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Characterization of 1000 breast cancer genomes and transcriptomes

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Breast cancer is driven by the acquisition of key genetic aberrations that confer clonal growth advantages. The identification of recurrent genomic alterations that impact gene expression can facilitate the elucidation of such ‘driver’ events. Given the substantial inter-individual variability of clinicopathological characteristics amongst breast cancer patients, genome-wide measurements of multiple data types combined with clinical variables provide an invaluable resource to dissect the complexity of this disease. Here I describe an integrated analysis of copy-number, allelic-ratios, and gene-expression for 1000 primary tumors aimed at further characterizing the genomic and transcriptional landscape of breast cancer.

This talk is part of the Computational and Systems Biology series.

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