Large deviations of cancer recurrence timing

Pranav Hanagal, Kevin Leder and Zicheng Wang

Stochastic Processes and their Applications, 2022, vol. 147, issue C, 1-50

Abstract: We study large deviation events in the timing of disease recurrence. In particular, we are interested in modeling cancer treatment failure due to mutation induced drug resistance. We first present a two-type branching process model of this phenomenon, where an initial population of cells that are sensitive to therapy can produce mutants that are resistant to the therapy. In this model we investigate two random times, the recurrence time and the crossover time. Recurrence time is defined as the first time that the population size of mutant cells exceeds a given proportion of the initial population size of drug-sensitive cells. Crossover time is defined as the first time that the resistant cell population dominates the total population. We establish convergence in probability results for both recurrence and crossover time. We then develop expressions for the large deviations rate of early recurrence and early crossover events. We characterize how the large deviation rates and rate functions depend on the initial size of the mutant cell population. We finally look at the large deviations rate of early recurrence conditioned on the number of mutant clones present at recurrence in the special case of deterministically decaying sensitive population. We find that if recurrence occurs before the predicted law of large numbers limit then there will likely be an increase in the number of clones present at recurrence time, i.e., an increase in tumor diversity.

Keywords: Cancer evolution; Large deviation principle; Branching processes (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.spa.2022.01.001

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