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Results of the GSEA enrichment

Purpose: Throughout their development, tumors are challenged by the immune system, and they acquire features to evade its surveillance. A systematic view of these traits, which shed light on how tumors respond to immunotherapies, is still lacking.Experimental Design: Here, we computed the relative abundance of an array of immune cell populations to measure the immune infiltration pattern of 9,174 tumors of 29 solid cancers. We then clustered tumors with similar infiltration pattern to define immunophenotypes. Finally, we identified genomic and transcriptomic traits associated to these immunophenotypes across cancer types.Results: In highly cytotoxic immunophenotypes, we found tumors with low clonal heterogeneity enriched for alterations of genes involved in epigenetic regulation, ubiquitin-mediated proteolysis, antigen presentation, and cell–cell communication, which may drive resistance in combination with the ectopic expression of negative immune checkpoints. Tumors with immunophenotypes of intermediate cytotoxicity are characterized by an upregulation of processes involved in neighboring tissue invasion and remodeling that may foster the recruitment of immunosuppressive cells. Tumors with poorly cytotoxic immunophenotype tend to be of more advanced stages and bear a greater burden of copy number alterations and frequent alterations of cell cycle, hedgehog, β-catenin, and TGFβ pathways, which may cause immune depletion.Conclusions: We provide a comprehensive landscape of the characteristics of solid tumors that may influence (or be influenced by) the characteristics of their immune infiltrate. These results may help interpret the response of solid tumors to immunotherapies and guide the development of novel drug combination strategies. Clin Cancer Res; 24(15); 3717–28. ©2018 AACR.

Supplementary Figure 1: HER2-positive Breast Cancer Study cohorts Supplementary Figure 2: TI-NK cells as categorical variable predicted pCR to anti-HER2 mAb-based neoadjuvant treatment with high sensitivity and specificity Supplementary Table I: TIL and TI-NK cell distribution according to tumor characteristics Supplementary Figure 3: Baseline TI-NK cell numbers association with DMFS in HER2-positive breast cancer patients receiving anti-HER2 mAb-based neoadjuvant therapy Supplementary Table II: Tumor HLA-I levels in relation to clinicopathological factors. Supplementary Figure 4: TI-NK cells associate with pCR regardless of tumor HLA-I expression levels. Supplementary Figure 5: Comparable clinical behavior of HLA low and HLA normal expressing tumors upon stratification by TI-NK cells.

Purpose:We investigated the value of tumor-infiltrating NK (TI-NK) cells and HLA class I tumor expression as biomarkers of response to neoadjuvant anti-HER2 antibody–based treatment in breast cancer.Experimental Design:TI-NK cells and HLA-I were determined by IHC in pretreatment tumor biopsies from two cohorts of patients with HER2-positive breast cancer [discovery cohort (n = 42) and validation cohort (n = 71)]. Tumor-infiltrating lymphocytes (TIL) were scored according to international guidelines. Biomarker association with pathologic complete response (pCR) and disease-free survival (DFS) was adjusted for prognostic factors. Gene set variation analysis was used for determining immune cell populations concomitant to NK-cell enrichment in HER2-positive tumors from the Cancer Genome Atlas (n = 190).Results:TI-NK cells were significantly associated with pCR in the discovery cohort as well as in the validation cohort (P < 0.0001), independently of clinicopathologic factors. A ≥3 TI-NK cells/50x high-power field (HPF) cutoff predicted pCR in the discovery and validation cohort [OR, 188 (11–3154); OR, 19.5 (5.3–71.8)]. Presence of TI-NK cells associated with prolonged DFS in both patient cohorts [HR, 0.07 (0.01–0.6); P = 0.01; HR, 0.3 (0.08–1.3); P = 0.1]. NK-, activated dendritic- and CD8 T-cell gene expression signatures positively correlated in HER2-positive tumors, supporting the value of NK cells as surrogates of effective antitumor immunity. Stratification of patients by tumor HLA-I expression identified patients with low and high relapse risk independently of pCR.Conclusions:This study identifies baseline TI-NK cells as an independent biomarker with great predictive value for pCR to anti-HER2 antibody–based treatment and points to the complementary value of tumor HLA-I status for defining patient prognosis independently of pCR.

Supplementary Methods, Supplementary Note, and Supplementary Figures

PURPOSE: Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. While these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (e.g., population frequency, functional and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation and importantly impacts patient care. Therefore, it’s essential to address this unmet need. METHODS: Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium (CGC), and the Variant Interpretation for Cancer Consortium (VICC) used a consensus approach to develop a Standard Operating Procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS: This comprehensive SOP has been developed to improve consistency in somatic variant classification, and has been validated on 94 somatic variants in ten common cancer-related genes. CONCLUSION: The comprehensive SOP is now available for classification of oncogenicity of a somatic variant.

The associated publication reports proteogenomic analysis of non-small cell lung cancer (NSCLC), where we identified molecular subtypes with distinct immune evasion mechanisms and therapeutic targets, and validated our classification method in separate clinical cohorts. This protocol describes sections of the bioinformatics analysis of the multi-omics data, namely, data analysis and processing for panel sequencing, identification of cancer- and driver-related proteins in proteomics data, proteogenomics search, and machine learning-based classifiers for NSCLC subtyping. Specifically, a cohort classifier was built using support-vector machine-recursive feature elimination (SVM-RFE) algorithm applied to in-depth proteomics data from a cohort of 141 samples. The classifier was then validated in three external datasets. Another classifier, suitable for single-sample subtyping, was built using k-top scoring pairs (k-TSP) algorithm applied to label-free data from a cohort of 136 samples. The k-TSP-based classifier was validated in two independent cohorts and an additional external dataset.

The associated publication reports proteogenomic analysis of non-small cell lung cancer (NSCLC), where we identified molecular subtypes with distinct immune evasion mechanisms and therapeutic targets, and validated our classification method in separate clinical cohorts. The in-depth proteomic analysis pointed to a potential role of STK11 inactivation in liver-specific signaling and subsequent cancer growth and immune evasion mechanisms. This protocol describes in vitro validation of the downstream effects of STK11-AMPK signaling on HNF1A and FGL1 in a liver and two lung cancer cell lines.

The associated publication reports proteogenomic analysis of non-small cell lung cancer, where we identified molecular subtypes with distinct immune evasion mechanisms and therapeutic targets, and validated our classification method in separate clinical cohorts. This protocol describes the sample preparation and mass spectrometry (MS)-based in-depth and rapid proteomic analyses of tumor and biopsy samples. We deployed single-pot solid-phase-enhanced sample preparation (SP3). For the in-depth analysis, we used TMT labeling, followed by high-resolution isoelectric focusing (HiRIEF) prefractionation and LC-MS with data-dependent acquisition (DDA). The reported protocol achieved analytical depth of close to 14,000 quantified proteins and almost 10,000 across the entire cohort of 141 samples. The rapid analysis was label-free, based on LC-MS with data-independent acquisition (DIA). The median number of identified proteins was 3,967 and 3,552 in two independent cohorts of tumor samples (n = 141 and 208, respectively), and 2,494 in another cohort of biopsy material (n = 84).

The associated publication reports proteogenomic analysis of non-small cell lung cancer, where we identified molecular subtypes with distinct immune evasion mechanisms and therapeutic targets, and validated our classification method in separate clinical cohorts. This protocol describes histological, tertiary lymphoid structure (TLS), and immunohistochemical evaluation of clinical samples. Specifically, immunohistochemistry was performed for PD-L1, CD3, and CD8 on tumor microarrays (TMAs) derived from formalin-fixed paraffin embedded (FFPE) samples.

Purpose: We investigated the value of tumor-infiltrating NK (TI-NK) cells and HLA class I tumor expression as biomarkers of response to neoadjuvant anti-HER2 antibody–based treatment in breast cancer. Experimental Design: TI-NK cells and HLA-I were determined by IHC in pretreatment tumor biopsies from two cohorts of patients with HER2-positive breast cancer [discovery cohort (n = 42) and validation cohort (n = 71)]. Tumor-infiltrating lymphocytes (TIL) were scored according to international guidelines. Biomarker association with pathologic complete response (pCR) and disease-free survival (DFS) was adjusted for prognostic factors. Gene set variation analysis was used for determining immune cell populations concomitant to NK-cell enrichment in HER2-positive tumors from the Cancer Genome Atlas (n = 190). Results: TI-NK cells were significantly associated with pCR in the discovery cohort as well as in the validation cohort (P < 0.0001), independently of clinicopathologic factors. A ≥3 TI-NK cells/50x high-power field (HPF) cutoff predicted pCR in the discovery and validation cohort [OR, 188 (11–3154); OR, 19.5 (5.3–71.8)]. Presence of TI-NK cells associated with prolonged DFS in both patient cohorts [HR, 0.07 (0.01–0.6); P = 0.01; HR, 0.3 (0.08–1.3); P = 0.1]. NK-, activated dendritic- and CD8 T-cell gene expression signatures positively correlated in HER2-positive tumors, supporting the value of NK cells as surrogates of effective antitumor immunity. Stratification of patients by tumor HLA-I expression identified patients with low and high relapse risk independently of pCR. Conclusions: This study identifies baseline TI-NK cells as an independent biomarker with great predictive value for pCR to anti-HER2 antibody–based treatment and points to the complementary value of tumor HLA-I status for defining patient prognosis independently of pCR.

In precision oncology, tailored treatment decisions are largely driven by genomic alterations in a patient’s tumor. To support epidemiological research, we initiated a registry that enrolls patients diagnosed with a solid tumor and profiled with NGS. Collecting and standardizing variables of interest across the patient journey is critical for data harmonization and understanding clinical relevance. We propose a framework for identifying core variables most critical for solid tumor-based, real-world data studies and registries in precision oncology. A patient representative and panel of experts in NGS, oncology, epidemiology, bioinformatics, molecular tumor boards, clinical coordination, and molecular pathology was assembled to compile a draft variables list. To reduce the burden of data collection and missingness, the variables were categorized by decreasing importance and reconciled with draft European Medicines Agency guidelines at the time of writing (final guidelines released October 26, 2021) on registry-based studies. If applicable, internationally harmonized coding dictionaries were used to standardize the variables. The variable list was created to: describe a patient’s cancer history and journey effectively; allow conduct of comparative effectiveness studies and comparisons with clinical trials; and represent key risk factors or important confounders for prognosis or statistical analysis adjustments. The draft list comprised ~500 variables and was reconciled to ~150; highest priority was given to patient information (e.g. consent, demographics, risk and prognostic factors, dates of cancer-related events), NGS test results (e.g. actionable alterations, genomic signatures), cancer details (e.g. disease ontology, staging, metastases), and therapy details/outcomes (e.g. treatment information, response, progression, death). Variables with moderate to lower priority comprised NGS (e.g. technical, quality criteria), familial cancer history, adherence to molecular tumor board recommendations, and primary cause of death. This list provides viable guidance for conducting research- and regulatory-grade real-world data-based precision oncology studies, and facilitating standardized data collection and thus pooling from various datasets. Citation Format: Rodrigo Dienstmann, Clare Turnbull, Allan Hackshaw, Jean-Yves Blay, Maud Kamal, Nicolas Servant, Jan Geissler, David Tamborero, Janick Weberpals, Simon Fear, Camille Perret, Laura Perez, Martina von Meyenn, Christophe Le Tourneau. Conceptualization of core clinico-molecular variables for registries enrolling patients diagnosed with a solid tumor and profiled with next-generation sequencing (NGS) [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 5246.

There is a growing need for systems that efficiently support the work of medical teams at the precision oncology point-of-care. Here we present the implementation of the Molecular Tumor Board Portal (MTBP), an academic clinical decision support system that creates a unified legal, scientific and technological platform to share and harness next-generation sequencing data across the Cancer Core Europe network. Automating the interpretation and reporting of sequencing results decreased drastically the need for manual procedures that are time consuming and prone to errors. In addition, the adoption of an expert-agreed process to systematically link tumor molecular profiles with clinical actions promoted consistent decision-making and structured data capture across centers. Finally, the use of information-rich patient reports with interactive content facilitated collaborative discussion of complex cases during virtual molecular tumor board meetings. Overall, we believe that streamlined digital systems like the MTBP are crucial to better address the challenges brought by precision oncology and accelerate the use of emerging biomarkers.

There is a growing need for systems that efficiently support the work of medical teams at the precision-oncology point of care. Here, we present the implementation of the Molecular Tumor Board Portal (MTBP), an academic clinical decision support system developed under the umbrella of Cancer Core Europe that creates a unified legal, scientific and technological platform to share and harness next-generation sequencing data. Automating the interpretation and reporting of sequencing results decrease the need for time-consuming manual procedures that are prone to errors. The adoption of an expert-agreed process to systematically link tumor molecular profiles with clinical actions promotes consistent decision-making and structured data capture across the connected centers. The use of information-rich patient reports with interactive content facilitates collaborative discussion of complex cases during virtual molecular tumor board meetings. Overall, streamlined digital systems like the MTBP are crucial to better address the challenges brought by precision oncology and accelerate the use of emerging biomarkers.

Despite major advancements in lung cancer treatment, long-term survival is still rare and a deeper understanding of molecular phenotypes would allow the identification of specific cancer dependencies and immune-evasion mechanisms. Here we performed in-depth mass-spectrometry-based proteogenomic analysis of 141 tumors representing all major histologies of non-small cell lung cancer (NSCLC). We identified six distinct proteome subtypes with striking differences in immune cell composition and subtype-specific expression of immune checkpoints. Unexpectedly, high neoantigen burden was linked to global hypomethylation and complex neoantigens mapped to genomic regions, such as endogenous retroviral elements and introns, in immune-cold subtypes. Further, we linked immune evasion with LAG-3 via STK11 mutation-dependent HNF1A activation and FGL1 expression. Finally, we develop a data-independent acquisition mass-spectrometry-based NSCLC subtype classification method, validate it in an independent cohort of 208 NSCLC cases and demonstrate its clinical utility by analyzing an additional cohort of 84 late-stage NSCLC biopsy samples. Lehtio and colleagues perform proteogenomic analysis of non-small cell lung cancer and identify molecular subtypes with distinct immune-evasion mechanisms and therapeutic targets and validate their classification method in separate clinical cohorts.

Myelodysplastic syndromes (MDS) are hematological disorders at high risk of progression to secondary acute myeloid leukemia (sAML). However, the mutational dynamics and clonal evolution underlying disease progression are poorly understood at present. To elucidate the mutational dynamics of pathways and genes occurring during the evolution to sAML, next generation sequencing was performed on 84 serially paired samples of MDS patients who developed sAML (discovery cohort) and 14 paired samples from MDS patients who did not progress to sAML during follow-up (control cohort). Results were validated in an independent series of 388 MDS patients (validation cohort). We used an integrative analysis to identify how mutations, alone or in combination, contribute to leukemic transformation. The study showed that MDS progression to sAML is characterized by greater genomic instability and the presence of several types of mutational dynamics, highlighting increasing (STAG2) and newly-acquired (NRAS and FLT3) mutations. Moreover, we observed cooperation between genes involved in the cohesin and Ras pathways in 15-20% of MDS patients who evolved to sAML, as well as a high proportion of newly acquired or increasing mutations in the chromatin-modifier genes in MDS patients receiving a disease-modifying therapy before their progression to sAML., This work was supported by grants from the Spanish Fondo de Investigaciones Sanitarias FIS PI18/01500, PI17/01741, Instituto de Salud Carlos III (ISCIII), Fondo de Investigación Sanitaria (Instituto de Salud Carlos III – Contratos Río Hortega (CM17/0017), European Regional Development Fund (ERDF), Una manera de hacer Europa, European Union Seventh Framework Programme [FP7/2007-2013] under Grant Agreement nº306242-NGS-PTL, SYNtherapy: Synthetic Lethality for Personalized Therapy-based Stratification in Acute Leukemia (ERAPERMED2018-275); ISCIII (AC18/00093), Proyectos de Investigación del SACYL, Gerencia Regional de Salud de Castilla y León: GRS1850/A18, GRS1653/A17, and Centro de Investigación Biomédica en Red de Cáncer (CIBERONC CB16/12/00233). MMI is supported by a predoctoral grant from the Junta de Castilla y León, and by the Fondo Social Europeo (JCYL-EDU/556/2019 PhD scholarship) and JMHS is supported by a research grant from Fundación Española de Hematología y Hemoterapia.

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases., This analysis presents a harmonized meta-knowledgebase to facilitate clinical interpretation of somatic genomic variants in cancer. This community-based project highlights the need for cooperative efforts to curate clinical interpretations of somatic variants for robust practice of precision oncology.

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.

Over the past few years, several large-scale studies using next-generation sequencing (NGS) of whole-genomes (WGS) and whole-exomes (WES) have defined the mutational landscape of chronic lymphocytic leukemia (CLL) [1,2,3,4]. NGS studies have also revealed the clonal heterogeneity in CLL and showed that clonal evolution contributes to the variability in clinical course among CLL patients [3]. Clonal evolution is considered a key condition in CLL progression and relapse after treatment. Most CLL cases are diagnosed during the inactive disease phase, genetic aberrations’ underlying progress in CLL activity leading to the need for therapy are poorly understood and should be explored. A large number of frequently mutated genes have been identified and several putative driver mutations likely to confer selective growth advantage to CLL tumor cells have been proposed [1,2,3]. In addition, clonal shifts between paired treatment-naïve and relapsed CLL samples have been reported due to pre-existing subclone expansion under therapeutic pressure, demonstrating that clonal evolution likely underlies CLL relapse [3, 5]. Nevertheless, there are still a limited amount of longitudinal WES studies analyzing consecutive CLL samples before treatment intervegntion [6]. The acquisition of driver mutations accompanied by selectively neutral passenger changes during disease prior to therapy influence is therefore poorly documented. Here, WES was performed on consecutive treatment-naïve samples of CLL patients from three groups with different disease course: Active disease (AD) group: patients with an active disease before the second analyzed time-point (TP2); Stable disease (SD) group: cases with a period of stable phase after diagnosis followed by progression within 3 years after; and Indolent disease (ID) group: those with a long-term stable indolent disease. Moreover, we applied a novel integrative bioinformatics tool called “Cancer Genome Interpreter” to identify driver mutations [7]., The research leading to these results has mainly received funding from the European Union Seventh Framework Programme [FP7/2007–2013] under Grant Agreement no 306242-NGS-PTL. In addition, this work was supported by grants from the Spanish Fondo de Investigaciones Sanitarias PI15/01471, PI18/01500, Instituto de Salud Carlos III (ISCIII), European Regional Development Fund (ERDF) “Una manera de hacer Europa”, “Consejería de Educación, Junta de Castilla y León” (SA085U16), “Proyectos de Investigación del SACYL”, Spain: GRS 994/A/14, BIO/SA10/14, BIO/SA31/13, GRS 1172/A/15,“Fundación Memoria Don Samuel Solórzano Barruso”, by grants (RD12/0036/0069) from Red Temática de Investigación Cooperativa en Cáncer (RTICC), Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) CB16/12/00233 and USAL >Programa XIII>. M. Hernández-Sánchez is supported by FEHH-Janssen (“Sociedad Española de Hematología y Hemoterapia”). M Quijada-Álamo is supported by an “Ayuda Predoctoral de la Junta de Castilla y León” (JCYL-EDU/529/2017). We are grateful to I. Rodríguez, S. González, T.Prieto, M. Á. Ramos, A. Martín, A. Díaz, A. Simón, M.del Pozo, V. Gutiérrez and S. Pujante from Centro de Investigación del Cáncer, Salamanca, for their technical assistance. D. Tamborero is supported by project SAF2015–74072-JUN, which is funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Dearrollo Regional (FEDER). This work was supported by Seventh Framework Programme (NGS-PTL/2012–2015/no.306242) and Ministry of Education, Youth and Sports (2013–2015, no. 7E13008); by the Ministry of Education, Youth and Sports of the Czech Republic under the CEITEC 2020 project (LQ1601); by the Ministry of Health, Czech Republic - conceptual development of research organization (FNBr, 65269705); by the Specific University Research (nr. MUNI/A/0968/2017) provided by MEYS; and by the project CZ.02.1.01/0.0/0.0/16_013/0001634 National Center for Medical Genomic - modernization of infrastructure and research of genetic variation in the population, funded by OP RDE.

Comprehensive interpretation of a patient's cancer genome is a cross-disciplinary process requiring the expertise of oncologists, molecular pathologists, genetic counselors, genomic scientists, and bioinformaticians. The Variant Interpretation for Cancer Consortium (VICC) has created a non-clinical forum for the variant interpretation process by connecting experts from multiple academic institutions and industry to create a Virtual Molecular Tumor Board (VMTB). During VICC-VMTB sessions, experts retrospectively discuss de-identified, challenging patient cases in the context of genomic interpretation guidelines to evaluate a patient's profile across several distinct components: genomic variants; disease context; therapeutic history; family history; demographic information; genomic assay information; and variant allele frequency. These components are processed through the prism of publically available knowledge and expertise of VMTB members, resulting in a broad spectrum of clinically relevant assertions about the patient's disease that may be of prognostic, diagnostic, or predictive relevance. Through these evaluations, the VICC-VMTB primarily works as a means of illuminating challenges in genomic interpretation and advancing the objectives of domain-specific VICC working groups. Examples of challenges that have been highlighted by the VICC-VMTB forum include: 1) inconsistent disease and therapy ontologies across resources are being addressed by Disease Harmonization & Drug Harmonization working groups, respectively; 2) use of machine-learning and natural language processing into clinical-grade variant interpretation are being addressed by the AI-Assisted Curation working group; 3) aggregation and harmonization of knowledgebase data into a centralized resource is being addressed by the Data Licensing and Variant Harmonization working groups; 4) inefficiencies in knowledgebase search functions are being addressed by the Search working group; 5) determination of accurate prediction of biological consequences of rare variants is being addressed by the In Silico Interpretation working group; and 6) standardization of genomic variant curation is being addressed by the Knowledge Curation and Interpretation Standards working group. These VICC working groups provide dedicated expert assemblies intended to specifically tackle these challenges, further increasing the breadth for which genomic information can be made useful in cancer variant interpretation. The VICC-VMTB has been a pertinent link between cancer genomes and clinical interpretation. We are confident VICC-VMTB will continue to be a gateway of acceleration and progress needed in the field of cancer interpretation to provide accurate and efficient answers for our patients in the future. Citation Format: Beth A. Pitel, Shruti Rao, Catherine Del Vecchio Fitz, Subha Madhavan, Rodrigo Dientsmann, Peter Horak, Ian King, Susan M. Mockus, Gordana Raca, Damian T. Rieke, Peter Rogan, Dmitriy Sonkin, David Tamborero, Ioannis S. Vlachos, Brian Walsh, Jeremy L. Warner, Malachi Griffith, Obi L. Griffith, Debyani Chakravarty, Alex H. Wagner. The Virtual Molecular Tumor Board of the Variant Interpretation for Cancer Consortium: A systematic gateway connecting cancer genome interpretation and progress in genomic knowledgebases in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3222.

We investigated the value of tumor-infiltrating NK (TI-NK) cells and HLA class I tumor expression as biomarkers of response to neoadjuvant anti-HER2 antibody-based treatment in breast cancer.TI-NK cells and HLA-I were determined by IHC in pretreatment tumor biopsies from two cohorts of patients with HER2-positive breast cancer [discovery cohort (TI-NK cells were significantly associated with pCR in the discovery cohort as well as in the validation cohort (This study identifies baseline TI-NK cells as an independent biomarker with great predictive value for pCR to anti-HER2 antibody-based treatment and points to the complementary value of tumor HLA-I status for defining patient prognosis independently of pCR.

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors. A comprehensive analysis of oncogenic driver genes and mutations in >9,000 tumors across 33 cancer types highlights the prevalence of clinically actionable cancer driver events in TCGA tumor samples., 0, info:eu-repo/semantics/published

Purpose: Throughout their development, tumors are challenged by the immune system, and they acquire features to evade its surveillance. A systematic view of these traits, which shed light on how tumors respond to immunotherapies, is still lacking. Experimental Design: Here, we computed the relative abundance of an array of immune cell populations to measure the immune infiltration pattern of 9,174 tumors of 29 solid cancers. We then clustered tumors with similar infiltration pattern to define immunophenotypes. Finally, we identified genomic and transcriptomic traits associated to these immunophenotypes across cancer types. Results: In highly cytotoxic immunophenotypes, we found tumors with low clonal heterogeneity enriched for alterations of genes involved in epigenetic regulation, ubiquitin-mediated proteolysis, antigen presentation, and cell–cell communication, which may drive resistance in combination with the ectopic expression of negative immune checkpoints. Tumors with immunophenotypes of intermediate cytotoxicity are characterized by an upregulation of processes involved in neighboring tissue invasion and remodeling that may foster the recruitment of immunosuppressive cells. Tumors with poorly cytotoxic immunophenotype tend to be of more advanced stages and bear a greater burden of copy number alterations and frequent alterations of cell cycle, hedgehog, β-catenin, and TGFβ pathways, which may cause immune depletion. Conclusions: We provide a comprehensive landscape of the characteristics of solid tumors that may influence (or be influenced by) the characteristics of their immune infiltrate. These results may help interpret the response of solid tumors to immunotherapies and guide the development of novel drug combination strategies. Clin Cancer Res; 24(15); 3717–28. ©2018 AACR.

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases. The resource is publicly available at http://www.cancergenomeinterpreter.org. This project has received funding from Fundació La Marató de TV3, the European Union's Horizon 2020 research and innovation programme 2014-2020 under grant agreement number 634143, and by the European Research Council (Consolidator Grant 682398). IRB Barcelona is a recipient of a Severo Ochoa Centre of Excellence Award from the Spanish Ministry of Economy and Competitiveness (MINECO; Government of Spain) and is supported by CERCA (Generalitat de Catalunya). DT is supported by project SAF2015-74072-JIN funded by the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). CR-P is funded by a FPI MINECO grant (BES-2013-063354). AG-P is supported by a Ramon y Cajal fellowship (RYC-2013-14554).

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5’UTRs ofTP53, RFTN1, RNF34,andMTG2,in the 3’UTRs ofNFKBIZandTOB1,and in the non-coding RNARMRP.We provide evidence that the previously reported non-coding RNAsNEAT1andMALAT1may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyondTERT.

The development of targeted therapies against altered driver proteins holds the promise of selectively and efficiently eliminating cancer cells. However, high intertumor heterogeneity is a major obstacle to develop and apply therapeutic targeted agents to treat most cancer patients. Here, we present the first large-scale therapeutic landscape of cancer as it stands today in a 6.792 sample cohort covering 28 tumor types. To pursue this goal, we developed a three-step in silico drug prescription strategy. 1) To discover actionable driver events, we first comprehensively identified mutational cancer driver genes by detecting complementary signals of positive selection in the pattern of their mutations across the tumor cohorts. We also identified actionable copy number alteration (CNA) and fusion cancer driver genes. Second, we detected which of these driver genes would have an oncogenic role in the tumor and which ones would lose their function. With these two steps we generated the Drivers Database. 2) Next, we systematically gathered all information available on therapeutic agents; FDA approved and in clinical or pre-clinical stages. We considered three different types of targeting strategies for the cancer driver genes: direct targeting, indirect targeting and gene therapies in clinical trials. Moreover, we designed a set of rules for assigning therapeutic agents to specific genomic alterations beard for the driver genes. By doing this last step, we generated the Drivers Actionability Database. 3) Finally, by combining data of Drivers Database, Drivers Actionability Database and sample data, we developed in silico drug prescription, a novel approach to determine which of the drugs could benefit each of the tumor individuals. In all, in the Driver Database we identified 460 mutational cancer driver genes acting in one or more of the tumor types along with 39 driver genes acting via CNAs or fusions. Fifty of these cancer driver genes are targeted by FDA approved agents, 63 by molecules currently in clinical trials and 74 are bound by pre-clinical ligands. We also identified 81 therapeutically unexploited targetable cancer genes. Lastly, by applying in silico drug prescription we found that only 6.7% of the patients could be treated following clinical guidelines, and were concentrated in only 6 tumor types. Moreover, considering repurposing strategies the fraction of patients that could benefit from FDA approved drugs would increase up to 40%, increasing remarkably the fraction of targetable patients in some tumor types like glioblastoma and thyroid cancer, and up to 72% if considering targeted therapies in clinical trials. In summary, the in silico drug prescription based on Drivers and Drivers Actionability Databases was tested on one of the largest cohorts of tumor samples currently collected for research. The main result highlights the current scope of targeted anti-cancer therapies and its prospects for growth in view of the drugs that are currently in clinical trials or at pre-clinical stages. Additionally, another important output of this work is a ranked list of novel target opportunities for anticancer drug development. Continuous update of drug-target interactions information, and the application of the strategy to larger cohorts, will improve the in silico prescription rules contained within the two databases, thus enhancing its usefulness within personalized cancer medicine. Citation Format: Carlota Rubio-Perez, David Tamborero, Michael P. Schroeder, Albert A. Antolín, Jordi Deu-Pons, Christian Perez-Llamas, Jordi Mestres, Abel Gonzalez-Perez, Nuria Lopez-Bigas. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-45.

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

Novel agents have increased survival of multiple myeloma (MM) patients, however high-risk and relapsed/refractory patients remain challenging to treat and their outcome is poor. To identify novel therapies and aid treatment selection for MM, we assessed the ex vivo sensitivity of 50 MM patient samples to 308 approved and investigational drugs. With the results we i) classified patients based on their ex vivo drug response profile; ii) identified and matched potential drug candidates to recurrent cytogenetic alterations; and iii) correlated ex vivo drug sensitivity to patient outcome. Based on their drug sensitivity profiles, MM patients were stratified into four distinct subgroups with varied survival outcomes. Patients with progressive disease and poor survival clustered in a drug response group exhibiting high sensitivity to signal transduction inhibitors. Del(17p) positive samples were resistant to most drugs tested with the exception of histone deacetylase and BCL2 inhibitors. Samples positive for t(4; 14) were highly sensitive to immunomodulatory drugs, proteasome inhibitors and several targeted drugs. Three patients treated based on the ex vivo results showed good response to the selected treatments. Our results demonstrate that ex vivo drug testing may potentially be applied to optimize treatment selection and achieve therapeutic benefit for relapsed/refractory MM.

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes. We would like to thank the people working at The Cancer Genome Atlas for their efforts and for making all the data publicly available. E.P.-P. and A.G. acknowledge the support from the Cancer Center grants P30 CA030199 (to our institute) and R35 GM118187 (A.G.). A.K. was supported by startup funds of G.G. and by a collaboration with Bayer AG. D.T. is supported by project SAF2015- 74072-JIN, which is funded by the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). N.L.-B. acknowledges funding from the European Research Council (consolidator grant 682398). A.V. and T.P. acknowledge funding by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 305444 (RD-Connect).

Motivation: Several computational methods have been developed to identify cancer drivers genes—genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role. Result: OncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role. Availability and implementation: The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role. Contact: abel.gonzalez@upf.edu or nuria.lopez@upf.edu Supplementary information: Supplementary data are available at Bioinformatics online.

TPS3151 Background: Basket trials with targeted agents can show high response rates for tumors with specific molecular profiles, granting extension of the label of some drugs. In other cases, study results were disappointing, likely due to the rarity of molecular alterations, limits in trial design and the difficulties in applying molecular tumor profiling in the clinical setting. Methods: Basket of Baskets (BoB), NCT03767075, aims to bridge the gap between Academic Genomics and clinical applications (ready-to market multi-marker Companion Diagnostics) by providing a sustainable and adaptable (to new technologies, markers, and therapeutic agents) platform for co-development of drug/companion diagnostic. BoB is a novel platform trial from Cancer Core Europe, a recently established sustainable European network for innovative cancer research. This protocol has two parts: (1) Part A includes a molecular profiling program for subjects with advanced solid tumors (iPROFILER), a variant annotation tool, and a molecular tumor board to select the most appropriate treatment. It also enables testing/developing companion diagnostics linked with the therapeutic part (part B). (2) Part B includes iBASKET, a modular multi-arm basket trial for subjects with tumors harboing selected molecular alterations. Each module is focused on a certain molecular pathway or on certain molecular alterations that may confer sensitivity to the study drug or study drug combination evaluated in that module/arm. The current version of iBASKET (Module 1- Atezolizumab in genomically-selected patients) is open for enrollment for patients with advanced neoplasms bearing one of the following alterations: Arm 1A: BRCA1 or BRCA2; Arm 1B: MLH1, MSH2, MSH6, PMS2; Arm 1C: POLE, POLD1 mutations; Arm 1D: hypermutated tumors; Arm 1E: other mutations in DNA-repair genes; Arm 1F: PDL1 gene amplification. All patients enrolled in Module 1 will receive single-agent atezolizumab. New Modules for genomically selected populations can be added through amendments. Our final aim is to achieve drug repurposing of treatments, co-develop multi-marker companion diagnostics and a large database of knowledge in Precision Medicine. Clinical trial information: NCT03767075.

In our individualized systems medicine program, personalized treatment options are identified and administered to chemorefractory acute myeloid leukemia (AML) patients based on exome sequencing and ex vivo drug sensitivity and resistance testing data. Here, we analyzed how clonal heterogeneity affects the responses of 13 AML patients to chemotherapy or targeted treatments using ultra-deep (average 68 000 × coverage) amplicon resequencing. Using amplicon resequencing, we identified 16 variants from 4 patients (frequency 0.54–2%) that were not detected previously by exome sequencing. A correlation-based method was developed to detect mutation-specific responses in serial samples across multiple time points. Significant subclone-specific responses were observed for both chemotherapy and targeted therapy. We detected subclonal responses in patients where clinical European LeukemiaNet (ELN) criteria showed no response. Subclonal responses also helped to identify putative mechanisms underlying drug sensitivities, such as sensitivity to azacitidine in DNMT3A mutated cell clones and resistance to cytarabine in a subclone with loss of NF1 gene. In summary, ultra-deep amplicon resequencing method enables sensitive quantification of subclonal variants and their responses to therapies. This approach provides new opportunities for designing combinatorial therapies blocking multiple subclones as well as for real-time assessment of such treatments.

The bone marrow (BM) provides a protective microenvironment to support the survival of leukemic cells and influence their response to therapeutic agents. In acute myeloid leukemia (AML), the high rate of relapse may in part be a result of the inability of current treatment to effectively overcome the protective influence of the BM niche. To better understand the effect of the BM microenvironment on drug responses in AML, we conducted a comprehensive evaluation of 304 inhibitors, including approved and investigational agents, comparing ex vivo responses of primary AML cells in BM stroma-derived and standard culture conditions. In the stroma-based conditions, the AML patient cells exhibited significantly reduced sensitivity to 12% of the tested compounds, including topoisomerase II, B-cell chronic lymphocytic leukemia/lymphoma 2 (BCL2), and many tyrosine kinase inhibitors (TKIs). The loss of TKI sensitivity was most pronounced in patient samples harboring FLT3 or PDGFRB alterations. In contrast, the stroma-derived conditions enhanced sensitivity to Janus kinase (JAK) inhibitors. Increased cell viability and resistance to specific drug classes in the BM stroma-derived conditions was a result of activation of alternative signaling pathways mediated by factors secreted by BM stromal cells and involved a switch from BCL2 to BCLXL-dependent cell survival. Moreover, the JAK1/2 inhibitor ruxolitinib restored sensitivity to the BCL2 inhibitor venetoclax in AML patient cells ex vivo in different model systems and in vivo in an AML xenograft mouse model. These findings highlight the potential of JAK inhibitors to counteract stroma-induced resistance to BCL2 inhibitors in AML.

BACKGROUND: Profiling the somatic mutations of genes which may inform about tumor evolution, prognostics and treatment is becoming a standard tool in clinical oncology. Commercially available cancer gene panels rely on manually gathered cancer-related genes, in a "one-size-fits-many" solution. The design of new panels requires laborious search of literature and cancer genomics resources, with their performance on cohorts of patients difficult to estimate. RESULTS: We present OncoPaD, to our knowledge the first tool aimed at the rational design of cancer gene panels. OncoPaD estimates the cost-effectiveness of the designed panel on a cohort of tumors and provides reports on the importance of individual mutations for tumorigenesis or therapy. With a friendly interface and intuitive input, OncoPaD suggests researchers relevant sets of genes to be included in the panel, because prior knowledge or analyses indicate that their mutations either drive tumorigenesis or function as biomarkers of drug response. OncoPaD also provides reports on the importance of individual mutations for tumorigenesis or therapy that support the interpretation of the results obtained with the designed panel. We demonstrate in silico that OncoPaD designed panels are more cost-effective-i.e. detect a maximum fraction of tumors in the cohort by sequencing a minimum quantity of DNA-than available panels. CONCLUSIONS: With its unique features, OncoPaD will help clinicians and researchers design tailored next-generating sequencing (NGS) panels to detect circulating tumor DNA or biopsy specimens, thereby facilitating early and accurate detection of tumors, genomics informed therapeutic decisions, patient follow-up and timely identification of resistance mechanisms to targeted agents. OncoPaD may be accessed through http://www.intogen.org/oncopad. A.G.-P. is supported by a Ramón y Cajal contract (RYC-2013-14554), which also funds the publication of this article. We also acknowledge funding from the Spanish Ministry of Economy and Competitiveness (grant no. SAF2012-36199), the Marató de TV3 Foundation, and the Spanish National Institute of Bioinformatics (INB). C.R.-P. is supported by an FPI fellowship. D.T. is supported by the People Programme (Marie Curie Actions) of the Seventh Framework Programme of the European Union (FP7/2007- 2013) under REA grant agreement no. 600388 and by the Agency of Competitiveness for Companies of the Government of Catalonia, ACCIÓ.

Electrocardiographic VV Optimization. Introduction: Echocardiographic optimization of the VV interval may improve CRT response, but it is time-consuming and not routinely performed. The aim of this study was to compare the response to cardiac resynchronization therapy (CRT) when the interventricular pacing (VV) interval was optimized by Tissue Doppler Imaging (TDI) to CRT response when it was optimized following QRS width criteria. Methods and Results: The study included 156 consecutive CRT patients with severe heart failure and left bundle-branch block configuration. Atrioventricular interval was selected according to a pulsed Doppler assessment, and VV optimization was randomly assigned to echocardiography (ECHO group, n = 78) or electrocardiography (ECG group, n = 78). Optimal VV was defined for the ECHO group as producing the best LV intraventricular synchrony according to TDI displacement curves and for the ECG group as resulting in the narrowest QRS measured from the earliest deflection. At 6-month follow-up, percentage of echocardiographic responders (defined as neither death nor heart transplantation and a LV end-systolic volume reduction >10%) was higher in the ECG optimized group (50.0% vs 67.9%; P = 0.023), whereas clinical response (defined as neither death nor heart transplantation and >10% improvement in the 6-minute walking test) was similar in both groups (71.8% vs 73.1%; P = 0.858). Conclusions: VV optimization based on QRS width obtained a higher percentage of responders in terms of LV reverse remodeling compared to the TDI method. (J Cardiovasc Electrophysiol, Vol. 22, pp. 1129-1134, October 2011)

Background: Best practice for cardiac resynchronization therapy (CRT) device optimization is not established. This study compared Tissue Doppler Imaging (TDI) to study left ventricular (LV) synchrony and left ventricular outflow tract velocity-time integral (LVOT VTI) to assess hemodynamic performance. Methods: LVOT VTI and LV synchrony were tested in 50 patients at three interventricular (VV) delays (LV preactivation at −30 ms, simultaneous biventricular pacing, and right ventricular preactivation at +30 ms), selecting the highest VTI and the greatest degree of superposition of the displacement curves, respectively, as the optimum VV delay. Results: In 39 patients (81%), both techniques agreed (Kappa = 0.65, p < 0.0001) on the optimum VV delay. LV preactivation (VV − 30) was the interval most frequently chosen. Conclusions: Both TDI and LVOT VTI are useful CRT programming methods for VV optimization. The best hemodynamic response correlates with the best synchrony. In most patients, the optimum VV interval is LV preactivation. (PACE 2011; 34:984–990)

Aims Transoesophageal echocardiography (TEE) is recommended prior to circumferential pulmonary vein ablation (CPVA) in patients with atrial fibrillation (AF) to identify left atrial (LA) or left atrial appendage (LAA) wall thrombi. It is not clear whether all patients undergoing CPVA should receive pre-procedural TEE. We wanted to assess the incidence of LA thrombus in these patients and to identify factors associated with its presence. Methods and results Consecutive patients referred for CPVA from 2004 to 2009 underwent TEE within 48 h prior to the procedure. Of 408 patients included in the study, 6 patients (1.47%) had LA thrombi, persistent AF, and LA dilation. Compared with patients without thrombus, these six patients had larger LA diameter ( P = 0.0001) and more frequently were women ( P = 0.002), had persistent AF ( P = 0.04), and had underlying structural cardiac disease ( P = 0.014). The likelihood of presenting LA thrombus increased with the number of these four risk factors present ( P < 0.001). None of the patients with paroxysmal AF and without LA dilation had LA thrombus. A cut-off value of 48.5 mm LA diameter yielded 83% sensitivity, 92% specificity, and a 10.1 likelihood ratio to predict LA thrombus appearance. Conclusion The incidence of LA thrombus prior to CPVA is low. Persistent AF, female sex, structural cardiopathy, and LA dilation were associated with the presence of LA thrombus. Our data suggest that the use of TEE prior to CPVA to detect LA thrombi might not be needed in patients with paroxysmal AF and no LA dilation or structural cardiopathy.

Aims Although the benefit of cardiac resynchronization therapy (CRT) in selected patients with heart failure is well established, its effect on mortality in New York Heart Association (NYHA) class IV patients remains unclear. Our study evaluated the effect of CRT on urgent transplant-free survival in NYHA class IV patients treated with CRT, compared with medication-only treatment. Methods and results Forty NYHA class IV patients treated with CRT (80% men, 62.5% ischaemic, mean age of 65) were matched 1:1 by age, gender and aetiology of cardiomyopathy with patients treated with optimal medical therapy (OPT group). No significant differences were found between the groups in left ventricular diastolic diameter (71 ± 6 vs. 73 ± 9 mm), left ventricular systolic diameter (58 ± 7 vs. 61 ± 11 mm), and left ventricular ejection fraction (23 ± 5 vs. 22 ± 6%). Mean follow-up was 13.2 ± 9.5 months for the CRT group and 17.3 ± 11.6 months for the OPT group. Time to all-cause death or urgent transplantation [hazard ratios (HR), 1.29; 95% CI: 0.59–2.83; P = 0.52] or to cardiovascular death or urgent transplantation (HR, 1.53; 95% CI: 0.64–3.67; P = 0.34) was not reduced significantly in patients treated with CRT. Conclusion In this study, CRT did not significantly improve survival of NYHA class IV heart failure patients compared with pharmacological therapy.

Aims Atrial fibrillation (AF) ablation efficacy varies according to patients' clinical characteristics. Although the association of obstructive sleep apnoea (OSA) and AF is well established, data on AF ablation efficacy in OSA are scarce. The aim of this study was to clarify the effect of OSA on the outcome of AF ablation. Methods and results A series of 174 consecutive patients without polysomnography submitted to circumferential pulmonary vein ablation were included in the study. All patients were assessed by Berlin Questionnaire (BQ) and underwent an echocardiogram and a clinical evaluation. Patients with a high BQ score, indicating high risk for OSA, participated in a sleep study. Diagnoses were classified according to the apnoea–hypoapnoea index (AHI) as mild (AHI < 10/h), non-severe (AHI < 30/h), or severe (AHI ≥ 30/h) OSA. Follow-up consisted of outpatient visits and 24 or 48 h Holter monitoring at 1, 4, and 7 months, and every 6 months thereafter. Any episode of AF or left atrial (LA) flutter was considered recurrence. Fifty-one (29.3%) patients had high BQ scores. The sleep study showed that 17 (9.8%) and 25 (14.4%) of these patients had non-severe and severe OSA, respectively. One-year arrhythmia-free probability after a single ablation procedure was 48.5% in patients with low risk for OSA (low BQ score or AHI < 10/h), 30.4% in the non-severe OSA group (10 < AHI < 30/h) and 14.3% in the severe OSA group (AHI ≥ 30). Anteroposterior LA diameter [hazard ratio (HR) = 1.046, 95% confidence interval (CI): 1.005–1.089; P = 0.029] and severe OSA (HR = 1.870, 95% CI: 1.106–3.161; P = 0.019) were the independent predictors of arrhythmia recurrence. Conclusion In patients with AF ablation, the presence of severe OSA is an independent predictor for AF ablation failure.

Aims Matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) play a role in left ventricular structural remodelling. The aim of our study was to analyse MMP-2 and TIMP-1 levels as predictors of poor response to cardiac resynchronization therapy (CRT). Methods and results A cohort of 42 CRT patients from our centre was prospectively evaluated at baseline and after 12-month follow-up. MMP-2 and TIMP-1 assays were performed prior to CRT implant. Cardiac resynchronization therapy responders were defined as patients who survived, were not transplanted, and increased their basal 6 min walking distance test (6MWDT) by ≥10% or improved their NYHA functional class. Overall, 25 patients (60%) were classed as responders. At 12-month follow-up, six patients (14.2%) had died and one (2.4%) patient had been transplanted. Compared with responders, non-responders had higher levels of TIMP-1 (277 ± 59 vs. 216 ± 46 ng/mL, P = 0.001), MMP-2 (325 ± 115 vs. 258 ± 56 ng/mL, P = 0.02), and creatinine (1.76 ± 0.8 vs. 1.25 ± 0.3 mg/dL, P = 0.01). In a multivariate analysis, TIMP-1 was the only independent predictor of non-response to CRT [OR 0.97, 95% (CI 0.96–0.99) P = 0.005]. TIMP-1≥248 ng/mL predicted non-response with 71% sensitivity and 72% specificity. Conclusion TIMP-1 is an independent predictor of non-response in patients treated with CRT.