Supplementary Figure 9: Stochastic simulations of neutral evolution recapitulate the observed NGS data.

(a) We produced realistic synthetic NGS data using a stochastic simulation of tumor growth that accounts for the neutral accumulation of mutations in the tumor as well as the different sources of sequencing noise (sampling, sequencing depth and normal contamination). (b) The prediction of the analytical model on the cumulative distribution of subclonal allelic frequencies agrees with the stochastic simulation. We generated synthetic data to test the accuracy with which tumor parameters could be reliably recovered when faced with confounding factors. Illustrative synthetic data are shown for (c) low mutation rate, (d) a high number of clonal mutations, (e) significant normal contamination and (f) a low detection limit. (g) Over 10,000 simulations, the interquartile range of the percentage error in the estimates of the mutation rate is <5%, demonstrating the ability of the analytical model to accurately estimate tumor growth parameters from NGS data. (h) The R² values of the fits are consistently high over 10,000 simulations. Unless otherwise stated, the input parameters for the simulation and subsequent sampling were μ=100 mutations/cell division, λ=ln(2), detection limit=10%, normal contamination=0%, mean N_i=100 and number of clonal mutations=200.