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      Genomic and Transcriptomic Characterization Links Cell Lines with Aggressive Head and Neck Cancers

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          SUMMARY

          Cell lines are important tools for biological and preclinical investigation, and establishing their relationship to genomic alterations in tumors could accelerate functional and therapeutic discoveries. We conducted integrated analyses of genomic and transcriptomic profiles of 15 human papillomavirus (HPV)-negative and 11 HPV-positive head and neck squamous cell carcinoma (HNSCC) lines to compare with 279 tumors from The Cancer Genome Atlas (TCGA). We identified recurrent amplifications on chromosomes 3q22–29, 5p15, 11q13/22, and 8p11 that drive increased expression of more than 100 genes in cell lines and tumors. These alterations, together with loss or mutations of tumor suppressor genes, converge on important signaling pathways, recapitulating the genomic landscape of aggressive HNSCCs. Among these, concurrent 3q26.3 amplification and TP53 mutation in most HPV(–) cell lines reflect tumors with worse survival. Our findings elucidate and validate genomic alterations underpinning numerous discoveries made with HNSCC lines and provide valuable models for future studies.

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          In Brief

          Cheng et al. reveal that head and neck squamous cell cancer cell lines display recurrent chromosome copy number alterations and mutations found in tumors with worse prognosis. The copy alterations correlate with expression of multiple genes implicated in squamous cell signaling pathways and programs that provide candidates for mechanistic studies and targeted therapy.

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          Most cited references45

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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            A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

            Heng Li (2011)
            Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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              High-throughput oncogene mutation profiling in human cancer.

              Systematic efforts are underway to decipher the genetic changes associated with tumor initiation and progression. However, widespread clinical application of this information is hampered by an inability to identify critical genetic events across the spectrum of human tumors with adequate sensitivity and scalability. Here, we have adapted high-throughput genotyping to query 238 known oncogene mutations across 1,000 human tumor samples. This approach established robust mutation distributions spanning 17 cancer types. Of 17 oncogenes analyzed, we found 14 to be mutated at least once, and 298 (30%) samples carried at least one mutation. Moreover, we identified previously unrecognized oncogene mutations in several tumor types and observed an unexpectedly high number of co-occurring mutations. These results offer a new dimension in tumor genetics, where mutations involving multiple cancer genes may be interrogated simultaneously and in 'real time' to guide cancer classification and rational therapeutic intervention.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                13 November 2018
                30 October 2018
                05 December 2018
                : 25
                : 5
                : 1332-1345.e5
                Affiliations
                [1 ]Tumor Biology Section, Head and Neck Surgery Branch, National Institute on Deafness and Other Communication Disorders, NIH, Bethesda, MD 20892, USA
                [2 ]Translational Bioinformatics, MedImmune, Gaithersburg, MD 20878, USA
                [3 ]Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
                [4 ]Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15260, USA
                [5 ]Division of Head and Neck Surgery, Departments of Otolaryngology, Radiation Oncology, and Immunology, University of Pittsburgh Cancer Institute, Pittsburgh, PA 15232, USA
                [6 ]Department of Surgery, Division of Otolaryngology, Molecular Virology Research Program, Smilow Cancer Hospital, Yale Cancer Center, Yale University Medical School, New Haven, CT 06520, USA
                [7 ]Cancer Biology Program, Program in the Biomedical Sciences, Rackham Graduate School, and the Department of Otolaryngology-Head and Neck Surgery, University of Michigan, Ann Arbor, MI 48109, USA
                [8 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, C.V.W. and Z.C.; Resources, T.E.C., M.E.P., R.L.F., S.M.G., N.I., and W.G.Y.; Methodology, X.Y., H.C., J.C., A.D.S. and Z.C.; Sequencing, X.Y; Computational and Statistical Analysis, H.C., H.S., and W.X.; Figure and Table Preparation, H.C., Z.C.; Writing – Original Draft, H.C., Z.C., and C.V.W.; Writing – Review and Editing, all authors.

                [* ]Correspondence: vanwaesc@ 123456nidcd.nih.gov (C.V.W.), chenz@ 123456nidcd.nih.gov (Z.C.)
                Article
                NIHMS1511993
                10.1016/j.celrep.2018.10.007
                6280671
                30380422
                1f7141be-10f5-48c6-834f-87bfc23c92b3

                This is an open access article under the CC BY license.

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                Cell biology
                Cell biology

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