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Lacy, M. S.

Publications and source records attributed to Lacy, M. S..

2 recordsLinked to original sources

A stochastic model of T cell expansion in activating micro-rod scaffolds and its continuum limit: Importance of IL-2 loading and scaffold homogeneity

T cells are immune cells that are known to be effective at killing cancer cells, however, an individual patients tumour-specific T cell counts are often insufficient to control cancer growths. Adoptive T cell therapy aims to address this by activating and expanding highly effective T cells ex vivo before injecting them into the patient to employ their cancer-killing functions. Recent experimental setups using activating micro-rod scaffolds have significantly improved T cell expansion over conventional methods, but there is still much to understand regarding the factors that maximise the expansion of functional T cells in these scaffolds. We present a stochastic agent-based model of T cell expansion alongside its continuum limit to simulate the average interactions between T cells and micro-rods, which enable us to explore several behaviours of the experimental system. Stochastic simulations demonstrate that T cell expansion is driven by activated cell clusters around micro-rods. Using our spatial models and a mean-field approximation, we discover that this cluster-driven expansion is most supported by scaffolds with initially homogeneous micro-rod concentrations. Our simulations also reveal that loading the T cell growth factor, interleukin-2 (IL-2), into micro-rod pores for secretion significantly prolongs expansion compared to the more conventional method of IL-2 supplementation.

immunology↗

Impact of resistance on therapeutic design: a Moran model of cancer growth

Resistance of cancers to treatments, such as chemotherapy, largely arise due to cell mutations. These mutations allow cells to resist apoptosis and inevitably lead to recurrence and often progression to more aggressive cancer forms. Sustained-low dose therapies are being considered as an alternative over maximum tolerated dose treatments, whereby a smaller drug dosage is given over a longer period of time. However, understanding the impact that the presence of treatment-resistant clones may have on these new treatment modalities is crucial to validating them as a therapeutic avenue. In this study, a Moran process is used to capture stochastic mutations arising in cancer cells, inferring treatment resistance. The model is used to predict the probability of cancer recurrence given varying treatment modalities. The simulations predict that sustained-low dose therapies would be virtually ineffective for a cancer with a non-negligible probability of developing a sub-clone with resistance tendencies. Furthermore, calibrating the model to in vivo measurements for breast cancer treatment with Herceptin, the model suggests that standard treatment regimens are ineffective in this mouse model. Using a simple Moran model, it is possible to explore the likelihood of treatment success given a non-negligible probability of treatment resistant mutations and suggest more robust therapeutic schedules.

cancer biology↗