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.
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|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|
|Wang, Zehua; Yang, Bo; Zhang, Min et al. (2018) lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer. Cancer Cell 33:706-720.e9|
|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|
|Taylor, Alison M; Shih, Juliann; Ha, Gavin et al. (2018) Genomic and Functional Approaches to Understanding Cancer Aneuploidy. Cancer Cell 33:676-689.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|
|Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15|
|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|
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