Making sense of cancer genomic data in the light of tumour evolution: measuring clonal dynamics in individual patients from next-generation sequencing
Cancer is the result of an evolutionary process in which tumour cells accumulate somatic alterations that provide a selective advantage. This accumulation of malignant traits allows evading the normal homeostatic regulation, proliferate uncontrollably, and finally disseminate to the rest of the body. Despite extraordinary efforts to profile cancer genomes on a large scale, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. This is complicated by the pervasive inter-patient variation (different molecular profiles between patients with the same tumour type) and extensive intra-tumour heterogeneity (distinct molecular patterns of cells within the same tumour). Here we present a novel model-based framework founded on evolutionary theory and population genetics to interpret cancer genomic data and measure clonal dynamics in individual patients. In particular, we will present a null model of genomic intra-tumour heterogeneity that can be applied to next-generation sequencing data from human malignancies. This mathematical framework is based on neutral evolution and allows identifying those tumours that are characterized by complex evolutionary dynamics, such as clonal selection, and which ones are not. Importantly, reanalysing cancer genomic data within a quantitative modelling framework allows the measurement, in each individual patient, of novel parameters of somatic evolution that are not directly accessible in humans.