2008 Scientific Report

VARI | 2008
Research Interests
As high-throughput technologies such as DNA sequencing, gene and protein expression profiling, DNA copy number analysis,
and single nucleotide polymorphism genotyping become more available to researchers, extracting the most significant biological
information from the large amount of data produced by these technologies becomes increasingly difficult. Computational
disciplines such as bioinformatics and computational biology have emerged to develop methods that assist in the storage,
distribution, integration, and analysis of these large data sets. The Computational Biology laboratory at VARI currently focuses
on using mathematical and computer science approaches to analyze and integrate complex data sets in order to develop a
better understanding of how cancer cells differ from normal cells at the molecular level. In addition, members of the lab provide
assistance in data analysis and other computational projects on a collaborative and/or fee-for-service basis.
In the past year the laboratory has contributed to several gene expression microarray analysis projects ranging from mechanisms
of oncogene transformation to the identification of genes associated with drug sensitivity. For example, in recent work
led by the Laboratory of Molecular Oncology, we combined cytogenetic, phenotypic, and gene expression profiling data to help
elucidate the role of chromosomal abnormalities during tumor cell progression. We also worked closely with the Laboratory of
Cancer Genetics in the development of gene expression–based models for the diagnosis and prognosis of renal cell carcinoma.
Moreover, we and other groups have demonstrated that several types of biological information, in addition to relative transcript
abundance, can be derived from high-density gene expression profiling data. Taking advantage of this additional information
can lead to the rapid development of plausible computational models of disease development and progression.
Changes in DNA copy number result in dramatic changes in gene expression within the abnormal region and are detectable
by examining the population of mRNAs generated from the genes that map to each chromosome. Additionally, activation of
certain oncogenes or inactivation of certain tumor suppressor genes can produce context-independent gene signatures that
can be detected in a gene expression profile. For example, genes that are up-regulated by overexpression of RAS in breast
epithelial cells also tend to be overexpressed in other samples having activated RAS signaling, such as lung tumors that
contain activating RAS mutations. We have invested a reasonable portion of the past several years developing and evaluating
computational methods to predict deregulated signal transduction pathways and chromosomal abnormalities using gene
expression data. We have worked closely with the Laboratory of Cancer Genetics on computational models to describe the
development and progression of renal cell carcinoma. An example of the successful application of this analytic approach is in
the examination of gene expression profiling data derived from papillary renal cell carcinoma (RCC).
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