In Silico Cancer Research

The In Silico Research Centers of Excellence Program is the flagship effort of former caBIG® program aimed at supporting investigator-initiated research using data-mining and other in silico methods into the etiology, diagnosis, treatment, and prevention of cancer. Georgetown is one of a consortium of Centers with the mission of broadening the usage of in silico research and data-mining methodologies across the caBIG® and general cancer research communities.

The Georgetown ISRCE supports research using a variety of informatics technologies to mine primarily existing data in an effort to gain fundamentally new insights into common cancer types, particularly breast and gastrointestinal cancers (colorectal and pancreatic) with the goal of delivering improvements in survival and therapeutic outcomes. We have developed prototypes of new data analysis and mining workflows and algorithms capable of working with disparate data resources in the areas of gene expression, copy number, pathway networks, miRNA, and metabolomics data, and we have started to analyze next generation sequencing (NGS) data including RNA-seq, ChIP-seq, whole genome, exome, and others. To enhance these strengths, we added significant external expertise, through collaborations with Virginia Tech in advanced bioinformatics modeling and analytics development.

The platform that we have developed to disseminate the tools, algorithms, and workflows developed in the ISRCE, as well as clinical data, is the Georgetown Database of Cancer (G-DOC). This web tool was designed to provide advanced bioinformatics capabilities and patient molecular data to clinicians and researchers with non-informatics backgrounds to enable the integration of discovery science with clinical research. G-DOC houses a large array of multi-omics data from the ISRCE project in addition to other public and consortia datasets, a collection which is expected to grow considerably as more public users participate and request inclusion of their data. 

Publications

Subha Madhavan, Yuriy Gusev, Thanemozhi G. Natarajan, Lei Song, Krithika Bhuvaneshwar, Robinder Gauba, Abhishek Pandey, Bassem Haddad, David Goerlitz, Amrita Cheema, Hartmut Juhl, Bhaskar Kallakury, John Marshall, Stephen Byers and Louis M. Weiner. Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse.  Frontiers in Oncology (in press) 
 
Yuriy Gusev*, Rebecca B. Riggins*, Krithika Bhuvaneshwar#, Robinder Gauba#, Laura Sheahan, Robert Clarke, and Subha Madhavan. In silico discovery of mitosis regulation networks associated with early distant metastases in estrogen receptor positive breast cancers. Cancer Informatics 2013:12 31-51
 
Madhavan S, Gusev Y, Harris M, Tanenbaum DM, Gauba R, Bhuvaneshwar K, Shinohara A, Rosso K, Carabet L, Song L, Riggins R, Dakshanamurthy S, Wang Y, Byers S, Clarke R, Weiner LM.  G-DOC: A Systems Medicine Platform for Personalized Oncology.  Neoplasia.  2011 Sep;13(9):771-83. PMID: 21969811
 
Hu ZZ, Huang H, Wu CH, Jung M, Dritschilo A, Riegel AT, Wellstein A. Omics-Based Molecular Target and Biomarker Identification. Methods Mol Biol. 2011; 719:547-71.
 
Chen L, Chan TH, Choyke PL, Hillman EM, Chi CY, Bhujwalla ZM, Wang G, Wang SS, Szabo Z, Wang Y. CAM-CM: A Signal Deconvolution Tool for In Vivo Dynamic Contrast-enhanced Imaging of Complex Tissues. Bioinformatics. 2011 Sep 15;27(18):2607-9.  PMID: 21785131 
 
Yu G, Zhang B, Bova GS, Xu J, Shih M, Wang Y. BACOM: in silico detection of  genomic deletion types and correction of normal cell contamination in copy number data. Bioinformatics. 2011 Jun 1;27(11):1473-80. PMID: 21498400
 
Madhavan S, Sanders AE, Chou WY, Shuster A, Boone KW, Dente MA, Shad AT, Hesse BW. Pediatric palliative care and eHealth opportunities for patient-centered care. Am J Prev Med. 2011 May;40(5 Suppl 2):S208-16. PMID: 21521596
 
Zhang B, Tian Y, Jin L, Li H, Shih IeM, Madhavan S, Clarke R, Hoffman EP, Xuan J, Hilakivi-Clarke L, Wang Y. DDN: a caBIG™ analytical tool for differential network analysis. Bioinformatics. 2011 Apr 1;27(7):1036-8.  PMID: 21296752
 
Yu G, Li H, Ha S, Shih IeM, Clarke R, Hoffman EP, Madhavan S, Xuan J, Wang Y.  PUGSVM: a caBIG™ analytical tool for multiclass gene selection and predictive classification. Bioinformatics. 2011 Mar 1;27(5):736-8. PMID: 21186245
 
Riggins RB, Mazzotta MM, Maniya OZ, and Clarke R.  Orphan nuclear receptors in breast cancer pathogenesis and therapeutic response.  Endocrine-Related Cancer. 2010 Aug 16;17(3):R213-31.
 
Feng Y, Yu G, Wang T, Shih I, Wang Y. Analyzing DNA copy number changes using Fused Margin Regression, Int. J. Functional Informatics and Personalized Medicine, 3(1):3-15, 2010.
 
Yu G, Feng Y, Miller DJ, Xuan J, Hoffman EP, Clarke R, Davidson B, Shih I, Wang Y. Matched gene selection and committee classifier for molecular classification of heterogeneous diseases. Journal of Machine Learning Research 11 (2010) 2141-2167.
 
Zhang B, Li H, Riggins RB, Zhan M, Xuan J, Zhang Z, Hoffman EP, Clarke R, Wang Y. Differential dependency network analysis to identify condition-specific topological changes in biological networks, Bioinformatics. 2009 Feb 15;25(4):526-32.