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Abstract Background Epstein-Barr virus (EBV) infection is a well characterized etiopathogenic factor for a variety of immune-related conditions, including lymphomas, lymphoproliferative disorders and autoimmune diseases. EBV-mediated transformation of resting B cells to proliferating lymphoblastoid cells occurs in early stages of infection and is an excellent model for investigating the mechanisms associated with acquisition of unlimited growth. Results We investigated the effects of experimental EBV infection of B cells on DNA methylation profiles by using high-throughput analysis. Remarkably, we observed hypomethylation of around 250 genes, but no hypermethylation. Hypomethylation did not occur at repetitive sequences, consistent with the absence of genomic instability in lymphoproliferative cells. Changes in methylation only occurred after cell divisions started, without the participation of the active demethylation machinery, and were concomitant with acquisition by B cells of the ability to proliferate. Gene Ontology analysis, expression profiling, and high-throughput analysis of the presence of transcription factor binding motifs and occupancy revealed that most genes undergoing hypomethylation are active and display the presence of NF-κB p65 and other B cell-specific transcription factors. Promoter hypomethylation was associated with upregulation of genes relevant for the phenotype of proliferating lymphoblasts. Interestingly, pharmacologically induced demethylation increased the efficiency of transformation of resting B cells to lymphoblastoid cells, consistent with productive cooperation between hypomethylation and lymphocyte proliferation. Conclusions Our data provide novel clues on the role of the B cell transcription program leading to DNA methylation changes, which we find to be key to the EBV-associated conversion of resting B cells to proliferating lymphoblasts. ; This work was supported by grants PI081346 (FIS) and SAF2011-29635 from the Spanish Ministry of Science and Innovation (MICINN) and grant 2009SGR184 from AGAUR (Catalan Government). AI was supported by fellowship from AGAUR, Government of Catalonia, Spain. MP is supported by Ramon y Cajal Programme. ; Peer Reviewed

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user's guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation. ; The ICGC Data Portal development is supported by the Ontario Institute for Cancer Research (OICR) through funding provided by the government of Ontario. UCSC Xena development is supported by the National Cancer Institute of the National Institutes of Health under award numbers 5U24CA180951-04 and 5U24CA210974-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Chromothripsis Explorer development is supported by the European Union's Framework Programme For Research and Innovation Horizon 2020 (2014-2020) under the Marie Curie Sklodowska-Curie Grant Agreement No. 703543 (I.C.-C.). Expression Atlas development is supported by the European Molecular Biology Laboratory (EMBL) member states, the Single Cell Gene Expression Atlas from the Wellcome Trust (grant numbers 108437/Z/15/Z), the National Science Foundation of USA grant to Gramene database [NSF IOS #1127112], Open Targets, and Chan Zuckerberg Initiative. PCAWG-Scout development is supported by joint BSC-IRB-CRG Program in Computational Biology and Severo Ochoa Award SEV 2015-0493. In addition, this work has been supported by the Spanish Government (SEV 2015-0493) and from the BSC-Lenovo Master Collaboration Agreement (2015). We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment, and harmonized variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. ; Peer Reviewed ; Postprint (published version)

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user's guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation. ; The ICGC Data Portal development is supported by the Ontario Institute for Cancer Research (OICR) through funding provided by the government of Ontario. UCSC Xena development is supported by the National Cancer Institute of the National Institutes of Health under award numbers 5U24CA180951-04 and 5U24CA210974-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Chromothripsis Explorer development is supported by the European Union's Framework Programme For Research and Innovation Horizon 2020 (2014-2020) under the Marie Curie Sklodowska-Curie Grant Agreement No. 703543 (I.C.-C.). Expression Atlas development is supported by the European Molecular Biology Laboratory (EMBL) member states, the Single Cell Gene Expression Atlas from the Wellcome Trust (grant numbers 108437/Z/15/Z), the National Science Foundation of USA grant to Gramene database [NSF IOS #1127112], Open Targets, and Chan Zuckerberg Initiative. PCAWG-Scout development is supported by joint BSC-IRB-CRG Program in Computational Biology and Severo Ochoa Award SEV 2015-0493. In addition, this work has been supported by the Spanish Government (SEV 2015-0493) and from the BSC-Lenovo Master Collaboration Agreement (2015). We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment, and harmonized variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. ; Sí

Triple-negative breast cancer (TNBC) lacks prognostic and predictive markers. Here, we use high-throughput phosphoproteomics to build a functional TNBC taxonomy. A cluster of 159 phosphosites is upregulated in relapsed cases of a training set (n = 34 patients), with 11 hyperactive kinases accounting for this phosphoprofile. A mass-spectrometry-to-immunohistochemistry translation step, assessing 2 independent validation sets, reveals 6 kinases with preserved independent prognostic value. The kinases split the validation set into two patterns: one without hyperactive kinases being associated with a >90% relapse-free rate, and the other one showing ≥1 hyperactive kinase and being associated with an up to 9.5-fold higher relapse risk. Each kinase pattern encompasses different mutational patterns, simplifying mutation-based taxonomy. Drug regimens designed based on these 6 kinases show promising antitumour activity in TNBC cell lines and patient-derived xenografts. In summary, the present study elucidates phosphosites and kinases implicated in TNBC and suggests a target-based clinical classification system for TNBC. ; We are grateful to the Spanish Biobanks integrated within the Spanish Network of National Biobanks for the tumor samples used in our investigations and to the Breast Cancer Group at VHIO for providing study materials. The present study was funded by FIS PI10/00288, FIS PI13/00430, and AECC Scientific Foundation "Beca de Retorno 2010"awarded to MQF; FIS PI11-00832, FIS PI14-00726, and ISCIII PIE15/00068 awarded to RC. I.Z. is a recipient of a La Caixa PhD Fellowship, 2011. S.F. is a recipient of a FPI grant (SEV-2015-0510-16-6). V.S. is supported by the Miguel Servet Program (CP14/00228). This work was also supported by the PRIME-XS project, Grant Agree- ment Number 262067, funded by the European Union Seventh Framework Program and the project Proteins At Work (project 184.032.201), a program of the Netherlands Proteomics Centre financed by the Netherlands Organisation for Scientific Research (NWO). ...