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.
Bibliographical noteFunding Information:
Abbreviations: CIN, chromosomal instability; CRC, colorectal cancer; dbSNP, the single nucleotide polymorphism database at the NCBI; G-DOC, Georgetown Database of Cancer; GI, gastrointestinal; miRNAs, microRNAs; OMIM, Online Mendelian Inheritance in Man; PCA, principal component analysis Address all correspondence to: Subha Madhavan, PhD, Lombardi Comprehensive Cancer Center, 2115 Wisconsin Ave NW, Suite 110, Washington, DC 20007. E-mail: email@example.com 1The G-DOC development effort was partly funded by the National Cancer Institute’s In Silico Centers of Excellence Program (HHSN261200800001E) as well as the Center for Cancer Systems Biology (U54-CA149147). 2These authors equally contributed to this study. Received 9 June 2011; Revised 28 July 2011; Accepted 1 August 2011 Copyright © 2011 Neoplasia Press, Inc. All rights reserved 1522-8002/11/$25.00 DOI 10.1593/neo.11806