G-DOC: a systems medicine platform for personalized oncology - PubMed (original) (raw)

Yuriy Gusev, Michael Harris, David M Tanenbaum, Robinder Gauba, Krithika Bhuvaneshwar, Andrew Shinohara, Kevin Rosso, Lavinia A Carabet, Lei Song, Rebecca B Riggins, Sivanesan Dakshanamurthy, Yue Wang, Stephen W Byers, Robert Clarke, Louis M Weiner

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G-DOC: a systems medicine platform for personalized oncology

Subha Madhavan et al. Neoplasia. 2011 Sep.

Abstract

Currently, cancer therapy remains limited by a "one-size-fits-all" approach, whereby treatment decisions are based mainly on the clinical stage of disease, yet fail to reference the individual's underlying biology and its role driving malignancy. Identifying better personalized therapies for cancer treatment is hindered by the lack of high-quality "omics" data of sufficient size to produce meaningful results and the ability to integrate biomedical data from disparate technologies. Resolving these issues will help translation of therapies from research to clinic by helping clinicians develop patient-specific treatments based on the unique signatures of patient's tumor. Here we describe the Georgetown Database of Cancer (G-DOC), a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. While several rich data repositories for high-dimensional research data exist in the public domain, most focus on a single-data type and do not support integration across multiple technologies. Currently, G-DOC contains data from more than 2500 breast cancer patients and 800 gastrointestinal cancer patients, G-DOC includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major "omics" types: DNA, mRNA, microRNA, and metabolites. We believe that G-DOC will help facilitate systems medicine by providing identification of trends and patterns in integrated data sets and hence facilitate the use of better targeted therapies for cancer. A set of representative usage scenarios is provided to highlight the technical capabilities of this resource.

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Figures

Figure 1

Figure 1

G-DOC quick search showing the number of breast cancer studies available in the database patient annotations for neoplastic relapse.

Figure 2

Figure 2

Analysis of miRNA expression data in G-DOC: Analysis and visualization of differentially expressed miRNA in CRC samples versus normal samples. Left, Heat map viewer showing clusters of coexpressed miRNAs. Middle, PCA scatter plot of tumor versus normal samples based on expression data for 61 miRNA showing well-separated clusters of tumors and normal samples. Right, Venn diagram showing only partial overlap between miRNAs differentially expressed in CRC stage II, III, and IV, with only eight miRNAs found to be in common for all three sets of miRNAs. Far right, WNT signaling pathways with predicted targets of the eight miRNAs shown in gray. Analysis of predicted targets has shown that this small group of miRNA regulates WNT signaling pathway known to be affected in colorectal cancer.

Figure 3

Figure 3

PCA using 42 differentially expressed metabolite peaks between relapse and nonrelapse cases with colorectal cancer; fold change of 1.5 or higher; P ≤ .01.

Figure 4

Figure 4

Analysis of processed copy number data in conjunction with clinical information within the G-DOC genome browser. Patient tracks can be dragged to the workspace to view genomic and clinical details. The “omics” tracks can be dragged in to see features that map to various locations on the genome.

Figure 5

Figure 5

Overview of data and analysis features in G-DOC. Data (public or private) are uniformly processed through standard bioinformatics pipelines and made available to various analysis tools through a clinician-and researcher-friendly Web interface.

Figure 6

Figure 6

The results from a clinical search are shown in a sortable table in G-DOC. A variety of options for saving and exporting these results are supported.

Figure 7

Figure 7

A. It is possible to very specifically configure analyses within G-DOC to generate results to enable scientific discovery or validation of hypotheses. Both basic (e.g., fold change cutoff) and advanced (e.g., multiple hypothesis correction) options are available. B. Results of an analysis in G-DOC are displayed clearly, and a variety of options for saving or exporting are supported within the tool. This is the results page from the comparison of the two cohorts identified from the study of Loi et al. [29].

Figure 8

Figure 8

G-DOC supports the generation of Kaplan-Meier plots to help visualize the effect of gene overexpression on patient survival. Note here that the effect of strong overexpression (red) versus strong underexpression (blue) of MYBL1, as seen in the data set of Loi et al. [29], shows a statistically significant impact on patient survival. Differences in survival between either of these groups and patients with intermediate expression (yellow line) are not statistically significant.

Figure 9

Figure 9

Many links from G-DOC to external resources are supported, enabling investigators to use G-DOC as a central resource for their scientific explorations of public or private data sets.

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