? The Cancer Genome Atlas (TCGA) project aims to determine the feasibility of identifying and cataloging the genomic-level alterations associated with human cancers, including alterations in DNA copy number and transcript levels, epigenetic modifications, cancer somatic mutations, and inherited genetic variance that contribute to cancer susceptibility. As experimental techniques for a comprehensive survey of the cancer landscape mature, there is a great demand in the cancer research field to develop advanced analysis and visualization tools for the characterization and integrative analysis of the large, complex genomic datasets arising from different technology platforms. ? The proposed UCSC Cancer Genomics Browser is a suite of web-based tools designed to integrate, visualize and analyze genomic and clinical data generated by the TCGA project. The browser, which will be available at https://cancer.cse.ucsc.edu/, currently consists of three major components: hgHeatmap, hgFeatureSorter and hgPathSorter. The main panel, hgHeatmap, displays a whole-genome-oriented view of genome-wide experimental measurements for individual and sets of samples/patients along with their clinical information. hgFeatureSorter and hgPathSorter together enable investigators to order, filter, aggregate and display data interactively based on any given feature set ranging from clinical features to annotated biological pathways to user-edited collections of genes. Standard and advanced statistical tools will be installed on the server's side through a caBIG-compatible mechanism to provide quantitative analysis of the whole genomic data or any of its subsets. The UCSC Cancer Genomics Browser is an extension of the UCSC Genome Browser; thus it inherits and integrates the Genome Browser's existing rich set of human biology and genetics data to enhance the interpretability of the cancer genomic data. A pathway- centric, multi-layer machine learning algorithm, BioIntegrator, will be built on top of the UCSC Cancer Genomics Browser for the integration of cancer genomics and clinical data to assess the levels of perturbation of biological pathways in cancers and during the course of therapy, and to predict clinical outcomes. ? Collectively, these proposed tools will facilitate a synergistic interaction among clinicians, experimental biologists and bioinformaticians. They will enable cancer researchers to better explore the breadth and depth of the TCGA cancer genomics data resources, and to further characterize molecular pathways that influence cellular dynamics and stability in cancer. Ultimately, insights gained by applying these tools may advance our knowledge of human cancer biology and stimulate the discovery of new prognostic and diagnostic markers, as well as the development of therapeutic and prevention strategies. ? ? ?
| Ng, Sam; Collisson, Eric A; Sokolov, Artem et al. (2012) PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis. Bioinformatics 28:i640-i646 |
| Sanborn, J Zachary; Benz, Stephen C; Craft, Brian et al. (2011) The UCSC Cancer Genomics Browser: update 2011. Nucleic Acids Res 39:D951-9 |
| Vaske, Charles J; Benz, Stephen C; Sanborn, J Zachary et al. (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26:i237-45 |
