The proposal titled """"""""TCGA Data Analysis Center at Berkeley"""""""" focuses on integrating data from The Cancer Genome Atlas (TCGA) and providing that integrated analysis to the community by timely computation and redistribution through the TCGA Data Coordinating Center. The proposal includes six key elements (1) quality control of TCGA data, (2) systematic classification of tumors by molecular data, (3) analysis and integration of histopathology images (H&E), (4) interpretation of gene expression data in the context of other molecular data, (5) identification of interacting genetic loci by aberration co-occurrence, and (6) creation of genetic influence diagrams. These analyses will be performed on the mutation, copy number, genotype, expression, methylation and miRNA analyses that are likely to be key components of TCGA. Data analyses will begin after a cohesive analytical strategy is developed in a design document that clearly lays out the process by which our group will receive, analyze, and redistribute information. Our proposal includes collaborators who are pathologists, cancer biologists, geneticists, genomicists, computer scientists, and statisticians who have a proven track record of working together on large projects.
| Huang, Kuan-Lin; Mashl, R Jay; Wu, Yige et al. (2018) Pathogenic Germline Variants in 10,389 Adult Cancers. Cell 173:355-370.e14 |
| Ding, Li; Bailey, Matthew H; Porta-Pardo, Eduard et al. (2018) Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell 173:305-320.e10 |
| 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 |
Showing the most recent 10 out of 75 publications
