Microarrays in cancer research.

摘要: Microarray technology has presented the scientific community with a compelling approach that allows for simultaneous evaluation of all cellular processes at once. Cancer, being one most challenging diseases due to its polygenic nature, presents itself as perfect candidate by this approach. Several recent articles have provided significant insight into strengths and limitations microarrays. Nevertheless, there are strong indications will provide new molecular markers could be used in diagnosis prognosis cancers (1, 2). To achieve these goals it is essential seamless integration clinical biological data us elucidate genes pathways involved various cancers. effect we currently evaluating gene expression profiles human brain, ovarian, breast hematopoetic, lung, colo- rectal, head neck biliary tract address issues joint team scientists, doctors computer scientists from two Virginia Universities major healthcare provider. The study been divided several focus groups include; Tissue Bank Clinical & Pathology Laboratory Data, Chip Fabrication, QA/QC, Devitalization, Database Design Data Analysis, using multiple microarray platforms. Currently over 300 consenting patients enrolled largest number cancer patients. on each patient compiled secure interactive relational database elements accomplished common programming interface. This contains key parameters including demographic (risk factors, nutrition, co-morbidity, familial history), histopathology (non genetic predictors), tumor, treatment follow-up information. Gene derived tissue samples linked database, which query levels. challenge acquisition processing paramount importance success venture. A devitalization timeline protocol was devised ensure sample RNA integrity. Stringent protocols employed ascertain accurate tumor homogeneity, serial dissection 10IM frozen sections followed histopathological evaluation. platforms utilized include Affimetrix, Oligo- Chips custom-designed cDNA arrays. Selected evaluated platform between groups. Analysis steps involve normalization standardization hierarchical clustering determine co-regulation profiles. aim conjoint effort cancers, resistance mechanisms, prognosis.