title: G-DOC: a systems medicine platform for personalized oncology.
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.
authors.affiliation: Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA. sm696@georgetown.edu
authors.forename: Subha
authors.initials: S
authors.lastname: Madhavan
authors.forename: Yuriy
authors.initials: Y
authors.lastname: Gusev
authors.forename: Michael
authors.initials: M
authors.lastname: Harris
authors.forename: David M
authors.initials: DM
authors.lastname: Tanenbaum
authors.forename: Robinder
authors.initials: R
authors.lastname: Gauba
authors.forename: Krithika
authors.initials: K
authors.lastname: Bhuvaneshwar
authors.forename: Andrew
authors.initials: A
authors.lastname: Shinohara
authors.forename: Kevin
authors.initials: K
authors.lastname: Rosso
authors.forename: Lavinia A
authors.initials: LA
authors.lastname: Carabet
authors.forename: Lei
authors.initials: L
authors.lastname: Song
authors.forename: Rebecca B
authors.initials: RB
authors.lastname: Riggins
authors.forename: Sivanesan
authors.initials: S
authors.lastname: Dakshanamurthy
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Byers
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.forename: Louis M
authors.initials: LM
authors.lastname: Weiner
mesh_terms: D001943:Breast Neoplasms / Q000628:therapy; D019295:Computational Biology / Q000379:methods*; D019992:Databases as Topic; D003658:Decision Making, Computer-Assisted; D005260:Female; D005770:Gastrointestinal Neoplasms / Q000628:therapy; D023281:Genomics; D057166:High-Throughput Screening Assays; D006801:Humans; D020407:Internet; D055432:Metabolomics; D057285:Precision Medicine; D049490:Systems Biology; D059467:Transcriptome; D016896:Treatment Outcome