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      Neural crest-derived tumor neuroblastoma and melanoma share 1p13.2 as susceptibility locus that shows a long-range interaction with the SLC16A1 gene

      1 , 2 , 2 , 1 , 1 , 2 , 3 , 4 , 1 , 2 , 5 , 1 , 2 , 2 , 5 , 6 , 7 , 7 , 1 , 8 , 9 , 10 , 4 , 11 , 12 , 13 , 4 , 11 , 14 , 15 , 16 , 17 , 3 , 4 , 1 , 2 , 1 , 2 , 1 , 2 , 5 , 1
      Carcinogenesis
      Oxford University Press (OUP)

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          Abstract

          Neuroblastoma (NB) and malignant cutaneous melanoma (CMM) are neural crest cells (NCC)-derived tumors and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association studies (GWAS). We took a three-staged approach to conduct cross-disease meta-analysis of GWAS for NB and CMM (2101 NB cases and 4202 controls; 12 874 CMM cases and 23 203 controls) to identify shared loci. Findings were replicated in 1403 NB cases and 1403 controls of European ancestry and in 636 NB, 508 CMM cases and 2066 controls of Italian origin. We found a cross-association at locus 1p13.2 (rs2153977, odds ratio = 0.91, P = 5.36 × 10−8). We also detected a suggestive (P < 10−7) NB-CMM cross-association at 2q37.1 with opposite effect on cancer risk. Pathway analysis of 110 NB-CMM risk loci with P < 10−4 demonstrated enrichment of biological processes such as cell migration, cell cycle, metabolism and immune response, which are essential of human NCC development, underlying both tumors. In vitro and in silico analyses indicated that the rs2153977-T protective allele, located in an NB and CMM enhancer, decreased expression of SLC16A1 via long-range loop formation and altered a T-box protein binding site. Upon depletion of SLC16A1, we observed a decrease of cellular proliferation and invasion in both NB and CMM cell lines, suggesting its role as oncogene. This is the largest study to date examining pleiotropy across two NC cell-derived tumors identifying 1p13.2 as common susceptibility locus for NB and CMM risk.

          We demonstrate that combining genome-wide association studies results across cancers with same origins can identify new loci common to neuroblastoma and melanoma arising from tissues which originate from neural crest cells. Our results also show 1p13.2 confer risk to neuroblastoma and melanoma by regulating SLC16A1.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Is Open Access

            METAL: fast and efficient meta-analysis of genomewide association scans

            Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
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              CADD: predicting the deleteriousness of variants throughout the human genome

              Abstract Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs.washington.edu.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Carcinogenesis
                Oxford University Press (OUP)
                0143-3334
                1460-2180
                March 2020
                May 14 2020
                September 07 2019
                March 2020
                May 14 2020
                September 07 2019
                : 41
                : 3
                : 284-295
                Affiliations
                [1 ]Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Naples, Italy
                [2 ]CEINGE Biotecnologie Avanzate, Naples, Italy
                [3 ]Division of Oncology and Center for Childhood Cancer Research, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
                [4 ]Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                [5 ]IRCCS SDN, Naples, Italy
                [6 ]Department of Oncogenomics, Academic Medical Center, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
                [7 ]Dipartimento di Medicina Oncologica Integrata, Università degli Studi di Genova,Genova, Italy
                [8 ]Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli Federico II, Naples, Italy
                [9 ]Dipartimento di Medicina clinica e Chirurgia, Università degli Studi di Napoli Federico II, Naples, Italy
                [10 ]National Cancer Institute, ‘Fondazione G. Pascale’-IRCCS, Naples, Italy
                [11 ]Division of Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
                [12 ]The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
                [13 ]Experimental Therapy in Oncology, Istituto Giannina Gaslini, Genova, Italy
                [14 ]Department of Translational and Precision Medicine, University of Rome Sapienza, Rome, Italy
                [15 ]Statistical Genetics, QIMR Berghofer Medical Research Institute Brisbane, Queensland, Australia
                [16 ]Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
                [17 ]Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
                Article
                10.1093/carcin/bgz153
                31605138
                e3dd30a9-1f40-4944-9a26-8cadbd0b5321
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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