#2022 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 UCSC Cancer Genomics Browser is a suite of web-based tools designed to integrate, visualize and analyze genomic and clinical data. The secured-access browser, available at https://cancer.cse.ucsc.edu/, 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 alongside 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 are available to provide quantitative analysis of 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 cancer genomics data.
 We demonstrate the UCSC Cancer Genomics Browser by integrating several independent studies on breast cancer including the I-SPY chemotherapy clinical trial and other studies focused on chemotherapeutic response or long-term survival. The types of data that are visualized and analyzed by the browser include microarray measurements of gene expression, copy number variation and phosphoprotein expression, MRI imaging measurements, and clinical parameters.
 Collectively, these tools facilitate a synergistic interaction among clinicians, experimental biologists, and bioinformaticians. They enable cancer researchers to better explore the breadth and depth of the 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.
 Funding sources: CALGB CA31964 and CA33601, ACRIN U01 CA079778 and CA080098, NCI SPORE CA58207, California Institute for Quantitative Biosciences, NHGRI. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2022.
Current efforts to understand the mechanism of cancer involve using various whole-genome -omics measurements over large patient cohorts. Since a patient response to treatments is highly variable, the challenge then is to integrate the data in order to infer patient-specific disease mechanisms. Recent advances in the analysis of cancer (TCGA ovarian serous carcinoma and glioblastoma multiforme) has shown that a pathway interpretation of DNA copy number, DNA methylation, mRNA expression, and mutations offers a powerful framework for interpreting complex data. The hope is that a pathway-level interpretation of -omics data can identify pathway signatures to predict differences in clinical outcome, whereas traditional machine learning algorithms do not take advantage of the pathway structure of biological data. We are developing a pathway prediction method based on PARADIGM to discriminate patient outcome based on pathway signatures. Utilizing conditional random fields (CRFs) allow for formal search for a graphical model that optimizes the prediction of a particular variable of interest (VOI) defined by the given classification task, as opposed to a generative model that optimizes the model to explain the data. The method first merges pathways to build a core network around the VOI. The model then seeks to extend the pathway to include new genes and interactions which improve the model's predictive ability on the training data. Application of our method to 50 breast cancer cell-lines treated with 80 different compounds revealed general and subtype-specific signatures of response in breast cancer. We compared our CRF-based method against a compendium of standard machine-learning algorithms and found that our CRF outperformed all methods on a majority of drugs tested. We also tested the method on a cancer benchmark consisting of a dozen prediction challenges all involving the prediction of clinical outcomes on large patient cohorts using gene expression and copy number data. Again, the CRF model outperformed a majority of classifiers and performed comparably to the best classifiers on most challenges. We expect our method to generalize to a wide variety of biological systems for which high-throughput genomics and functional genomics are available. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 49. doi:10.1158/1538-7445.AM2011-49
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