Supplementary Data from Sustained Coevolution in a Stochastic Model of Cancer–Immune Interaction

Abstract

<p>Main SI file: This file contains all supplementary information, including detailed mathematical derivations and supplemental figures: SF1: Probability densities for the inter-temporal clone arrival times SF2: Escape and elimination probabilities as a function of cycle number assuming clearance probabilities that decline as a function of cycle number. SF3: Escape probabilities as a function of cycle number assuming constant clearance probability as well as clearance probabilities that decline as a function of current clone size. SF4: Simulated population trajectories as a function of time for both the deterministic and adaptive immune recognition assumptions SF5: Simulated population trajectories as a function of time conditioned on ultimate escape. SF6: Empirical cancer incidence as a function of age. SF7: Changes in early cancer incidence as a function of per-cell mutation rate SF8: General features of the co-evolutionary framework, including initiation, branching, recognition, ultimate elimination, and ultimate escape. SF9: Example simulated trajectories as a function of time for various clearance probabilities and branching parameters. SF10: Detection size and mean dispersion assuming adaptive recognition as a function of the adaptive immune detection parameter and per-cell growth rate SF11: Mean recognition cycles and escape probabilities as a function of clearance probability and branching parameter. SF12: Cycle number and escape probability as a function of clearance probability and branching parameter, assuming mean cycle numbers +/- 1 standard deviation of estimated cycle number. ST1: TRACERx empirical mutational burden partitioned by lung cancer subtype.</p>