grid.240367.4 0000 0004 0445 7876 Norfolk and Norwich University Hospital NHS Trust, Norwich, UK
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417 grid.1008.9 0000 0001 2179 088X Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC Australia
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425 grid.411475.2 0000 0004 1756 948X Department of Medicine, Section of Endocrinology, University and Hospital Trust of Verona, Verona, Italy
426 grid.51462.34 0000 0001 2171 9952 Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY USA
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428 grid.5801.c 0000 0001 2156 2780 Department of Computer Science, ETH Zurich, Zurich, Switzerland
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430 grid.5386.8 000000041936877X Weill Cornell Medical College, New York, NY USA
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432 grid.415041.5 MRC Cancer Unit, University of Cambridge, Cambridge, UK
433 grid.10698.36 0000000122483208 Departments of Pediatrics and Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
434 grid.492568.4 Seven Bridges Genomics, Charlestown, MA USA
435 Annai Systems, Inc, Carlsbad, CA USA
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438 grid.8591.5 0000 0001 2322 4988 Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, CH Switzerland
439 grid.8591.5 0000 0001 2322 4988 Swiss Institute of Bioinformatics, University of Geneva, Geneva, CH Switzerland
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442 grid.10392.39 0000 0001 2190 1447 Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
443 grid.418377.e 0000 0004 0620 715X Computational and Systems Biology, Genome Institute of Singapore, Singapore, Singapore
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452 grid.5612.0 0000 0001 2172 2676 Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
453 grid.415224.4 0000 0001 2150 066X Division of Medical Oncology, Princess Margaret Cancer Centre, Toronto, ON Canada
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455 grid.5386.8 000000041936877X Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY USA
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457 Independent Consultant, Wellesley, USA
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459 grid.4367.6 0000 0001 2355 7002 Department of Medicine and Department of Genetics, Washington University School of Medicine, St. Louis, St. Louis, MO USA
460 grid.256896.6 0000 0001 0395 8562 Hefei University of Technology, Anhui, China
461 grid.5284.b 0000 0001 0790 3681 Translational Cancer Research Unit, GZA Hospitals St.-Augustinus, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
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463 grid.25879.31 0000 0004 1936 8972 University of Pennsylvania, Philadelphia, PA USA
464 grid.440820.a Faculty of Science and Technology, University of Vic—Central University of Catalonia (UVic-UCC), Vic, Spain
465 grid.52788.30 0000 0004 0427 7672 The Wellcome Trust, London, UK
466 grid.42327.30 0000 0004 0473 9646 The Hospital for Sick Children, Toronto, ON Canada
467 grid.511123.5 0000 0004 5988 7216 Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK
468 grid.1049.c 0000 0001 2294 1395 Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD Australia
469 grid.5335.0 0000000121885934 Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
470 grid.5335.0 0000000121885934 Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
471 grid.453281.9 0000 0004 4652 6665 Prostate Cancer Canada, Toronto, ON Canada
472 grid.5335.0 0000000121885934 University of Cambridge, Cambridge, UK
473 grid.4514.4 0000 0001 0930 2361 Department of Laboratory Medicine, Translational Cancer Research, Lund University Cancer Center at Medicon Village, Lund University, Lund, Sweden
474 grid.7700.0 0000 0001 2190 4373 Heidelberg University, Heidelberg, Germany
475 grid.6363.0 0000 0001 2218 4662 New BIH Digital Health Center, Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin, Berlin, Germany
476 grid.466571.7 0000 0004 1756 6246 CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
477 Research Group on Statistics, Econometrics and Health (GRECS), UdG, Barcelona, Spain
478 Quantitative Genomics Laboratories (qGenomics), Barcelona, Spain
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480 grid.233520.5 0000 0004 1761 4404 State Key Laboratory of Cancer Biology, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Shaanxi, China
481 grid.5608.b 0000 0004 1757 3470 Department of Medicine (DIMED), Surgical Pathology Unit, University of Padua, Padua, Italy
482 grid.475435.4 Rigshospitalet, Copenhagen, Denmark
483 grid.94365.3d 0000 0001 2297 5165 Center for Cancer Genomics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
484 grid.14848.31 0000 0001 2292 3357 Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, QC Canada
485 grid.1011.1 0000 0004 0474 1797 Australian Institute of Tropical Health and Medicine, James Cook University, Douglas, QLD Australia
486 Department of Neuro-Oncology, Istituto Neurologico Besta, Milano, Italy
487 grid.484025.f Bioplatforms Australia, North Ryde, NSW Australia
488 grid.83440.3b 0000000121901201 Department of Pathology (Research), University College London Cancer Institute, London, UK
489 grid.415224.4 0000 0001 2150 066X Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON Canada
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491 grid.415184.d 0000 0004 0614 0266 The University of Queensland Thoracic Research Centre, The Prince Charles Hospital, Brisbane, QLD Australia
492 grid.5808.5 0000 0001 1503 7226 CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources, Universidade do Porto, Vairão, Portugal
493 grid.420746.3 0000 0001 1887 2462 HCA Laboratories, London, UK
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495 grid.22098.31 0000 0004 1937 0503 The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
496 grid.15276.37 0000 0004 1936 8091 Department of Neurosurgery, University of Florida, Gainesville, FL USA
497 grid.26999.3d 0000 0001 2151 536X Department of Pathology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
498 grid.7563.7 0000 0001 2174 1754 University of Milano Bicocca, Monza, Italy
499 grid.21155.32 0000 0001 2034 1839 BGI-Shenzhen, Shenzhen, China
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501 grid.38142.3c 000000041936754X Center for Biomedical Informatics, Harvard Medical School, Boston, MA USA
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503 grid.94365.3d 0000 0001 2297 5165 Office of Cancer Genomics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
504 grid.7497.d 0000 0004 0492 0584 Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
505 grid.240145.6 0000 0001 2291 4776 Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
506 grid.240145.6 0000 0001 2291 4776 Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
507 grid.47100.32 0000000419368710 Department of Computer Science, Yale University, New Haven, CT USA
508 grid.47100.32 0000000419368710 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT USA
509 grid.47100.32 0000000419368710 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT USA
510 grid.32224.35 0000 0004 0386 9924 Center for Cancer Research, Massachusetts General Hospital, Boston, MA USA
511 grid.32224.35 0000 0004 0386 9924 Department of Pathology, Massachusetts General Hospital, Boston, MA USA
512 grid.51462.34 0000 0001 2171 9952 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
513 grid.66875.3a 0000 0004 0459 167X Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN USA
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515 grid.4991.5 0000 0004 1936 8948 University of Oxford, Oxford, UK
516 grid.5335.0 0000000121885934 Department of Surgery, Academic Urology Group, University of Cambridge, Cambridge, UK
517 grid.8379.5 0000 0001 1958 8658 Department of Medicine II, University of Würzburg, Wuerzburg, Germany
518 grid.26790.3a 0000 0004 1936 8606 Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL USA
519 grid.20522.37 0000 0004 1767 9005 Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Barcelona, Spain
520 grid.280664.e 0000 0001 2110 5790 Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences (NIEHS), Durham, NC USA
521 grid.425213.3 St. Thomas’s Hospital, London, UK
522 Osaka International Cancer Center, Osaka, Japan
523 grid.411843.b 0000 0004 0623 9987 Department of Pathology, Skåne University Hospital, Lund University, Lund, Sweden
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525 grid.94365.3d 0000 0001 2297 5165 National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
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528 grid.452463.2 German Center for Infection Research (DZIF), Partner Site Hamburg-Borstel-Lübeck-Riems, Hamburg, Germany
529 grid.7048.b 0000 0001 1956 2722 Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
530 grid.410865.e Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi, Delhi India
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533 grid.17091.3e 0000 0001 2288 9830 Department of Urologic Sciences, University of British Columbia, Vancouver, BC Canada
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536 grid.7445.2 0000 0001 2113 8111 Imperial College NHS Trust, Imperial College, London, INY UK
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538 grid.266100.3 0000 0001 2107 4242 Department of Medicine, Division of Biomedical Informatics, UC San Diego School of Medicine, San Diego, CA USA
539 grid.468222.8 Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX USA
540 Oxford Nanopore Technologies, New York, NY USA
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542 grid.205975.c 0000 0001 0740 6917 Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA USA
543 grid.412857.d 0000 0004 1763 1087 Wakayama Medical University, Wakayama, Japan
544 grid.10698.36 0000000122483208 Department of Internal Medicine, Division of Medical Oncology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
545 grid.267301.1 0000 0004 0386 9246 University of Tennessee Health Science Center for Cancer Research, Memphis, TN USA
546 grid.412346.6 0000 0001 0237 2025 Department of Histopathology, Salford Royal NHS Foundation Trust, Salford, UK
547 grid.5379.8 0000000121662407 Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
548 grid.11135.37 0000 0001 2256 9319 BIOPIC, ICG and College of Life Sciences, Peking University, Beijing, China
549 grid.11135.37 0000 0001 2256 9319 Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
550 grid.239552.a 0000 0001 0680 8770 Children’s Hospital of Philadelphia, Philadelphia, PA USA
551 grid.240145.6 0000 0001 2291 4776 Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
552 grid.4714.6 0000 0004 1937 0626 Karolinska Institute, Stockholm, Sweden
553 grid.17063.33 0000 0001 2157 2938 The Donnelly Centre, University of Toronto, Toronto, ON Canada
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555 grid.5612.0 0000 0001 2172 2676 Department of Experimental and Health Sciences, Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, Barcelona, Spain
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559 grid.272242.3 0000 0001 2168 5385 Department of Pathology and Clinical Laboratory, National Cancer Center Hospital, Tokyo, Japan
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561 grid.418245.e 0000 0000 9999 5706 Computational Biology, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
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567 grid.7497.d 0000 0004 0492 0584 Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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585 grid.39382.33 0000 0001 2160 926X Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX USA
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587 grid.5170.3 0000 0001 2181 8870 Technical University of Denmark, Lyngby, Denmark
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591 Science Writer, Garrett Park, MD USA
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598 grid.10698.36 0000000122483208 Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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602 Indivumed GmbH, Hamburg, Germany
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771 grid.10698.36 0000000122483208 Research Computing Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
772 grid.30064.31 0000 0001 2157 6568 School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA USA
773 grid.5254.6 0000 0001 0674 042X Finsen Laboratory and Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
774 grid.17063.33 0000 0001 2157 2938 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON Canada
775 grid.51462.34 0000 0001 2171 9952 Department of Pathology, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY USA
776 grid.411067.5 0000 0000 8584 9230 University Hospital Giessen, Pediatric Hematology and Oncology, Giessen, Germany
777 grid.418189.d 0000 0001 2175 1768 Oncologie Sénologie, ICM Institut Régional du Cancer, Montpellier, France
778 grid.9764.c 0000 0001 2153 9986 Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
779 grid.8379.5 0000 0001 1958 8658 Institute of Pathology, University of Wuerzburg, Wuerzburg, Germany
780 grid.418484.5 0000 0004 0380 7221 Department of Urology, North Bristol NHS Trust, Bristol, UK
781 grid.419385.2 0000 0004 0620 9905 SingHealth, Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore, Singapore
782 grid.17063.33 0000 0001 2157 2938 Department of Computer Science, University of Toronto, Toronto, ON Canada
783 grid.5734.5 0000 0001 0726 5157 Bern Center for Precision Medicine, University Hospital of Bern, University of Bern, Bern, Switzerland
784 grid.5386.8 000000041936877X Englander Institute for Precision Medicine, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY USA
785 grid.5386.8 000000041936877X Meyer Cancer Center, Weill Cornell Medicine, New York, NY USA
786 grid.5386.8 000000041936877X Pathology and Laboratory, Weill Cornell Medical College, New York, NY USA
787 grid.411083.f 0000 0001 0675 8654 Vall d’Hebron Institute of Oncology: VHIO, Barcelona, Spain
788 grid.411475.2 0000 0004 1756 948X General and Hepatobiliary-Biliary Surgery, Pancreas Institute, University and Hospital Trust of Verona, Verona, Italy
789 grid.22401.35 0000 0004 0502 9283 National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
790 grid.411377.7 0000 0001 0790 959X Indiana University, Bloomington, IN USA
791 grid.428965.4 0000 0004 7536 2436 Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
792 grid.422639.8 0000 0004 0372 3861 Analytical Biological Services, Inc, Wilmington, DE USA
793 grid.1013.3 0000 0004 1936 834X Sydney Medical School, University of Sydney, Sydney, NSW Australia
794 grid.38142.3c 000000041936754X cBio Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
795 grid.38142.3c 000000041936754X Department of Cell Biology, Harvard Medical School, Boston, MA USA
796 grid.410869.2 0000 0004 1766 7522 Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai, Maharashtra India
797 grid.266842.c 0000 0000 8831 109X School of Environmental and Life Sciences, Faculty of Science, The University of Newcastle, Ourimbah, NSW Australia
798 grid.410718.b 0000 0001 0262 7331 Department of Dermatology, University Hospital of Essen, Essen, Germany
799 grid.7497.d 0000 0004 0492 0584 Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany
800 grid.6363.0 0000 0001 2218 4662 Department of Urology, Charité Universitätsmedizin Berlin, Berlin, Germany
801 grid.13648.38 0000 0001 2180 3484 Martini-Clinic, Prostate Cancer Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
802 grid.9764.c 0000 0001 2153 9986 Department of General Internal Medicine, University of Kiel, Kiel, Germany
803 grid.7497.d 0000 0004 0492 0584 German Cancer Consortium (DKTK), Partner site Berlin, Berlin, Germany
804 grid.239395.7 0000 0000 9011 8547 Cancer Research Institute, Beth Israel Deaconess Medical Center, Boston, MA USA
805 grid.21925.3d 0000 0004 1936 9000 University of Pittsburgh, Pittsburgh, PA USA
806 grid.38142.3c 000000041936754X Department of Ophthalmology and Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA USA
807 grid.240372.0 0000 0004 0400 4439 Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL USA
808 grid.251017.0 0000 0004 0406 2057 Van Andel Research Institute, Grand Rapids, MI USA
809 grid.26999.3d 0000 0001 2151 536X Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
810 grid.480536.c 0000 0004 5373 4593 Japan Agency for Medical Research and Development, Tokyo, Japan
811 grid.222754.4 0000 0001 0840 2678 Korea University, Seoul, South Korea
812 grid.414467.4 0000 0001 0560 6544 Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD USA
813 grid.9764.c 0000 0001 2153 9986 Human Genetics, University of Kiel, Kiel, Germany
814 grid.38142.3c 000000041936754X Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA
815 grid.5288.7 0000 0000 9758 5690 Oregon Health and Science University, Portland, OR USA
816 grid.240145.6 0000 0001 2291 4776 Center for RNA Interference and Noncoding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX USA
817 grid.240145.6 0000 0001 2291 4776 Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX USA
818 grid.240145.6 0000 0001 2291 4776 Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX USA
819 grid.15628.38 0000 0004 0393 1193 University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
820 grid.10417.33 0000 0004 0444 9382 Department of Radiation Oncology, Radboud University Nijmegen Medical Centre, Nijmegen, GA The Netherlands
821 grid.170205.1 0000 0004 1936 7822 Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL USA
822 grid.459927.4 0000 0000 8785 9045 Clinic for Hematology and Oncology, St.-Antonius-Hospital, Eschweiler, Germany
823 grid.51462.34 0000 0001 2171 9952 Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY USA
824 grid.14013.37 0000 0004 0640 0021 University of Iceland, Reykjavik, Iceland
825 grid.7497.d 0000 0004 0492 0584 Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
826 grid.416266.1 0000 0000 9009 9462 Dundee Cancer Centre, Ninewells Hospital, Dundee, UK
827 grid.410712.1 0000 0004 0473 882X Department for Internal Medicine III, University of Ulm and University Hospital of Ulm, Ulm, Germany
828 grid.418596.7 0000 0004 0639 6384 Institut Curie, INSERM Unit 830, Paris, France
829 grid.268441.d 0000 0001 1033 6139 Department of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
830 grid.10417.33 0000 0004 0444 9382 Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, GA The Netherlands
831 grid.7497.d 0000 0004 0492 0584 Division of Cancer Genome Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
832 grid.163555.1 0000 0000 9486 5048 Department of General Surgery, Singapore General Hospital, Singapore, Singapore
833 grid.4280.e 0000 0001 2180 6431 Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
834 grid.7737.4 0000 0004 0410 2071 Department of Medical and Clinical Genetics, Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
835 grid.24029.3d 0000 0004 0383 8386 East Anglian Medical Genetics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
836 grid.21729.3f 0000000419368729 Irving Institute for Cancer Dynamics, Columbia University, New York, NY USA
837 grid.418812.6 0000 0004 0620 9243 Institute of Molecular and Cell Biology, Singapore, Singapore
838 grid.410724.4 0000 0004 0620 9745 Laboratory of Cancer Epigenome, Division of Medical Science, National Cancer Centre Singapore, Singapore, Singapore
839 Universite Lyon, INCa-Synergie, Centre Léon Bérard, Lyon, France
840 grid.66875.3a 0000 0004 0459 167X Department of Urology, Mayo Clinic, Rochester, MN USA
841 grid.416177.2 0000 0004 0417 7890 Royal National Orthopaedic Hospital - Stanmore, Stanmore, Middlesex UK
842 grid.6312.6 0000 0001 2097 6738 Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain
843 Giovanni Paolo II / I.R.C.C.S. Cancer Institute, Bari, BA Italy
844 grid.7497.d 0000 0004 0492 0584 Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
845 grid.414603.4 Fondazione Policlinico Universitario Gemelli IRCCS, Rome, Italy, Rome, Italy
846 grid.5611.3 0000 0004 1763 1124 University of Verona, Verona, Italy
847 grid.418135.a 0000 0004 0641 3404 Centre National de Génotypage, CEA - Institute de Génomique, Evry, France
848 grid.5012.6 0000 0001 0481 6099 CAPHRI Research School, Maastricht University, Maastricht, ER The Netherlands
849 grid.418116.b 0000 0001 0200 3174 Department of Biopathology, Centre Léon Bérard, Lyon, France
850 grid.7849.2 0000 0001 2150 7757 Université Claude Bernard Lyon 1, Villeurbanne, France
851 grid.419082.6 0000 0004 1754 9200 Core Research for Evolutional Science and Technology (CREST), JST, Tokyo, Japan
852 grid.26999.3d 0000 0001 2151 536X Department of Biological Sciences, Laboratory for Medical Science Mathematics, Graduate School of Science, University of Tokyo, Yokohama, Japan
853 grid.265073.5 0000 0001 1014 9130 Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
854 grid.10306.34 0000 0004 0606 5382 Cancer Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, UK
855 grid.412563.7 0000 0004 0376 6589 University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
856 grid.4777.3 0000 0004 0374 7521 Centre for Cancer Research and Cell Biology, Queen’s University, Belfast, UK
857 grid.240145.6 0000 0001 2291 4776 Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
858 grid.21107.35 0000 0001 2171 9311 Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD USA
859 grid.4714.6 0000 0004 1937 0626 Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
860 grid.5491.9 0000 0004 1936 9297 School of Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
861 grid.6988.f 0000000110107715 Department of Gene Technology, Tallinn University of Technology, Tallinn, Estonia
862 grid.42327.30 0000 0004 0473 9646 Genetics and Genome Biology Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON Canada
863 grid.189967.8 0000 0001 0941 6502 Departments of Neurosurgery and Hematology and Medical Oncology, Winship Cancer Institute and School of Medicine, Emory University, Atlanta, GA USA
864 grid.5947.f 0000 0001 1516 2393 Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
865 Argmix Consulting, North Vancouver, BC Canada
866 grid.5342.0 0000 0001 2069 7798 Department of Information Technology, Ghent University, Interuniversitair Micro-Electronica Centrum (IMEC), Ghent, Belgium
867 grid.4991.5 0000 0004 1936 8948 Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
868 grid.9845.0 0000 0001 0775 3222 Institute of Mathematics and Computer Science, University of Latvia, Riga, LV Latvia
869 grid.1013.3 0000 0004 1936 834X Discipline of Pathology, Sydney Medical School, University of Sydney, Sydney, NSW Australia
870 grid.5335.0 0000000121885934 Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK
871 grid.51462.34 0000 0001 2171 9952 Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
872 grid.21729.3f 0000000419368729 Department of Statistics, Columbia University, New York, NY USA
873 grid.8993.b 0000 0004 1936 9457 Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
874 grid.43169.39 0000 0001 0599 1243 School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
875 grid.24029.3d 0000 0004 0383 8386 Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
876 grid.4991.5 0000 0004 1936 8948 Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
877 grid.410427.4 0000 0001 2284 9329 Georgia Regents University Cancer Center, Augusta, GA USA
878 grid.417286.e 0000 0004 0422 2524 Wythenshawe Hospital, Manchester, UK
879 grid.4367.6 0000 0001 2355 7002 Department of Genetics, Washington University School of Medicine, St.Louis, MO USA
880 grid.423940.8 0000 0001 2188 0463 Department of Biological Oceanography, Leibniz Institute of Baltic Sea Research, Rostock, Germany
881 grid.4991.5 0000 0004 1936 8948 Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
882 grid.39382.33 0000 0001 2160 926X Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX USA
883 grid.66875.3a 0000 0004 0459 167X Thoracic Oncology Laboratory, Mayo Clinic, Rochester, MN USA
884 grid.240344.5 0000 0004 0392 3476 Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH USA
885 grid.66875.3a 0000 0004 0459 167X Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Mayo Clinic, Rochester, MN USA
886 grid.510975.f 0000 0004 6004 7353 International Institute for Molecular Oncology, Poznań, Poland
887 grid.22254.33 0000 0001 2205 0971 Poznan University of Medical Sciences, Poznań, Poland
888 grid.7497.d 0000 0004 0492 0584 Genomics and Proteomics Core Facility High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany
889 grid.410724.4 0000 0004 0620 9745 NCCS-VARI Translational Research Laboratory, National Cancer Centre Singapore, Singapore, Singapore
890 grid.4367.6 0000 0001 2355 7002 Edison Family Center for Genome Sciences and Systems Biology, Washington University, St. Louis, MO USA
891 grid.301713.7 0000 0004 0393 3981 MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
892 grid.5288.7 0000 0000 9758 5690 Department of Medical Informatics and Clinical Epidemiology, Division of Bioinformatics and Computational Biology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR USA
893 grid.33199.31 0000 0004 0368 7223 School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
894 grid.21107.35 0000 0001 2171 9311 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD USA
895 grid.136593.b 0000 0004 0373 3971 Department of Cancer Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan
896 grid.7700.0 0000 0001 2190 4373 Institute of Computer Science, Heidelberg University, Heidelberg, Germany
897 grid.1013.3 0000 0004 1936 834X School of Mathematics and Statistics, University of Sydney, Sydney, NSW Australia
898 grid.170205.1 0000 0004 1936 7822 Ben May Department for Cancer Research, University of Chicago, Chicago, IL USA
899 grid.170205.1 0000 0004 1936 7822 Department of Human Genetics, University of Chicago, Chicago, IL USA
900 grid.5386.8 000000041936877X Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY USA
901 grid.43169.39 0000 0001 0599 1243 The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China
902 grid.10784.3a 0000 0004 1937 0482 Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong China
903 grid.240145.6 0000 0001 2291 4776 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX USA
904 grid.428397.3 0000 0004 0385 0924 Duke-NUS Medical School, Singapore, Singapore
905 grid.16821.3c 0000 0004 0368 8293 Department of Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
906 grid.8756.c 0000 0001 2193 314X School of Computing Science, University of Glasgow, Glasgow, UK
907 grid.55325.34 0000 0004 0389 8485 Division of Orthopaedic Surgery, Oslo University Hospital, Oslo, Norway
908 grid.1002.3 0000 0004 1936 7857 Eastern Clinical School, Monash University, Melbourne, VIC Australia
909 grid.414539.e 0000 0001 0459 5396 Epworth HealthCare, Richmond, VIC Australia
910 grid.38142.3c 000000041936754X Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA USA
911 grid.261331.4 0000 0001 2285 7943 Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH USA
912 grid.413944.f 0000 0001 0447 4797 The Ohio State University Comprehensive Cancer Center (OSUCCC – James), Columbus, OH USA
913 grid.267308.8 0000 0000 9206 2401 The University of Texas School of Biomedical Informatics (SBMI) at Houston, Houston, TX USA
914 grid.10698.36 0000000122483208 Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
915 grid.16753.36 0000 0001 2299 3507 Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
916 grid.1013.3 0000 0004 1936 834X Faculty of Medicine and Health, University of Sydney, Sydney, NSW Australia
917 grid.5645.2 000000040459992X Department of Pathology, Erasmus Medical Center Rotterdam, Rotterdam, GD The Netherlands
918 grid.430814.a 0000 0001 0674 1393 Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, CX The Netherlands
919 grid.7400.3 0000 0004 1937 0650 Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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© The Author(s) 2020, corrected publication 2023
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Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 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 analyses3–15, 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.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

Subject terms

Cancer genetics
Mutation
issue-copyright-statement© The Author(s), under exclusive licence to Springer Nature Limited 2020
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pmcMain

Somatic mutations in cancer genomes are caused by mutational processes of both exogenous and endogenous origin that operate during the cell lineage between the fertilized egg and the cancer cell16. Each mutational process may involve components of DNA damage or modification, DNA repair and DNA replication (which may be normal or abnormal), and generates a characteristic mutational signature that potentially includes base substitutions, small insertions and deletions (indels), genome rearrangements and chromosome copy-number changes1. The mutations in an individual cancer genome may have been generated by multiple mutational processes, and thus incorporate multiple superimposed mutational signatures. Therefore, to systematically characterize the mutational processes that contribute to cancer, mathematical methods have previously been used to decipher mutational signatures from somatic mutation catalogues, estimate the number of mutations that are attributable to each signature in individual samples and annotate each mutation class in each tumour with the probability that it arose from each signature6,9,17–27.

Previous studies of multiple types of cancer have identified more than 30 single-base substitution (SBS) signatures, some of known—but many of unknown—aetiologies, some ubiquitous and others rare, some part of normal cell biology and others associated with abnormal exposures or neoplastic progression3–5,7–15. Genome rearrangement signatures have also previously been described11,25,28–30. However, the analysis of other classes of mutation has been relatively limited3,11,31–33.

Mutational signature analysis has predominantly used cancer exome sequences. However, the many-fold-greater numbers of somatic mutations in whole genomes provide substantially increased power for signature decomposition, enabling the better separation of partially correlated signatures and the extraction of signatures that contribute relatively small numbers of mutations. Furthermore, technical artefacts and differences in sequencing technologies and mutation-calling algorithms can themselves generate mutational signatures. Therefore, the uniformly processed and highly curated sets of all classes of somatic mutations from the 2,780 cancer genomes of the PCAWG project2, combined with most other suitable cancer genomes (accession code syn11801889, available at https://www.synapse.org/#!Synapse:syn11801889), present a notable opportunity to establish the repertoire of mutational signatures and determine their activities across different types of cancer. The timing of these signatures during the evolution of individual cancers and the repertoire of signatures of structural variation have been explored in other PCAWG analyses30,34.

Mutational signature analysis

The 23,829 samples—which include most types of cancer, and comprise the 2,780 PCAWG whole genomes2, 1,865 additional whole genomes and 19,184 exomes—yielded 79,793,266 somatic SBSs, 814,191 doublet-base substitutions (DBSs) and 4,122,233 small indels that were analysed for mutational signatures, about 10-fold-more mutations than any previous study of which we are aware (syn11801889)6.

We developed classifications for each type of mutation. For SBSs, the primary classification comprised 96 classes (available at https://cancer.sanger.ac.uk/cosmic/signatures/SBS) constituted by the 6 base substitutions C>A, C>G, C>T, T>A, T>C and T>G (in which the mutated base is represented by the pyrimidine of the base pair), plus the flanking 5′ and 3′ bases. In some analyses, two flanking bases 5′ and 3′ to the mutated base were considered (producing 1,536 classes) or mutations within transcribed genome regions were selected and classified according to whether the mutated pyrimidine fell on the transcribed or untranscribed strand (producing 192 classes). We also derived a classification for DBSs (78 classes; available at https://cancer.sanger.ac.uk/cosmic/signatures/DBS). Indels were classified as deletions or insertions and—when of a single base—as C or T, and according to the length of the mononucleotide repeat tract in which they occurred. Longer indels were classified as occurring at repeats or with overlapping microhomology at deletion boundaries, and according to the size of indel, repeat and microhomology (83 classes; available at https://cancer.sanger.ac.uk/cosmic/signatures/ID).

The PCAWG whole-genome sequences, the additional whole-genome sequences and the exome sequences were each analysed separately (syn11801889)2. Signatures were extracted from each type of cancer individually, from all cancer types together, as separate SBS, DBS and indel signatures, and as composite signatures of all three types of mutation (Supplementary Note 2).

We used two methods based on nonnegative matrix factorization (NMF): SigProfiler, an elaborated version of the framework used for the previous ‘Catalogue Of Somatic Mutations In Cancer’ (COSMIC) compendium of mutational signatures (COSMIC v.2, available at https://cancer.sanger.ac.uk/cosmic/signatures_v2)11,17, and SignatureAnalyzer, which is based on a Bayesian variant of NMF9,27,35. NMF determines the signature profiles and contributions of each signature to each cancer genome as part of its factorization of the input matrix of mutation spectra. However, with many signatures and/or heterogeneous mutation burdens across samples, the mutations observed in a particular sample can be reconstructed in multiple ways—often with small and/or biologically implausible contributions from many signatures. Therefore, each method has developed a separate procedure for estimating the contributions of signatures to each sample (Methods).

We tested SignatureAnalyzer and SigProfiler on 11 sets of synthetic data (including 64,400 synthetic samples), generated from known signature profiles (Methods, Supplementary Note 2). Both methods performed well in re-extracting known signatures from realistically complex data. Extracted signatures that were discordant from the known input usually arose from difficulties in selecting the correct number of signatures. The results confirm that use of NMF-based approaches for extracting mutational signatures is not a purely algorithmic process, but also requires consideration of evidence from experimentally determined mutational signatures and the DNA damage and repair literature, prior evidence of biological plausibility and human-guided sensitivity analysis confirming that extractions from different groupings of tumours yield consistent results. We used these types of evidence and approaches in determining the signature profiles reported here. The findings are consistent with results regarding NMF, and the related areas of probabilistic topic modelling and latent Dirichlet allocation, in multiple problem domains36,37. It is widely understood that the choice of the number of latent variables (for our purposes, the number of mutational signatures) is rarely amenable to complete automation.

The results from our SigProfiler and SignatureAnalyzer analyses of cancer data exhibited many similarities, and we assigned the same identifiers to similar signatures extracted using the two methods (syn12016215). However, there were also noteworthy differences. The numbers of SBS signatures found in PCAWG tumours with a low mutation burden (94.4% of cases that contain 47% of mutations) were similar: 31 using SigProfiler and 35 using SignatureAnalyzer. However, the numbers of additional SBS signatures extracted from hypermutated PCAWG samples (5.6% of cases, containing 53% of mutations) were different: 13 using SigProfiler and 25 using SignatureAnalyzer. There were also differences in SBS signature profiles, including among signatures found in cases with a low mutation burden. The latter primarily involved relatively featureless (‘flat’) signatures, which are mathematically challenging to deconvolute. Finally, there were differences in signature attributions to individual samples. SignatureAnalyzer used more signatures to reconstruct the mutational profiles (Extended Data Fig. 1) (syn12169204 and syn12177011) and attributions to flat signatures were different (Extended Data Fig. 2a, b) (syn12169204). The DBS and indel signatures were generally similar between the two methods (Extended Data Fig. 2c, d).

The final reference mutational signatures were determined from the PCAWG set, supplemented by additional signatures from the other datasets (COSMIC, available at https://cancer.sanger.ac.uk/cosmic/signatures). Each signature was allocated an identifier consistent with, and extending, the COSMIC v.2 annotation. Some previous signatures split into multiple constituent signatures: these were numbered as in the previous annotation, but with additional letter suffixes (for example, SBS17 was split into SBS17a and SBS17b). DNA sequencing and analysis artefacts also generate mutational signatures. We indicate which signatures are possible artefacts but do not present them below (full information is available at https://cancer.sanger.ac.uk/cosmic/signatures). The results of both SignatureAnalyzer and SigProfiler were used throughout the study. However, for brevity and for continuity with the signature set previously displayed in COSMIC v.2—which has been widely used as a reference—SigProfiler results are outlined here, and SignatureAnalyzer results are provided in Extended Data Figs. 3, 4 and at syn11738307.

Single-base substitution signatures

There were substantial differences in the numbers of SBSs between samples (ranging from hundreds to millions) and between cancer types38 (Fig. 1). In total, 67 SBS mutational signatures were extracted, of which 49 were considered likely to be of biological origin (Fig. 2, Methods; available at https://cancer.sanger.ac.uk/cosmic/signatures/SBS/). Except for signature SBS25, all signatures reported in COSMIC v.2 (ref. 6) were confirmed; the median cosine similarity between the newly derived signatures and those on COSMIC v.2 was 0.95, excluding the ‘split’ signatures (discussed below). SBS25 was previously found in cell lines derived from Hodgkin lymphomas treated with chemotherapy, and no primary cancers of this type were available. The newly derived signatures showed much improved separation from each other and more-distinct signature profiles, as compared with COSMIC v.2 signatures (see ‘Better separation compared to COSMIC v.2 signatures’ in Supplementary Note 2 for more information).Fig. 1 Mutation burdens of SBSs, DBSs and small indels  across PCAWG tumour types.

The numbers of cases of each tumour type are shown next to the labels. Each dot represents one tumour. Tumour types are ordered by the median numbers of single-base substitutions. Only tumour types with >20 samples are shown. AdenoCA, adenocarcinoma; BNHL, B-cell non-Hodgkin lymphoma; ChRCC, chromophobe renal cell carcinoma; CLL, chronic lymphocytic leukaemia; CNS, central nervous system; ColoRect, colorectal; Eso, oesophageal; GBM, glioblastoma; HCC, hepatocellular carcinoma; Medullo, medulloblastoma; MH, microhomology; MPN, myeloproliferative neoplasm; Osteosarc, osteosarcoma; Panc, pancreatic; PiloAstro, pilocytic astrocytoma; Prost; prostate; RCC, renal cell carcinoma; SCC, squamous cell carcinoma; TCC, transitional cell carcinoma; Thy, thyroid.

Fig. 2 Profiles of SBS, DBS and small indel mutational signatures.

The classifications of each mutation type (SBS, 96 classes; DBS, 78 classes; and indels, 83 classes) are described in the main text. Magnified versions of signatures SBS4, DBS2 and ID3 (all of which are associated with tobacco smoking) are shown to illustrate the positions of each mutation subtype on each plot. The plotted data are available in digital form (along with the x axis labels) at syn12025148.

Thirteen of the SBS signatures we extracted (excluding those due to signature splitting) represent newly identified and probably real signatures, not present in COSMIC v.2. Some were rare (SBS31, SBS32, SBS35, SBS36, SBS42 and SBS44). Others were more common, but contributed relatively few mutations and/or were similar to previously discovered signatures (SBS38, SBS39 and SBS40). Notably, SBS40 is a flat signature similar to SBS5. It contributes to multiple types of cancer, but its similarity to SBS5 renders the extent of this contribution uncertain. For some of the newly identified signatures, there were plausible underlying aetiologies (Fig. 3, Extended Data Figs. 4, 5): for SBS31 and SBS35, platinum compound chemotherapy39; for SBS32, azathioprine therapy; for SBS36, inactivating germline or somatic mutations in MUTYH (which encodes a component of the base excision repair machinery)40,41; for SBS38, additional effects of exposure to ultraviolet (UV) light; for SBS42, occupational exposure to haloalkanes13; and for SBS44, defective DNA mismatch repair42.Fig. 3 The number of mutations contributed by each mutational signature to the PCAWG tumours.

The size of each dot represents the proportion of samples of each tumour type that shows the mutational signature. The colour of each dot represents the median mutation burden of the signature in samples that show the signature. Tumours that had few mutations or that were poorly reconstructed by the signature assignment were excluded. The contributions of composite signatures to the PCAWG cancers, and SBS signatures to the complete set of cancer samples analysed, are shown in Extended Data Figs. 4 and 5, respectively. AML, acute myeloid leukaemia; liposarc, liposarcoma; MDS, myelodysplastic syndrome.

Three previously characterized base substitution signatures (SBS7, SBS10 and SBS17) split into multiple constituent signatures (Fig. 2). Signature splitting probably reflects the existence of multiple distinct mutational processes initiated by the same exposure that have closely—but not perfectly—correlated activities. We previously regarded SBS7 as a single signature composed predominantly of C>T at CCN and TCN trinucleotides (the mutated base is underlined) together with many fewer T>N mutations. It was found in malignant melanomas and squamous skin carcinomas, and is probably due to the UV-light-induced formation of pyrimidine dimers, followed by translesion DNA synthesis by error-prone polymerases predominantly inserting A opposite to damaged cytosines. SBS7 has now been decomposed into four constituent signatures. SBS7a and SBS7b (consisting mainly of C>T at TCN and C>T at CCN, respectively) may reflect different pyrimidine-dimer photoproducts. SBS7c and SBS7d (consisting predominantly of T>A at NTT and T>C at NTT, respectively43) may be due to low frequencies of the misincorporation of T and G opposite to thymines in pyrimidine dimers. The splitting of SBS10 and SBS17 is described at https://cancer.sanger.ac.uk/cosmic/signatures/SBS/.

Several base substitution signatures showed transcriptional strand bias, which may be attributable to transcription-coupled nucleotide excision repair acting on DNA damage and/or to an excess of DNA damage on untranscribed strands of genes44. Both mechanisms result in more mutations of damaged bases on untranscribed than on transcribed strands of genes. Assuming that either mechanism is responsible for the observed transcriptional strand biases, DNA damage to cytosine (SBS7a and SBS7b), guanine (SBS4, SBS8, SBS19, SBS23, SBS24, SBS31, SBS32, SBS35 and SBS42), thymine (SBS7c, SBS7d, SBS21, SBS26 and SBS33) and adenine (SBS5, SBS12, SBS16, SBS22 and SBS25) may underlie these mutational signatures (plots of strand bias are available at https://cancer.sanger.ac.uk/cosmic/signatures/SBS/). The likely DNA-damaging agents are known for SBS4 (tobacco mutagens), SBS7a, SBS7b, SBS7c and SBS7d (UV light), SBS22 (aristolochic acid), SBS24 (aflatoxin), SBS25 (chemotherapy), SBS31 and SBS35 (platinum compounds), SBS32 (azathioprine) and SBS42 (haloalkanes).

Using the SBS classification of 1,536 mutation types, which uses the sequence context two bases 5′ and two bases 3′ to each mutated base, yielded signatures that are largely consistent with those based on substitutions in trinucleotide contexts. Notably, however, two forms of both SBS2 and SBS13 were extracted, one with mainly a pyrimidine and the other with mainly a purine at the −2 base (the second base 5′ to the mutated cytosine). These may represent the activities of the cytidine deaminases APOBEC3A and APOBEC3B, respectively45. If so, APOBEC3A accounts for many more mutations than APOBEC3B in cancers with high APOBEC activity. Other signatures showed nonrandom sequence contexts at +2 and −2 positions (for example, SBS17a, SBS17b and SBS9), but sequence context effects were generally much stronger for bases immediately 5′ and 3′ to mutated bases.

SBS signatures showed substantial variation in the numbers of cancer types and cancer samples in which they were found, and in the mutations attributed per cancer sample (Fig. 3). Almost all individual cancer samples exhibited multiple signatures, with a mode of three in the PCAWG set (syn12169204). The assigned signatures reconstruct well the mutational spectra of the cancer samples (in PCAWG samples, the median cosine similarity was 0.97; 96.3% of samples with cosine similarity >0.90): Fig. 4 shows illustrative examples.Fig. 4 Illustrative examples of mutational spectra of individual cancer samples.

The contributory SBS, DBS and small indel mutational signatures in two tumours are shown.

Some mutational processes generate base substitutions that cluster in small genomic regions. The limited numbers of such mutations may result in a failure to detect their signatures using standard methods. We therefore identified clustered mutations in each genome and analysed them separately (Methods). Four main clustered mutational signatures were identified (Fig. 2), as previously reported4,27,32. Two, which are found in multiple types of cancer, were similar to SBS2 and SBS13 (which have been attributed to APOBEC enzyme activity) and represent foci of kataegis3,32,46. Two further clustered signatures, one characterized by C>T and C>G mutations at (A or G)C(C or T) trinucleotides47 and the other T>A and T>C mutations at (A or T)T(A or T), were found in lymphoid neoplasms; they probably represent the direct and indirect consequences of activation-induced cytidine deaminase mutagenesis and translesion DNA synthesis by error-prone polymerases (SBS84 and SBS85, respectively)27.

Doublet-base substitution signatures

Tandem doublet, triplet, quadruplet, quintuplet and sextuplet base substitutions (syn11801938 and syn11726620) were observed at about 1% the prevalence of SBSs. In most cancer genomes, the number of DBSs was considerably higher than would be expected from the random adjacency of SBSs (syn12177057), indicating the existence of commonly occurring, single mutagenic events that cause substitutions at neighbouring bases. There was substantial variation in the number of DBSs, ranging from 0 to 20,818 in a sample. The numbers of DBSs were generally proportional to the numbers of SBSs (Fig. 1), although colorectal adenocarcinomas had fewer than expected, and lung cancers and melanomas had more (Extended Data Table 1). We extracted eleven DBS signatures (Fig. 2, of which three have previously been reported33,48.

Signature DBS1 was characterized by CC>TT mutations (Fig. 2), contributed hundreds to tens of thousands of mutations in malignant melanomas with SBS7a and SBS7b (Fig. 3), exhibited transcriptional strand bias consistent with damage to cytosines (syn12177063) and is a known consequence of DNA damage induced by UV light33,49. Excluding cancers associated with exposure to UV light also yielded a signature (DBS11) that was characterized predominantly by CC>TT mutations, but only contributing tens of mutations in many samples from multiple types of cancer (Figs. 2, 3). DBS11 was associated with SBS2, which is due to APOBEC activity: APOBEC activity may, therefore, also generate DBS11.

DBS2 was composed predominantly of CC>AA mutations, with smaller numbers of CC>AG and CC>AT mutations, and contributed hundreds to thousands of mutations in lung adenocarcinoma, lung squamous and head and neck squamous carcinomas, which are often caused by tobacco smoking33 (Figs. 2, 3). DBS2 showed transcriptional strand bias indicative of guanine damage (syn12177064) and was associated with SBS4, which is caused by exposure to tobacco smoke. It is likely, therefore, that DBS2 can be a consequence of DNA damage by tobacco-smoke mutagens.

A signature similar to DBS2 contributed hundreds of mutations to liver cancers and tens of mutations to other types of cancer without evidence of exposure to tobacco smoke. A pattern resembling DBS2 also dominates DBSs in healthy mouse cells50. The nature of the mutational processes that underlie these signatures in human cancers that are unrelated to smoking, and in healthy mice, is unknown. However, in experimental systems, acetaldehyde exposure has been shown to generate a mutational signature characterized primarily by CC>AA mutations, and lower burdens of CC>AG and CC>AT mutations, together with C>A SBSs48. Acetaldehyde is an oxidation product of alcohol and a constituent of cigarette smoke. The role of acetaldehyde, and perhaps other aldehydes, in generating DBS2 merits further investigation51.

DBS3, DBS7, DBS8 and DBS10 showed hundreds to thousands of mutations in rare colorectal, stomach and oesophageal cancers, some of which showed evidence of defective DNA mismatch repair (DBS7 and DBS10) or polymerase epsilon exonuclease domain mutations (DBS3) that generate hypermutator phenotypes (Figs. 2, 3). DBS5 was found in cancers exposed to platinum chemotherapy, and is associated with SBS31 and SBS35.

Small insertion-and-deletion signatures

Indels were usually present at about 10% of the frequency of base substitutions (Fig. 1). There was substantial variation between cancer genomes in the number of indels, even when cancers with evidence of defective DNA mismatch repair were excluded. Overall, the numbers of deletions and insertions were similar, but there was variation between cancer types: some cancers showed more deletions and others more insertions of various subtypes (Fig. 1). We extracted 17 indel mutational signatures (Fig. 2).

Indel signature 1 (ID1) was composed predominantly of insertions of thymine and ID2 was composed predominantly of deletions of thymine, both at long (≥5) thymine mononucleotide repeats (Fig. 2). Tens to hundreds of mutations of both signatures were found in most samples of most types of cancer, but were particularly common in colorectal, stomach, endometrial and oesophageal cancers and in diffuse large B cell lymphoma (Fig. 3). Together, ID1 and ID2 accounted for 97% and 45% of indels in hypermutated and non-hypermutated cancer genomes, respectively (Extended Data Table 2). They are probably due to slippage of either the nascent (ID1) or template strand (ID2) during DNA replication of long mononucleotide tracts.

ID3 was characterized predominantly by deletions of cytosine at short (≤5-bp long) mononucleotide cytosine repeats and exhibited hundreds of mutations in cancers of the lung, head and neck that are associated with tobacco smoking (Figs. 2, 3). There was transcriptional strand bias of mutations, with more guanine deletions than cytosine deletions on the untranscribed strands of genes, which is compatible with transcription-coupled nucleotide excision repair of damaged guanine (syn12177065 and syn12177066). The numbers of ID3 mutations positively correlated with the numbers of SBS4 and DBS2 mutations, which we have shown are associated with tobacco smoking (Extended Data Figs. 6, 7). Thus, DNA damage by components of tobacco smoke probably underlie ID3.

ID13 was characterized predominantly by deletions of thymine at thymine–thymine dinucleotides and exhibited large numbers of mutations in malignant melanomas of the skin (Figs. 2, 3). The numbers of ID13 mutations correlated with the numbers of SBS7a, SBS7b and DBS1 mutations, which we have attributed to DNA damage induced by UV light (Extended Data Figs. 6, 7). However, deletions of cytosine at cytosine–cytosine dinucleotides did not feature strongly in ID13, which may reflect the predominance of thymine compared to cytosine dimers induced by UV light52.

ID6 and ID8 were both characterized predominantly by ≥5-bp deletions (Fig. 2). ID6 exhibited overlapping microhomology at deletion boundaries with a mode of 2 bp (and often longer stretches) and correlated with SBS3, which we have attributed to defective homologous-recombination-based repair (Extended Data Figs. 6, 7). By contrast, ID8 deletions showed shorter or no microhomology at deletion boundaries and did not strongly correlate with SBS3. Both deletion patterns may be characteristic of DNA double-strand-break repair by non-homologous-recombination-based end-joining mechanisms and—if so—this suggests that at least two distinct forms are operative in human cancer53.

A small fraction of cancers exhibited very large numbers of ID1 and ID2 mutations (>10,000) (Fig. 3) (shown at https://cancer.sanger.ac.uk/cosmic/signatures/ID). These were usually accompanied by SBS6, SBS14, SBS15, SBS20, SBS21, SBS26 and/or SBS44, which are associated with deficiency in DNA mismatch repair—sometimes combined with POLE or POLD1 proofreading deficiency (SBS14 and SBS20)35. Occasional cases with these signatures additionally showed large numbers of indels attributed to ID7 (syn11738668), and rare samples showed large numbers of ID4, ID11, ID14, ID15, ID16 or ID17 mutations but did not show large numbers of ID1 and ID2 mutations or the SBS signatures associated with deficiency in DNA mismatch repair.

Correlations with age

A positive correlation between age of cancer diagnosis and the number of mutations attributable to a signature suggests that the underlying mutational process has been operative (at a more or less constant rate) throughout the cell lineage from fertilized egg to cancer cell, and thus in the normal cells from which that type of cancer develops6,54. Confirming previous reports6,54, the numbers of SBS1 and SBS5 mutations correlate with age, and exhibit different rates in different types of tissue (q values provided in syn12030687, syn20317940 and syn12217988). SBS40 also correlated with age in multiple types of cancer, although—given its similarity to SBS5—misattribution cannot be excluded. DBS2 and DBS4 correlated with age; consistent with activity in normal cells and, when combined their profiles closely resemble the spectrum of DBS mutations found in normal mouse cells50. ID1, ID2, ID5 and ID8 showed correlations with age in multiple tissues. ID1 and ID2 indels are probably due to slippage at poly T repeats during DNA replication and correlated with the numbers of SBS1 substitutions, which have previously been proposed to reflect the number of mitoses a cell has experienced6. Thus, SBS1, ID1 and ID2 may all be generated during DNA replication at mitosis. The number of ID5 mutations correlated with the number of SBS40 mutations, and the mutational processes that underlie these two age-correlated signatures may therefore contain common components. ID8, which is predominantly composed of ≥5-bp deletions with no or 1 bp of microhomology at their boundaries, is probably due to DNA double-strand breaks repaired by a non-homologous-end-joining mechanism. The results indicate that multiple mutational processes operate in normal cells.

Discussion

There are important constraints, limitations and assumptions in the analytic frameworks used here to characterize mutational signatures. Signatures extracted from sample sets in which multiple processes are operative remain mathematical approximations, with profiles that are potentially influenced by the mathematical approach used and other factors. For conceptual and practical simplicity, we assume that a single signature is associated with each mutational process and provide an average reference signature to represent it. However, we do not discount the possibility that further nuances and variations of signature profiles exist. We have estimated the contributions from each signature to the mutation burden in each sample. However, with increasing numbers of signatures and differences of multiple orders of magnitude in mutation burdens between some signatures, prior knowledge has helped to avoid biologically implausible results. Thus, the further development of methods for deciphering and attributing mutational signatures is warranted, ideally supported by signatures derived from experimental systems in which the causes are known. Nevertheless, signatures with many similarities and some differences can be found by different mathematical approaches, and these can be confirmed in several ways, including experimentally elucidated signatures5,31,39,42,43,54–62 and tumours dominated by a single signature (syn12016215).

This analysis includes most publicly available exome and whole-genome cancer sequences. Some rare or geographically restricted signatures may not have been captured, signatures conferring limited mutation burdens may have been missed and signatures of therapeutic mutagenic exposures have not been exhaustively explored. Nevertheless, it is likely that a substantial proportion of the naturally occurring mutational signatures found in human cancer have now been described. This comprehensive repertoire provides a foundation for research into the aetiologies of geographical and temporal differences in cancer incidence, the mutational processes that operate in healthy tissues and non-neoplastic disease states, clinical and public health applications of signatures and mechanistic understanding of the mutational processes that underlie carcinogenesis.

Methods

No statistical methods were used to predetermine sample size. The experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment.

These online methods contain an abridged description of the methodology used in the current manuscript; extensive details about the methodology we used are provided in Supplementary Note 2. Importantly, two independently developed computational frameworks (SigProfiler and SignatureAnalyzer) based on NMF were applied separately to the examined sets of mutational catalogues. SigProfiler and SignatureAnalyzer take different approaches for deciphering mutational signatures and for assigning each signature to each sample. By using two methods, we aimed to provide a perspective on the effect that different methodologies can have on the numbers of signatures generated, signature profiles and attributions. In addition to applying SigProfiler and SignatureAnalyzer to cancer data, the tools were also applied to realistic synthetic data with known solutions.

Analysis of mutational signatures with SigProfiler

SigProfiler incorporates two distinct steps for identification of mutational signatures, based on the previously described methodology6,11,17 (Extended Data Fig. 8). The first step (SigProfilerExtraction) encompasses a hierarchical de novo extraction of mutational signatures based on somatic mutations and their immediate sequence context, and the second step (SigProfilerAttribution) focuses on accurately estimating the number of somatic mutations associated with each extracted mutational signature in each sample. SigProfilerExtraction is an extension of a previous framework for the analysis of mutational signatures11,17. In brief, for a given set of mutational catalogues, the algorithm deciphers a minimal set of mutational signatures that optimally explains the proportion of each mutation type and estimates the contribution of each signature to each sample. More specifically, for each NMF iteration, SigProfilerExtraction minimizes a generalized Kullback–Leibler divergence constrained for nonnegativity (Supplementary Note 2). The algorithm uses multiple NMF iterations (in most cases 1,024) to identify the matrix of mutational signatures and the matrix of the activities of these signatures, as previously described17. The unknown number of signatures is determined by human assessment of the stability and accuracy of solutions for a range of values, as previously described17. The framework is applied hierarchically to increase its ability to find mutational signatures that generate few mutations or are present in few samples.

After signatures are discovered by SigProfilerExtraction, SigProfilerAttribution estimates their contributions to individual samples. For each examined sample, the estimation algorithm involves finding the minimum of the Frobenius norm of a constrained function using a nonlinear convex optimization programming solver using the interior-point algorithm63. See Supplementary Note 2 and Extended Data Fig. 8b for further details.

Analysis of mutational signatures with SignatureAnalyzer

SignatureAnalyzer uses a Bayesian variant of NMF that infers the number of signatures through the automatic relevance determination technique and delivers highly interpretable and sparse representations for both signature profiles and attributions that strike a balance between data fitting and model complexity. Further details of the actual implementation of the computational approach have previously been published9,27,64. SignatureAnalyzer was applied by using a two-step signature extraction strategy using 1,536 pentanucleotide contexts for SBSs, 83 indel features and 78 DBS features. In addition to the separate extraction of SBS, indel and DBS signatures, we performed a ‘COMPOSITE’ signature extraction based on all 1,697 features (1,536 SBS + 78 DBS + 83 indel). For SBSs, the 1,536 SBS COMPOSITE signatures are preferred; for DBSs and indels, the separately extracted signatures are preferred.

In step 1 of the two-step extraction process, global signature extraction was performed for the samples with a low mutation burden (n = 2,624). These excluded hypermutated tumours: those with putative polymerase epsilon (POLE) defects or mismatch repair defects (microsatellite instable tumours), skin tumours (which had intense UV-light mutagenesis) and one tumour with temozolomide (TMZ) exposure. Because the underlying algorithm of SignatureAnalyzer performs a stochastic search, different runs can produce different results. In step 1, we ran SignatureAnalyzer 10 times and selected the solution with the highest posterior probability. In step 2, additional signatures unique to hypermutated samples were extracted (again selecting the highest posterior probability over ten runs) while allowing all signatures found in the samples with low mutation burden, to explain some of the spectra of hypermutated samples. This approach was designed to minimize a well-known ‘signature bleeding’ effect or a bias of hyper- or ultramutated samples on the signature extraction. In addition, this approach provided information about which signatures are unique to the hypermutated samples, which was later used when attributing signatures to samples.

A similar strategy was used for signature attribution: we performed a separate attribution process for low- and hypermutated samples in all COMPOSITE, SBS, DBS and indel signatures. For downstream analyses, we preferred to use the COMPOSITE attributions for SBSs and the separately calculated attributions for DBSs and indels. Signature attribution in samples with a low mutation burden was performed separately in each tumour type (for example, Biliary–AdenoCA, Bladder–TCC, Bone–Osteosarc, and so on). Attribution was also performed separately in the combined microsatellite instable tumours (n = 39), POLE (n = 9), skin melanoma (n = 107) and TMZ-exposed samples (syn11738314). In both groups, signature availability (which signatures were active, or not) was primarily inferred through the automatic relevance determination process applied to the activity matrix H only, while fixing the signature matrix W. The attribution in samples with a low mutation burden was performed using only signatures found in the step 1 of the signature extraction. Two additional rules were applied in SBS signature attribution to enforce biological plausibility and minimize a signature bleeding: (i) allow SBS4 (smoking signature) only in lung, head and neck cases; and (ii) allow SBS11 (TMZ signature) in a single GBM sample. This was enforced by introducing a binary, signature-by-sample signature indicator matrix Z (1, allowed; 0, not allowed), which was multiplied by the H matrix in every multiplication update of H. No additional rules were applied to indel or DBS signature attributions, except that signatures found in hypermutated samples were not allowed in samples with a low mutation burden.

Application of SigProfiler and SignatureAnalyzer to synthetic data

Our goal was to evaluate SignatureAnalyzer and SigProfiler on realistic synthetic data to identify any potential limitations of these two methods. SignatureAnalyzer and SigProfiler were tested on 11 sets of synthetic data, encompassing a total of 64,400 synthetic samples, in which known signature profiles were used to generate catalogues of synthetic mutational spectra. We operationally defined ‘realistic’ data as those based on the characteristics of either SignatureAnalyzer’s or SigProfiler’s analysis of the PCAWG genome data. SignatureAnalyzer’s reference signature profiles were based on COMPOSITE signatures, consisting of 1,536 types of strand-agnostic SBSs in pentanucleotide context, 78 types of DBSs and 83 types of small indels, for a total of 1,697 mutation types. SigProfiler’s reference analysis was based on strand-agnostic SBSs in the context of one 5′ and one 3′ base. For each test, we generated two sets of realistic data: SigProfiler-realistic (based on SigProfiler’s reference signatures and attributions) and SignatureAnalyzer-realistic (based on SignatureAnalyzer’s reference signatures and attributions), as well as two other types of data that involved using SignatureAnalyzer profiles with SigProfiler attributions and vice versa. A detailed description of each of the 11 sets of synthetic data and the results from applying SigProfiler and SignatureAnalyzer are provided in Supplementary Note 2.

Analysis of clustered mutational signatures

Somatic SBSs were considered clustered if they had intermutational distances < 1,000 bp. More specifically, for each sample, an SBS mutational catalogue was generated for substitutions that were <1,000 bp from another substitution. Subsequently, the set of SBS mutational catalogues containing clustered mutations underwent de novo extraction of mutational signatures. Any novel mutational signature (one that was not previously observed in the complete SBS catalogues) was reported as a clustered mutational signature.

Better separation compared to COSMIC v.2 signatures

As described in the manuscript, all mutational signatures previously reported in COSMIC v.2 were confirmed in the new set of analyses with median cosine similarity of 0.95. However, the separation between the COSMIC v.2 mutational signatures (https://cancer.sanger.ac.uk/cosmic/signatures_v2) is much worse than the separation between the mutational signatures reported here. For example, in COSMIC v.2, signatures 5 and 16 had a cosine similarity of 0.90, making them hard to distinguish from one another. By contrast, in the current analysis, SBS5 and SBS16 have a cosine similarity of 0.65. This allows us to unambiguously assign SBS5 and SBS16 to different samples. In the current analysis, the larger number of samples has allowed the reduction of bleeding between signatures and has given more unique and easily distinguishable signatures. One can evaluate the overall separation of a set of mutational signatures by examining the distribution of cosine similarities between the signatures in the set. The signatures in COSMIC v.2 had a median cosine similarity of 0.238. By contrast, the current signatures have a much lower median cosine similarity of 0.098. This twofold reduction in similarity is highly statistically significant (P value 9.1 × 10−25) and indicates a better separation between the signatures in the current analysis.

Correlations of mutational signature activity with age

Before evaluating the association between age and the activity of a mutational signature, all outliers for both age and numbers of mutations attributed to a signature in a cancer type were removed from the data. An outlier was defined as any value outside three standard deviations from the mean value. A robust linear regression model that estimated the slope of the line and whether this slope was significantly different from zero (F test; P value < 0.05) was performed using the MATLAB function robustfit (https://www.mathworks.com/help/stats/robustfit.html) with default parameters. The P values from the F tests were corrected using the Benjamini–Hochberg procedure for false discovery rates. Results are available at syn12030687 and syn20317940.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

Online content

Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-020-1943-3.

Supplementary information

Supplementary Table This file contains Supplementary Table 1: Summary of datasets used in this paper.

Reporting Summary

Supplementary Information Supplementary Note 1: Members of the TCGA/ICGC Pan-Cancer Analysis of Whole Genomes Network.

Supplementary Information Supplementary Note 2: Additional methods details.

Extended data figures and tables

Extended Data Fig. 1 Histogram of the number of signatures attributed in each of 2,780 PCAWG samples by SigProfiler and SignatureAnalyzer.

Hypermutated tumours and melanomas (156) are listed at syn11738314.

Extended Data Fig. 2 Comparisons between results of SigProfiler and SignatureAnalyzer.

a, b, Comparison of the attributions for corresponding SigProfiler (a) and SignatureAnalyzer (b) signatures. Each one of the SBS signatures extracted by SigProfiler and SignatureAnalyzer was paired with the signature of highest cosine similarity in the extraction by the other method (if one with >0.85 cosine similarity exists). The first column of the plot corresponds to the fraction of mutations assigned by one method (summed across samples and mutation types) that was also assigned by the other method. The remaining mutations were then redistributed to the other signatures in the extraction, weighted by their relative probabilities of having been generated by each signature and the resulting fraction of mutations was then plotted. Signatures on the x axis are shown only if they contribute at least a 0.1 fraction of mutations to at least one signature on the y axis. c, d, Cosine similarities between SigProfiler and SignatureAnalyzer DBS (c) and indel (d) signatures. Brown nodes represent SigProfiler signatures; green nodes represent SignatureAnalyzer signatures. Matches with cosine similarities > 0.8 are shown as edges; the width of the edge indicates the strength of the similarity. The locations of the nodes have no meaning. Signatures with no matches of >0.8 cosine similarity are shown below. SigProfiler ID15 and ID17 were extracted from data that were not analysed by SignatureAnalyzer. The suffix ‘P’ on a SignatureAnalyzer signature name indicates a signature extracted from non-hypermutated, non-melanoma tumours. The suffix ‘S’ on a SignatureAnalyzer signature name indicates a signature extracted from hypermutated or melanoma tumours.

Extended Data Fig. 3 SignatureAnalyzer reference signatures.

The classifications of each mutation type (SBS, 96 classes; DBS, 78 classes; and indels, 83 classes) are described in the main text.

Extended Data Fig. 4 The number of SBS mutations attributed to each mutational signature for each cancer type over the PCAWG tumours by SignatureAnalyzer.

Conventions are as in Fig. 3; see this figure for explanation.

Extended Data Fig. 5 The number of SBS mutations attributed to each mutational signature to each cancer type over the complete set of PCAWG and non-PCAWG cancer samples analysed by SigProfiler.

Conventions are as in Fig. 3; see this figure for explanation.

Extended Data Fig. 6 Associations between SBS, DBS and indel signature activities for SigProfiler and SignatureAnalyzer.

a, b, Each node represents an SBS (light green), DBS (dark green) or indel (black) signature. Any two signatures with sample attributions that significantly correlated with R2 > 0.3 (SigProfiler) (a) or > 0.5 (SignatureAnalyzer) (b) are connected by edges. Edge widths are proportional to the strength of the correlation. Signatures with no significant correlation to any other signature above the relevant threshold are not shown. Signature locations are fit for display purposes only, and do not indicate similarity.

Extended Data Fig. 7 Mutational signatures extracted from the COMPOSITE feature set consisting of the concatenation of SBSs in pentanucleotide context, DBSs and indels.

For each of the 4 COMPOSITE mutational signatures shown, the top panel shows the SBS signature in pentanucleotide context (1,536 mutation classes) after being collapsed to 96 SBS mutation classes, the middle panel is the co-extracted DBS signature and the bottom panel is the co-extracted indel signature. There are similarities between the DBS portion of Composite-4 and DBS2, and between the indel portion of Composite-4 and ID3; other similarities are noted in the figure.

Extended Data Fig. 8 SigProfiler signature extraction and attribution.

A full description is provided in Supplementary Note 2. a, Procedure for extracting (discovering) mutational signatures. Step A, apply the approach to a set of samples D; initially D contains all samples (that is, D = M). This step has previously been described in detail17. Step B, solution evaluation and re-iteration. Extracted mutational signatures and their activities in individual samples are saved into a set (S). The activity of any signature that does not increase the cosine similarity of a sample by > 0.01 was removed from the sample (assigned a value of 0). Step A is repeated for all samples for which the identified signatures do not explain their patterns (cosine similarity < 0.95). The algorithm continues to step C when step A cannot find any stable signatures. Step C, clustering of mutational signatures. Hierarchical consensus clustering was applied to the set S to derive the consensus mutational signatures across the set of samples M. b, Attribution of activities of mutational signatures in samples.

Extended Data Table 1 The number of DBSs is proportional to the number of SBSs, with few exceptions

The number of DBSs is proportional to the number of SBSs, with few exceptions

The exceptions are colorectal adenocarcinoma (Colorect–AdenoCA), lung adenocarcinoma (Lung–AdenoCA), lung squamous cell carcinoma (Lung–SCC) and skin–melanoma, as analysed by the following linear regression (computed by an R function call): glm(DBS.count ~ SBS.count + Cancer.Type). This function call fits a model in which the number of DBSs depends linearly on the number of SBSs and on the cancer type. P values associated with the coefficients are two-sided.

Extended Data Table 2 Numbers of insertion and deletion mutations due to ID1, ID2 and all other indel signatures in hypermuted and non-hypermutated tumours

Numbers of insertion and deletion mutations due to ID1, ID2 and all other indel signatures in hypermutated and non-hypermutaed tumourst

Supplementary information

is available for this paper at 10.1038/s41586-020-1943-3.

Extended data

is available for this paper at 10.1038/s41586-020-1943-3.

Acknowledgements

The results here are based in part on data generated by the TCGA research network (http://cancergenome.nih.gov/) and the ICGC and TCGA PCAWG network. This work was supported by Wellcome grant reference 206194 (M.R.S.), Singapore National Medical Research Council grants NMRC/CIRG/1422/2015 and MOH-000032/MOH-CIRG18may-0004 and the Singapore Ministry of Health via the Duke-NUS Signature Research Programmes (M.N.H., A.W.T.N., Y.W., A.B. and S.G.R.), US National Institute of Health Intramural Research Program Project Z1AES103266 (D.A.G.), the European Research Council Consolidator Grant 682398 (N.L.-B.), US National Cancer Institute U24CA143843 (D.A.W.) and Cancer Research UK Grand Challenge Award C98/A24032 (E.N.B., S.M.A.I., L.B.A. and M.R.S.). G.G and J.K were partially supported by the National Cancer Institute grants U24CA210999 and U24CA143845. G.G. was partially supported by the Paul C. Zamecnick, MD, Chair in Oncology at the Massachusetts General Hospital Cancer Center. N.J.H. and G.G. were partially supported by G.G.’s funds at the Broad Institute and Massachusetts General Hospital. N.J.H. was partially funded by the Molecular Biophysics Training Grant NIH/ NIGMS T32 GM008313 (PI: Venkatesh N. Murthy).We acknowledge the contributions of the many clinical networks across the ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonized variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. The members of the PCAWG Consortium are listed in Supplementary Note 1.

Author contributions

The ICGC and TCGA contributed collectively to this work under the guidance of PCAWG Steering and Executive Committees, and the Ethics and Legal Working Group. The International Cancer Genome Consortium and TCGA tumour specific providers provided tumour and matched non-tumour samples, and the PCAWG Technical Working Group, the PCAWG Quality Control Working Group and the PCAWG Novel Somatic Mutation Calling Methods Working Group provided standardized mutation calls for the 2,780 PCAWG whole genomes. G.G., S.G.R. and M.R.S. were project leaders; L.B.A., G.G., S.G.R. and M.R.S. obtained funding for this study; L.B.A., J.K., N.J.H., G.G., S.G.R. and M.R.S. designed this study; M.N.H., A.W.T.N., A.B., E.N.B., J.R.M. and S.G.R. collected and prepared data for analysis; L.B.A., J.K., E.N.B. and S.M.A.I. created mutational signature analysis software; L.B.A., J.K., N.J.H., A.W.T.N., A.B., K.R.C., D.A.G., N.L.-B., L.J.K., S.M., R.S., D.A.W., V.M., G.G., S.G.R. and M.R.S. analysed data and reviewed results; L.B.A., J.K., N.J.H., G.G., S.G.R. and M.R.S. wrote the paper; L.B.A., J.K., N.J.H., M.N.H. and A.W.T.N. created figures; and Y.W. and S.G.R. generated synthetic data and benchmarked signature analysis software.

Data availability

Somatic and germline variant calls, mutational signatures, subclonal reconstructions, transcript abundance, splice calls and other core data generated by the ICGC and TCGA PCAWG Consortium are described in ref. 2, and are available for download at https://dcc.icgc.org/releases/PCAWG. Additional information on accessing the data, including raw read files, can be found at https://docs.icgc.org/pcawg/data/. In accordance with the data access policies of the ICGC and TCGA projects, most molecular, clinical and specimen data are in an open tier that does not require access approval. To access information that could potentially identify participants, such as germline alleles and the underlying sequencing data, researchers will need to apply to the TCGA data access committee via dbGaP (https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?page=login) for access to the TCGA portion of the dataset, and to the ICGC data access compliance office (http://icgc.org/daco) for the ICGC portion of the dataset. In addition, to access somatic single nucleotide variants derived from TCGA donors, researchers will also need to obtain dbGaP authorization. For each mutational signature as extracted by SigProfiler, there is a ‘vignette’ that consists of plots and a short textual description at COSMIC (available at https://cancer.sanger.ac.uk/cosmic/signatures/). Beyond the core sequence data generated by the ICGC and TCGA PCAWG Consortium, other derived datasets were generated by the research reported in this paper. These derived datasets are available at Synapse (https://www.synapse.org/#!Synapse:syn11726601/wiki/513478), and are denoted by accession numbers (synXXXXXXXX). All these datasets are mirrored at https://dcc.icgc.org/releases/PCAWG/mutational_signatures/ with full links, filenames, accession numbers and descriptions as detailed in Supplementary Table 1. These datasets include (1) CSV files comprising all catalogues of observed mutational spectra that were used as input to signature extraction (syn11801889), (2) CSV files and plots of signatures extracted by SigProfiler (syn11738306) and SignatureAnalyzer (syn11738307), (3) CSV files with estimates of the numbers of mutations generated by each signature in individual tumours (syn11804065), (4) estimates of the probability that each signature was responsible for each mutational type (for example, CTG>CAG) in individual tumours (syn11804068) and (5) synthetic test input data plus the results of tests of signature extraction (discovery) on the synthetic test data (syn18497223). All derived datasets are open access, and can be downloaded without registration or logging in.

Code availability

SigProfiler is available both as a MATLAB framework and as a Python package. In both cases, SigProfiler is a fully functional, free and open-source tool distributed under the permissive 2-Clause BSD License. SigProfiler in MATLAB can be downloaded from: https://www.mathworks.com/matlabcentral/fileexchange/38724-sigprofiler. SigProfiler in Python can be downloaded from: https://github.com/AlexandrovLab/SigProfilerExtractor. SignatureAnalyzer code is available at https://github.com/broadinstitute/getzlab-SignatureAnalyzer (github.com). The code used to generate the synthetic data and summarize SignatureAnalyzer and SigProfiler results is open source and freely available as the SynSig package: https://github.com/steverozen/SynSig/tree/v0.2.0 under the GNU General Public License v.3.0. The core computational pipelines used by the PCAWG Consortium for alignment, quality control and variant calling are available to the public at https://dockstore.org/search?search=pcawg under the GNU General Public License v.3.0, which allows for reuse and distribution.

Competing interests

G.G. receives research funds from IBM and Pharmacyclics and is an inventor on patent applications related to MuTect, ABSOLUTE, MutSig, MSMuTect and POLYSOLVER. All the other authors have no competing interests.

Peer review information Nature thanks Arul Chinnaiyan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Ludmil B. Alexandrov, Jaegil Kim, Nicholas J. Haradhvala, Mi Ni Huang

These authors jointly supervised this work: Gad Getz, Steven G. Rozen, Michael R. Stratton

A list of members and their affiliations appears at the end of the paper

A list of members and their affiliations appears online

Change history

1/25/2023

A Correction to this paper has been published: 10.1038/s41586-022-05600-5
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