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      Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer

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          Abstract

          Extensive transcriptional alterations are observed in cancer, many of which activate core biological processes established in unicellular organisms or suppress differentiation pathways formed in metazoans. Through rigorous, integrative analysis of genomics data from a range of solid tumors, we show many transcriptional changes in tumors are tied to mutations disrupting regulatory interactions between unicellular and multicellular genes within human gene regulatory networks (GRNs). Recurrent point mutations were enriched in regulator genes linking unicellular and multicellular subnetworks, while copy-number alterations affected downstream target genes in distinctly unicellular and multicellular regions of the GRN. Our results depict drivers of tumourigenesis as genes that created key regulatory links during the evolution of early multicellular life, whose dysfunction creates widespread dysregulation of primitive elements of the GRN. Several genes we identified as important in this process were associated with drug response, demonstrating the potential clinical value of our approach.

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          Cancers arise when harmful changes happen in the genetic information of certain cells. These ‘mutations’ are different from person to person, but overall, they disrupt healthy cells in similar ways. In particular, cancer cells tend to lose features that help cells work together in the body. Researchers have suggested that cancers may emerge when cells stop being able to cooperate with each other as part of an organism.

          Our bodies still rely on old genes that were present in our earliest, single-cell ancestors. However, we also have newer genes that evolved when the organisms in our lineage started to have more than one cell. A complex network of signals exists to ensure that both sets of genes work together smoothly, and previous studies have suggested that cancers may appear when this delicate balance is disrupted.

          To address this question, Trigos et al. have now used a computational approach to analyse different types of tumours from over 9,000 patients. This showed that, in cancer, many mutations disrupt the genes that coordinate old and new genes. These mutations were usually small, punctual changes in the genetic sequence. However, large modifications, such as an entire gene being deleted or repeated, took place more often in the old or the new genes themselves. Therefore, different classes of mutations have specific roles when disrupting how old and new genes work in cancer.

          While certain genes highlighted during this analysis were already known to be associated with cancer, others were not – including genes present during the evolution of the earliest animals on Earth. Looking more closely into how these genes can cause disease may help us better understand and fight cancer.

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          Most cited references33

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          Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

          Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.
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            The BioGRID interaction database: 2017 update

            The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
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              SomaticSniper: identification of somatic point mutations in whole genome sequencing data.

              The sequencing of tumors and their matched normals is frequently used to study the genetic composition of cancer. Despite this fact, there remains a dearth of available software tools designed to compare sequences in pairs of samples and identify sites that are likely to be unique to one sample. In this article, we describe the mathematical basis of our SomaticSniper software for comparing tumor and normal pairs. We estimate its sensitivity and precision, and present several common sources of error resulting in miscalls. Binaries are freely available for download at http://gmt.genome.wustl.edu/somatic-sniper/current/, implemented in C and supported on Linux and Mac OS X. delarson@wustl.edu; lding@wustl.edu Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Role: Senior Editor
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                26 February 2019
                2019
                : 8
                : e40947
                Affiliations
                [1 ]deptComputational Cancer Biology Program Peter MacCallum Cancer Centre MelbourneAustralia
                [2 ]deptSir Peter MacCallum Department of Oncology The University of Melbourne ParkvilleAustralia
                [3 ]deptDepartment of Biochemistry and Molecular Biology The University of Melbourne ParkvilleAustralia
                [4 ]deptDepartment of Biochemistry and Molecular Biology Monash University ClaytonAustralia
                [5 ]deptBioinformatics Division The Walter & Eliza Hall Institute of Medical Research ParkvilleAustralia
                Max-Planck Institute for Evolutionary Biology Germany
                Barcelona Supercomputing Center Spain
                Barcelona Supercomputing Center Spain
                Francis Crick Institute United Kingdom
                Author information
                http://orcid.org/0000-0002-5915-2952
                http://orcid.org/0000-0002-3277-6562
                Article
                40947
                10.7554/eLife.40947
                6402835
                30803482
                a59e67cc-a3d4-4ae6-b73e-062c706ad7f4
                © 2019, Trigos et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 09 August 2018
                : 06 February 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001782, University of Melbourne;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award Recipient :
                Funded by: The Peter MacCallum Cancer Centre Foundation;
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Cancer Biology
                Computational and Systems Biology
                Custom metadata
                Cancer is a consequence of the release of basal cellular functions inherited from our unicellular ancestors from the control of regulatory networks that evolved during the emergence of multicellularity.

                Life sciences
                cancer,evolution,networks,orthology,mutations,multicellularity,human
                Life sciences
                cancer, evolution, networks, orthology, mutations, multicellularity, human

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