Computational dissection of intratumour genetic heterogeneity and applications to the study of cancer treatment, evolution and metastasis


Session type:

Scott Carter1,2
1Broad Institute of MIT-Harvard, Cambridge, MA, USA,2Dana-Farber Cancer Institute, Boston, MA, USA,3Massachusetts General Hospital, Boston, MA, USA


Starting from a normal cell, cancers evolve via multiple rounds of mutation, selection, and expansion. Continued application of this process to the growing cancer cell population results in branched genetic variegation, whereby multiple cancer subclones relate to each other in a tree-like fashion. Consequently, cancer tissues are substantially heterogeneous both across different anatomical regions and within single cancer biopsies. Here we give an overview of a suite of computational tools for dissecting intra-tumour heterogeneity using whole-exome sequencing data. We describe how high-quality copy-number profiles can be generated and integrated with germline SNP and somatic mutation data to resolve the subclonal structure of individual tumour samples. We then describe methods for reconstructing the phylogeny of multiple related tumour samples, taking subclonal structure into account. We then describe the application of these techniques to the analysis of cancer evolution and metastasis, with examples from chronic lymphocytic leukaemia, small cell lung cancer models, brain metastases, and Barrett's oesophagus.