Extended Data Fig. 8: Bayesian inference framework for cancer dynamics in space and time.

a, Schematic representation of the spatial cellular automaton model of tumour growth. b, Instance of simulation of a neutrally expanding cancer with a single ‘functional’ clone (blue, top), and corresponding neutral mutation lineages (bottom). c, Simulation of a tumour containing a differentially selected subclone (red, top) and corresponding neutral mutation lineages (bottom). d, Simulation with two branching subclonal selection events. e, In this neutral simulation we illustrate peripheral versus exponential growth and the effects on lineage mixing. f, Spatial sampling annotated during tissue collection for tumour C539 and corresponding simulated spatial sampling. g, Real data from patient C539 (top) versus simulated data from an instance selected by the inference framework (bottom). h–i, Inference framework based on Approximate Bayesian Computation - Sequential Monte Carlo (ABC-SMC) allows for (h) model selection and (i) posterior parameter estimation given the data. In this case birthrates.2 is the birth rate of the selected subclone, clone_start_times.2 is the time when the subclone arose during the growth of the tumour, push_power.1 is the coefficient of boundary driven growth and mutation_rate is the rate of accumulation of mutations per genome per division.