DF/HCC Program Affiliation

Biostatistics
Breast Cancer

Lab Website

Center for Cancer Computational BiologyLaboratory of Compuatational Biology and Functional Genomics

Research Abstract

The transforming event of the human genome project has neither been the production of a draft reference genome sequence nor the creation of a preliminary catalogue of human genes, but rather the creation of technologies that allow us to generate data on a genomic scale. Applications such as microarray transcriptional profiling, proteomics, and metabolomics/metabonomics, have begun to provide insight into fundamental biological processes and the hope is that they will help us to understand the pathways and networks that underlie the biological processes we are studying. In practice, however, these technologies often generate long lists of “significant” genes, proteins, or metabolites, and a good deal of additional work is necessary to turn this data into a clear picture that reveals insight into the mechanism responsible for the phenotype we observe. Our group uses a variety of bioinformatics and computational approaches, biostatistical analyses, and fundamental laboratory investigation to explore fundamental questions about the nature of human cancers. Our approach is based on using high-throughput assays and analyzing the results by integrating diverse datatypes, including the genome sequence, its annotation, genetic information, phenotype, and the vast body of knowledge captured in the literature. Our goal is not only to develop insight into these processes, but to provide tools, protocols, and databases to the broader community that will accelerate research.

Publications

• Chen DT,Nasir A,Culhane A,Venkataramu C,Fulp W,Rubio R,Wang T,Agrawal D,McCarthy SM,Gruidl M,Bloom G,Anderson T,White J,Quackenbush J,Yeatman T.Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue.Breast Cance
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• Djebbari A,Quackenbush J.Seeded Bayesian Networks: constructing genetic networks from microarray data.BMC Syst Biol 2008;2:57.
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• Mar JC,Rubio R,Quackenbush J.Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples.Genome Biol 2006;7(12):R119.
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• Maher EA, Brennan C, Wen PY, Durso L, Ligon KL, Richardson A, Khatry D, Feng B, Sinha R, Louis DN, Quackenbush J, Black PM, Chin L, DePinho RA.Marked genomic differences characterize primary and secondary glioblastoma subtypes and identify two distinct mo
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• Saeed AI,Bhagabati NK,Braisted JC,Liang W,Sharov V,Howe EA,Li J,Thiagarajan M,White JA,Quackenbush J.TM4 microarray software suite.Methods Enzymol 2006;411:134-93.
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• Quackenbush J.From 'omes to biology.Anim Genet 2006 Aug;37 Suppl 1:48-56.
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• Quackenbush J.Microarray analysis and tumor classification.N Engl J Med 2006 Jun 8;354(23):2463-72.
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• Eschrich S,Yang I,Bloom G,Kwong KY,Boulware D,Cantor A,Coppola D,Kruhoffer M,Aaltonen L,Orntoft TF,Quackenbush J,Yeatman TJ.Molecular staging for survival prediction of colorectal cancer patients.J Clin Oncol 2005 May 20;23(15):3526-35.
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• Agrawal D,Chen T,Irby R,Quackenbush J,Chambers AF,Szabo M,Cantor A,Coppola D,Yeatman TJ.Osteopontin identified as colon cancer tumor progression marker.C R Biol 2003 Oct-Nov;326(10-11):1041-3.
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• Bloom G,Yang IV,Boulware D,Kwong KY,Coppola D,Eschrich S,Quackenbush J,Yeatman TJ.Multi-platform, multi-site, microarray-based human tumor classification.Am J Pathol 2004 Jan;164(1):9-16.
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• Chen T,Yang I,Irby R,Shain KH,Wang HG,Quackenbush J,Coppola D,Cheng JQ,Yeatman TJ.Regulation of caspase expression and apoptosis by adenomatous polyposis coli.Cancer Res 2003 Aug 1;63(15):4368-74.
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