Supplementary Figure 10: Robustness of the parameter estimation analysis.
From: Identification of neutral tumor evolution across cancer types
By varying the parameters of the simulation, we show that the analytical model can accurately identify neutrality of tumor growth and recover the mutation rate in the face of (a) different numbers of clonal mutations and (b) different detection limits, and that we can correct for normal contamination accurately (to within 5%) for contamination below 30% (c,d). (e,f) The effect of varying read depth. Less accurate mutation rate estimates were achieved at low read depth (<25) and poorer fits of the analytical model. (g,h) The effect of mutation rate: lower mutation rates lead to poorer model fits and a higher variance in the mutation estimate because fewer variants are available to fit the model. (i,j) The effect of growth rate λ: the variance in the mutation rate estimate increases as tumor growth slows (l decreases) and the fit of the model becomes worse. The offspring probability distributions for the different values of λ were Pλ= ln(2) = (p0 = 0, p1 = 0, p2 = 1), Pλ = ln(1.8) = (p0 = 0.05, p1 = 0.1, p2 = 0.85), Pλ= ln(1.6) = (p0 = 0.1, p1 = 0.2, p2 = 0.7), Pλ= ln(1.4) = (p0 = 0.2, p1 = 0.2, p2 = 0.6) and Pλ = ln(2) = (p0 = 0.2, p1 = 0.4, p2 = 0.4).
