Computational analysis of cancer cytogenetic data

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DESCRIPTION (provided by applicant): Characterization of non-random recurrent chromosomal aberrations have proven to be a useful instrument for identification of genes related to cancers and other genetic diseases and for improving diagnosis and prognosis of certain types of cancer. Current studies on characterization of cytogenetic signatures have been limited to the use of data available as individual clinical cases or as small-scale epidemiologic surveys with a primary focus on individual abnormalities and breakpoints using manual approaches. Due to the limitation on data size and the simplicity of methods, more complex patterns and critical breakpoints have not been revealed. Comprehensive cytogenetic data analyzed with new strategies and methodologies are needed to identify novel cytogenetic signatures for human cancer. The proposed project involves analysis of the unprecedented comprehensive cancer etiologic/cytogenetic data deposited in the Mitelman Database of Chromosomal Aberrations in Cancers using computational approaches. By first establishing a computational algorithm with the capability to decode karyotype data to reveal all cytogenetic changes implied by the simplified cytogenetics nomenclature, and by taking advantage of the first digitally compiled comprehensive data of cancer chromosomal aberrations, the project aims: 1) to attempt to establish cytogenetic signatures for cancers without previously known cytogenetic characteristics; 2) to identify new cytogenetic signatures for cancers with established patterns; and 3) to study relationships among different cancers and general patterns for all human chromosomal aberrations. The data resulting from this project should shed light on the molecular basis of poorly understood cancers, the multistage process of tumorigenesis, and relationships among different cancers, and it may provide a global picture of all cancers at the cytogenetic level.