TY  - INPR
A1  - Blath, Jochen
A1  - Kraut, Anna
A1  - Paul, Tobias
A1  - Tóbiás, András József
T1  - A stochastic population model for the impact of cancer cell dormancy on therapy success
T2  - bioRxiv
N2  - Therapy evasion – and subsequent disease progression – is a major challenge in current oncology. An important role in this context seems to be played by various forms of cancer cell dormancy. For example, therapy-induced dormancy, over short timescales, can create serious obstacles to aggressive treatment approaches such as chemotherapy, and long-term dormancy may lead to relapses and metastases even many years after an initially successful treatment. The underlying dormancy-related mechanisms are complex and highly diverse, so that the analysis even of basic patterns of the population-level consequences of dormancy requires abstraction and idealization, as well as the identification of the relevant specific scenarios.
In this paper, we focus on a situation in which individual cancer cells may switch into and out of a dormant state both spontaneously as well as in response to treatment, and over relatively short time-spans. We introduce a mathematical ‘toy model’, based on stochastic agent-based interactions, for the dynamics of cancer cell populations involving individual short-term dormancy, and allow for a range of (multi-drug) therapy protocols. Our analysis shows that in our idealized model, even a small initial population of dormant cells can lead to therapy failure under classical (and in the absence of dormancy successful) single-drug treatments. We further investigate the effectiveness of several multidrug regimes (manipulating dormant cancer cells in specific ways) and provide some basic rules for the design of (multi-)drug treatment protocols depending on the types and parameters of dormancy mechanisms present in the population.
KW  - cancer cell dormancy
KW  - therapy evasion
KW  - treatment success
KW  - multi-drug treatment
KW  - resistance mutation
KW  - treatment protocol design
KW  - stochastic population dynamics
KW  - individual-based models
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/83025
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-830252
UR  - https://www.biorxiv.org/content/10.1101/2023.12.15.571717v1
IS  - 2023.12.15.571717 Version 1
PB  - bioRxiv
ER  -