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Oncogenic alterations to DNA are not transforming in all cellular contexts1,2. This may be due to pre-existing transcriptional programmes in the cell of origin. Here we define anatomic position as a major determinant of why cells respond to specific oncogenes. Cutaneous melanoma arises throughout the body, whereas the acral subtype arises on the palms of the hands, soles of the feet or under the nails3. We sequenced the DNA of cutaneous and acral melanomas from a large cohort of human patients and found a specific enrichment for BRAF mutations in cutaneous melanoma and enrichment for CRKL amplifications in acral melanoma. We modelled these changes in transgenic zebrafish models and found that CRKL-driven tumours formed predominantly in the fins of the fish. The fins are the evolutionary precursors to tetrapod limbs, indicating that melanocytes in these acral locations may be uniquely susceptible to CRKL. RNA profiling of these fin and limb melanocytes, when compared with body melanocytes, revealed a positional identity gene programme typified by posterior HOX13 genes. This positional gene programme synergized with CRKL to amplify insulin-like growth factor (IGF) signalling and drive tumours at acral sites. Abrogation of this CRKL-driven programme eliminated the anatomic specificity of acral melanoma. These data suggest that the anatomic position of the cell of origin endows it with a unique transcriptional state that makes it susceptible to only certain oncogenic insults.

Germline genetic variants have been identified, which predispose individuals and families to develop melanoma. Tumor thickness is the strongest predictor of outcome for clinically localized primary melanoma patients. We sought to determine whether there is a heritable genetic contribution to variation in tumor thickness. If confirmed, this will justify the search for specific genetic variants influencing tumor thickness. To address this, we estimated the proportion of variation in tumor thickness attributable to genome-wide genetic variation (variant-based heritability) using unrelated patients with measured primary cutaneous melanoma thickness. As a secondary analysis, we conducted a genome-wide association study (GWAS) of tumor thickness. The analyses utilized 10 604 individuals with primary cutaneous melanoma drawn from nine GWAS datasets from eight cohorts recruited from the general population, primary care and melanoma treatment centers. Following quality control and filtering to unrelated individuals with study phenotypes, 8125 patients were used in the primary analysis to test whether tumor thickness is heritable. An expanded set of 8505 individuals (47.6% female) were analyzed for the secondary GWAS meta-analysis. Analyses were adjusted for participant age, sex, cohort and ancestry. We found that 26.6% (SE 11.9%, P = 0.0128) of variation in tumor thickness is attributable to genome-wide genetic variation. While requiring replication, a chromosome 11 locus was associated (P

Cancers, including cutaneous melanoma, can cluster in families. In addition to environmental etiological factors such as ultraviolet radiation, cutaneous melanoma has a strong genetic component. Genetic risks for cutaneous melanoma range from rare, high-penetrance mutations to common, low-penetrance variants. Known high-penetrance mutations account for only about half of all densely affected cutaneous melanoma families, and the causes of familial clustering in the remainder are unknown. We hypothesize that some clustering is due to the cumulative effect of a large number of variants of individually small effect. Common, low-penetrance genetic risk variants can be combined into polygenic risk scores. We used a polygenic risk score for cutaneous melanoma to compare families without known high-penetrance mutations with unrelated melanoma cases and melanoma-free controls. Family members had significantly higher mean polygenic load for cutaneous melanoma than unrelated cases or melanoma-free healthy controls (Bonferroni-corrected t-test P = 1.5 × 10−5 and 6.3 × 10−45, respectively). Whole genome sequencing of germline DNA from 51 members of 21 families with low polygenic risk for melanoma identified a CDKN2A p.G101W mutation in a single family but no other candidate high-penetrance melanoma susceptibility genes. This work provides further evidence that melanoma, like many other common complex disorders, can arise from the joint action of multiple predisposing factors, including rare high-penetrance mutations, as well as via a combination of large numbers of alleles of small effect.

Germline mutations of BRCA1 and BRCA2 predispose individuals to a high risk of breast and ovarian cancer, and elevated risk of other cancers, including those of the pancreas and prostate. BRCA2 mutation carriers may have increased risk of uveal melanoma (UM) and cutaneous melanoma (CM), but associations with these cancers in BRCA1 mutation carriers have been mixed. Here, we further assessed whether UM and CM are associated with BRCA1 or BRCA2 by assessing the presence, segregation and reported/predicted pathogenicity of rare germline mutations (variant allele frequency < 0.01) in families with multiple members affected by these cancers. Whole-genome or exome sequencing was performed on 160 CM and/or UM families from Australia, the Netherlands, Denmark and Sweden. Between one and five cases were sequenced from each family, totalling 307 individuals. Sanger sequencing was performed to validate BRCA1 and BRCA2 germline variants and to assess carrier status in other available family members. A nonsense and a frameshift mutation were identified in BRCA1, both resulting in premature truncation of the protein (the first at p.Q516 and the second at codon 91, after the introduction of seven amino acids due to a frameshift deletion). These variants co-segregated with CM in individuals who consented for testing and were present in individuals with pancreatic, prostate and breast cancer in the respective families. In addition, 33 rare missense mutations (variant allele frequency ranging from 0.00782 to 0.000001 in the aggregated ExAC data) were identified in 34 families. Examining the previously reported evidence of functional consequence of these variants revealed all had been classified as either benign or of unknown consequence. Seeking further evidence of an association between BRCA1 variants and melanoma, we examined two whole-genome/exome sequenced collections of sporadic CM patients (total N = 763). We identified one individual with a deleterious BRCA1 variant, however, this allele was lost (with the wild-type allele remaining) in the corresponding CM, indicating that defective BRCA1 was not a driver of tumorigenesis in this instance. Although this is the first time that deleterious BRCA1 mutations have been described in high-density CM families, we conclude that there is an insufficient burden of evidence to state that the increased familial CM or UM susceptibility is because of these variants. In addition, in conjunction with other studies, we conclude that the previously described association between BRCA2 mutations and UM susceptibility represents a rare source of increased risk.

The benign melanocytic nevus is the most common tumor in humans and rarely transforms into cutaneous melanoma. Elucidation of the nevus genome is required to better understand the molecular steps of progression to melanoma. We performed whole genome sequencing on a series of 14 benign melanocytic nevi consisting of both congenital and acquired types. All nevi had driver mutations in the MAPK signaling pathway, either BRAF V600E or NRAS Q61R/L. No additional definite driver mutations were identified. Somatic mutations in nevi with higher mutation loads showed a predominance of mutational signatures 7a and 7b, consistent with UVR exposure, whereas nevi with lower mutation loads (including all three congenital nevi) had a predominance of the ubiquitous signatures 1 and 5. Two nevi had mutations in promoter regions predicted to bind E26 transformation-specific family transcription factors, as well as subclonal mutations in the TERT promoter. This paper presents whole genome data from melanocytic nevi. We confirm that UVR is involved in the etiology of a subset of nevi. This study also establishes that TERT promoter mutations are present in morphologically benign skin nevi in subclonal populations, which has implications regarding the interpretation of this emerging biomarker in sensitive assays.

Knowledge of key drivers and therapeutic targets in mucosal melanoma is limited due to the paucity of comprehensive mutation data on this rare tumor type. To better understand the genomic landscape of mucosal melanoma, here we describe whole genome sequencing analysis of 67 tumors and validation of driver gene mutations by exome sequencing of 45 tumors. Tumors have a low point mutation burden and high numbers of structural variants, including recurrent structural rearrangements targeting TERT, CDK4 and MDM2. Significantly mutated genes are NRAS, BRAF, NF1, KIT, SF3B1, TP53, SPRED1, ATRX, HLA-A and CHD8. SF3B1 mutations occur more commonly in female genital and anorectal melanomas and CTNNB1 mutations implicate a role for WNT signaling defects in the genesis of some mucosal melanomas. TERT aberrations and ATRX mutations are associated with alterations in telomere length. Mutation profiles of the majority of mucosal melanomas suggest potential susceptibility to CDK4/6 and/or MEK inhibitors., Mucosal melanomas are challenging to treat partly because so little is known about the genetic drivers underpinning them. Here, the authors perform a genomic landscape analysis of samples collected from three continents, revealing a potential role for CDK4/6 or MEK inhibition in the treatment of the disease.

Although melanoma is initiated by acquisition of point mutations and limited focal copy number alterations in melanocytes-of-origin, the nature of genetic changes that characterise lethal metastatic disease is poorly understood. Here, we analyze the evolution of human melanoma progressing from early to late disease in 13 patients by sampling their tumours at multiple sites and times. Whole exome and genome sequencing data from 88 tumour samples reveals only limited gain of point mutations generally, with net mutational loss in some metastases. In contrast, melanoma evolution is dominated by whole genome doubling and large-scale aneuploidy, in which widespread loss of heterozygosity sculpts the burden of point mutations, neoantigens and structural variants even in treatment-naïve and primary cutaneous melanomas in some patients. These results imply that dysregulation of genomic integrity is a key driver of selective clonal advantage during melanoma progression., The genetic changes that occur in late stage metastatic melanoma are not well delineated. Here, the authors use rapid autopsy samples from metastatic melanoma patients and show that the late stage in the disease is characterised by whole genome doubling and aneuploidy.

To increase understanding of the genomic landscape of acral melanoma, a rare form of melanoma occurring on palms, soles or nail beds, whole genome sequencing of 87 tumors with matching transcriptome sequencing for 63 tumors was performed. Here we report that mutational signature analysis reveals a subset of tumors, mostly subungual, with an ultraviolet radiation signature. Significantly mutated genes are BRAF, NRAS, NF1, NOTCH2, PTEN and TYRP1. Mutations and amplification of KIT are also common. Structural rearrangement and copy number signatures show that whole genome duplication, aneuploidy and complex rearrangements are common. Complex rearrangements occur recurrently and are associated with amplification of TERT, CDK4, MDM2, CCND1, PAK1 and GAB2, indicating potential therapeutic options., Acral melanoma occurs on the soles of the feet, palms of the hands and in nail beds. Here, the authors reports the genomic landscape of 87 acral melanomas and find that some tumors harbor a UV signature and that the tumors are diverse at the levels of mutational signatures, structural aberrations and copy number signatures.

Estimates of seroprevalence of SARS-CoV-2 antibodies have been hampered by inadequate assay sensitivity and specificity. Using an ELISA-based approach to that combines data about IgG responses to both the Nucleocapsid and Spike-receptor binding domain antigens, we show that near-optimal sensitivity and specificity can be achieved. We used this assay to assess the frequency of virus-specific antibodies in a cohort of elective surgery patients in Australia and estimated seroprevalence in Australia to be 0.28% (0 to 0.72%). These data confirm the low level of transmission of SARS-CoV-2 in Australia before July 2020 and validate the specificity of our assay.

Meta-analysis of 36,760 cases and 375,188 controls identifies 54 loci associated with susceptibility to cutaneous melanoma. Further analysis combining nevus count and hair color GWAS results provide insights into the genetic architecture of melanoma.Most genetic susceptibility to cutaneous melanoma remains to be discovered. Meta-analysis genome-wide association study (GWAS) of 36,760 cases of melanoma (67% newly genotyped) and 375,188 controls identified 54 significant (P < 5 x 10(-8)) loci with 68 independent single nucleotide polymorphisms. Analysis of risk estimates across geographical regions and host factors suggests the acral melanoma subtype is uniquely unrelated to pigmentation. Combining this meta-analysis with GWAS of nevus count and hair color, and transcriptome association approaches, uncovered 31 potential secondary loci for a total of 85 cutaneous melanoma susceptibility loci. These findings provide insights into cutaneous melanoma genetic architecture, reinforcing the importance of nevogenesis, pigmentation and telomere maintenance, together with identifying potential new pathways for cutaneous melanoma pathogenesis.

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.

Cutaneous melanoma accounts for at least >10% of all cancers in adolescents and young adults (AYA, 15–30 years of age) in Western countries. To date, little is known about the correlations between germline variants and somatic mutations and mutation signatures in AYA melanoma patients that might explain why they have developed a cancer predominantly affecting those over 65 years of age. We performed genomic analysis of 50 AYA melanoma patients (onset 10–30 years, median 20); 25 underwent whole genome sequencing (WGS) of both tumor and germline DNA, exome data were retrieved from 12 TCGA AYA cases, and targeted DNA sequencing was conducted on 13 cases. The AYA cases were compared with WGS data from 121 adult cutaneous melanomas. Similar to mature adult cutaneous melanomas, AYA melanomas showed a high mutation burden and mutation signatures of ultraviolet radiation (UVR) damage. The frequencies of somatic mutations in BRAF (96%) and PTEN (36%) in the AYA WGS cohort were double the rates observed in adult melanomas (Q < 6.0 × 10 and 0.028, respectively). Furthermore, AYA melanomas contained a higher proportion of non-UVR-related mutation signatures than mature adult melanomas as a proportion of total mutation burden (p = 2.0 × 10). Interestingly, these non-UVR mutation signatures relate to APOBEC or mismatch repair pathways, and germline variants in related genes were observed in some of these cases. We conclude that AYA melanomas harbor some of the same molecular aberrations and mutagenic insults occurring in older adults, but in different proportions. Germline variants that may have conferred disease susceptibility correlated with somatic mutation signatures in a subset of AYA melanomas.

The replicative immortality of human cancer cells is achieved by activation of a telomere maintenance mechanism (TMM). To achieve this, cancer cells utilise either the enzyme telomerase, or the Alternative Lengthening of Telomeres (ALT) pathway. These distinct molecular pathways are incompletely understood with respect to activation and propagation, as well as their associations with clinical outcomes. We have identified significant differences in the telomere repeat composition of tumours that use ALT compared to tumours that do not. We then employed a machine learning approach to stratify tumours according to telomere repeat content with an accuracy of 91.6%. Importantly, this classification approach is applicable across all tumour types. Analysis of pathway mutations that were under-represented in ALT tumours, across 1,075 tumour samples, revealed that the autophagy, cell cycle control of chromosomal replication, and transcriptional regulatory network in embryonic stem cells pathways are involved in the survival of ALT tumours. Overall, our approach demonstrates that telomere sequence content can be used to stratify ALT activity in cancers, and begin to define the molecular pathways involved in ALT activation.

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.

Whole-genome sequencing of matched germline and tumour pairs in a well-characterized cohort of melanoma patients allowed investigation of associations between melanoma body site, age at melanoma onset and MC1R variant status with overall mutation burden and specific base pair changes observed in the corresponding melanoma. We observed statistically significant associations between mutation burden in melanoma and body site, age at onset and MC1R genotype, for both ultraviolet radiation (UVR) signature changes (C>T and CC>TT) and non-UVR base pair substitutions, as well as with overall variant load.

Background: Little is known about the prognostic significance of somatically mutated genes in metastatic melanoma (MM). We have employed a combined clinical and bioinformatics approach on tumor samples from cutaneous melanoma (SKCM) as part of The Cancer Genome Atlas project (TCGA) to identify mutated genes with potential clinical relevance.Methods: After limiting our DNA sequencing analysis to MM samples (n = 356) and to the CANCER CENSUS gene list, we filtered out mutations with low functional significance (snpEFF). We performed Cox analysis on 53 genes that were mutated in ≥3% of samples, and had ≥50% difference in incidence of mutations in deceased subjects versus alive subjects.Results: Four genes were potentially prognostic [RAC1, FGFR1, CARD11, CIITA; false discovery rate (FDR) < 0.2]. We identified 18 additional genes (e.g., SPEN, PDGFRB, GNAS, MAP2K1, EGFR, TSC2) that were less likely to have prognostic value (FDR < 0.4). Most somatic mutations in these 22 genes were infrequent (< 10%), associated with high somatic mutation burden, and were evenly distributed across all exons, except for RAC1 and MAP2K1. Mutations in only 9 of these 22 genes were also identified by RNA sequencing in >75% of the samples that exhibited corresponding DNA mutations. The low frequency, UV signature type and RNA expression of the 22 genes in MM samples were confirmed in a separate multi-institution validation cohort (n = 413). An underpowered analysis within a subset of this validation cohort with available patient follow-up (n = 224) showed that somatic mutations in SPEN and RAC1 reached borderline prognostic significance [log-rank favorable (p = 0.09) and adverse (p = 0.07), respectively]. Somatic mutations in SPEN, and to a lesser extent RAC1, were not associated with definite gene copy number or RNA expression alterations. High (>2+) nuclear plus cytoplasmic expression intensity for SPEN was associated with longer melanoma-specific overall survival (OS) compared to lower (≤ 2+) nuclear intensity (p = 0.048). We conclude that expressed somatic mutations in infrequently mutated genes beyond the well-characterized ones (e.g., BRAF, RAS, CDKN2A, PTEN, TP53), such as RAC1 and SPEN, may have prognostic significance in MM.

The identification of recurrent driver mutations by whole-exome sequencing (WES) of fresh-frozen human cancers and the subsequent development of novel targeted therapies have recently transformed the treatment of many cancers including melanoma. In routine clinical practice, fresh-frozen tissue is rarely available and mutation testing usually needs to be carried out on archival formalin fixed, paraffin embedded (FFPE) tissue, from which DNA is typically fragmented, cross-linked and of lower quality. In this study we aimed to determine whether WES data generated from genomic DNA (gDNA) extracted from FFPE tissues can be produced reliably and of clinically-actionable standard. In this study of ten melanoma patients, we compared WES data produced from analysis of gDNA isolated from FFPE tumour tissue with that isolated from fresh-frozen tumour tissue from the same specimen. FFPE samples were sequenced using both Illumina's Nextera and NimbleGen SeqCap exome capture kits. To examine mutations between the two tissue sources and platforms, somatic mutations in the FFPE exomes were called using the matched fresh tissue sequence as a reference. Of the 10 FFPE DNA samples, seven Nextera and four SeqCap samples passed library preparation. On average, there were 5341 and 2246 variants lost in FFPE compared to matched fresh tissue utilising Nextera and SeqCap kits, respectively. In order to explore the feasibility of future clinical implementation of WES, FFPE variants in 27 genes of important clinical relevance in melanoma were assessed. The average concordance rate was 43.2% over a total of 1299 calls for the chosen genes in the FFPE DNA. For the current clinically most important melanoma mutations, 0/3 BRAF and 6/8 (75%) NRAS FFPE calls were concordant with the fresh tissue result, which was confirmed using a Sequenom OncoCarta Panel. The poor performance of FFPE WES indicates that specialised library construction to account for low quality DNA and further refinements will be necessary before this approach could be used for routine clinical decision making over currently preferred techniques.

Background: Elevated serum LDH is a poor prognostic factor in pts with MM. It is currently unknown if serum LDH levels correlate with molecular or immune differences in tumors, including LDH expression. We analyzed resected melanoma metastases to identify features that correlate with serum LDH elevation. Methods: Metastases with serum LDH levels measured within 30 days of surgery were included in the study: (1) a TMA of resected stage IV metastases from MD Anderson (MDACC) (n=207); (2) resected stage IV metastases from MDACC with previously acquired RNA-sequencing (RNA-seq) and CD8+ immunohistochemistry (IHC) data (n=24); (3) publicly available data for regional metastases from the melanoma TCGA (n=104) from MDACC and the Melanoma Institute of Australia. IHC was performed for LDHA, LDHB, PTEN, PD-L1, MITF, and Ki67 on the stage IV TMA, and publicly available data for DNA, RNA, and proteins for the TCGA samples were downloaded. Data were analyzed to identify features that differed between tumors with elevated and normal serum LDH levels. TCGA and stage IV tumors expressing the 25% highest and lowest LDHA and LDHB by RNA-seq were also compared. Results: IHC of the stage IV TMA identified no significant associations with serum LDH levels, including tumor LDHA (p=0.69) and LDHB (p=0.93) expression. Serum LDH was elevated in 33.3% of stage IV metastases with RNA-seq data and 21.6 % of TCGA regional metastases, which by Ensemble of Gene Set Enrichment Analyses (EGSEA) correlated with decreased expression of interferon alpha (q=8.26 × 10−14 and q=2.31 × 10−14), interferon gamma (q=9.46 × 10−14 and q=2.34 × 10−19), and inflammatory response (q= 1.26 × 10−20 and q=4.17 × 10−28) gene sets. Fewer (p=0.0464) CD8+ T cells were detected in stage IV tumors from pts with elevated serum LDH. Comparison of stage IV and TCGA tumors with high vs. low LDHA and LDHB mRNA expression levels did not correlate with serum LDH status, but significant enrichment of oxidative phosphorylation (q=6.32 × 10−21 and q=5.69 × 10−19), pyruvate metabolism (q= 7.90 × 10−16 and q=1.78 × 10−14), and citrate cycle (q= 1.51 × 10−06 and q=5.44 × 10−06) gene sets were detected in LDHA-high tumors. Increased LDHB expression correlated with enrichment of citrate cycle (q= 4.04 × 10−16 and q= 1.68 × 10−14), pyruvate metabolism (q= 2.34 × 10−11 and q=1.50 × 10−10), and glycolysis (q= 2.92 × 10−9 and q=1.55 × 10−8) gene sets. Conclusions: Elevated serum LDH levels correlated with decreased expression of immune response genes, but not LDHA or LDHB expression, in melanoma regional and distant metastases. Elevated serum LDH also correlated with decreased CD8+ T cell infiltrates by IHC. LDHA and LDHB expression levels in regional metastases correlated with distinct metabolic pathways, but not serum LDH levels. Citation Format: Fernando Cintra Lopes Carapet, Grant Fischer, Aron Joon, Huiqin Chen, Lauren Haydu, Sandra Lee, Melissa Saul, Savi Appana, John Thompson, Graham Mann, Jennifer McQuade, Alexander Lazar, Michael Tetzlaff, John Kirkwood, Richard Scolyer, Georgina Long, Michael Davies. Analysis of molecular and immune features that correlate with serum lactate dehydrogenase (LDH) levels in patients (pts) with metastatic melanoma [abstract]. In: Proceedings of the AACR Special Conference on Melanoma: From Biology to Target; 2019 Jan 15-18; Houston, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(19 Suppl):Abstract nr A04.

© 2018 UICC Despite recent advances in targeted and immune-based therapies, advanced stage melanoma remains a clinical challenge with a poor prognosis. Understanding the genes and cellular processes that drive progression and metastasis is critical for identifying new therapeutic strategies. Here, we found that the GTPase RAB27A was overexpressed in a subset of melanomas, which correlated with poor patient survival. Loss of RAB27A expression in melanoma cell lines inhibited 3D spheroid invasion and cell motility in vitro, and spontaneous metastasis in vivo. The reduced invasion phenotype was rescued by RAB27A-replete exosomes, but not RAB27A-knockdown exosomes, indicating that RAB27A is responsible for the generation of pro-invasive exosomes. Furthermore, while RAB27A loss did not alter the number of exosomes secreted, it did change exosome size and altered the composition and abundance of exosomal proteins, some of which are known to regulate cancer cell movement. Our data suggest that RAB27A promotes the biogenesis of a distinct pro-invasive exosome population. These findings support RAB27A as a key cancer regulator, as well as a potential prognostic marker and therapeutic target in melanoma.

Merkel cell carcinoma (MCC) is an uncommon, but highly malignant, cutaneous tumor. Merkel cell polyoma virus (MCV) has been implicated in a majority of MCC tumors; however, viral-negative tumors have been reported to be more prevalent in some geographic regions subject to high sun exposure. While the impact of MCV and viral T-antigens on MCC development has been extensively investigated, little is known about the etiology of viral-negative tumors. We performed targeted capture and massively parallel DNA sequencing of 619 cancer genes to compare the gene mutations and copy number alterations in MCV-positive (n = 13) and -negative (n = 21) MCC tumors and cell lines. We found that MCV-positive tumors displayed very low mutation rates, but MCV-negative tumors exhibited a high mutation burden associated with a UV-induced DNA damage signature. All viral-negative tumors harbored mutations in RB1, TP53, and a high frequency of mutations in NOTCH1 and FAT1. Additional mutated or amplified cancer genes of potential clinical importance included PI3K (PIK3CA, AKT1, PIK3CG) and MAPK (HRAS, NF1) pathway members and the receptor tyrosine kinase FGFR2. Furthermore, looking ahead to potential therapeutic strategies encompassing immune checkpoint inhibitors such as anti-PD-L1, we also assessed the status of T-cell–infiltrating lymphocytes (TIL) and PD-L1 in MCC tumors. A subset of viral-negative tumors exhibited high TILs and PD-L1 expression, corresponding with the higher mutation load within these cancers. Taken together, this study provides new insights into the underlying biology of viral-negative MCC and paves the road for further investigation into new treatment opportunities. Cancer Res; 75(24); 5228–34. ©2015 AACR.

Summary Whole genome sequencing (WGS) of cancer patients’ tumours offers the most comprehensive method of identifying both novel and known clinically-actionable genomic targets. However, the practicalities of performing WGS on clinical samples are poorly defined. This study was designed to test sample preparation, sequencing specifications and bioinformatic algorithms for their effect on accuracy and cost-efficiency in a large WGS analysis of human melanoma samples. WGS was performed on melanoma cell lines (n = 15) and melanoma fresh frozen tumours (n = 222). The appropriate level of coverage and the optimal mutation detection algorithm for the project pipeline were determined. An incremental increase in sequencing coverage from 36X to 132X in melanoma tissue samples and 30X to 103X for cell lines only resulted in a small increase (1–2%) in the number of mutations detected, and the quality scores of the additional mutations indicated a low probability that the mutations were real. The results suggest that 60X coverage for melanoma tissue and 40X for melanoma cell lines empower the detection of 98–99% of informative single nucleotide variants (SNVs), a sensitivity level at which clinical decision making or landscape research projects can be carried out with a high degree of confidence in the results. Likewise the bioinformatic mutation analysis methodology strongly influenced the number and quality of SNVs detected. Detecting mutations in the blood genomes separate to the tumour genomes generated 41% more SNVs than if the blood and melanoma tissue genomes were analysed simultaneously. Therefore, simultaneous analysis should be employed on matched melanoma tissue and blood genomes to reduce errors in mutation detection. This study provided valuable insights into the accuracy of SNV with WGS at various coverage levels in human clinical cancer specimens. Additionally, we investigated the accuracy of the publicly available mutation detection algorithms to detect cancer specific SNVs which will aid researchers and clinicians in study design and implementation of WGS for the identification of somatic mutations in other cancers.

Desmoplastic melanoma is an uncommon variant of melanoma with sarcomatous histology, distinct clinical behavior and unknown pathogenesis. We performed low-coverage genome and high-coverage exome sequencing of 20 desmoplastic melanomas, followed by targeted sequencing of 293 genes in a validation cohort of 42 cases. A high mutation burden (median of 62 mutations/Mb) ranked desmoplastic melanoma among the most highly mutated cancers. Mutation patterns strongly implicate ultraviolet radiation as the dominant mutagen, indicating a superficially located cell of origin. Newly identified alterations included recurrent promoter mutations of NFKBIE, encoding NF-κB inhibitor ɛ (IκBɛ), in 14.5% of samples. Common oncogenic mutations in melanomas, in particular in BRAF (encoding p.Val600Glu) and NRAS (encoding p.Gln61Lys or p.Gln61Arg), were absent. Instead, other genetic alterations known to activate the MAPK and PI3K signaling cascades were identified in 73% of samples, affecting NF1, CBL, ERBB2, MAP2K1, MAP3K1, BRAF, EGFR, PTPN11, MET, RAC1, SOS2, NRAS and PIK3CA, some of which are candidates for targeted therapies.

The role of microRNAs (miRNAs) in melanoma is unclear. We examined global miRNA expression profiles in fresh-frozen metastatic melanomas in relation to clinical outcome and BRAF mutation, with validation in independent cohorts of tumours and sera. We integrated miRNA and mRNA information from the same samples and elucidated networks associated with outcome and mutation. Associations with prognosis were replicated for miR-150-5p, miR-142-3p and miR-142-5p. Co-analysis of miRNA and mRNA uncovered a network associated with poor prognosis (PP) that paradoxically favoured expression of miRNAs opposing tumorigenesis. These miRNAs are likely part of an autoregulatory response to oncogenic drivers, rather than drivers themselves. Robust association of miR-150-5p and the miR-142 duplex with good prognosis and earlier stage metastatic melanoma supports their potential as biomarkers. miRNAs overexpressed in association with PP in an autoregulatory fashion will not be suitable therapeutic targets.

Background Extraordinary progress has been made in our understanding of common variants in many diseases, including melanoma. Because the contribution of rare coding variants is not as well characterized, we performed an exome-wide, gene-based association study of familial cutaneous melanoma (CM) and ocular melanoma (OM). Methods Using 11 990 jointly processed individual DNA samples, whole-exome sequencing was performed, followed by large-scale joint variant calling using GATK (Genome Analysis ToolKit). PLINK/SEQ was used for statistical analysis of genetic variation. Four models were used to estimate the association among different types of variants. In vitro functional validation was performed using three human melanoma cell lines in 2D and 3D proliferation assays. In vivo tumor growth was assessed using xenografts of human melanoma A375 melanoma cells in nude mice (eight mice per group). All statistical tests were two-sided. Results Strong signals were detected for CDKN2A (Pmin = 6.16 × 10-8) in the CM cohort (n = 273) and BAP1 (Pmin = 3.83 × 10‐6) in the OM (n = 99) cohort. Eleven genes that exhibited borderline association (P < 10‐4) were independently validated using The Cancer Genome Atlas melanoma cohort (379 CM, 47 OM) and a matched set of 3563 European controls with CDKN2A (P = .009), BAP1 (P = .03), and EBF3 (P = 4.75 × 10‐4), a candidate risk locus, all showing evidence of replication. EBF3 was then evaluated using germline data from a set of 132 familial melanoma cases and 4769 controls of UK origin (joint P = 1.37 × 10‐5). Somatically, loss of EBF3 expression correlated with progression, poorer outcome, and high MITF tumors. Functionally, induction of EBF3 in melanoma cells reduced cell growth in vitro, retarded tumor formation in vivo, and reduced MITF levels. Conclusions The results of this large rare variant germline association study further define the mutational landscape of hereditary melanoma and implicate EBF3 as a possible CM predisposition gene.

Summary Outcomes for melanoma patients with stage III disease differ widely even within the same subcategory. Molecular signatures that more accurately predict prognosis are needed to stratify patients according to risk. Proteomic analyses were used to identify differentially abundant proteins in extracts of surgically excised samples from patients with stage IIIc melanoma lymph node metastases. Analysis of samples from patients with poor (n = 14, 4 yr) survival outcomes identified 84 proteins that were differentially abundant between prognostic groups. Subsequent selected reaction monitoring analysis verified 21 proteins as potential biomarkers for survival. Poor prognosis patients are characterized by increased levels of proteins involved in protein metabolism, nucleic acid metabolism, angiogenesis, deregulation of cellular energetics and methylation processes, and decreased levels of proteins involved in apoptosis and immune response. These proteins are able to classify stage IIIc patients into prognostic subgroups (P

Approximately 50% of melanomas depend on mutant B-RAF for proliferation, metastasis and survival. The inhibition of oncogenic B-RAF with highly targeted compounds has produced remarkable albeit short-lived clinical responses in B-RAF mutant melanoma patients. Reactivation of signaling downstream of B-RAF is frequently associated with acquired resistance to B-RAF inhibitors, and the identification of B-RAF targets may provide new strategies for managing melanoma. Oncogenic B-RAF(V600E) is known to promote the stabilizing phosphorylation of the anti-apoptotic protein Mcl-1, implicated in melanoma survival and chemoresistance. We now show that B-RAF(V600E) signaling also induces the transcription of Mcl-1 in melanocytes and melanoma. We demonstrate that activation of STAT3 serine-727 and tyrosine-705 phosphorylations is promoted by B-RAF(V600E) activity and that the Mcl-1 promoter is dependent on a STAT consensus-site for B-RAF-mediated activation. Consequently, suppression of STAT3 activity disrupted B-RAF(V600E)-mediated induction of Mcl-1 and reduced melanoma cell survival. We propose that STAT3 has a central role in the survival and contributes to chemoresistance of B-RAF(V600E) melanoma.

Genome-wide association studies (GWASs) have mainly focused on top significant single nucleotide polymorphisms (SNPs), most of which did not have clear biological functions but were just surrogates for unknown causal variants. Studying SNPs with modest association and putative functions in biologically plausible pathways has become one complementary approach to GWASs. To unravel the key roles of mitogen-activated protein kinase (MAPK) pathways in cutaneous melanoma (CM) risk, we re-evaluated the associations between 47 818 SNPs in 280 MAPK genes and CM risk using our published GWAS dataset with 1804 CM cases and 1026 controls. We initially found 105 SNPs with P ≤ 0.001, more than expected by chance, 26 of which were predicted to be putatively functional SNPs. The risk associations with 16 SNPs around DUSP14 (rs1051849) and a previous reported melanoma locus MAFF/PLA2G6 (proxy SNP rs4608623) were replicated in the GenoMEL dataset (P < 0.01) but failed in the Australian dataset. Meta-analysis showed that rs1051849 in the 3ʹ untranslated regions of DUSP14 was associated with a reduced risk of melanoma (odds ratio = 0.89, 95% confidence interval: 0.82–0.96, P = 0.003, false discovery rate = 0.056). Further genotype–phenotype correlation analysis using the 90 HapMap lymphoblastoid cell lines from Caucasians showed significant correlations between two SNPs (rs1051849 and rs4608623) and messenger RNA expression levels of DUSP14 and MAFF (P = 0.025 and P = 0.010, respectively). Gene-based tests also revealed significant SNPs were over-represented in MAFF, PLA2G6, DUSP14 and other 16 genes. Our results suggest that functional SNPs in MAPK pathways may contribute to CM risk. Further studies are warranted to validate our findings.

A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers.

The contribution of melanocortin-1 receptor (MC1R) gene variants to the development of early-onset melanoma is unknown. Using an Australian population-based, case-control-family study, we sequenced MC1R for 565 cases with invasive cutaneous melanoma diagnosed between ages 18 and 39 years, 409 unrelated controls and 518 sibling controls. Variants were classified a priori into "R" variants (D84E, R142H, R151C, I155T, R160W, D294H) and "r" variants (all other nonsynonymous variants). We estimated odds ratios (OR) for melanoma using unconditional (unrelated controls) and conditional (sibling controls) logistic regression. The prevalence of having at least one R or r variant was 86% for cases, 73% for unrelated controls and 81% for sibling controls. R151C conferred the highest risk (per allele OR 2.57, 95% confidence interval 1.86-3.56 for the case-unrelated-control analysis and 1.70 (1.12-2.60) for the case-sibling-control analysis). When mutually adjusted, the ORs per R allele were 2.23 (1.77-2.80) and 2.06 (1.47-2.88), respectively, from the two types of analysis, and the ORs per r allele were 1.69 (1.33-2.13) and 1.25 (0.88-1.79), respectively. The associations were stronger for men and those with none or few nevi or with high childhood sun exposure. Adjustment for phenotype, nevi and sun exposure attenuated the overall log OR for R variants by approximately 18% but had lesser influence on r variant risk estimates. MC1R variants explained about 21% of the familial aggregation of melanoma. Some MC1R variants are important determinants of early-onset melanoma. The strength of association with melanoma differs according to the type and number of variants.

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.

We report a genome-wide association study for melanoma that was conducted by the GenoMEL Consortium. Our discovery phase included 2,981 individuals with melanoma and 1,982 study-specific control individuals of European ancestry, as well as an additional 6,426 control subjects from French or British populations, all of whom were genotyped for 317,000 or 610,000 single-nucleotide polymorphisms (SNPs). Our analysis replicated previously known melanoma susceptibility loci. Seven new regions with at least one SNP with P < 10(-5) and further local imputed or genotyped support were selected for replication using two other genome-wide studies (from Australia and Texas, USA). Additional replication came from case-control series from the UK and The Netherlands. Variants at three of the seven loci replicated at P < 10(-3): an SNP in ATM (rs1801516, overall P = 3.4 × 10(-9)), an SNP in MX2 (rs45430, P = 2.9 × 10(-9)) and an SNP adjacent to CASP8 (rs13016963, P = 8.6 × 10(-10)). A fourth locus near CCND1 remains of potential interest, showing suggestive but inconclusive evidence of replication (rs1485993, overall P = 4.6 × 10(-7) under a fixed-effects model and P = 1.2 × 10(-3) under a random-effects model). These newly associated variants showed no association with nevus or pigmentation phenotypes in a large British case-control series.

The heterogeneity of multiple case breast cancer families that do not carry mutations in BRCA1 or BRCA2 (non-BRCA1/2 families) poses a challenge to the identification of breast cancer susceptibility genes. The aim of this study was to determine whether intrafamilial concordance in breast cancer pathology could identify subgroups of non-BRCA1/2 families with consistent genotypic features. Invasive breast cancers were reviewed from 84 individuals belonging to 30 multiple-case families; BRCA1 (n = 9), BRCA2 (n = 10), and non-BRCA1/2 (n = 11). Hierarchical cluster analysis based on histopathology and age at first diagnosis was then used to specify three subgroups designated Clusters 1-3. The genomic features of non-BRCA1/2 families were examined by genome wide linkage and FGFR2 SNP genotyping, according to whether they showed cluster-concordant or cluster-mixed familial pathology. The majority of pathogenic BRCA1 mutation carriers (80%) fell into a single cluster. In contrast pathogenic BRCA2 mutation carriers were distributed across all three clusters and within families, cluster groups were also generally mixed. Most non-BRCA1/2 mutation carriers belonged to Cluster 3 (71%). Genome wide linkage data from five non-BRCA1/2 Cluster 3-concordant families were compared with four mixed cluster non-BRCA1/2 families. This revealed a number of distinct linkage peaks, including some regions previously associated with breast cancer susceptibility. The distribution of low risk alleles in FGFR2 was not different between these two subgroups (P = 0.237). The pattern of breast cancer pathology concordance amongst family members may assist the investigation of breast cancer susceptibility in multiple case families.

An antibody microarray was developed for profiling the surface proteome of melanoma cells, which may facilitate melanoma sub-classification and provide important prognostic information useful in predicting the clinical behavior of the melanoma (e.g., likely sites of metastatic spread), patient outcome and treatment response. Forty-eight antibodies were selected based on their correlation with melanoma development, progression and/or prognosis and printed on nitrocellulose slides. The immobilised antibodies capture live cells expressing corresponding antigens to produce a cell binding dot pattern representing the surface antigen profile (immunophenotype) of the melanoma. Surface antigen signatures were determined for a normal melanocyte and 6 melanoma cell lines and cell suspensions prepared from 10 surgically excised melanoma lymph node metastases. A procedure for obtaining separate surface antigen profiles for melanoma cells and leukocytes from clinical lymph node samples was also developed using anti-CD45 magnetic beads. The capture of live, bead-bound leukocytes on these antibody microarrays provides a significant enhancement of this microarray technology. The antibody microarray will be used to profile panels of surgically excised melanoma lymph node metastases (melanoma and leukocyte fractions) to determine whether the immunophenotypes correlate with clinicopathological characteristics, disease progression and clinical outcome.

The CDKN2A locus encodes for two distinct tumor suppressor proteins, p16 INK4A and p14 ARF , involved in cell cycle regulation. CDKN2A germline mutations have been associated with familial predisposition to melanoma and other tumor types. Besides bona-fide pathogenic mutations, many sequence variants have been identified, but their effect is not well known. We detected the p.Gly23Asp missense mutation in one of the two tested melanoma patients of a family with three melanoma cases. Even though the mutated amino acid is located in a conserved domain that specifically binds to and blocks the function of CDK4/6, its lack of segregation with disease suggested a series of functional assays to discriminate between a pathogenic variant and a neutral polymorphism. The effect of this mutation has been investigated exploiting four p16 INK4A properties: its ability (i) to bind CDK4, (ii) to inhibit pRb phosphorylation, (iii) to evenly localize in the cell, and (iv) to cause cell cycle arrest. The mutant protein properties were evaluated transfecting three different cell lines (U2-OS and NM-39, both p16-null, and SaOS 2, p53 and pRb-null) with plasmids expressing either p16 wt , p16 23Asp , or the p16 32Pro pathogenic variant. We found that p16 23Asp was less efficient than p16 wt in CDK4 binding, in inhibiting pRb phosphorylation, in inducing G1 cell cycle arrest; moreover, its pattern of distribution throughout the cell was suggestive of protein aggregation, thus assessing a pathogenic role for p16 23Asp in familial melanoma.

We report a genome-wide association study of melanoma conducted by the GenoMEL consortium based on 317K tagging SNPs for 1,650 selected cases and 4,336 controls, with replication in an additional two cohorts (1,149 selected cases and 964 controls from GenoMEL, and a population-based case-control study in Leeds of 1,163 cases and 903 controls). The genome-wide screen identified five loci with genotyped or imputed SNPs reaching P < 5 x 10(-7). Three of these loci were replicated: 16q24 encompassing MC1R (combined P = 2.54 x 10(-27) for rs258322), 11q14-q21 encompassing TYR (P = 2.41 x 10(-14) for rs1393350) and 9p21 adjacent to MTAP and flanking CDKN2A (P = 4.03 x 10(-7) for rs7023329). MC1R and TYR are associated with pigmentation, freckling and cutaneous sun sensitivity, well-recognized melanoma risk factors. Common variants within the 9p21 locus have not previously been associated with melanoma. Despite wide variation in allele frequency, these genetic variants show notable homogeneity of effect across populations of European ancestry living at different latitudes and show independent association to disease risk.

Summary Background Accurate classification of primary melanocytic tumours as benign or malignant is crucial for prognostic prediction and appropriate patient management. Several chromosomal aberrations have been frequently identified in melanomas, but are absent in melanocytic naevi. We performed four-colour fluorescence in situ hybridisation (FISH) analysis of melanocytic tumours to determine the accuracy of the technique in classifying melanocytic tumours as benign or malignant. Methods FISH was performed on paraffin-embedded tissue from 40 histologically unequivocal melanocytic tumours (10 metastatic melanomas, 10 primary melanomas and 20 benign melanocytic naevi) using the product Vysis LSI RREB1/LSI MYB/LSI CCND1/CEP 6 probes (Abbott Molecular Laboratories, USA), which is designed to detect the copy number of the RREB1 (6p25), MYB (6q23), and CCND1 (11q13) genes and FISH positivity is defined by means of a scoring algorithm. Results FISH distinguished the melanomas and the naevi with a sensitivity of 90% (10/10 primary melanoma cases and 8/10 metastatic melanoma cases, respectively), and a specificity of 95%. The most common abnormalities in the melanomas were increased copies of 11q (70%) and 6p (70%), followed by 6q loss relative to cep6 (50%). Fifteen of the 18 positive melanomas were positive by more than one criterion. Conclusions The results of this study show that FISH, using a panel of four probes, is a sensitive and specific method of classifying benign and malignant melanocytic tumours. The four-colour FISH technique has the potential to assist in the stratification of the subgroup of melanocytic tumours which are difficult to classify using conventional histology.

We report that 10% of melanoma tumors and cell lines harbor mutations in the fibroblast growth factor receptor 2 (FGFR2) gene. These novel mutations include three truncating mutations and 20 missense mutations occurring at evolutionary conserved residues in FGFR2 as well as among all four FGFRs. The mutation spectrum is characteristic of those induced by UV radiation. Mapping of these mutations onto the known crystal structures of FGFR2 followed by in vitro and in vivo studies show that these mutations result in receptor loss of function through several distinct mechanisms, including loss of ligand binding affinity, impaired receptor dimerization, destabilization of the extracellular domains, and reduced kinase activity. To our knowledge, this is the first demonstration of loss-of-function mutations in a class IV receptor tyrosine kinase in cancer. Taken into account with our recent discovery of activating FGFR2 mutations in endometrial cancer, we suggest that FGFR2 may join the list of genes that play context-dependent opposing roles in cancer. (Mol Cancer Res 2009;7(1):41–54)

Metastatic cutaneous melanoma is highly resistant to cytotoxic drugs, and this contributes to poor prognosis. In vivo studies on the chemosensitivity of metastatic melanoma are rare and hampered by poor response rates to systemic chemotherapeutics. Patients who undergo isolated limb infusion (ILI) with cytotoxic drugs show high response rates and are, therefore, a good cohort for studying chemosensitivity in vivo. We used tumors from patients who underwent ILI to study the role of melanoma tumor-suppressor genes and oncogenes on melanoma chemosensitivity. Prospectively acquired tumors from 30 patients who subsequently underwent ILI with melphalan and actinomycin-D for metastatic melanoma were investigated for mRNA expression levels of p14(ARF), p16(INK4a), and MITFm. The mutation status of B-RAF, N-RAS, and PTEN were also determined. A high percentage of tumors had activating mutations in either B-RAF (15/30) or N-RAS (10/30) and only two tumors carried altered PTEN. High expression of p16(INK4a) and absence of an activating B-RAF mutation independently predicted response to treatment. Further, inducible expression of p16(INK4a) sensitized a melanoma cell line to death induced by melphalan or actinomycin-D. This study shows that high expression of p16(INK4a) or the absence of activated B-RAF correlates with in vivo response of melanoma to cytotoxic drugs.

Mutations in the DNA mismatch repair gene MSH2 lead to increased replication error and microsatellite instability and account for a substantial proportion of hereditary non-polyposis colorectal cancer (Lynch syndrome). A recent international collaborative genome-wide linkage scan (GWS) for breast cancer susceptibility loci found some evidence for there being a breast cancer susceptibility gene in a genomic region on chromosome 2p close to MSH2. We sought to investigate the possibility that mutations in MSH2 might explain the multiple cases of breast cancer in some families that were included in the international GWS. DNA samples from the affected probands of 59 multiple-case breast cancer families, many of whom gave LOD scores >0.5 in the MSH2 region, were screened for large genomic alterations in MSH2 via the Multiplex Ligation-dependant Probe Amplification (MLPA) assay and for coding region mutations via exonic sequencing. Several of the families also contained cases of colorectal cancer in addition to breast cancer and had been included in the GWS that had identified a positive LOD score on chromosome 2p. Using MLPA, c.1236C > T was identified in one proband but this variant was not predicted to create an alternate acceptor/donor site within exon 7 MSH2 using in silico analyses. A c.1734T > C was identified in a second proband via exonic sequencing but testing of the variant in other family members did not support segregation of this variant with disease. Extensive screening of 59 multiple-case breast cancer families did not identify any coding region mutations or larger genomic alterations in MSH2 that might implicate MSH2 as a breast cancer susceptibility gene.

© 2015, National Academy of Sciences. All rights reserved. Melanoma is difficult to treat once it becomes metastatic. However, the precise ancestral relationship between primary tumors and their metastases is not well understood. We performed whole-exome sequencing of primary melanomas and multiple matched metastases from eight patients to elucidate their phylogenetic relationships. In six of eight patients, we found that genetically distinct cell populations in the primary tumor metastasized in parallel to different anatomic sites, rather than sequentially from one site to the next. In five of these six patients, the metastasizing cells had themselves arisen from a common parental subpopulation in the primary, indicating that the ability to establish metastases is a late-evolving trait. Interestingly, we discovered that individual metastases were sometimes founded by multiple cell populations of the primary that were genetically distinct. Such establishment of metastases by multiple tumor subpopulations could help explain why identical resistance variants are identified in different sites after initial response to systemic therapy. One primary tumor harbored two subclones with different oncogenic mutations in CTNNB1, which were both propagated to the same metastasis, raising the possibility that activation of wingless-type mouse mammary tumor virus integration site (WNT) signaling may be involved, as has been suggested by experimental models.

Summary We describe the landscape of genomic alterations in cutaneous melanomas through DNA, RNA, and protein-based analysis of 333 primary and/or metastatic melanomas from 331 patients. We establish a framework for genomic classification into one of four subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF , mutant RAS , mutant NF1 , and Triple-WT (wild-type). Integrative analysis reveals enrichment of KIT mutations and focal amplifications and complex structural rearrangements as a feature of the Triple-WT subtype. We found no significant outcome correlation with genomic classification, but samples assigned a transcriptomic subclass enriched for immune gene expression associated with lymphocyte infiltrate on pathology review and high LCK protein expression, a T cell marker, were associated with improved patient survival. This clinicopathological and multi-dimensional analysis suggests that the prognosis of melanoma patients with regional metastases is influenced by tumor stroma immunobiology, offering insights to further personalize therapeutic decision-making.

Background Inherited susceptibility genes have been associated with histopathologic characteristics of tumors. Objective We sought to identify associations between histology of melanomas and CDKN2A genotype. Methods This was a case-control study design comparing 28 histopathologic tumor features among individuals with sporadic melanomas (N = 81) and cases from melanoma families with (N = 123) and without (N = 120) CDKN2A germline mutations. Results Compared with CDKN2A − cases, mutation carriers tended to have histologic features of superficial spreading melanoma subtype including higher pigmentation ( P trend = .02) and increased pagetoid scatter ( P trend = .07) after adjusting for age at diagnosis, sex, and American Joint Committee on Cancer thickness category. Similar associations were observed when comparing mutation carriers with a combined group of CDKN2A − (wild type) and sporadic melanomas. The presence of spindle cell morphology in the vertical growth phase was also an important predictor of genotype. Of the 15 cases with this phenotype, none were observed to harbor a CDKN2A mutation. Limitations Our study examined rare mutations and may have been underpowered to detect small, but biologically significant associations between histology and genotype. Conclusion Familial melanomas with CDKN2A mutations preferentially express a histologic phenotype of dense pigmentation, high pagetoid scatter, and a non-spindle cell morphology in the vertical growth phase.

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.

Most of the known breast cancer susceptibility genes (BRCA1, BRCA2, CHEK2 and ATM) are involved in the damage response pathway. Other members of this pathway are therefore good candidates for additional breast cancer susceptibility genes. ATR, along with ATM, plays a central role in DNA damage recognition and Chk1 relays checkpoint signals from both ATR and ATM. PPP2R1B and PPP2R5B code for subunits of protein phosphatase 2A (PP2A), which regulates autophosphorylation of ATM. In addition, EIF2S6/Int-6, which was originally identified as a common integration site for the mouse mammary tumour virus in virally induced mouse mammary tumours, is a candidate breast cancer susceptibility gene because of its putative role in maintaining chromosome stability. To investigate the role of ATR, CHK1, PPP2R1B, PPP2R5B and EIF2S6/Int-6, we carried out mutation analysis of these genes in the index cases from non-BRCA1/BRCA2 breast cancer families. We also screened sporadic breast tumours for somatic mutations in PPP2R1B and PPP2R5B. Although we identified many novel variants, we found no evidence that highly penetrant germline mutations in these five genes contribute to familial breast cancer susceptibility.

Germ-line mutations of the tumor-suppressor gene CDKN2A predispose individuals to melanoma in families worldwide. However, coding mutations of CDKN2A have not been detected in a significant proportion of those affected. The identification of a disease-associated intronic mutation of CDKN2A in UK families, which has proved to be the most common CDKN2A mutation as yet identified in this population, has highlighted the possibility that additional causal mutations may lie within the intronic sequence of the gene. In this article, we describe the comprehensive screening of 109 English and 26 Australian melanoma pedigrees for intronic mutations of CDKN2A. In total, 24 sequence variants were identified across the two introns of the gene. We show evidence that two of the CDKN2A intronic variants (IVS1 + 1104 C > A and IVS1 - 1104 C > G) predispose to melanoma. IVS1 + 1104 was shown to result in the aberrant splicing of both p16(INK4a) and p14(ARF) mRNA. Overall, however, the proportion of English melanoma families with these variants is small.

Melanoma-associated germline mutations affecting the tumor suppressor and cyclin-dependent kinase (CDK) inhibitor, CDKN2A/p16INK4a, have been identified in over 100 melanoma-prone families worldwide. To predict the melanoma risk for carriers of specific mutations, mutant p16INK4a can be tested in biochemical and cellular assays. In most cases, p16INK4a mutations with predicted disease relation, due to segregation with melanoma, are functionally impaired in such assays. The N-terminal 24 base pair duplication of CDKN2A, however, encodes a p16INK4a variant previously shown to have wild-type function, despite segregating with melanoma in at least 5 melanoma families. To clarify whether the duplication mutation has a cell cycle regulatory defect or behaves like wild-type p16INK4a, we reanalyzed the cell cycle-inhibitory activity of this mutation. Stable cell clones of the p16-null WMM1175 melanoma cell line inducible for ectopic p16INK4a were used in this study. In these cells, p16INK4a expression can be controlled at physiologic levels. Our results show that in comparison to wild-type p16INK4a, the duplication mutant induced weaker S-phase inhibition and cells expressing this mutant form of p16INK4a retained colony formation ability. We also show that the cell cycle-regulatory defect of the p16INK4a duplication mutant was associated with decreased inhibition of pRb phosphorylation even though it retained significant binding to CDK4.