Comprehensive
Analysis of Microarray Gene Expression Data (CA109872)
The
challenge of cancer treatment has been to target specific therapies
to pathogenetically distinct tumor subtypes, to maximize efficacy
and minimize toxicity. However, tumors with similar histopathological
appearance can follow significantly different clinical courses
and show different responses to therapy.
The
recent development of gene microarrays provides an opportunity
to take a genome-wide approach to predict clinical heterogeneity
in cancer treatment. Although such global views are likely to
reveal previously unrecognized patterns of gene regulation and
generate new hypotheses warranting further study, widespread use
of microarray profiling methods is limited by the need for further
technology developments, particularly comprehensive bioinformatics
tools not previously included by the instruments.
The
long-term goal of the proposed work is to develop, test, and disseminate
effective bioinformatics tools to interpret the rich information
about underlying cancer biology (e.g., molecular biomarkers) present
in gene microarray data and to facilitate molecular classification/prediction
of cancer and response to therapy. This technology-driven project
is inspired by the underlying hypothesis that microarray based
gene expression profiling and integrated intelligent bioinformatics
tools can, at the molecular level: (1) confirm existing and discover
previously unrecognized cancer phenotypes; (2) identify most relevant
diagnostic or therapeutic biomarkers; and (3) predict diagnosis,
prognosis, and response to therapy.
The
R21 project will focus on: (1) Perform rigorous and quantitative
tests to compare the proposed methods with comparable existing
methods, and (2) Perform quantitative tests to show the feasibility
of the proposed methods where no comparable method exists. The
R33 project will focus on: (1) Establish a database of gene expression
profiles derived from two human cancers (breast & childhood tumors);
(2) Extract and refine most relevant biomarkers associated with
previously and newly defined cancer phenotypes; and (3) Develop,
optimize, and validate neural network classifiers to predict tumor
phenotype and response to therapy with confidence values. These
novel bioinformatics tools will be developed based on state-of-the-art
and/or latest inventions in engineering, computer science, advanced
statistics, and neural networks, and will produce a major advance
in molecular analysis of cancer.
Copyright
©2004, Computational Bioinformatics and Bioimaging Laboratory
(CBIL), Advanced Research Institute, Virginia Tech.
Last
Updated: 03/03/2009. Suggestions/Comments
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