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PDF file - 381K, Results of Melanoma Specific Mutation Panel.

PDF file - 270K, Mutation List used in confirmation phase.

XLS file - 50K, Confirmation panel assay design.

The ABO histo-blood group, the critical determinant of transfusion incompatibility, was the first genetic polymorphism discovered in humans. Remarkably, ABO antigens are also polymorphic in many other primates, with the same two amino acid changes responsible for A and B specificity in all species sequenced to date. Whether this recurrence of A and B antigens is the result of an ancient polymorphism maintained across species or due to numerous, more recent instances of convergent evolution has been debated for decades, with a current consensus in support of convergent evolution. We show instead that genetic variation data in humans and gibbons as well as in Old World monkeys are inconsistent with a model of convergent evolution and support the hypothesis of an ancient, multiallelic polymorphism of which some alleles are shared by descent among species. These results demonstrate that the A and B blood groups result from a trans-species polymorphism among distantly related species and has remained under balancing selection for tens of millions of years—to date, the only such example in hominoids and Old World monkeys outside of the major histocompatibility complex.

Background Public health sun safety campaigns introduced during the 1980s have successfully reduced skin cancer rates in Australia. Despite this success, high rates of sunburn continue to be reported by youth and young adults. As such, new strategies to reinforce sun protection approaches in this demographic are needed. Objective This study aims to develop a virtual reality (VR) game containing preventive skin cancer messaging and to assess the safety and satisfaction of the design based on end user feedback. Methods Using a two-phase design approach, we created a prototype VR game that immersed the player inside the human body while being confronted with growing cancer cells. The first design phase involved defining the problem, identifying stakeholders, choosing the technology platform, brainstorming, and designing esthetic elements. In the second design phase, we tested the prototype VR experience with stakeholders and end users in focus groups and interviews, with feedback incorporated into refining and improving the design. Results The focus groups and interviews were conducted with 18 participants. Qualitative feedback indicated high levels of satisfaction, with all participants reporting the VR game as engaging. A total of 11% (2/8) of participants reported a side effect of feeling nauseous during the experience. The end user feedback identified game improvements, suggesting an extended multistage experience with visual transitions to other environments and interactions involving cancer causation. The implementation of the VR game identified challenges in sharing VR equipment and hygiene issues. Conclusions This study presents key findings highlighting the design and implementation approaches for a VR health intervention primarily aimed at improving sun protection behaviors. This design approach can be applied to other health prevention programs in the future.

BACKGROUND Public health sun safety campaigns introduced during the 1980s have successfully reduced skin cancer rates in Australia. Despite this success, high rates of sunburn continue to be reported by youth and young adults. As such, new strategies to reinforce sun protection approaches in this demographic are needed. OBJECTIVE This study aims to develop a virtual reality (VR) game containing preventive skin cancer messaging and to assess the safety and satisfaction of the design based on end user feedback. METHODS Using a two-phase design approach, we created a prototype VR game that immersed the player inside the human body while being confronted with growing cancer cells. The first design phase involved defining the problem, identifying stakeholders, choosing the technology platform, brainstorming, and designing esthetic elements. In the second design phase, we tested the prototype VR experience with stakeholders and end users in focus groups and interviews, with feedback incorporated into refining and improving the design. RESULTS The focus groups and interviews were conducted with 18 participants. Qualitative feedback indicated high levels of satisfaction, with all participants reporting the VR game as engaging. A total of 11% (2/8) of participants reported a side effect of feeling nauseous during the experience. The end user feedback identified game improvements, suggesting an extended multistage experience with visual transitions to other environments and interactions involving cancer causation. The implementation of the VR game identified challenges in sharing VR equipment and hygiene issues. CONCLUSIONS This study presents key findings highlighting the design and implementation approaches for a VR health intervention primarily aimed at improving sun protection behaviors. This design approach can be applied to other health prevention programs in the future. CLINICALTRIAL

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Melanoma of the skin is a common cancer only in Europeans, whereas it arises in internal body surfaces (mucosal sites) and on the hands and feet (acral sites) in people throughout the world. Here we report analysis of whole-genome sequences from cutaneous, acral and mucosal subtypes of melanoma. The heavily mutated landscape of coding and non-coding mutations in cutaneous melanoma resolved novel signatures of mutagenesis attributable to ultraviolet radiation. However, acral and mucosal melanomas were dominated by structural changes and mutation signatures of unknown aetiology, not previously identified in melanoma. The number of genes affected by recurrent mutations disrupting non-coding sequences was similar to that affected by recurrent mutations to coding sequences. Significantly mutated genes included BRAF, CDKN2A, NRAS and TP53 in cutaneous melanoma, BRAF, NRAS and NF1 in acral melanoma and SF3B1 in mucosal melanoma. Mutations affecting the TERT promoter were the most frequent of all; however, neither they nor ATRX mutations, which correlate with alternative telomere lengthening, were associated with greater telomere length. Most melanomas had potentially actionable mutations, most in components of the mitogen-activated protein kinase and phosphoinositol kinase pathways. The whole-genome mutation landscape of melanoma reveals diverse carcinogenic processes across its subtypes, some unrelated to sun exposure, and extends potential involvement of the non-coding genome in its pathogenesis.

The treatment of melanoma has been markedly improved by the introduction of targeted therapies and checkpoint blockade immunotherapy. Unfortunately, resistance to these therapies remains a limitation. Novel anticancer therapeutics targeting the MCL1 anti-apoptotic protein have shown impressive responses in haematological cancers but are yet to be evaluated in melanoma. To assess the sensitivity of melanoma to new MCL1 inhibitors, we measured the response of 51 melanoma cell lines to the novel MCL1 inhibitor, S63845. Additionally, we assessed combination of this drug with inhibitors of the bromodomain and extra-terminal (BET) protein family of epigenetic readers, which we postulated would assist MCL1 inhibition by downregulating anti-apoptotic targets regulated by NF-kB such as BCLXL, BCL2A1 and XIAP, and by upregulating pro-apoptotic proteins including BIM and NOXA. Only 14% of melanoma cell lines showed sensitivity to S63845, however, combination of S63845 and I-BET151 induced highly synergistic apoptotic cell death in all melanoma lines tested and in an in vivo xenograft model. Cell death was dependent on caspases and BAX/BAK. Although the combination of drugs increased the BH3-only protein, BIM, and downregulated anti-apoptotic proteins such as BCL2A1, the importance of these proteins in inducing cell death varied between cell lines. ABT-199 or ABT-263 inhibitors against BCL2 or BCL2 and BCLXL, respectively, induced further cell death when combined with S63845 and I-BET151. The combination of MCL1 and BET inhibition appears to be a promising therapeutic approach for metastatic melanoma, and presents opportunities to add further BCL2 family inhibitors to overcome treatment resistance.

We asked young scientists: Are our schools and universities adequately prepared to educate young people for future challenges? What is the most pressing issue in your field, and what one improvement could your country make to its current education system to prepare students to face it? The responses expressed concerns about the current state of education in countries around the world. Many students lack access to the information they need, and those with access are often constrained by curriculum that emphasizes rote learning and isolated subjects. Our respondents suggested a variety of improvements to prepare the next generation for success.

In Mary Shelley's Frankenstein , Victor Frankenstein's well-intentioned research goes awry, creating a monster. The novel was first adapted to film in 1910, and many movie remakes and variations followed. We asked young scientists to craft their own Frankenstein-inspired science fiction by pitching

We asked young scientists this question: What is your New Year's resolution for your field? Describe one thing that your field's research community could do better in the coming year . We received responses from scientists around the world representing a variety of fields. Excerpts of their

Despite recent therapeutic advances in the management ofBRAFV600-mutant melanoma, there is still a compelling need for more effective treatments for patients who developedBRAF/NRASwild type disease. Since the activity of single targeted agents is limited by innate and acquired resistance, we performed a high-throughput drug screen using 180 drug combinations to generate over 18,000 viability curves, with the aim of identifying agents that synergise to killBRAF/NRASwild type melanoma cells. From this screen we observed strong synergy between the tyrosine kinase inhibitor nilotinib and MEK inhibitors and validated this combination in an independent cell line collection. We found that AXL expression was associated with synergy to the nilotinib/MEK inhibitor combination, and that both drugs work in concert to suppress pERK. This finding was supported by genome-wide CRISPR screening which revealed that resistance mechanisms converge on regulators of the MAPK pathway. Finally, we validated the synergy of nilotinib/trametinib combinationin vivousing patient-derived xenografts. Our results indicate that a nilotinib/MEK inhibitor combination may represent an effective therapy inBRAF/NRASwild type melanoma patients.

We asked young scientists to describe an example of artificial intelligence or machine learning in research, its broader implications in the field, and the challenges scientists face when using such technologies. Our survey's responses reflected a variety of countries and fields, but only 6% came

While melanoma is not the most common form of skin cancer, it represents the vast majority of skin cancer-related deaths. Indeed, while combination therapies such as Dabrafenib and Trametinib have shown great promise in clinical trials for treating metastatic disease, some melanoma subtypes nevertheless develop resistances to front-line treatments. Under in vitro conditions, some metastatic human melanoma cell lines have been observed to evolve resistance to treatment while simultaneously changing color under brightfield microscopy, hinting at perturbations in pigment synthesis. The process known as melanogenesis gives rise to the two forms of melanin found in mammals: eumelanin, a dark brown/black pigment, and pheomelanin, a much more pale red/blond pigment. Interestingly, pheomelanin has been shown to contribute to the onset and development of melanoma in an ultraviolet-radiation-independent manner through a mechanism of oxidative stress. Eumelanin, on the other hand, is a known antioxidant whose chemical properties seem to shield cells against oxidative damage. To study these pigments in closer detail, nonlinear optical microscopy including coherent anti-Stokes Raman scattering (CARS) was used for the specific visualization and quantification of the relative abundance of pheomelanin and eumelanin within these treatment resistant cell lines. These microscopy toolkits provide a means to monitor changes in pigmentation in a noninvasive and non-destructive manner without the use of exogenous dyes to better understand the molecular basis of treatment resistance.

We have investigated the potential for the p16-cyclin D-CDK4/6-retinoblastoma protein pathway to be exploited as a therapeutic target in melanoma. In a cohort of 143 patients with primary invasive melanoma, we used fluorescence in situ hybridization to detect gene copy number variations (CNVs) in CDK4, CCND1, and CDKN2A and immunohistochemistry to determine protein expression. CNVs were common in melanoma, with gain of CDK4 or CCND1 in 37 and 18% of cases, respectively, and hemizygous or homozygous loss of CDKN2A in 56%. Three-quarters of all patients demonstrated a CNV in at least one of the three genes. The combination of CCND1 gain with either a gain of CDK4 and/or loss of CDKN2A was associated with poorer melanoma-specific survival. In 47 melanoma cell lines homozygous loss, methylation or mutation of CDKN2A gene or loss of protein (p16(INK) (4A) ) predicted sensitivity to the CDK4/6 inhibitor PD0332991, while RB1 loss predicted resistance.

Patients with advanced metastatic melanoma have poor prognosis and the genetics underlying its pathogenesis are poorly understood. High-throughput sequencing has allowed comprehensive discovery of somatic mutations in cancer samples. Here, on analysis of our whole-genome and whole-exome sequencing data of 29 melanoma samples, we identified several genes that harbor recurrent nonsynonymous mutations. These included MAP3K5 (mitogen-activated protein kinase kinase kinase-5), which in a prevalence screen of 288 melanomas was found to harbor a R256C substitution in 5 cases. All MAP3K5-mutated samples were wild type for BRAF, suggesting a mutual exclusivity for these mutations. Functional analysis of the MAP3K5 R256C mutation revealed attenuation of MKK4 (mitogen-activated protein kinase kinase 4) activation through increased binding of the inhibitory protein thioredoxin (TXN/TRX-1/Trx), resulting in increased proliferation and anchorage-independent growth of melanoma cells. This mutation represents a potential target for the design of new therapies to treat melanoma.

Background: Transcriptional ‘cell states’ (defined as the genes expressed in a cell in any given point in time) can determine response to targeted and immune-based therapies in late-stage melanoma treatment. MITF and AXL are common markers of drug-sensitive and drug-resistant ‘cell states’, respectively; and extensive heterogeneity of both cell populations have been observed in melanoma cell lines and tumors. Understanding the mechanisms controlling these ‘cell-states’ may lead to therapeutic strategies to overcome resistance and improve patient survival. Problem: To date, certain experiments assessing cell population heterogeneity dynamics in melanoma (such as live cell sorting applications) has been limited by the lack of surface markers defining the MITF ‘cell-state’. Furthermore, existing antibodies against MITF commonly detect multiple expressed isoforms which can limit the accuracy of downstream experimental assays, such as in-cell western blot detection. Methods and Results: To find a suitable cell surface marker defining the MITF ‘cell state’, we used the PARIS GenePattern module with data from the Cancer Cell Encyclopedia. TNFRSF14, our highest ranked gene, was highly correlated to MITF transcript expression in melanoma cell lines. We confirmed this at the protein level through western blot detection and flow cytometry. Using flow cytometry, we could accurately follow ‘cell-state’ population dynamics of AXL and MITF following shRNA knockdown of MITF and during acquired drug resistance in culture. Outcomes: We have identified TNFRSF14 as a robust cell surface marker of MITF in melanoma. Our TNFRSF14 and AXL live cell flow protocol will be useful for those exploring ‘cell-state’ population dynamics and heterogeneity in melanoma and could contribute to new mechanistic insights of drug resistance. Citation Format: Ken Dutton-Regester, Nicholas K. Hayward. TNFRSF14 is a cell surface marker of MITF expression in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4226.

Background: Acral melanoma is a rare subtype of melanoma, which has a distinct genomic profile from cutaneous melanoma. While survival outcomes for late-stage cutaneous melanoma have significantly improved in the last decade, treatment options for acral melanoma remain limited. The purpose of this study was to assess therapeutically targetable genomic alterations in acral melanoma. Methods: The AACR project GENIE database was accessed via cBioPortal (http://cbioportal.org/genie/; version 3) to identify recurrent gene alterations in acral melanomas analyzed using the MSK-IMPACT oncopanel. These alterations were assessed through Cancer Genome Interpreter (http://cancergenomeinterpreter.org) to identify clinically actionable alterations. Results: Thirty-six patients with acral melanoma were identified from the cBioPortal-GENIE database. These patients were sequenced using three different versions of oncopanel, which included 341 (n = 6), 410 (n = 22) and 468 (n = 8) genes. Seventy-six genes were found to be somatically mutated in this cohort in at least one sample. The most commonly mutated genes were NF1 (n = 5), BRAF (n = 5), PTPRT (n = 4), NOTCH3, HRAS and KRAS (n = 3). Copy number variations greatly contributed to aberration burden, with 132 unique genes found to either carry an amplification or deletion event. The most commonly amplified genes were CDK4 (n = 9), CCND1 (n = 9), FGF19/FGF4 (n = 8), PAK1 (n = 8), MDM2 (n = 7) and FGF3 (n = 7). The most recurrently deleted genes included CDKN2A (n = 9) and CDKN2B (n = 8), and less commonly JAK2 and PTEN (n = 2). Some genes were altered by various mechanisms; for example, some tumors had KIT activated by amplification while other tumors carried missense mutations. BRAF exhibited two fusion events: BRAF-METTL2B (with concomitant amplification) and BRAF-KIAA1549. Cancer Genome Interpreter identified several therapeutic targets, some of which have been the focus of clinical trials in cutaneous melanoma and other solid tumors, e.g. CDK4/6 inhibitors to target amplified CDK4 and CCND1, or mTOR inhibitors for tumors with NF1 mutations. Conclusion: Several inhibitors are available which could show efficacy in a subset of acral melanomas with particular genomic alterations. Many of these inhibitors have yet to be tested in the context of acral melanoma and should be explored. Citation Format: Natasa Broit, Ken Dutton-Regester, Peter Johansson, Antonia L. Pritchard, Glen M. Boyle, Nicholas K. Hayward. Clinically targetable genomic alterations in acral melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 744.

Synonymous mutations, which do not alter the protein sequence, have been shown to affect protein function [Sauna ZE, Kimchi-Sarfaty C (2011) Nat Rev Genet 12(10):683–691]. However, synonymous mutations are rarely investigated in the cancer genomics field. We used whole-genome and -exome sequencing to identify somatic mutations in 29 melanoma samples. Validation of one synonymous somatic mutation in BCL2L12 in 285 samples identified 12 cases that harbored the recurrent F17F mutation. This mutation led to increased BCL2L12 mRNA and protein levels because of differential targeting of WT and mutant BCL2L12 by hsa-miR-671–5p. Protein made from mutant BCL2L12 transcript bound p53, inhibited UV-induced apoptosis more efficiently than WT BCL2L12 , and reduced endogenous p53 target gene transcription. This report shows selection of a recurrent somatic synonymous mutation in cancer. Our data indicate that silent alterations have a role to play in human cancer, emphasizing the importance of their investigation in future cancer genome studies.

To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies., National Cancer Institute (U.S.) (1U24CA180922), National Cancer Institute (U.S.) (P30-CA14051)

We characterized the mutational landscape of melanoma, the form of skin cancer with the highest mortality rate, by sequencing the exomes of 147 melanomas. Sun-exposed melanomas had markedly more ultraviolet (UV)-like CT somatic mutations compared to sun-shielded acral, mucosal and uveal melanomas. Among the newly identified cancer genes was PPP6C, encoding a serine/threonine phosphatase, which harbored mutations that clustered in the active site in 12% of sun-exposed melanomas, exclusively in tumors with mutations in BRAF or NRAS. Notably, we identified a recurrent UV-signature, an activating mutation in RAC1 in 9.2% of sun-exposed melanomas. This activating mutation, the third most frequent in our cohort of sun-exposed melanoma after those of BRAF and NRAS, changes Pro29 to serine (RAC1(P29S)) in the highly conserved switch I domain. Crystal structures, and biochemical and functional studies of RAC1(P29S) showed that the alteration releases the conformational restraint conferred by the conserved proline, causes an increased binding of the protein to downstream effectors, and promotes melanocyte proliferation and migration. These findings raise the possibility that pharmacological inhibition of downstream effectors of RAC1 signaling could be of therapeutic benefit.

Success with molecular-based targeted drugs in the treatment of cancer has ignited extensive research efforts within the field of personalized therapeutics. However, successful application of such therapies is dependent on the presence or absence of mutations within the patient's tumor that can confer clinical efficacy or drug resistance. Building on these findings, we developed a high-throughput mutation panel for the identification of frequently occurring and clinically relevant mutations in melanoma. An extensive literature search and interrogation of the Catalogue of Somatic Mutations in Cancer database identified more than 1,000 melanoma mutations. Applying a filtering strategy to focus on mutations amenable to the development of targeted drugs, we initially screened 120 known mutations in 271 samples using the Sequenom MassARRAY system. A total of 252 mutations were detected in 17 genes, the highest frequency occurred in BRAF ( n = 154, 57%), NRAS ( n = 55, 20%), CDK4 ( n = 8, 3%), PTK2B ( n = 7, 2.5%), and ERBB4 ( n = 5, 2%). Based on this initial discovery screen, a total of 46 assays interrogating 39 mutations in 20 genes were designed to develop a melanoma-specific panel. These assays were distributed in multiplexes over 8 wells using strict assay design parameters optimized for sensitive mutation detection. The final melanoma-specific mutation panel is a cost effective, sensitive, high-throughput approach for identifying mutations of clinical relevance to molecular-based therapeutics for the treatment of melanoma. When used in a clinical research setting, the panel may rapidly and accurately identify potentially effective treatment strategies using novel or existing molecularly targeted drugs. Mol Cancer Ther; 11(4); 888–97. ©2012 AACR . This article is featured in Highlights of This Issue, [p. 793][1] [1]: /lookup/volpage/11/793?iss=4

High density SNP arrays can be used to identify DNA copy number changes in tumors such as homozygous deletions of tumor suppressor genes and focal amplifications of oncogenes. Illumina Human CNV370 Bead chip arrays were used to assess the genome for unbalanced chromosomal events occurring in 39 cell lines derived from stage III metastatic melanomas. A number of genes previously recognized to have an important role in the development and progression of melanoma were identified including homozygous deletions of CDKN2A (13 of 39 samples), CDKN2B (10 of 39), PTEN (3 of 39), PTPRD (3 of 39), TP53 (1 of 39), and amplifications of CCND1 (2 of 39), MITF (2 of 39), MDM2 (1 of 39), and NRAS (1 of 39). In addition, a number of focal homozygous deletions potentially targeting novel melanoma tumor suppressor genes were identified. Because of their likely functional significance for melanoma progression, FAS, CH25H, BMPR1A, ACTA2, and TFG were investigated in a larger cohort of melanomas through sequencing. Nonsynonymous mutations were identified in BMPR1A (1 of 43), ACTA2 (3 of 43), and TFG (5 of 103). A number of potentially important mutation events occurred in TFG including the identification of a mini mutation "hotspot" at amino acid residue 380 (P380S and P380L) and the presence of multiple mutations in two melanomas. Mutations in TFG may have important clinical relevance for current therapeutic strategies to treat metastatic melanoma.

To identify microRNAs potentially involved in melanomagenesis, we compared microRNA expression profiles between melanoma cell lines and cultured melanocytes. The most differentially expressed microRNA between the normal and tumor cell lines was miR-211. We focused on this pigment-cell-enriched miRNA as it is derived from the microphthalmia-associated transcription factor (MITF)-regulated gene, TRPM1 (melastatin). We find that miR-211 expression is greatly decreased in melanoma cells and melanoblasts compared to melanocytes. Bioinformatic analysis identified a large number of potential targets of miR-211, including POU3F2 (BRN2). Inhibition of miR-211 in normal melanocytes resulted in increased BRN2 protein, indicating that endogenous miR-211 represses BRN2 in differentiated cells. Over-expression of miR-211 in melanoma cell lines changed the invasive potential of the cells in vitro through directly targeting BRN2 translation. We propose a model for the apparent non-overlapping expression levels of BRN2 and MITF in melanoma, mediated by miR-211 expression.

Summary Somatic mutation analysis of melanoma has been performed at the single gene level extensively over the past several decades. This has provided considerable insight into the critical pathways controlling melanoma initiation and progression. During the last 5 yr, next-generation sequencing (NGS) has enabled even more comprehensive mutational screening at the level of multigene panels, exomes and genomes. These studies have uncovered many new and unexpected players in melanoma development. The recent landmark study from The Cancer Genome Atlas (TCGA) consortium describing the genomic architecture of 333 cutaneous melanomas provides the largest and broadest analysis to date on the somatic aberrations underlying melanoma genesis. It thus seems timely to review the mutational landscape of melanoma and highlight the key genes and cellular pathways that appear to drive this cancer.

The shelterin complex protects chromosomal ends by regulating how the telomerase complex interacts with telomeres. Following the recent finding in familial melanoma of inactivating germline mutations in POT1, encoding a member of the shelterin complex, we searched for mutations in the other five components of the shelterin complex in melanoma families.Next-generation sequencing techniques were used to screen 510 melanoma families (with unknown genetic etiology) and control cohorts for mutations in shelterin complex encoding genes: ACD, TERF2IP, TERF1, TERF2, and TINF 2. Maximum likelihood and LOD [logarithm (base 10) of odds] analyses were used. Mutation clustering was assessed with χ(2) and Fisher's exact tests. P values under .05 were considered statistically significant (one-tailed with Yates' correction).Six families had mutations in ACD and four families carried TERF2IP variants, which included nonsense mutations in both genes (p.Q320X and p.R364X, respectively) and point mutations that cosegregated with melanoma. Of five distinct mutations in ACD, four clustered in the POT1 binding domain, including p.Q320X. This clustering of novel mutations in the POT1 binding domain of ACD was statistically higher (P = .005) in melanoma probands compared with population control individuals (n = 6785), as were all novel and rare variants in both ACD (P = .040) and TERF2IP (P = .022). Families carrying ACD and TERF2IP mutations were also enriched with other cancer types, suggesting that these variants also predispose to a broader spectrum of cancers than just melanoma. Novel mutations were also observed in TERF1, TERF2, and TINF2, but these were not convincingly associated with melanoma.Our findings add to the growing support for telomere dysregulation as a key process associated with melanoma susceptibility.

Analysis of 501 melanoma exomes identified RASA2, encoding a RasGAP, as a tumor-suppressor gene mutated in 5% of melanomas. Recurrent loss-of-function mutations in RASA2 were found to increase RAS activation, melanoma cell growth and migration. RASA2 expression was lost in ≥30% of human melanomas and was associated with reduced patient survival. These findings identify RASA2 inactivation as a melanoma driver and highlight the importance of RasGAPs in cancer.

Melanoma is the deadliest form of human skin cancer. The incidence of melanoma continues to rise. Recent advances in knowledge of melanoma genetics, genomics and biology has led to an optimistic view of the therapeutic outlook for melanoma patients. We analyzed sequence data from >500 melanoma genomes/exomes to identify novel tumor suppressor genes in melanoma. RASA2 was identified as the most highly somatically mutated novel tumor suppressor gene. RASA2 was mutated in 5% of melanomas and deleted in an additional 16.4% of cases. RASA2 is a GTPase Activating Protein (GAP) that regulates RAS; which is one of the most highly mutated oncogenes in melanoma but drugs targeting RAS have as yet shown poor efficacy. The role of RASA2 has not been investigated in melanoma. NF1, which encodes another RAS- specific GAP, was found to be frequently mutated in melanoma. Interestingly, mutations in RASA2 and NF1 co-occur in the same patients with high frequency. We plan to elucidate the roles of RASA2 in melanomagenesis and to understand why RASA2 and NF1 mutations co-occur despite the fact that both proteins are RasGAPs. Ras includes three isoforms: NRas, KRas and HRas. Our preliminary data show that RASA2 is more specific to NRAS and that NF1 is more specific to KRAS and HRAS. This finding highlights the existence of a paradigm of cooperativity in which combined loss of multiple negative regulators (RASA2 and NF1) of the RAS pathway is required for melanoma development. Therefore, this type of enhancement of RAS signaling is possibly selected for in some melanomas. We will apply a proteomic screen using BioID to identify RASA2 and NF1 binding partners to provide insights into the functional effects and consequences of alterations in RASA2 and NF1. We expect that these studies will not only identify the cellular components that contribute to the Ras signaling pathway but will also identify potential novel therapeutic targets. Citation Format: Rand Arafeh, Nouar Qutob, Rafi Rafi Emmanuel, Jason Madore, Abdel Elkahloun, James S. James S. Wilmott, Jared J. Gartner, Antonella Di Pizio, Ron Rotkopf, Ken Dutton-Regester, Victoria Hill, Antonia Pritchard, Jimmy C. Lin, Steven A Rosenberg, Javed Khan, Shifra Ben-Dor, Masha Y. Masha Y. Niv, Igor Ulitsky, Graham J Mann, Richard A. Scolyer, Nicholas K. Hayward, Yardena Samuels. Deciphering distinct roles of RASA2 in melanomagenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-031. doi:10.1158/1538-7445.AM2017-LB-031

Melanoma represents the common tumor whose incidence has increased the most in the last 30 years and causes more than one death every hour in the US alone. Despite significant advances in targeted and immunotherapies, most patients cannot still be cured. Our aim is to identify new drug combinations that are synergistic in BRAF/NRAS wild type melanoma, a sub-type representing 30% of cases for which targeted therapies are not currently available. We high-throughput screened a collection of 20 BRAF/NRAS wild type melanoma cell lines with 180 drug combinations (60 library drugs used at 5 different concentrations combined with 3 clinically relevant anchor drugs) and generated over 8000 survival curves . We found that 25% of cell lines are highly sensitive to a combination of nilotinib plus trametinib and confirmed this finding with 2 independent assays. We further validated the drug synergy firstly using an independent collection of BRAF/NRAS wild type melanoma cell lines (n=7), then a collection of BRAF/NRAS wild type patient derived xenotransplant cultures (n=3), and finally with a collection of BRAFV600E and NRASQ61 melanoma cell lines (n=12). Further, we generated a gene expression signature of cell lines that display synergy for the nilotinib/trametinib combination, and used it to classify human melanomas from Leeds Melanoma Project (N=171) and TCGA (n=470) cohorts. Tumors classified as “synergistic-like” (27.9 and 36.7%, respectively) are associated to decreased overall and recurrence free survival (P Citation Format: Marco Ranzani, Kristel Kemper, Magali Michaut, Oscar Krijgsman, Vivek Iyer, Anneliese Speak, Jeremie Nsengimana, Kim Wong, Vera Grinkevich, Nanne Aben, Martin Del Castillo Velasco-Herrera, Clara Alsinet, Marcela Sjoberg, Mamunur Rashid, Gemma Turner, Fiona Behan, Emmanuelle Supper, Nicola Thompson, Graham Bignell, Ken Dutton-Regester, Antonia Pritchard, Chi Wong, Ultan McDermott, Nicholas K. Hayward, Kosuke Yusa, Julia Newton-Bishop, Lodewyk Wessels, Mathew Garnett, Daniel Peeper, David Adams. New therapies for the treatment of BRAF/NRAS wild type melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3717. doi:10.1158/1538-7445.AM2017-3717

Dear Editor, Despite recent advances in the management of metastatic melanoma using targeted therapies, options for patients with tumours that are BRAF and NRAS wild type remain limited (Dummer et al., 2012). BRAF/NRAS wild-type melanoma accounts for 13–26% of all melanoma cases (Hodis et al., 2012; Mar et al., 2013) and is generally characterized by a high C > T mutation burden, loss of function mutations and deletions of NF1, and activating mutations of KIT. Amplification of KIT, CCND1 and TERT is also observed in this disease (Hodis et al., 2012; Mar et al., 2013). Dacarbazine chemotherapy is the standard of care for patients with this molecular class of melanoma, but response rates in advanced disease are disappointing (Dummer et al., 2012; Tsao et al., 2004). More recently, immunotherapy has been deployed for this disease, eliciting marked responses in a subset of patients, but in most only a modest improvement in survival over chemotherapy has been observed (Robert et al., 2011) (see also J Clin Oncol 31, 2013; suppl; abstr 9025). In the subset of patients with BRAF/NRAS wild-type melanoma carrying KIT mutations, KIT inhibitors have shown some efficacy, particularly in patients with exon 11 or 13 mutations (Goldinger et al., 2013). This therapeutic modality is, however, only applicable to the 10–22% of patients with KIT mutant BRAF/NRAS wild-type disease (Hodis et al., 2012; Mar et al., 2013). Collectively, the survival of patients with metastatic BRAF/NRAS wild-type melanoma remains dismal. Trametinib (a competitive MEK1/2 inhibitor) alone or in combination with BRAF inhibitor treatment has significantly improved the survival of patients with BRAF mutant melanoma (Flaherty et al., 2012). Additionally, some patients with NRAS mutant disease have been shown to respond to MEK inhibitor-based therapy (Ascierto et al., 2013). Although partial responses have been described in patients with BRAF/NRAS wild-type melanoma in a Phase 1 clinical trial of trametinib, the validity of this therapy has not been fully explored in this subclass of disease (Falchook et al., 2012). Recently, Nissan et al. (2014) showed that trametinib efficiently inhibited cell growth and ERK signalling in BRAF/NRAS wild-type melanoma cell lines that had lost NF1, a negative regulator of RAS signalling. As 56–76% of BRAF/NRAS wild-type melanomas do not carry loss of function mutations of NF1 (Hodis et al., 2012; Mar et al., 2013), we investigated the sensitivity to trametinib of cell lines retaining NF1 expression. We assembled a collection of 25 patient-derived melanoma cell lines and determined their mutational status for a panel of 19 melanoma cancer genes (Table S1). Our collection comprised 9 cell lines carrying activating mutations of BRAF and 16 BRAF/NRAS wild-type cell lines (Table S1). The sensitivity of each cell line to trametinib was assessed using Syto60, a nucleic acid-based assay, after 6 days of exposure to 9 different escalating doses of trametinib (range 0.08–10 nM). This assay provides a robust estimate of cell viability (Garnett et al., 2012), and is consistent with live cell assays (see Figure S1 and Data S1). All BRAF/NRAS wild-type melanoma lines displayed a IC50 for trametinib in the nanomolar range, which was comparable to the IC50 for the BRAF-mutated cell lines that were tested in parallel (mean IC50 ± standard error mean = 2.54 nM ± 0.85 and 2.46 nM ± 1.05 for BRAF/NRAS wild-type and BRAF-mutated melanomas, respectively; P = 0.96 by two tailed unpaired t-test; Figure​Figure1A1A and Table​Table1).1). Compared to the IC50 of a panel of 316 cancer cell lines screened for trametinib sensitivity, BRAF/NRAS wild-type melanoma lines are scored as highly sensitive (Figure S2 and Table S2) suggesting that utilisation of the MAPK pathway is an intrinsic feature of these melanomas. These results confirm and extend the validity of a recent study showing that BRAF/NRAS wild-type melanoma cell lines are sensitive to trametinib and suggest that they can be as sensitive to MEK inhibition as melanomas with BRAF mutations (Stones et al., 2013). To further stratify the BRAF/NRAS wild-type melanomas in our cell line collection, we assessed NF1 status by Western blotting (see Data S1) and sequencing (Table S1). Nine of the 16 BRAF/NRAS wild-type cell lines analysed by Western blotting displayed undetectable NF1 protein levels while 7 expressed NF1 protein (Figure​(Figure1B,1B, Table​Table11 and Table S1). Remarkably, the 7 BRAF/NRAS wild-type melanoma cell lines that expressed NF1 protein showed a similar sensitivity to trametinib as cell lines in which NF1 protein was undetectable (IC50 1.81 nM ± 1.20 and 3.10 nM ± 1.22 for NF1-positive and NF1-negative melanomas, respectively; P = 0.47 by two tailed unpaired t-test; Figure​Figure1C1C and Table​Table1).1). To confirm the effectiveness of MEK inhibition by trametinib in cell lines of different NF1 expression status, we measured the levels of phospho-ERK, a downstream effector of the MAPK pathway. Treatment with escalating doses of trametinib (0.01–10 nM) revealed reduced levels of phospho-ERK at 1 and 10 nM trametinib in all the cell lines tested (Figure​(Figure1D1D and Figure S3). We then measured the expression levels of downstream transcriptional targets of ERK: ETV5 and PHLDA1 (see Data S1). Trametinib induced a significant decrease of ETV5 and PHLDA1 levels in 3/4 and 4/4 cell lines, respectively (Figure​(Figure1E).1E). Overall, these results show that trametinib induces a functional downregulation of the ERK pathway (Figure ​(Figure1D–E)1D–E) in BRAF/NRAS wild-type melanoma cell lines, and that lines that express NF1 protein can also be defined as sensitive to MEK inhibition. Table 1 Sensitivity of the 25 melanoma cell lines to trametinib and their mutation status Figure 1 BRAF/NRAS wild-type melanoma cell lines, NF1 expression and sensitivity to trametinib. (A) The IC50 values for trametinib in BRAF/NRAS wild-type melanoma cell lines are comparable to those that are BRAF mutant. The box extends from the 25th to 75th percentiles, ... In summary, we show that BRAF/NRAS wild-type melanomas are highly sensitive to the MEK inhibitor trametinib, and that loss of NF1 protein expression alone does not stratify sensitive cell lines. Overall, our findings mandate further investigation of the efficacy of trametinib in BRAF/NRAS wild-type melanoma. Given the limited therapeutic options for BRAF/NRAS wild-type melanoma, trametinib may represent a useful therapeutic tool for patients with this subclass of the disease.

The incidence of melanoma continues to rise globally and is increasing at a rate greater than any other cancer. To systematically search for new genes involved in melanomagenesis, we collated exome sequencing data from independent melanoma cohort datasets, including those in the public domain. We identified recurrent mutations that may drive melanoma growth, survival or metastasis, and which may hold promise for the design of novel therapies to treat melanoma. These included a frequent recurrent (i.e. hotspot) mutation in the 5' untranslated region of RPS27 in ~10% of samples. We show that the mutation expands the 5'TOP element, a motif known to regulate the expression of most of the ribosomal protein family, to which RPS27 belongs, and thus might sensitize the mutated transcript to growth-mediated regulation. This finding highlights not only the important role of non-protein coding genetic aberrations in cancer development but also their potential as novel therapeutic targets.

5-year survival for melanoma metastasis to regional lymph nodes (American Joint Committee on Cancer stage III) is50%. Knowledge of outcomes following therapeutic lymphadenectomy for stage III melanoma related to BRAF status may guide adjuvant use of BRAF/MEK inhibitors along with established and future therapies.To determine patterns of melanoma recurrence and survival following therapeutic lymph node dissection (TLND) associated with oncogenic mutations.DNA was obtained from patients who underwent TLND and had ⩾2 positive nodes, largest node3cm or extracapsular invasion. Mutations were detected using an extended Sequenom MelaCARTA panel.Mutations were most commonly detected in BRAF (57/124 [46%] patients) and NRAS (26/124 [21%] patients). Patients with BRAF mutations had higher 3-year recurrence rate (77%) versus 54% for BRAF wild-type patients (hazard ratio (HR) 1.8, p=0.008). The only prognostically significant mutations occurred in BRAF: median recurrence-free (RFS) and disease-specific survival (DSS) for BRAF mutation patients was 7 months and 16 months, versus 19 months and not reached for BRAF wild-type patients, respectively. Multivariate analysis identified BRAF mutant status and number of positive lymph nodes as the only independent prognostic factors for RFS and DSS.Patients with BRAF mutations experienced rapid progression of metastatic disease with locoregional recurrence rarely seen in isolation, supporting incorporation of BRAF status into melanoma staging and use of BRAF/MEK inhibitors post-TLND.

Melanoma of unknown primary (MUP) is an uncommon phenomenon whereby patients present with metastatic disease without an evident primary site. To determine their likely site of origin, we combined exome sequencing from 33 MUPs to assess the total rate of somatic mutations and degree of UV mutagenesis. An independent cohort of 91 archival MUPs was also screened for 46 hot spot mutations highly prevalent in melanoma including BRAF, NRAS, KIT, GNAQ, and GNA11. Results showed that the majority of MUPs exhibited high somatic mutation rates, high ratios of C>T/G>A transitions, and a high rate of BRAF (45 of 101, 45%) and NRAS (32 of 101, 32%) mutations, collectively indicating a mutation profile consistent with cutaneous sun-exposed melanomas. These data suggest that a significant proportion of MUPs arise from regressed or unrecognized primary cutaneous melanomas or arise de novo in lymph nodes from nevus cells that have migrated from the skin.

The individuals carrying melanocortin-1 receptor (MC1R) variants, especially those associated with red hair color, fair skin, and poor tanning ability (RHC trait), are more prone to melanoma; however, the underlying mechanism is poorly defined. Here, we report that UVB exposure triggers phosphatase and tensin homolog (PTEN) interaction with wild-type (WT), but not RHC-associated MC1R variants, which protects PTEN from WWP2-mediated degradation, leading to AKT inactivation. Strikingly, the biological consequences of the failure of MC1R variants to suppress PI3K/AKT signaling are highly context dependent. In primary melanocytes, hyperactivation of PI3K/AKT signaling leads to premature senescence; in the presence of BRAF(V600E), MC1R deficiency-induced elevated PI3K/AKT signaling drives oncogenic transformation. These studies establish the MC1R-PTEN axis as a central regulator for melanocytes' response to UVB exposure and reveal the molecular basis underlying the association between MC1R variants and melanomagenesis.

Tumors are heterogeneous ecosystems composed of genetically and epigenetically distinct cancer cell populations embedded in an intricate tumor microenvironment. The complexity and cell-to-cell interactions within this system pose a tremendous therapeutic challenge and opportunity. Due to technical constraints, current profiling technologies only provide average signals that do not reflect this intrinsic genetic and phenotypic variability. Here, we applied single-cell RNA-sequencing to examine 4,645 single cells isolated from 19 freshly procured melanomas, profiling malignant, immune and stromal cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, stem-like cells, spatial context, and a drug treatment resistance program. All tumors harbored malignant cells from two distinct transcriptional cell states, such that treatment-sensitive “MITF-high” tumors also contained drug-resistant “AXL-high” tumor cells; similar heterogeneity was present in 18 established melanoma cell lines. The frequency of AXL-high cells increased in post-relapse resistant tumors following treatment with BRAF/MEK inhibitors. Using multiplexed, quantitative single-cell immunofluorescence analysis and FACS, we validated these observations in melanoma cell lines treated with BRAF±MEK inhibitors. Signatures of cell types identified from single-cell analysis revealed distinct patterns of the tumor microenvironment. We inferred cell-to-cell interactions between stromal, immune and malignant cells, and identified factors, including known secreted gene products (e.g. CXCL12) and several complement factors. We validated the association between cancer-associated fibroblast (CAF)-expressed complement factor 3 (C3) and TIL infiltration in an independent set of 308 melanomas. Finally, analysis of TILs revealed T-cell activation dependent and independent exhaustion programs that varied among patients dependent on their exposure to treatment with immune checkpoint-inhibitors. In addition to co-expression of several known co-inhibitory receptors, including PD1, CTLA-4, and TIM-3, we identified common markers associated with cytotoxicity-independent T-cell exhaustion across patients. To identify potential T-cell clones, we classified single T-cells by their isoforms of the V and J segments of the alpha and beta TCR chains, allowing us to identify expanded T-cell clones. We found that clonally expanded T-cells expressed a strong exhaustion program, while non-expanded T-cells lacked this phenotype. This study represents the most comprehensive single-cell genomics analysis in humans to date and begins to unravel the cellular ecosystem of tumors. Single-cell genomics offer new insights with implications for both targeted and immune therapies by simultaneously profiling numerous aspects of a tumor with a single assay. Citation Format: Benjamin Izar, Itay Tirsh, Sanjay Prakadan, Marc Wadsworth, Daniel Treacy, John Trombetta, Asaf Rotem, Christine Lian, George Murphy, Mohammad Fallahi-Sichani, Ken Dutton-Regester, Jia-Ren Lin, Judit Jane-Valbuena, Orit Rozenblatt-Rosen, Charles Yoon, Alex Shalek, Aviv Regev, Levi Garraway. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4380.

BRAF and MEK inhibitors have become a standard of care for patients with metastatic BRAF mutant (V600) melanoma. Despite their success, 10- 20% of patients exhibit ‘intrinsic’ resistance and fail to respond to treatment. Recently, distinct transcriptional profiles have been associated with sensitivity to these drugs with intrinsically resistant melanomas exhibiting an AXL-high/ MITF-low phenotype (1). Using melanoma cell lines from the Cancer Cell Line Encyclopedia with matched gene expression data, we confirmed that AXL-high/ MITF-low cell lines had increased resistance to both BRAF (Dabrafenib) and MEK (Trametinib) inhibitors, either singly or in combination. Using flow cytometry with an AXL antibody, we observed that AXL-high/ MITF-low resistant cell lines frequently exhibited a high percentage of AXL+ cells (≥90%) while AXL-low/ MITF-high sensitive cell lines showed the opposite (≤5%). We hypothesized that the small percentage of AXL+ cells in the sensitive cell lines may be responsible for mechanisms of adaptive or acquired resistance (a feature that is frequently observed with the use of BRAF and MEK inhibitors in vitro and in the clinic). To explore this, we cultured sensitive cell lines in vitro for 5 days in the presence of combined Dabrafenib and Trametinib (DT) and performed flow cytometry to determine if there was a change in the percentage of AXL+ cells. All sensitive cell lines exhibited an enrichment of AXL+ cells with increasing concentrations of DT (maximum increase compared to DMSO control across individual cell lines ranged from 10% to 85%). It is unknown whether this phenomenon could be explained by a clonal selection or ‘plastic’ epigenetic reprogramming process and as such, we are currently performing experiments to determine this. Looking to the future, identifying potential dependencies of AXL-low/ MITF-high melanomas will deepen our understanding of the biology supporting this resistant state and provide a platform for the design of future clinical intervention strategies in this subset of patients. 1) Konieczkowski et al. 2014. Cancer Discovery. A melanoma cell state distinction influences sensitivity to MAPK pathway inhibitors. 4(7):816-27. Citation Format: Ken Dutton-Regester, Levi Garraway. Enrichment of AXL-high/ MITF-low melanoma cells in the presence of MAPK inhibitors in vitro. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 237.

Melanoma causes 9000 deaths each year in the USA alone, more than one every hour. It represents the common tumor whose incidence has increased the most in the last 30 years. Despite promising advances, including immunotherapies, the therapeutic regimens are curative in only a fraction of patients. Implementation and combination of different therapeutic modalities is therefore required to improve patient survival. Given the clinical efficacy of drug combinations for BRAF mutant melanomas, we performed a high-throughput drug screening to identify new drug combinations for the treatment of BRAF/NRAS wild type melanomas. Effective targeted therapies are not currently available for this sub-type of the disease, which represents up to 30% of melanomas. By viability assays we characterized the sensitivity to 240 combinations of clinically relevant drugs in a collection of 21 BRAF/NRAS wild type melanoma cell lines. We analysed 8360 survival curves and found that 16 (73%) and 5 (23%) cell lines are highly sensitive to temozolomide plus olaparib and to nilotinib plus trametinib combinations, respectively. Two independent experimental approaches validated these drug synergies. By -omics technologies we deeply characterized the cell line profiles of mutations, copy number changes, DNA methylation, and gene and microRNA expression. We generated gene expression signatures of synergistic and resistant cell lines for the 2 drug combinations and used them to classify 374 human melanomas. Tumors significantly associated to nilotinib plus trametinib synergism signature were significantly enriched for high immune response and proliferative Jonsson's expression classes, while tumors associated to the signature of drug resistance are enriched for pigmentation class. Melanomas associated to temozolomide plus olaparib resistance signature displayed enrichment for normal class. We are currently confirming the representation of these signatures in an independent cohort of melanomas and looking for other clinical correlates. We are also validating the predictivity of these gene expression signatures in independent cohorts of melanoma cell lines. Prospectively, this may represent an approach to identify patients that could have the maximal benefit from these drug combinations. Additionally, we have recently performed a genome-wide CRISPR/Cas9 screen to identify drug resistance genes for these 2 drug combinations. Preliminary results indicate a prominent role of tuberous sclerosis complex in the regulation of the sensitivity to nilotinib plus trametinib combination. These results may pave the way to the development of novel patient-tailored targeted therapies for the efficient eradication of BRAF/NRAS wild type melanomas. Citation Format: Marco Ranzani, Magali Michaut, Clara Alsinet, Vera Grinkevich, Kim Wong, Vivek Iyer, Nanne Aben, Martin Del Castillo Velasco-Herrera, Marcela Sjoberg, Mamunur Rashid, Graham Bignell, Ken Dutton-Regester, Antonia Pritchard, Daniel Vis, Gemma Turner, Ultan McDermott, Nicholas K. Hayward, Kosuke Yusa, Lodewyk Wessels, Mathew Garnett, David Adams. Identification of new therapies for the treatment of BRAF/NRAS wild-type melanomas by functional screening approaches. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4783.

11503Background: Heterogeneity of malignant and non-malignant tumor components and their interactions pose tremendous clinical challenges and opportunities in clinical care. Due to technical constr...

Melanoma has historically been refractive to traditional therapeutic approaches. As such, the development of novel drug strategies has been needed to improve rates of overall survival in patients with melanoma, particularly those with late stage or disseminated disease. Recent success with molecularly based targeted drugs, such as Vemurafenib in BRAF-mutant melanomas, has now made "personalized medicine" a reality within some oncology clinics. In this sense, tailored drugs can be administered to patients according to their tumor "mutation profiles." The success of these drug strategies, in part, can be attributed to the identification of the genetic mechanisms responsible for the development and progression of metastatic melanoma. Recently, the advances in sequencing technology have allowed for comprehensive mutation analysis of tumors and have led to the identification of a number of genes involved in the etiology of metastatic melanoma. As the methodology and costs associated with next-generation sequencing continue to improve, this technology will be rapidly adopted into routine clinical oncology practices and will significantly impact on personalized therapy. This review summarizes current and emerging molecular targets in metastatic melanoma, discusses the potential application of next-generation sequencing within the paradigm of personalized medicine, and describes the current limitations for the adoption of this technology within the clinic.

Family and twin studies indicate that melanoma susceptibility has a strong genetic component. Very rarely, melanoma runs in families in which there is an inherited mutation in a single ‘high penetrance’ gene, but in the general population melanoma susceptibility is thought to be governed by variation in a series of ‘low penetrance’ genes. We sought to identify new melanoma risk genes of both classes by conducting an Australian genome-wide association study (GWAS) of ~2200 melanoma cases and ~4300 matched controls (from the AMFS and Q-MEGA studies), in parallel with whole-genome sequencing of cases from densely affected melanoma families with follow up genotyping of interesting variants in the GWAS sample and other highly case-loaded melanoma families. Genotyping of the GWAS sample was carried out using Illumina Hap610K or OMNI 1M arrays. All 25 SNPs that reached genome-wide statistical significance (i.e. p

Metastatic melanoma has traditionally been difficult to treat, and although molecularly based targeted therapies have shown promising results, they have yet to show consistent improvements in overall survival rates. Thus, identifying the key mutation events underlying the etiology of metastatic melanoma will no doubt lead to the improvement of existing therapeutic approaches and the development of new treatment strategies. Significant advances toward understanding the complexity of the melanoma genome have recently been achieved using next-generation sequencing (NGS) technologies. However, identifying those mutations driving tumorigenesis will continue to be a challenge for researchers, in part because of the high rates of mutation compared to other cancers. This article will review the catalog of mutations identified in melanoma through a variety of approaches, including the use of unbiased exome and whole-genome NGS platforms, as well discuss complementary strategies for identifying driver mutations. The promise of personalized medicine afforded by better understanding these mutation events should provide impetus for increased activity and rapid advances in this field.

Melanoma has historically been refractive to traditional therapeutic approaches. As such, the development of novel drug strategies has been needed to improve rates of overall survival in patients with melanoma, particularly those with late stage or disseminated disease. Recent success with molecularly based targeted drugs, such as Vemurafenib in BRAF-mutant melanomas, has now made “personalized medicine” a reality within some oncology clinics. In this sense, tailored drugs can be administered to patients according to their tumor “mutation profiles.” The success of these drug strategies, in part, can be attributed to the identification of the genetic mechanisms responsible for the development and progression of metastatic melanoma. Recently, the advances in sequencing technology have allowed for comprehensive mutation analysis of tumors and have led to the identification of a number of genes involved in the etiology of metastatic melanoma. As the methodology and costs associated with next-generation sequencing continue to improve, this technology will be rapidly adopted into routine clinical oncology practices and will significantly impact on personalized therapy. This review summarizes current and emerging molecular targets in metastatic melanoma, discusses the potential application of next-generation sequencing within the paradigm of personalized medicine, and describes the current limitations for the adoption of this technology within the clinic.

We sequenced eight melanoma exomes to identify new somatic mutations in metastatic melanoma. Focusing on the mitogen-activated protein (MAP) kinase kinase kinase (MAP3K) family, we found that 24% of melanoma cell lines have mutations in the protein-coding regions of either MAP3K5 or MAP3K9. Structural modeling predicted that mutations in the kinase domain may affect the activity and regulation of these protein kinases. The position of the mutations and the loss of heterozygosity of MAP3K5 and MAP3K9 in 85% and 67% of melanoma samples, respectively, together suggest that the mutations are likely to be inactivating. In in vitro kinase assays, MAP3K5 I780F and MAP3K9 W333* variants had reduced kinase activity. Overexpression of MAP3K5 or MAP3K9 mutants in HEK293T cells reduced the phosphorylation of downstream MAP kinases. Attenuation of MAP3K9 function in melanoma cells using siRNA led to increased cell viability after temozolomide treatment, suggesting that decreased MAP3K pathway activity can lead to chemoresistance in melanoma.

To identify microRNAs potentially involved in melanomagenesis, we compared microRNA expression profiles between melanoma cell lines and cultured melanocytes. The most differentially expressed microRNA between the normal and tumor cell lines was miR-211. We focused on this pigment-cell-enriched miRNA as it is derived from the microphthalmia-associated transcription factor (MITF)-regulated gene, TRPM1 (melastatin). We find that miR-211 expression is greatly decreased in melanoma cells and melanoblasts compared to melanocytes. Bioinformatic analysis identified a large number of potential targets of miR-211, including POU3F2 (BRN2). Inhibition of miR-211 in normal melanocytes resulted in increased BRN2 protein, indicating that endogenous miR-211 represses BRN2 in differentiated cells. Over-expression of miR-211 in melanoma cell lines changed the invasive potential of the cells in vitro through directly targeting BRN2 translation. We propose a model for the apparent non-overlapping expression levels of BRN2 and MITF in melanoma, mediated by miR-211 expression.

So far, two genes associated with familial melanoma have been identified, accounting for a minority of genetic risk in families. Mutations in CDKN2A account for approximately 40% of familial cases, and predisposing mutations in CDK4 have been reported in a very small number of melanoma kindreds. Here we report the whole-genome sequencing of probands from several melanoma families, which we performed in order to identify other genes associated with familial melanoma. We identify one individual carrying a novel germline variant (coding DNA sequence c.G1075A; protein sequence p.E318K; rs149617956) in the melanoma-lineage-specific oncogene microphthalmia-associated transcription factor (MITF). Although the variant co-segregated with melanoma in some but not all cases in the family, linkage analysis of 31 families subsequently identified to carry the variant generated a log of odds (lod) score of 2.7 under a dominant model, indicating E318K as a possible intermediate risk variant. Consistent with this, the E318K variant was significantly associated with melanoma in a large Australian case-control sample. Likewise, it was similarly associated in an independent case-control sample from the United Kingdom. In the Australian sample, the variant allele was significantly over-represented in cases with a family history of melanoma, multiple primary melanomas, or both. The variant allele was also associated with increased naevus count and non-blue eye colour. Functional analysis of E318K showed that MITF encoded by the variant allele had impaired sumoylation and differentially regulated several MITF targets. These data indicate that MITF is a melanoma-predisposition gene and highlight the utility of whole-genome sequencing to identify novel rare variants associated with disease susceptibility.