Conventional methods dramatically underestimate the rate of of viral escape from CTL responses.

We ran stochastic simulations of viral escape assuming CTL response at 6 epitopes with an early CTL response at a single epitope followed roughly a week later by CTL response at five additional epitopes. For each simulation, we estimate the escape rate at the 5 additional epitopes using Eq (1) assuming sampling at t1 = 30 and t2 = 60 and then calculate the relative error of the estimated escape rate, (estimate-true rate)/true rate, based on sampling N = 15 sequences at t1 and t2 (column “sampled freq”), based on the exact frequencies of wild type and mutant variants at t1 and t2 (column “exact freq”), and based on exact frequencies as well as a model in which no mutations occur after t1 (column “exact freq/no mutation”). Due to the later CTL response, the 5 additional epitopes correspond to expansion variants. We show relative errors of escape rate estimates under linear and full escape graphs. The linear escape graphs can include only the variants 000000, 100000, 110000, …, 111111. The full escape graphs can include all 26 possible haplotypes formed by wild type and mutants at the 6 epitopes. Strong and weak CTL response reflect simulations in which the killing rate at the 5 additional epitopes had a maximum value of k = 0.3 day−1 and k = 0.12 day−1, respectively, with the exact kill rate varying across simulations (see Methods for details). We assume equal replicative fitness across all variants. Confidence intervals (CIs) presented are based on 1,000 simulations for each case of CTL response and escape graph type.