The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

Summary Current therapies for medulloblastoma (MB), a highly malignant childhood brain tumor, impose debilitating effects on the developing child, warranting deployment of molecularly targeted treatments with reduced toxicities. Prior studies failed to disclose the full spectrum of driver genes and molecular processes operative in MB subgroups. Herein, we detail the somatic landscape across 491 sequenced MBs and molecular heterogeneity amongst 1,256 epigenetically analyzed cases, identifying subgroup-specific driver alterations including previously unappreciated actionable targets. Driver mutations explained the majority of Group 3 and Group 4 patients, remarkably enhancing previous knowledge. Novel molecular subtypes were differentially enriched for specific driver events, including hotspot in-frame insertions targeting KBTBD4 and ‘enhancer hijacking’ driving PRDM6 activation. Thus, application of integrative genomics to an unprecedented cohort of clinical samples derived from a single childhood cancer entity disclosed a series of new cancer genes and biologically relevant subtype diversity that represent attractive therapeutic targets for treating MB patients.

Highlights d Clock-like mutation process attributed to APOBEC3 mediates earliest mutations in PC d Identification of four molecular subgroups that stratifies intermediate-risk disease d Rearrangements at the ESRP1 locus associated with aggressive and proliferative cancer d Development of method to predict clinical trajectories of PC from DNA sequencing data

BackgroundA healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability.ResultsWe apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16− monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers.ConclusionsOur data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1156-8) contains supplementary material, which is available to authorized users.

1 SUMMARYThe advance of personalized cancer medicine requires the accurate identification of the mutations driving each patient's tumor. However, to date, we have only been able to obtain partial insights into the contribution of genomic events to tumor development. Here, we design a comprehensive approach to identify the driver mutations in each patient's tumor and obtain a whole-genome panorama of driver events across more than 2,500 tumors from 37 types of cancer. This panorama includes coding and non-coding point mutations, copy number alterations and other genomic rearrangements of somatic origin, and potentially predisposing germline variants. We demonstrate that genomic events are at the root of virtually all tumors, with each carrying on average 4.6 driver events. Most individual tumors harbor a unique combination of drivers, and we uncover the most frequent co-occurring driver events. Half of all cancer genes are affected by several types of driver mutations. In summary, the panorama described here provides answers to fundamental questions in cancer genomics and bridges the gap between cancer genomics and personalized cancer medicine.Tumors arise from genomic mutations, often referred to as 'drivers', that confer proliferative advantage to a The characterization of more than 2,500 tumors by the International Cancer Genomics Consortium (ICGC) and The Cancer Genome Atlas (TCGA) under the Pan-Cancer Analysis of Whole Genomes 2 (PCAWG) 16 initiative provides an unprecedented opportunity to obtain a comprehensive whole-genome view of driver events in cancer. Here, we set the goal of identifying all driver events (somatic point mutations, SCNAs, SGRs, and potentially predisposing germline variants) across the whole genome of each tumor in the PCAWG cohort. We call this list of driver events the per-patient panorama of driver mutations (or simply panorama). To obtain it, we designed an incremental approach ( Fig. 1a; Methods and Suppl. Notes 1 and 2)that exploits the power of this cohort to discover novel drivers, both coding and non-coding, and the knowledge accumulated through decades of cancer genetics research 2 . We found driver mutations in virtually all tumors, thereby providing definitive evidence of the oft-quoted maxim that cancer is fundamentally a genomics disease. We also demonstrated the presence of a small number of driver mutations in each tumor -4.6 on average, a number that is relatively stable regardless of of the variability in mutational burden. While the contribution of somatic point mutations and SVs to tumorigenesis across cancer types differs, their relative proportions across the entire cohort are very similar. We found that most individual tumors harbor a unique combination of driver mutations, and we uncovered the most frequently co-occurring driver events.Some of these combinations may have a synergistic effect in the emergence of cancer, as in the cases of somatic point mutations of KRAS and SMAD4 in pancreatic adenocarcinomas (Panc-AdenoCA) and DAXX and MEN1 across pancreatic ...

Cancer sequencing studies have implicated regulators of pre-mRNA splicing as important disease determinants in acute myeloid leukemia (AML), but the underlying mechanisms have remained elusive. We hypothesized that “non-mutated” splicing regulators may also play a role in AML biology and therefore conducted an in vivo shRNA screen in a mouse model of CEBPA mutant AML. This has led to the identification of the splicing regulator RBM25 as a novel tumor suppressor. In multiple human leukemic cell lines, knockdown of RBM25 promotes proliferation and decreases apoptosis. Mechanistically, we show that RBM25 controls the splicing of key genes, including those encoding the apoptotic regulator BCL-X and the MYC inhibitor BIN1. This mechanism is also operative in human AML patients where low RBM25 levels are associated with high MYC activity and poor outcome. Thus, we demonstrate that RBM25 acts as a regulator of MYC activity and sensitizes cells to increased MYC levels.

In many applications, the data of interest comprises multiple sequences that evolve over time. Examples include currency exchange rates, network tra c data, and demographic data on multiple variables. We develop a fast method to analyze such co-evolving time sequences jointly to allow (a) estimation/forecasting of missing/delayed/future values, (b) quantitative data mining, discovering correlations (with or without lag) among the given sequences, and (c) outlier detection. Our method, MUSCLES, adapts to changing correlations among time sequences. It can handle inde nitely long sequences e ciently using an incremental algorithm and requires only small amount of storage so that it works well with limited main memory size and does not cause excessive I/O operations. To scale for a large number of sequences, we present a variation, the Selective MUSCLES method and propose an e cient algorithm to reduce the problem size. Experiments on real datasets show that MUSCLES outperforms popular competitors in prediction accuracy up to 10 times, and discovers interesting correlations. Moreover, Selective MUSCLES scales up very well for large numbers of sequences, reducing response time up to 110 times over MUSCLES, and sometimes even improves the prediction quality.

Cancers develop through somatic mutagenesis, however germline genetic variation can markedly contribute to tumorigenesis via diverse mechanisms. We discovered and phased 88 million germline single nucleotide variants, short insertions/deletions, and large structural variants in whole genomes from 2,642 cancer patients, and employed this genomic resource to study genetic determinants of somatic mutagenesis across 39 cancer types. Our analyses implicate damaging germline variants in a variety of cancer predisposition and DNA damage response genes with specific somatic mutation patterns. Mutations in the MBD4 DNA glycosylase gene showed association with elevated C>T mutagenesis at CpG dinucleotides, a ubiquitous mutational process acting across tissues. Analysis of somatic structural variation exposed complex rearrangement patterns, involving cycles of templated insertions and tandem duplications, in BRCA1-deficient tumours. Genome-wide association analysis implicated common genetic variation at the APOBEC3 gene cluster with reduced basal levels of somatic mutagenesis attributable to APOBEC cytidine deaminases across cancer types. We further inferred over a hundred polymorphic L1/LINE elements with somatic retrotransposition activity in cancer. Our study highlights the major impact of rare and common germline variants on mutational landscapes in cancer.