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Understanding tumour heterogeneity in glioblastoma

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We have undertaken an integrated genomic analysis of the evolution of Glioblastoma (GB) in individual patients across multiple spatial scales. Our data reveal early clonal diversification generating a genetically complex and highly evolved disease environment at clinical presentation. We propose that these fundamental patient-specific tumor evolutionary dynamics underlie clinical phenotypic heterogeneity and may have implications for the emergence of resistant disease. Using a Fluorescence-Guided Multiple Sampling technique we obtained samples from the tumor mass, the sub-ependymal zone and the non-fluorescent tumor margin. These data allow us to study intra-tumour heterogeneity, which is likely to be the key to understanding treatment failure. I will describe our approach, and discuss a statistical problem that arises in the analysis of dependent samples.

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

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