Recent advances in therapeutics have improved survival rates for many cancers. However, nearly all metastatic tumors are incurable, and resistance to therapeutic interventions is nearly universal. There are many reasons for our lack of progress-foremost of which is lack of understanding of mechanisms of response and resistance and lack of markers to identify subsets of patients ideally-suited for specific treatments. Our team brings enormous experience in TCGA and other multi-disciplinary coordinated projects, such as BEAT-AML, and the Stand Up to Cancer West Coast Prostate Cancer Dream Team. The Cancer Genome Atlas (TCGA) was successful because multi-disciplinary teams worked together to create new and innovative knowledge about cancer and we intend to ensure that the GDAN is equally successful. In this application, we have assembled a team of proven investigators from four of the different TCGA groups to extend the successes of TCGA to the projects managed by the Center for Cancer Genomics. Our team will continue our outstanding capabilities at analyzing and interpreting cancer genomic data by deploying data analysis pipelines that support the key capabilities of the network. In addition to the our experience in coordinated network studies, we bring experience with clinical trial design and interpretation. Further, the team has deep expertise at building the computational infrastructure necessary for the GDAN to succeed. For example, in the domain of distributed computing we have deploy pipelines for execution at many sites. We also bring novel methods for integrative pathways and analysis. Finally, we bring our experiences with competitive challenges that ensure that we can identify and deploy the most effective methods for genomic data analyses. Using these strengths we will support the GDAN and the Analysis Working Groups (AWGs) that it serves with two principal objectives. The first objective and second objective, per the RFA, are the ?Development of innovative bioinformatics and computational tools and methodologies?, which will allow us to make clinical and biological correlations and to ?Conduct Integrative analysis of data sets generated by GCCs using the bioinformatics tools developed by each GDAC.? We will achieve these objectives in five specific aims, one administrative aim to support the GDAN and four aims, one for each of the areas where we propose a competency.
The GDAN represents an attempt to bring retrospective precision medicine to the NCI's clinical trial infrastructure. As such it is a great opportunity to learn why trials that have occurred worked at a broad level, or identify patients who likely benefited from therapy, even when the trials were not successful. Our participation in this network will bring the most robust approaches for mutation calling and expression analysis, will bring novel pathway analysis approaches, and will bring an analysis of tumor genetic heterogeneity and evolution.
| 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 |
| Berger, Ashton C; Korkut, Anil; Kanchi, Rupa S et al. (2018) A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers. Cancer Cell 33:690-705.e9 |
| Hoadley, Katherine A; Yau, Christina; Hinoue, Toshinori et al. (2018) Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell 173:291-304.e6 |
| Schaub, Franz X; Dhankani, Varsha; Berger, Ashton C et al. (2018) Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas. Cell Syst 6:282-300.e2 |
| Liu, Jianfang; Lichtenberg, Tara; Hoadley, Katherine A et al. (2018) An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173:400-416.e11 |
| Bailey, Matthew H; Tokheim, Collin; Porta-Pardo, Eduard et al. (2018) Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 173:371-385.e18 |
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