Modelling of killer T-cell and cancer cell subpopulation dynamics under immuno- and chemotherapies

Halkola AS, Parvinen K, Kasanen H, Mustjoki S, & Aittokallio T (2020). Modelling of killer T-cell and cancer cell subpopulation dynamics under immuno- and chemotherapies. Journal of Theoretical Biology 488: e110136. DOI:10.1016/j.jtbi.2019.110136.

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Abstract

Each patient’s cancer has a unique molecular makeup, often comprised of distinct cancer cell subpopulations. Improved understanding of dynamic processes between cancer cell populations is therefore critical for making treatment more effective and personalized. It has been shown that immunotherapy increases the survival of melanoma patients. However, there remain critical open questions, such as timing and duration of immunotherapy and its added benefits when combined with other types of treatments. We introduce a model for the dynamics of active killer T-cells and cancer cell subpopulations. Rather than defining the cancer cell populations based on their genetic makeup alone, we consider also other, non-genetic differences that make the cell populations either sensitive or resistant to a therapy. Using the model, we make predictions of possible outcomes of the various treatment strategies in virtual melanoma patients, providing hypotheses regarding therapeutic efficacy and side-effects. It is shown, for instance, that starting immunotherapy with a denser treatment schedule may enable changing to a sparser schedule later during the treatment. Furthermore, combination of targeted and immunotherapy results in a better treatment effect, compared to mono-immunotherapy, and a stable disease can be reached with a patient-tailored combination. These results offer better understanding of the competition between T-cells and cancer cells, toward personalized immunotherapy regimens.

Item Type: Article
Uncontrolled Keywords: combination therapy; immunotherapy; personalized medicine; killer T-cells; side-effects
Research Programs: Evolution and Ecology (EEP)
Depositing User: Luke Kirwan
Date Deposited: 07 Jan 2020 09:26
Last Modified: 17 Jan 2020 10:30
URI: http://pure.iiasa.ac.at/16250

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