Abstract
BackgroundTumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers.ResultsIn this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times.ConclusionsOur results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.
Highlights
Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and are termned drivers
We present a discrete-time Wright-Fisher stochastic model to study the evolutionary dynamics of superdrivers in combination with common drivers by extensively simulating tumorigenesis under a wide range of parameters
Error modeling To understand the agreement between the model simulations and analytical waiting time approximations, we fitted a linear regression model to predict the residual between simulation and approximation, using the following predictors: driver fitness, superdriver fitness factor, number of superdriver mutations to wait for, and number of driver mutations to wait for: are modeled with a selective advantage of r = c s, where c > 1 is the superdriver fitness increase parameter and s ∊ [0,1] is the driver advantage
Summary
To describe the evolutionary dynamics of tumorigenesis with superdrivers and drivers, we employ a Wrightand τkl ≈ Tk S+ TlD. While superdriver waves without driver mutations had higher frequency when superdriver selection was high, superdriver waves with one or two driver mutations had relatively lower frequency when superdriver selection was high (Supplementary Fig. 2). Curvature of superdriver waves with two driver mutations and at least one superdriver mutation tended to be higher for high superdriver selection (non-monotonic increase, Supplementary Fig. 3) and the growth rate of superdriver clones was lower compared to the situation when the superdriver selection was high. We observed that our theoretical approximation tends to slightly underestimates the simulated waiting times To both understand the source of this deviation and correct for it, we empirically learned the residuals using a linear regression model, regressing them on the covariates s, r, k, and l (i.e., driver fitness, superdriver fitness, number of driver mutations waited for, and number of superdrivers waited for, respectively).
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