Large deviations of cancer recurrence timing

Pranav Hanagal, Kevin Leder, Zicheng Wang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1-50
Number of pages50
JournalStochastic Processes and their Applications
StatePublished - May 2022

Bibliographical note

Funding Information:
The authors would like to thank Einar Gunnarsson for helpful comments on the draft. Also KL and ZW were supported by NSF grants CMMI-1552764. KL was also supported by Research Council of Norway [] R&D Grant 309273.

Publisher Copyright:
© 2022 Elsevier B.V.


  • Branching processes
  • Cancer evolution
  • Large deviation principle


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