The MSKCC Center for Translational Cancer Genomic Analysis, a Genome Data Analysis Center type B (GDAC-B), aims to develop novel integrative analysis methods for studying cancer genomic data, thereby enabling the translation of genomic insights into new clinical applications. The planned novel translational genomics analyses are presented in four specific aims organized around four themes: Subtype discovery. Pathway analysis, Therapy nomination, and Software development. These methods will be critical to helping the Cancer Genome Atlas (TCGA) project meet its stated objective of accelerating our understanding of the molecular basis of cancer, and improving our ability to diagnose, treat, and prevent cancer. The analysis work and method development will be closely coordinated with the GDACs working group (GDAC-WG), software tools will be fully integrated with the TCGA analytic pipelines and results of analyses will be made freely available to the scientific community via TCGA internet portals, with well-established plans for caBIG interoperability. The work of this GDAC-B will build on the flow of data from Genome Characterization Centers and Genome Sequencing Centers, as integrated by the GDAC-A data analysis centers. The applicant group of the MSKCC Center for Translational Cancer Genomic Analysis has a strong track record in large scale collaborative cancer genomics within the TCGA pilot phase and other consortia, and benefits from a computational biology program that is uniquely embedded in a comprehensive cancer center with a major focus on basic and translational research.
Over the next five years, The Cancer Genome Atlas (TCGA) will generate the most detailed information ever obtained on the abnormalities present in many different types of cancer. To make sense of this unprecedented amount of information, the MSKCC Center for Translational Cancer Genomic Analysis will develop new ways of analyzing this information, to enable and hasten the translation of these novel insights into new clinical advances in the diagnosis and treatment of cancer.
| Seiler, Michael; Peng, Shouyong; Agrawal, Anant A et al. (2018) Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types. Cell Rep 23:282-296.e4 |
| Liu, Yang; Sethi, Nilay S; Hinoue, Toshinori et al. (2018) Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas. Cancer Cell 33:721-735.e8 |
| Jayasinghe, Reyka G; Cao, Song; Gao, Qingsong et al. (2018) Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281.e3 |
| Saltz, Joel; Gupta, Rajarsi; Hou, Le et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 23:181-193.e7 |
| Ellrott, Kyle; Bailey, Matthew H; Saksena, Gordon et al. (2018) Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst 6:271-281.e7 |
| Campbell, Joshua D; Yau, Christina; Bowlby, Reanne et al. (2018) Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas. Cell Rep 23:194-212.e6 |
| Gao, Qingsong; Liang, Wen-Wei; Foltz, Steven M et al. (2018) Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep 23:227-238.e3 |
| Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14 |
| Radovich, Milan; Pickering, Curtis R; Felau, Ina et al. (2018) The Integrated Genomic Landscape of Thymic Epithelial Tumors. Cancer Cell 33:244-258.e10 |
| Shen, Hui; Shih, Juliann; Hollern, Daniel P et al. (2018) Integrated Molecular Characterization of Testicular Germ Cell Tumors. Cell Rep 23:3392-3406 |
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