Modeling How Heterogeneity in Cell Cycle Length Affects Cancer Cell Growth Dynamics in Response to Treatment

Tumours are complex, dynamic and adaptive biological systems characterized by high heterogeneity at genetic, epigenetic, phenotypic, as well as tissue-micro-environmental level. In this work, utilizing cellular automata methods, we focus on intrinsic heterogeneity with respect to cell-cycle duration and explore whether and to what extent this heterogeneity affects cancer cell growth dynamics, when cytotoxic treatment is applied. We assume that treatment acts on cancer cells specifically during mitosis and compare it with a (cell-cycle non-specific) cytotoxic treatment that acts randomly irrespective of the cell-cycle phase. We simulate the spatiotemporal evolution of tumour cells with different initial spatial configurations and different cell-length probability distributions. We observed that in heterogeneous populations, strong selection forces act on cancer cells favouring the faster cells, when the death rates are lower than the proliferation rates. However, at higher mitotic death rates, selection of the slower proliferative cells is favoured, leading to slower post-treatment regrowth rates, as compared to untreated growth. Of note, random cell death progressively eliminates the slower proliferative cells, consistently, favouring highly proliferative phenotypes. Interestingly, compared to the monoclonal populations, that exhibit complete response at high random death rates, emergent resistance arises naturally in heterogeneous populations during treatment. As divergent selection forces may act on a heterogeneous cancer cell population, we argue that treatment plan selection can considerably alter the post-treatment tumour dynamics, cell survival and emergence of resistance proving its significant biological and therapeutic impact.