Predicting neuroblastoma outcomes with molecular evolution NatureGenet
\ and fit the model to the clock-like mutation densities.Denoting the rate at which tumor cells divide by, with an exponential growth model,. We assumed that some SSNVs are already present in the founder cell of the tumor and denote their number byf; these mutations are present in a subset of the tumor only and will thus be found at< 1. The VAF distribution of a neuroblastoma is hence a superposition of clonal and subclonal mutations accumulated before and during tumor growth, respectively.
To model the number of subclonal mutations, we used a model of neutrally evolving tumors that accounts for the stochastic expansion of neutral subclones while assuming exponential expansion of the tumor mass overalland drift stochastically according to a supercritical birth–death process, where$$P_{1,i}\left=\left\{ {\begin{array}{*{20}{l}} {\alpha ,i=0} \hfill \\ {)\left} \right)\beta \left^{i - 1},i \ge 1} \hfill \end{array}} \right.
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