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UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer: * Is overexpression of this gene associated with lower/higher survival? * What genes are differentially expressed between these two groups of samples? * What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: * Genome-wide differential gene expression analysis * Select samples directly from the screen for filtering and creating subgroups * Violin plots on any numerical data * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2066.

PURPOSE Patients with metastatic triple-negative breast cancer (mTNBC) have poor outcomes. The Intensive Trial of Omics in Cancer (ITOMIC) sought to determine the feasibility and potential efficacy of informing treatment decisions through multiple biopsies of mTNBC deposits longitudinally over time, accompanied by analysis using a distributed network of experts. METHODS Thirty-one subjects were enrolled and 432 postenrollment biopsies performed (clinical and study-directed) of which 332 were study-directed. Molecular profiling included whole-genome sequencing or whole-exome sequencing, cancer-associated gene panel sequencing, RNA-sequencing, and immunohistochemistry. To afford time for analysis, subjects were initially treated with cisplatin (19 subjects), or another treatment they had not received previously. The results were discussed at a multi-institutional ITOMIC Tumor Board, and a report transmitted to the subject's oncologist who arrived at the final treatment decision in conjunction with the subject. Assistance was provided to access treatments that were predicted to be effective. RESULTS Multiple biopsies in single settings and over time were safe, and comprehensive analysis was feasible. Two subjects were found to have lung cancer, one had carcinoma of unknown primary site, tumor samples from three subjects were estrogen receptor–positive and from two others, human epidermal growth factor receptor 2–positive. Two subjects withdrew. Thirty-four of 112 recommended treatments were accessed using approved drugs, clinical trials, and single-patient investigational new drugs. After excluding the three subjects with nonbreast cancers and the two subjects who withdrew, 22 of 26 subjects (84.6%) received at least one ITOMIC Tumor Board–recommended treatment. CONCLUSION Further exploration of this approach in patients with mTNBC is merited.

UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Data is kept on the researcher9s computer (we support Mac, Windows, and Linux) and is never uploaded to public servers. Questions Xena can help you answer: * Is overexpression of this gene associated with lower/higher survival? * Do my two subgroups have differential survival? * Is this gene differentially expressed in tumor vs normal samples? * What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotypes, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or URL links. We show multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. Other visualizations and analyses include dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: * Data from PCAWG, latest data from the GDC, ATAC-seq from TCGA, and other studies like MET500 * New visualizations for ATAC-seq and DNA methylation data * Multiple survival endpoints for Kaplan-Meier analyses from the PanCan Atlas * Export PDFs from Chart View * Genomic signatures now supported for all datasets, including data from the GDC * Updated navigation to make it easier to dive into any genomic region * Better support for probes (e.g. methylation probes like "cg16203911") We are now published in Nature Biotechnology! If you use us, cite us here: https://www.nature.com/articles/s41587-020-0546-8 We have also started to visualize scRNA-seq data including data from the HCA and the literature. Our beta prototype site delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. Citation Format: Mary J. Goldman, Brian Craft, Jing-chun Zhu, David Haussler. UCSC Xena for the visualization and analysis of cancer genomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 250.

UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. The toll supports Mac, Windows, and Linux. Questions Xena can help you answer: 1) Is overexpression of this gene associated with lower/higher survival? 2) What genes are differentially expressed between these two groups of samples? 3) What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression, and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: - Genome-wide differential gene expression analysis - New interface for filtering samples and creating subgroups - New interface to create charts and graphs - Violin plots on any numerical data - Loading of Microsoft Excel files - A new Publication Page showcasing publications and authors that use UCSC Xena Our beta prototype for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5039.

SUMMARY RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(–) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(–) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(–) cases are required to understand this important LUAD subset., In Brief Carrot-Zhang et al. perform whole-genome characterization of lung adenocarcinomas (LUADs) lacking RTK/RAS/RAF pathway alterations (RPAs) and identify mutations or structural variants in both coding and non-coding spaces that define a unique entity of RPA(–) LUADs and potentially explain the underlying biology of this disease., Graphical Abstract

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI), Funder: Ontario Institute for Cancer Research (Institut Ontarien de Recherche sur le Cancer); doi: https://doi.org/10.13039/100012118, Funder: EMBL Member States EU FP7 Programme projects EurocanPlatform (260791) CAGEKID (241669), Funder: European Union’s Framework Programme For Research and Innovation Horizon 2020 under the Marie Sklodowska-Curie grant agreement no. 703543, Funder: Michael & Susan Dell Foundation; Mary K. Chapman Foundation; CCSG Grant P30 CA016672 (Bioinformatics Shared Resource); ITCR U24 CA199461; GDAN U24 CA210949; GDAN U24 CA210950, Funder: European Commission's H2020 Programme, project SOUND, Grant Agreement no 633974, Funder: Spanish Government (SEV 2015-0493) BSC-Lenovo Master Collaboration Agreement (2015), The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

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.

This cohort study evaluates the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer., Key Points Question Is it feasible and useful to compare the tumor RNA sequencing data of a child or young adult with the tumor RNA sequencing data of thousands of other patients (of all ages) in a research setting? Findings Among 144 tumor samples from children and young adults, comparative RNA sequencing analysis, conducted across 4 precision medicine studies in the United States and Canada, was feasible and potentially useful for 99 of 144 pediatric and young adult cancer samples. In contrast, DNA mutation information was potentially useful for only 34 of 74 samples. Meaning This study’s findings suggest that open sharing and combined analysis of tumor RNA sequencing data from pediatric and young adult patients treated on different clinical trials may represent a feasible approach and may produce useful clinical and biological information for individual patients., Importance Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes. Objective To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer. Design, Setting, and Participants This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children’s Hospital (n = 31), Lucile Packard Children’s Hospital at Stanford University (n = 80), CHOC Children’s Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017. Exposures Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant’s tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient’s tumor. Results of the UCSC analysis were presented to clinical partners. Main Outcomes and Measures Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information. Results Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq–derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%). Conclusions and Relevance This study’s findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.

The respiratory tract surface is protected from inhaled pathogens by a secreted layer of mucus that is rich in mucin glycoproteins. Disrupted mucus production is a cardinal feature of chronic respiratory diseases but how this alteration affect interactions between mucins and pathogens is complex and poorly understood. Here, we identify a central and unexpected role for the major airway mucin MUC5AC in pathogenesis of virus-induced exacerbations of chronic obstructive pulmonary disease (COPD). Virus induction of MUC5AC is augmented in COPD compared to healthy subjects, is enhanced in frequent exacerbators and correlates with inflammation, symptom severity and secondary bacterial infection during exacerbation. MUC5AC is functionally related to inflammation as MUC5AC-deficient (Muc5ac-/-) mice had attenuated rhinovirus-induced airway inflammation whilst exogenous MUC5AC glycoprotein administration augmented virus-induced inflammatory responses and bacterial load. Mechanistically, MUC5AC-augmentation of rhinovirus-induced inflammation occurred through release of extracellular adenosine triphosphate (ATP). Therapeutic suppression of virus-induced MUC5AC release using an epidermal growth factor receptor (EGFR) inhibitor ameliorated exaggerated pro-inflammatory responses in a mouse COPD exacerbation model. Collectively, these studies demonstrate previously unrecognised pro-inflammatory effects of MUC5AC during infection and thus highlight a key unforeseen role in driving COPD exacerbation severity.

The mechanisms underlying rapid elimination of herpes simplex virus-2 (HSV-2) in the human genital tract despite low tissue-resident CD8+ T-cell density (TRM) are unknown. We analyzed shedding episodes during chronic HSV-2 infection: viral clearance always occurred within 24 hours of detection even if viral load exceeded 107HSV DNA copies; surges in granzyme B and interferon-γoccurred within the early hours after reactivation. We next developed a mathematical model of an HSV-2 genital ulcer to integrate mechanistic observations of TRMin situproliferation, trafficking, cytolytic effects and cytokine alarm signaling from murine studies with viral kinetics, histopathology and lesion size data from humans. A sufficiently high density of HSV-2 specific TRMpredicted rapid contact-mediated elimination of infected cells. At lower TRMdensities, TRMmust initiate a rapidly diffusing, polyfunctional cytokine response in order to eliminate of a majority of infected cells and eradicate briskly spreading HSV-2 infection.One Sentence SummaryControl of herpes simplex virus-2 is primarily mediated by rapidly diffusing cytokines secreted by tissue-resident T cells.

We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.

Whole-exome sequencing studies have transformed our understanding of recurrent somatic mutations that contribute to cancer pathogenesis; however, these studies limit our ability to identify cancer-associated mutations to those that cause protein-coding changes. To more comprehensively catalogue cancer-associated gene alterations, we have extensively characterized tumor transcriptomes from 1,220 donors with matched whole-genome sequence data to identify recurrent RNA-level aberrations. Specifically, we created a unified RNA-Seq analysis pipeline including sequence alignment and quality control and subsequently identified gene alterations through outlier detection from estimated gene expression levels, alternative splicing, alternative transcription starts, and allele-specific expression and through identified RNA-edited sites and gene fusions. Our data represent an extensive catalog of RNA aberrations for each gene across 27 cancer types. We have also tested for genetic associations with these RNA phenotypes. Using an integrative analysis approach, we have mapped genome-wide cis and trans effects on individual RNA phenotypes, considering both common germline variants as well as somatic SNVs in gene promoters, enhancers, and intronic and other regions. Many of the regulatory associations we identify are not accessible by exome sequencing, underlining the importance of whole-genome sequence data. Utilizing this RNA-centric view, we have identified genes that are recurrently altered, yet have not been previously characterized as cancer genes or identified through DNA-level driver gene analysis. To identify further supporting evidence that these recurrent alterations are potential drivers, we identified genes with mutually exclusive RNA-level alterations. Our findings reveal new insights into selective advantages of somatic changes and molecular mechanisms of cancer. This work is by the Transcriptome Working Group of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and authors are listed in alphabetical order. Citation Format: Samirkumar Amin, Philip Awadalla, Andrew Biankin, Paul Boutros, Alvis Brazma, Angela Norie Brooks, Claudia Calabrese, David Chang, Aurélien Chateigner, Ken Chen, Zechen Chong, Brian Craft, Chad Creighton, Deniz Demircioğlu, Nuno Fonseca, Milana Frenkel-Morgenstern, Gad Getz, Jonathan Göke, Mary Goldman, Liliana Greger, Syed Haider, Yao He, Katherine Hoadley, Yuan Ji, Andre Kahles, Ekta Khurana, Jan Korbel, Kjong Lehmann, Han Liang, Fenglin Liu, Maximillian Marin, Matthew Meyerson, Akinyemi Ojesina, Francis Ouellette, Chandra Pedamallu, Marc Perry, Gunnar Rätsch, Roland Schwarz, Yuichi Shiraishi, Cameron Soulette, Oliver Stegle, Patrick Tan, Alfonso Valencia, Linda Xiang, Christina Yung, Junjun Zhang, Fan Zhang, Zemin Zhang, Jingchun Zhu. Pan-cancer study of recurrent and heterogeneous RNA aberrations and association with whole-genome variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr SY10-02. doi:10.1158/1538-7445.AM2017-SY10-02

Toil is portable, open-source workflow software that supports contemporary workflow definition languages and can be used to securely and reproducibly run scientific workflows efficiently at large-scale. To demonstrate Toil, we processed over 20,000 RNA-seq samples to create a consistent meta-analysis of five datasets free of computational batch effects that we make freely available. Nearly all the samples were analysed in under four days using a commercial cloud cluster of 32,000 preemptable cores.

UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.

Clinical detection of sequence and structural variants in known cancer genes points to viable treatment options for a minority of children with cancer.1 To increase the number of children who benefit from genomic profiling, gene expression information must be considered alongside mutations.2,3 Although high expression has been used to nominate drug targets for pediatric cancers,4,5 its utility has not been evaluated in a systematic way.6 We describe a child with a rare sarcoma that was profiled with whole-genome and RNA sequencing (RNA-Seq) techniques. Although the tumor did not harbor DNA mutations targetable by available therapies, incorporation of gene expression information derived from RNA-Seq analysis led to a therapy that produced a significant clinical response. We use this case to describe a framework for inclusion of gene expression into the clinical genomic evaluation of pediatric tumors.

With the advent of cancer genome analysis, there is an enormous need for an integrative computational approach to understand the functional impact of the genomic aberrations that drive and characterize cancers. This requires mechanisms to aggregate and visualize both public and investigator-generated data on cancer genomes, transcriptomes, epigenomes and more. Extending the UCSC Cancer Genomics Browser, we are developing the UCSC Xena platform to achieve this. UCSC's Xena is a data server-based platform that stores functional genomics data and serves them in response to data requests in real-time and with minimal informatics overhead. Examples of these data requests include data visualization, integration and further downstream analysis. Xena can easily be installed on a laptop, or on servers behind a firewall. The UCSC Xena server provides access to TCGA open access data, with 526 datasets from 31 different TCGA cancer types. Types of hosted datasets include copy number, somatic mutation, DNA methylation, gene and exon expression, protein expression, PARADIGM pathway inference, and phenotype data. Our automated pipeline updates TCGA data periodically, ensuring we are visualizing the most recent data available. Additionally, our pipeline ingests TCGA phenotype data and attempts to assign more readable feature names and values. We further derive overall and recurrence free survival from TCGA phenotype data, allowing users to perform survival analysis. We are extending the UCSC Cancer Genomics Browser to access and visualize data hosted across multiple Xena servers while maintaining data privacy. This functionality allows viewing and interpretation of one's own data (e.g. stored on a private Xena) in the context of a large collection of cancer genomics datasets (e.g. TCGA data stored at UCSC). The outcome is a platform for researchers to store and analyze their datasets in an interoperable manner. We are integrating Xena with other tools such as MuPIT (enables visualization of somatic mutations on three-dimensional protein structures), and with Galaxy to allow integration with other bioinformatics tools such as Trinity (via Galaxy, RNA-seq data analysis to identify coding and non-coding transcripts, score them for aberrations, and quantify their expression). Integrating these tools provides researchers with a workflow with strong analysis and visualization capabilities, and brings sophisticated computational analyses within the reach of non-computational scientists. Citation Format: Jingchun Zhu, Brian Craft, Mary Goldman, Melissa Cline, Mark Diekhans, David Haussler. Using the UCSC Xena Platform to integrate, visualize, and analyze your own data in the context of large external genomic datasets. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-07.

We hypothesize that new insights into how cancers progress and respond to treatment will come from clinical trials that i) extensively characterize the molecular features of a patient’s cancer; ii) use results to predict drug susceptibilities; iii) treat in accordance with these predictions; and iv) learn from individual patient outcomes to iterate and improve over time. To investigate the feasibility of this type of clinical study, we launched the "Intensive Trial of OMics in Cancer" (ITOMIC) for patients with metastatic triple negative breast cancer (TNBC) (Clinicaltrials.gov ID: NCT01957514). Eligible patients have metastatic TNBC, are platinum-naive, and are scheduled to receive Cisplatin. Biopsies are performed under carefully controlled conditions prior to Cisplatin – starting all subjects on a common treatment path, and uncoupling the time needed for specimen analysis from immediate therapy. Biopsies are repeated upon completion of Cisplatin and following subsequent therapies. A subset of specimens is chosen for whole Exome Sequencing, deep sequencing of a panel of cancer associated genes, and RNA-sequencing. De-identified results are placed on a web-based server for analysis and discussed at a meeting of the ITOMIC tumor board. A report describing results and potential therapies is provided to the subject’s oncologist. Treatment decisions are left to the discretion of the oncologist. If a decision is taken to pursue treatments identified in our report we offer assistance in accessing those treatments. Ten patients have been screened and seven have enrolled. Subjects range in age from 40 to 77 years and all but one has received extensive prior treatment for metastatic TNBC. All seven underwent an initial set of biopsies, targeting between two and five metastatic sites. For most metastatic sites, multiple core needle passes are performed. All subjects tolerated the biopsies well without significant adverse events, and all started treatment with Cisplatin. Three subjects completed Cisplatin and underwent a second round of biopsies. Potential targets for therapy were identified in 5 of the first 6 subjects, and three subjects have received four predicted therapies: 1) a patient with somatic loss of BRCA1 and two linked FGFR2 activating mutations, who was treated first with Veliparib through a single-patient IND and then switched to Ponatinib which produced a partial response; 2) a patient with a novel missense ROS1 mutation treated with crizotinib; and 3) a patient with CYP3A4 copy gain treated with cyclophosphamide. Conclusion: Our early experience indicates that this approach is feasible and may increase the efficiency of learning from patients with advanced cancer. Citation Format: C Anthony Blau, Colin Pritchard, Michael O Dorschner, Sibel Blau, Brigham Mecham, Elisabeth Mahen, VK Gadi, Wayne Monsky, Kimberly Burton, Arturo Ramirez, Jackie Stilwell, Eric Kladjian, Carol Collins, Jeannine S McCune, William S Noble, Julie Gralow, Frank Senecal, Linda Dhaene, Nicole Kuderer, Jennifer Specht, Chaozhong Song, Carla Grandori, Nathan Price, Mary Goldman, Aime Radenbaugh, David Haussler, Jingchun Zhu. Assessing the safety and feasibility of efficient hypothesis testing in patients with metastatic triple negative breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-08-01.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project has generated, to our knowledge, the largest whole-genome cancer sequencing resource to date. Here we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper: The ICGC Data Portal, UCSC Xena, Expression Atlas, PCAWG-Scout, and Chromothripsis Explorer. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, as well as demonstrate how the tools can be used together to more deeply understand tumor biology. Together, these tools enable researchers to dynamically query complex genomics data and integrate external information, enabling and enhancing PCAWG data interpretation. More information on these tools and their capabilities is available from The PCAWG Data Portals and Visualizations Page (http://docs.icgc.org/pcawg).

We report a comprehensive molecular characterization of pheochromocytomas and paragangliomas (PCCs/PGLs), a rare tumor type. Multi-platform integration revealed that PCCs/PGLs are driven by diverse alterations affecting multiple genes and pathways. Pathogenic germline mutations occurred in eight PCC/PGL susceptibility genes. We identified CSDE1 as a somatically mutated driver gene, complementing four known drivers (HRAS, RET, EPAS1, and NF1). We also discovered fusion genes in PCCs/PGLs, involving MAML3, BRAF, NGFR, and NF1. Integrated analysis classified PCCs/PGLs into four molecularly defined groups: a kinase signaling subtype, a pseudohypoxia subtype, a Wnt-altered subtype, driven by MAML3 and CSDE1, and a cortical admixture subtype. Correlates of metastatic PCCs/PGLs included the MAML3 fusion gene. This integrated molecular characterization provides a comprehensive foundation for developing PCC/PGL precision medicine., National Institutes of Health (U.S.) (Grant U54HG003273), National Institutes of Health (U.S.) (Grant U54HG003067), National Institutes of Health (U.S.) (Grant U54HG003079), National Institutes of Health (U.S.) (Grant U24CA143799), National Institutes of Health (U.S.) (Grant U24CA143835), National Institutes of Health (U.S.) (Grant U24CA143840), National Institutes of Health (U.S.) (Grant U24CA143843), National Institutes of Health (U.S.) (Grant U24CA143845), National Institutes of Health (U.S.) (Grant U24CA143848), National Institutes of Health (U.S.) (Grant U24CA143858), National Institutes of Health (U.S.) (Grant U24CA143866), National Institutes of Health (U.S.) (Grant U24CA143867), National Institutes of Health (U.S.) (Grant U24CA143882), National Institutes of Health (U.S.) (Grant U24CA143883), National Institutes of Health (U.S.) (Grant U24CA144025), National Institutes of Health (U.S.) (Grant P30CA016672)

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/) is a web-based application that integrates relevant data, analysis and visualization, allowing users to easily discover and share their research observations. Users can explore the relationship between genomic alterations and phenotypes by visualizing various -omic data alongside clinical and phenotypic features, such as age, subtype classifications and genomic biomarkers. The Cancer Genomics Browser currently hosts 575 public datasets from genome-wide analyses of over 227 000 samples, including datasets from TCGA, CCLE, Connectivity Map and TARGET. Users can download and upload clinical data, generate Kaplan–Meier plots dynamically, export data directly to Galaxy for analysis, plus generate URL bookmarks of specific views of the data to share with others.

Reverse phase protein array (RPPA) technology introduced a miniaturized “antigen-down” or “dot-blot” immunoassay suitable for quantifying the relative, semi-quantitative or quantitative (if a well-accepted reference standard exists) abundance of total protein levels and post-translational modifications across a variety of biological samples including cultured cells, tissues, and body fluids. The recent evolution of RPPA combined with more sophisticated sample handling, optical detection, quality control, and better quality affinity reagents provides exquisite sensitivity and high sample throughput at a reasonable cost per sample. This facilitates large-scale multiplex analysis of multiple post-translational markers across samples from in vitro, preclinical, or clinical samples. The technical power of RPPA is stimulating the application and widespread adoption of RPPA methods within academic, clinical, and industrial research laboratories. Advances in RPPA technology now offer scientists the opportunity to quantify protein analytes with high precision, sensitivity, throughput, and robustness. As a result, adopters of RPPA technology have recognized critical success factors for useful and maximum exploitation of RPPA technologies, including the following: preservation and optimization of pre-analytical sample quality, application of validated high-affinity and specific antibody (or other protein affinity) detection reagents, dedicated informatics solutions to ensure accurate and robust quantification of protein analytes, and quality-assured procedures and data analysis workflows compatible with application within regulated clinical environments. In 2011, 2012, and 2013, the first three Global RPPA workshops were held in the United States, Europe, and Japan, respectively. These workshops provided an opportunity for RPPA laboratories, vendors, and users to share and discuss results, the latest technology platforms, best practices, and future challenges and opportunities. The outcomes of the workshops included a number of key opportunities to advance the RPPA field and provide added benefit to existing and future participants in the RPPA research community. The purpose of this report is to share and disseminate, as a community, current knowledge and future directions of the RPPA technology.

UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Our unique Visual Spreadsheet shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We offer dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, comparative transcript views, and bookmarks. We link out to the UCSC Genome Browser, giving users additional genomic context for any gene or coordinate, as well as MuPIT/CRAVAT and TumorMap, to give users complementary views of the same data. Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, PCAWG, GDC, GTEx, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data. In addition to the commonly available SNPs, INDELs, copy number variation, and gene expression datasets, we support DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotypes, subtype classifications and biomarkers. A recompute of TCGA, TARGET and GTEx datasets through the same bioinformatics pipeline allows users to compare expression between tumor and normal tissues. All of our data is available for download via our python API or through AWS S3 buckets. A researcher can host their own data securely via private hubs running on a laptop or behind a firewall, with visual and analytical integration occurring only within the Xena Browser. The lightweight Xena Data Hubs are straightforward to install on Windows, Mac and Linux. Loading data is easy using either our application or command line interface. Our newest features include: * a new, more intuitive wizard to load your data into a local Hub * URL bookmarks to save interactive views for yourself or to share with collaborators * genomic signatures: dynamically build as a weighted sum over a set of genes * hierarchically cluster a list of genes, regulons or probes * upper vs lower quartile in a KM plot Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. UCSC Xena for cancer genomics visualization and interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 911.

Objective: Genomic interrogation of tumors has changed the way we care for cancer patients. Monogenic, single mutation tests clearly and accurately define patient populations whose tumors will or will not likely respond to specific therapies. Next generation sequencing has enabled diagnostics derived from broader footprints, such as signatures of mutational processes. Currently, no universal guidelines exist on translating complex predictive signatures into the clinic, and their interpretability is a barrier to uptake by clinicians. Visually displaying genomic signatures may help clinicians interpret them. We designed a user-directed approach to developing understandable displays of an individual’s BRCAness signature through testing at clinical cancer conferences. Materials and Methods: We adopted a two-stage approach to optimizing user-testing methods. First, 44 attendees at the 2018 Gastrointestinal Cancer Symposium participated in a tablet-based test of a visualization of a patient’s BRCAness signature. We refined our testing methods and continued tabled-based testing of 51 attendees at the 2018 American Society of Clinical Oncology. Participants viewed slides explaining the signature and visualization. They were asked to interpret a patient’s BRCAness signature when provided information both as text and as a visualization. Participants received one of three similar versions of the visualization. Results: Participants interpreted patients’ BRCAness signatures faster when given a visual. When shown a visual indicating BRCAness scores of germline BRCA mutated tumors, participants interpreted the information fastest without sacrificing accuracy or confidence in their answer. Participants’ interpretation of an individual’s BRCAness signature was associated with their self-reported likelihood to recruit the individual to a clinical trial. Conclusion: Visualizing distributions of BRCAness signatures with germline BRCA mutations displayed helped participants respond faster without sacrificing accuracy or confidence in their answer. Testing attendees at cancer conferences presents a potential platform to include clinicians and researchers in the development of genomics visualization tools. Citation Format: Hannah R. Allegakoen, Mary Goldman, Bennett Caughey, Martin Consunji, Christopher C. Benz, David Haussler, Jingchun Zhu, Eric A. Collisson. Designing an intuitive visualization of BRCAness scores for clinicians [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2466.

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

Toil is portable, open-source workflow software that supports contemporary workflow definition languages and can be used to securely and reproducibly run scientific workflows efficiently at large-scale. To demonstrate Toil, we processed over 20,000 RNA-seq samples to create a consistent meta-analysis of five datasets free of computational batch effects that we make freely available. Nearly all the samples were analysed in under four days using a commercial cloud cluster of 32,000 preemptable cores.

Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient's evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs.

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/) is a set of web-based tools to display, investigate and analyse cancer genomics data and its associated clinical information. The browser provides whole-genome to base-pair level views of several different types of genomics data, including some next-generation sequencing platforms. The ability to view multiple datasets together allows users to make comparisons across different data and cancer types. Biological pathways, collections of genes, genomic or clinical information can be used to sort, aggregate and zoom into a group of samples. We currently display an expanding set of data from various sources, including 201 datasets from 22 TCGA (The Cancer Genome Atlas) cancers as well as data from Cancer Cell Line Encyclopedia and Stand Up To Cancer. New features include a completely redesigned user interface with an interactive tutorial and updated documentation. We have also added data downloads, additional clinical heatmap features, and an updated Tumor Image Browser based on Google Maps. New security features allow authenticated users access to private datasets hosted by several different consortia through the public website.

Many DNA-hypermethylated cancer genes are occupied by the Polycomb (PcG) repressor complex in embryonic stem cells (ESCs). Their prevalence in the full spectrum of cancers, the exact context of chromatin involved, and their status in adult cell renewal systems are unknown. Using a genome-wide analysis, we demonstrate that ∼75% of hypermethylated genes are marked by PcG in the context of bivalent chromatin in both ESCs and adult stem/progenitor cells. A large number of these genes are key developmental regulators, and a subset, which we call the “DNA hypermethylation module,” comprises a portion of the PcG target genes that are down-regulated in cancer. Genes with bivalent chromatin have a low, poised gene transcription state that has been shown to maintain stemness and self-renewal in normal stem cells. However, when DNA-hypermethylated in tumors, we find that these genes are further repressed. We also show that the methylation status of these genes can cluster important subtypes of colon and breast cancers. By evaluating the subsets of genes that are methylated in different cancers with consideration of their chromatin status in ESCs, we provide evidence that DNA hypermethylation preferentially targets the subset of PcG genes that are developmental regulators, and this may contribute to the stem-like state of cancer. Additionally, the capacity for global methylation profiling to cluster tumors by phenotype may have important implications for further refining tumor behavior patterns that may ultimately aid therapeutic interventions.

Background: Her-2/neu overexpression, by immunohistochemistry (IHC) or fluorescence in-situ hybridization (FISH), is highly correlated with response to trastuzumab and these are currently the gold-standard, FDA-cleared testing methods for assigning treatment to Her-2-directed therapies. However, substantial variability has been documented between community and central laboratory IHC and FISH testing. Biologically, Her-2 overexpression may reflect increased gene copy number, gene expression and/or protein production, and these can be measured by other platforms, including comparative genomic hybridization (CGH), expression arrays and quantitative protein assays, respectively. We sought to determine the degree to which community IHC/FISH results differed from centrally-assessed IHC, FISH, and other assessment platforms within the I-SPY Trial and whether response to neoadjuvant chemotherapy (NAC) differed by platform. Methods: The I-SPY Trial enrolled 237 women 2002–06 with invasive breast tumors at least 3 cm in clinical/radiographic size who subsequently underwent anthracycline/taxane NAC, serial core biopsies and imaging. Pathologic complete response (pCR) was determined at time of surgery and 3-year follow up has been reached. Trastuzumab was given to Her2+ patients at physician discretion, based upon community IHC/FISH results, and became more widespread after 2005. Central I-SPY laboratories determined Her2 copy number by MIP array, gene expression by Affymetrix and Agilent arrays, and Her2 protein by reverse-phase protein array (RPMA). Unsupervised clustering algorithms were used to evaluate expression patterns. Composite variables were constructed for DNA, RNA and protein positivity as well as for community and central IHC/FISH. Platforms were compared and Kaplan-Meier curves were constructed to compare outcomes by platform. Results: 222 women were evaluable, though not all patients had results for all platforms. Community composite IHC/FISH was positive in 64/214 (30%) but only 41 of these (64%) were confirmed by central IHC/FISH and 4 additional cases were centrally positive despite negative community testing. Concordance was high among centrally-assessed Her2 platforms, but was lower between community IHC/protein and central RNA (90%), DNA (91%) and protein (91%). Among patients receiving trastuzumab (n=36), the pCR rate was ∼50% regardless of Her2-assessment platform; in contrast, those not receiving trastuzumab had pCR rates below 30%. Among the 64 patients deemed Her2+ by community IHC/FISH, 30 (48%) had pCR and 15 (25%) have had distant relapse. Five distant relapses have occurred despite pCR; all received trastuzumab, all were Her2 positive by multiple central platforms and 3/5 were ER-positive. Sites of distant relapse included brain, bone and viscera; only 1 of 5 had isolated brain relapse. Conclusions: Community IHC/FISH testing for Her2 expression in the I-SPY Trial overcalled Her2 positivity compared to central testing while central results were highly concordant among DNA, RNA and protein platforms. Despite the high rate of community “false positives”, relapse after pCR occurred only in central Her2 “true positives,” exclusively among those receiving trastuzumab, and was rarely isolated to CNS sanctuary sites. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-06-11.

The UCSC Xena platform (http://xena.ucsc.edu/) allows biologists and bioinformaticians to securely analyze and visualize functional genomics data. Our unique Visual Spreadsheet shows multiple data types side by side enabling discovery of correlations across and within genes and genomic regions. Dynamic Kaplan-Meier survival analysis assesses survival stratification in addition to scatter plots, bar graphs, and boxplots all shown with statistical tests. In addition to the commonly available SNPs, INDELs, CNV, and gene expression datasets, we support DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotypes, subtype classifications and biomarkers. Our new whole genome views allow users to easily visualize non-coding regions for both copy number variation and somatic mutations. All of our data is available for download via our python API or through AWS S3 buckets. Our expanding public Xena Data Hubs currently host 1500+ datasets from more than 35 cancer types, as well as Pan-Cancer datasets. In addition to serving seminal cancer genomic datasets to the scientific community, including the latest from the GDC, TCGA, TARGET, and ICGC, we also host 'normal tissue' datasets from GTEx. A recompute of TCGA, TARGET and GTEx datasets through the same bioinformatics pipeline allows users to compare expression between tumor and normal tissues. In addition to exploring these public datasets, the UCSC Xena Browser can easily display an investigator's genomic and clinical data on their own Xena Hub. By empowering users to install and load data into their own hub, our architecture ensures that the investigator's data remains private. The lightweight Xena Data Hubs are straightforward to install on Windows, Mac and Linux operating systems and loading data is easy using either our application or command line interface. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Cancer genomics visualization and interpretation using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2274.

Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis.Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH).Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).

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.

The UCSC Xena platform (http://xena.ucsc.edu/) allows biologists and bioinformaticians to securely analyze and visualize their private functional genomics data in the context of public genomic and clinical data sets. The Xena platform consists of a set of federated data hubs and the Xena browser, which integrates across hubs, providing one location to analyze and visualize all data. Our expanding public Xena Data Hubs currently hosts 1400+ data sets from more than 35 cancer types, as well as Pan-Cancer data sets. Our public data hubs serve seminal cancer genomics and functional genomics data set to the scientific community, including the latest TCGA, TARGET, ICGC, and GTEx data sets. We support most data types including somatic and germline SNPs, INDELs, large structural variants, CNV, gene-, transcript-, exon- protein-, miRNA-expression, DNA methylation, phenotypes, clinical data, subtype classifications and genomic biomarkers. Additionally, investigators’ own functional genomics data can be hosted on private hubs running on their laptop or behind the firewall. Data is integrated on the UCSC Xena Browser, allowing biologists to view and interpretation of their genomic data in the context of a large collection of cancer genomics data sets such as TCGA. The lightweight Xena data hubs are straightforward to install on Windows, Mac and Linux operating systems and loading data is easy using either our application or command line interface. This system of the browser and hubs helps researchers combine new or preliminary results from their laptops or internal servers, or even data from a new paper, securely with vetted data from the public sphere. Visualizations and analyses include dynamic Kaplan-Meier survival analysis to assess survival stratification by any information in addition to our visual spreadsheet, scatter plots and bar graphs. We seek feedback at our poster on new visualizations and functionalities. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. The UCSC Xena system for cancer genomics data visualization and interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2584. doi:10.1158/1538-7445.AM2017-2584

Reverse phase protein array (RPPA) technology introduced a miniaturized "antigen-down" or "dot-blot" immunoassay suitable for quantifying the relative, semi-quantitative or quantitative (if a well-accepted reference standard exists) abundance of total protein levels and post-translational modifications across a variety of biological samples including cultured cells, tissues, and body fluids. The recent evolution of RPPA combined with more sophisticated sample handling, optical detection, quality control, and better quality affinity reagents provides exquisite sensitivity and high sample throughput at a reasonable cost per sample. This facilitates large-scale multiplex analysis of multiple post-translational markers across samples from in vitro, preclinical, or clinical samples. The technical power of RPPA is stimulating the application and widespread adoption of RPPA methods within academic, clinical, and industrial research laboratories. Advances in RPPA technology now offer scientists the opportunity to quantify protein analytes with high precision, sensitivity, throughput, and robustness. As a result, adopters of RPPA technology have recognized critical success factors for useful and maximum exploitation of RPPA technologies, including the following: preservation and optimization of pre-analytical sample quality, application of validated high-affinity and specific antibody (or other protein affinity) detection reagents, dedicated informatics solutions to ensure accurate and robust quantification of protein analytes, and quality-assured procedures and data analysis workflows compatible with application within regulated clinical environments. In 2011, 2012, and 2013, the first three Global RPPA workshops were held in the United States, Europe, and Japan, respectively. These workshops provided an opportunity for RPPA laboratories, vendors, and users to share and discuss results, the latest technology platforms, best practices, and future challenges and opportunities. The outcomes of the workshops included a number of key opportunities to advance the RPPA field and provide added benefit to existing and future participants in the RPPA research community. The purpose of this report is to share and disseminate, as a community, current knowledge and future directions of the RPPA technology.

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a method that combines the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating the DNA and RNA, we are able to rescue back calls that would be missed by traditional mutation calling algorithms that only examine the DNA. RADIA was developed for the identification of somatic mutations using both DNA and RNA from the same individual. We demonstrate high sensitivity (84%) and very high specificity (98% and 99%) in real data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to real data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back calls at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued back due to the incorporation of the RNA. Software available at https://github.com/aradenbaugh/radia/, 25 pages, 3 figures, 4 tables, 8 supplementary figures, submitted to Bioinformatics

Mutations in the no-mechanoreceptor-potential A (nompA) gene, which eliminate transduction in Drosophila mechanosensory organs, disrupt contacts between neuronal sensory endings and cuticular structures. nompA encodes a transmembrane protein with a large, modular extracellular segment that includes a zona pellucida (ZP) domain and several plasminogen N-terminal (PAN) modules. It is specifically expressed in type I sense organs of the peripheral nervous system by the support cells that ensheath the neuronal sensory process. A green fluorescent protein (GFP)-NompA fusion protein is localized to the dendritic cap, an extracellular matrix that covers the ciliary outer segment of the sensory process and that shows organizational defects in nompA mutants. The structure and location of NompA suggest that it forms part of a mechanical linkage required to transmit mechanical stimuli to the transduction apparatus.

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) offers interactive visualization and exploration of TCGA genomic, phenotypic, and clinical data, as produced by the Cancer Genome Atlas Research Network. Researchers can explore the impact of genomic alterations on phenotypes by visualizing gene and protein expression, copy number, DNA methylation, somatic mutation and pathway inference data alongside clinical features, Pan-Cancer subtype classifications and genomic biomarkers. Integrated Kaplan-Meier survival analysis helps investigators to assess survival stratification by any of the information.

Adenocarcinoma of the lung is the leading cause of cancer death worldwide. Here we report molecular profiling of 230 resected lung adenocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses. High rates of somatic mutation were seen (mean 8.9 mutations per megabase). Eighteen genes were statistically significantly mutated, including RIT1 activating mutations and newly described loss-of-function MGA mutations which are mutually exclusive with focal MYC amplification. EGFR mutations were more frequent in female patients, whereas mutations in RBM10 were more common in males. Aberrations in NF1, MET, ERBB2 and RIT1 occurred in 13% of cases and were enriched in samples otherwise lacking an activated oncogene, suggesting a driver role for these events in certain tumours. DNA and mRNA sequence from the same tumour highlighted splicing alterations driven by somatic genomic changes, including exon 14 skipping in MET mRNA in 4% of cases. MAPK and PI(3)K pathway activity, when measured at the protein level, was explained by known mutations in only a fraction of cases, suggesting additional, unexplained mechanisms of pathway activation. These data establish a foundation for classification and further investigations of lung adenocarcinoma molecular pathogenesis.

UCSC Xena (http://xena.ucsc.edu/) is a bioinformatics tool to visualize functional genomics data from multiple sources simultaneously, including both public and private data. The Xena system consists of a set of federated data hubs and the Xena browser, which integrates across hubs, providing one location to analyze and visualize all data. The lightweight Xena data hubs are straightforward to install on Windows, Mac and Linux operating systems and easily allow hub administrators to authenticate users, ensuring that only authorized users have access to secure data. Loading data into a Xena hub is easy using either our application or command line interface. Hosting public data from major projects, such as TCGA, on public Xena hubs gives users access without having to download these large datasets. The Xena system makes it easy to aggregate across many hubs, allowing users to integrate public datasets and private secure data together or view them separately. This system of the browser and hubs helps researchers combine new or preliminary results from their laptops or internal servers, or even data from a new paper, securely with vetted data from the public sphere. The largest public Xena hub, based at UCSC, currently hosts an expanding set of searchable data, including 806 public datasets from several large consortiums including TCGA (The Cancer Genome Atlas), ICGC (International Cancer Genome Consortium), Treehouse Childhood Cancer Project, CCLE (Cancer Cell Line Encyclopedia), and more. Xena hubs are flexible enough to handle most data types, including gene, exon, miRNA and protein expression, copy number, DNA methylation and somatic mutation data along with phenotypes, subtype classifications and genomic biomarkers. Dynamic Kaplan-Meier survival analysis helps investigators to assess survival stratification by any information while scatter plots and bar graphs offer new insights into the data. Integration with Galaxy gives users access to a myriad of bioinformatics tools for further analysis and hypothesis testing. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, Teresa Swatloski, Melissa Cline, David Haussler. The UCSC Xena system for integrating and visualizing functional genomics. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5270.

Lung squamous cell carcinoma is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in squamous cell lung cancers have not been comprehensively characterized, and no molecularly targeted agents have been specifically developed for its treatment. As part of The Cancer Genome Atlas, here we profile 178 lung squamous cell carcinomas to provide a comprehensive landscape of genomic and epigenomic alterations. We show that the tumour type is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumour. We find statistically recurrent mutations in 11 genes, including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations are seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2 and KEAP1 in 34%, squamous differentiation genes in 44%, phosphatidylinositol-3-OH kinase pathway genes in 47%, and CDKN2A and RB1 in 72% of tumours. We identified a potential therapeutic target in most tumours, offering new avenues of investigation for the treatment of squamous cell lung cancers., National Institutes of Health (U.S.) (Grant U24 CA126561), National Institutes of Health (U.S.) (Grant U24 CA126551), National Institutes of Health (U.S.) (Grant U24 CA126554), National Institutes of Health (U.S.) (Grant U24 CA126543), National Institutes of Health (U.S.) (Grant U24 CA126546), National Institutes of Health (U.S.) (Grant U24 CA126563), National Institutes of Health (U.S.) (Grant U24 CA126544), National Institutes of Health (U.S.) (Grant U24 CA143845), National Institutes of Health (U.S.) (Grant U24 CA143858), National Institutes of Health (U.S.) (Grant U24 CA144025), National Institutes of Health (U.S.) (Grant U24 CA143882), National Institutes of Health (U.S.) (Grant U24 CA143866), National Institutes of Health (U.S.) (Grant U24 CA143867), National Institutes of Health (U.S.) (Grant U24 CA143848), National Institutes of Health (U.S.) (Grant U24 CA143840), National Institutes of Health (U.S.) (Grant U24 CA143835), National Institutes of Health (U.S.) (Grant U24 CA143799), National Institutes of Health (U.S.) (Grant U24 CA143883), National Institutes of Health (U.S.) (Grant U24 CA143843), National Institutes of Health (U.S.) (Grant U54 HG003067), National Institutes of Health (U.S.) (Grant U54 HG003079), National Institutes of Health (U.S.) (Grant U54 HG003273)

To characterize somatic alterations in colorectal carcinoma, we conducted a genome-scale analysis of 276 samples, analysing exome sequence, DNA copy number, promoter methylation and messenger RNA and microRNA expression. A subset of these samples (97) underwent low-depth-of-coverage whole-genome sequencing. In total, 16% of colorectal carcinomas were found to be hypermutated: three-quarters of these had the expected high microsatellite instability, usually with hypermethylation and MLH1 silencing, and one-quarter had somatic mismatch-repair gene and polymerase ε (POLE) mutations. Excluding the hypermutated cancers, colon and rectum cancers were found to have considerably similar patterns of genomic alteration. Twenty-four genes were significantly mutated, and in addition to the expected APC, TP53, SMAD4, PIK3CA and KRAS mutations, we found frequent mutations in ARID1A, SOX9 and FAM123B. Recurrent copy-number alterations include potentially drug-targetable amplifications of ERBB2 and newly discovered amplification of IGF2. Recurrent chromosomal translocations include the fusion of NAV2 and WNT pathway member TCF7L1. Integrative analyses suggest new markers for aggressive colorectal carcinoma and an important role for MYC-directed transcriptional activation and repression., National Institutes of Health (U.S.) (Grant U24CA143799), National Institutes of Health (U.S.) (Grant U24CA143835), National Institutes of Health (U.S.) (Grant U24CA143840), National Institutes of Health (U.S.) (Grant U24CA143843), National Institutes of Health (U.S.) (Grant U24CA143845), National Institutes of Health (U.S.) (Grant U24CA143848), National Institutes of Health (U.S.) (Grant U24CA143858), National Institutes of Health (U.S.) (Grant U24CA143866), National Institutes of Health (U.S.) (Grant U24CA143867), National Institutes of Health (U.S.) (Grant U24CA143882), National Institutes of Health (U.S.) (Grant U24CA143883), National Institutes of Health (U.S.) (Grant U24CA144025), National Institutes of Health (U.S.) (Grant U54HG003067), National Institutes of Health (U.S.) (Grant U54HG003079), National Institutes of Health (U.S.) (Grant U54HG003273)

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) comprises a suite of web-based tools to integrate, visualize and analyze cancer genomics and clinical data. The browser displays whole-genome views of genome-wide experimental measurements for multiple samples alongside their associated clinical information. Multiple data sets can be viewed simultaneously as coordinated ‘heatmap tracks’ to compare across studies or different data modalities. Users can order, filter, aggregate, classify and display data interactively based on any given feature set including clinical features, annotated biological pathways and user-contributed collections of genes. Integrated standard statistical tools provide dynamic quantitative analysis within all available data sets. The browser hosts a growing body of publicly available cancer genomics data from a variety of cancer types, including data generated from the Cancer Genome Atlas project. Multiple consortiums use the browser on confidential prepublication data enabled by private installations. Many new features have been added, including the hgMicroscope tumor image viewer, hgSignature for real-time genomic signature evaluation on any browser track, and ‘PARADIGM’ pathway tracks to display integrative pathway activities. The browser is integrated with the UCSC Genome Browser; thus inheriting and integrating the Genome Browser’s rich set of human biology and genetics data that enhances the interpretability of the cancer genomics data.