Model-based tumour subclonal deconvolution accounting for spatio-temporal sampling biases

Bulk DNA sequencing has entered the clinic, and understanding tumour evolution in space and time has become critical for advancing precision oncology. A recent work by us has brought a population genetics perspective into the classical tumour subclonal deconvolution problem, showing how multiple spatiotemporal sampling biases arise when we collect more than one sample of the same tumour. Sampling biases complicate the mapping of the mutations into the evolutionary process, a complex issue undermined by existing multi-sample analysis methods. This work presents MOBSTERm, a novel mixture-within-mixture Bayesian framework that extends our earlier approach to a multi-dimensional formulation. Our model incorporates distinct mathematical distributions that capture sampling bias patterns in tumour data, allowing the deconvolution to resist the effect of some confounders. This works presents the simulation of one source of sampling bias analysed with this new approach, together with a large scale test based on simulations. Moreover, we apply our model to understand subclonal deconvolution from a multi-region whole-genome colorectal cancer sample and from longitudinal whole-genome glioblastoma samples of 15 patients collected before and after treatment. This new deconvolution approach offers a better account of the effect of spatio-temporal tumour biases, allowing us to better elucidate complex clonal dynamics from multi-sample cancer sequencing data.