RESUMEN
Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.
Asunto(s)
Antineoplásicos/farmacología , Bases de Datos Genéticas , Neoplasias/genética , Línea Celular Tumoral , Gráficos por Computador , Genes Relacionados con las Neoplasias , Marcadores Genéticos , Genómica , Humanos , Internet , Mutación , Neoplasias/tratamiento farmacológicoRESUMEN
Clear cell renal cell carcinoma (ccRCC) is the most common form of adult kidney cancer, characterized by the presence of inactivating mutations in the VHL gene in most cases, and by infrequent somatic mutations in known cancer genes. To determine further the genetics of ccRCC, we have sequenced 101 cases through 3,544 protein-coding genes. Here we report the identification of inactivating mutations in two genes encoding enzymes involved in histone modification-SETD2, a histone H3 lysine 36 methyltransferase, and JARID1C (also known as KDM5C), a histone H3 lysine 4 demethylase-as well as mutations in the histone H3 lysine 27 demethylase, UTX (KMD6A), that we recently reported. The results highlight the role of mutations in components of the chromatin modification machinery in human cancer. Furthermore, NF2 mutations were found in non-VHL mutated ccRCC, and several other probable cancer genes were identified. These results indicate that substantial genetic heterogeneity exists in a cancer type dominated by mutations in a single gene, and that systematic screens will be key to fully determining the somatic genetic architecture of cancer.
Asunto(s)
Carcinoma de Células Renales/genética , Genes de la Neurofibromatosis 2 , N-Metiltransferasa de Histona-Lisina/genética , Histonas/metabolismo , Neoplasias Renales/genética , Proteínas Nucleares/genética , Oxidorreductasas N-Desmetilantes/genética , Carcinoma de Células Renales/patología , Hipoxia de la Célula/genética , Cromatina/metabolismo , Regulación Neoplásica de la Expresión Génica , Histona Demetilasas , Humanos , Neoplasias Renales/patología , Mutación/genética , Análisis de Secuencia de ADNRESUMEN
High-throughput oligonucleotide microarrays are commonly employed to investigate genetic disease, including cancer. The algorithms employed to extract genotypes and copy number variation function optimally for diploid genomes usually associated with inherited disease. However, cancer genomes are aneuploid in nature leading to systematic errors when using these techniques. We introduce a preprocessing transformation and hidden Markov model algorithm bespoke to cancer. This produces genotype classification, specification of regions of loss of heterozygosity, and absolute allelic copy number segmentation. Accurate prediction is demonstrated with a combination of independent experimental techniques. These methods are exemplified with affymetrix genome-wide SNP6.0 data from 755 cancer cell lines, enabling inference upon a number of features of biological interest. These data and the coded algorithm are freely available for download.
Asunto(s)
Algoritmos , Alelos , Variaciones en el Número de Copia de ADN/genética , Pruebas Genéticas , Modelos Estadísticos , Neoplasias/genética , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Aneuploidia , Teorema de Bayes , Sesgo , Línea Celular Tumoral , Genes Supresores de Tumor , Genotipo , Humanos , Internet , Pérdida de Heterocigocidad/genética , Cadenas de Markov , Neoplasias/diagnóstico , Polimorfismo de Nucleótido Simple/genética , Poliploidía , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Programas InformáticosRESUMEN
Somatically acquired epigenetic changes are present in many cancers. Epigenetic regulation is maintained via post-translational modifications of core histones. Here, we describe inactivating somatic mutations in the histone lysine demethylase gene UTX, pointing to histone H3 lysine methylation deregulation in multiple tumor types. UTX reintroduction into cancer cells with inactivating UTX mutations resulted in slowing of proliferation and marked transcriptional changes. These data identify UTX as a new human cancer gene.
