Towards identification of clonal populations in tumours through deep mutational profiling
Human cancers evolve under the principles of Darwinian selection at the level of clonal populations of cells. Over time, tumour cells acquire mutations which can confer phenotypic advantages and act as substrates for Darwinian selection. As a result, when tumours are diagnosed, they are often composed of mixtures of heterogeneous clonal cell populations. Though heterogeneity and clonal evolution have been accepted features of cancer for decades, only recently have technological advances in high throughput DNA sequencing allowed for accurate quantification of these phenomena through identification and interpretation of somatic mutations. In this work we develop a novel statistical model which allows us to deconvolve the effect of genotype and normal cell contamination to simultaneously infer the fraction of cells containing known mutations and cluster mutations into groups reflective of the underlying population structure. We show using data from patient tumours that novel considerations encoded in our model are necessary to avoid spurious inference about population structure and in reconstruction of temporal mutational evolution. Finally, our model provides a single quantitative metric of clonal diversity from a patient tumour sample and we discuss how this could be leveraged to address hypotheses related to the influence of heterogeneity on treatment failure and disease progression.