Cancer heterogeneity: an evolutionary perspective


Simon Tavaré

Director, Cancer Research UK Cambridge Institute


The talk will focus on aspects of tumor dynamics, using glioblastoma as examples. Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intra-tumor heterogeneity is likely to be the key to understanding treatment failure. However, the extent of intra-tumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multi-sampling scheme to collect spatially distinct tumor fragments from GB patients. I will describe an integrated genomic analysis that uncovers extensive intra-tumor heterogeneity, with the majority of patients displaying different GB subtypes within the same tumor. We showed that copy number alterations in EGFR and CDKN2A/B/p14ARF are early events, and aberrations in PDGFRA and PTEN are later events in cancer progression. Our results reveal the genome-wide architecture of intra-tumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.

Simon Tavaré has worked for many years on statistical problems arising in cancer genomics, human genetics, population genetics, bioinformatics and computational biology. He is Director of the Cancer Research UK Cambridge Institute, a Professor in the Department of Applied Mathematics and Theoretical Physics and Professor of Cancer Research in the Oncology Department at the University of Cambridge. His group focuses mainly on next-gen sequencing, cancer genomics and evolutionary approaches to cancer. In 2009 he was elected a Fellow of the Academy of Medical Sciences, and in 2011 a Fellow of the Royal Society. Simon is also a Research Professor, and George and Louise Kawamoto Chair in Biological Sciences, at the University of Southern California. He is PI of the NIH Center of Excellence in Genomic Science at USC, which is developing computational and experimental approaches for understanding how genotype relates to phenotype.