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Shuttleworth, R.

Publications and source records attributed to Shuttleworth, R..

2 recordsLinked to original sources

PhysiMeSS - A New PhysiCell Addon for Extracellular Matrix Modelling

The extracellular matrix is a complex assembly of macro-molecules, such as collagen fibres, which provides structural support for surrounding cells. In the context of cancer metastasis, it represents a barrier for the cells, that the migrating cells needs to degrade in order to leave the primary tumor and invade further tissues. Agent-based frameworks, such as PhysiCell, are often use to represent the spatial dynamics of tumor evolution. However, typically they only implement cells as agents, which are represented by either a circle (2D) or a sphere (3D). In order to accurately represent the extracellular matrix as a network of fibres, we require a new type of agent represented by a segment (2D) or a cylinder (3D). In this article, we present PhysiMeSS, an addon of PhysiCell, which introduces a new type of agent to describe fibres, and their physical interactions with cells and other fibres. PhysiMeSS implementation is publicly available at https://github.com/PhysiMeSS/PhysiMeSS, as well as in the official Physi-Cell repository. We also provide simple examples to describe the extended possibilities of this new framework. We hope that this tool will serve to tackle important biological questions such as diseases linked to dis-regulation of the extracellular matrix, or the processes leading to cancer metastasis.

systems biology↗

Deconstructing the contributions of heterogeneity to combination treatment of hormone sensitive breast cancer

Combination therapies are fundamental to cancer treatments, including in breast cancer the most common invasive malignancy in women. Breast cancer treatment is determined based on molecular subtypes, and since 2016, combination palbociclib and fulvestrant has been used to treat hormone receptor-positive breast cancer. However, the impact of heterogeneity of the tumour landscape and tumour composition dynamics on scheduling decisions remains poorly understood. To elucidate the contributions of variability at multiple scales to treatment outcomes in hormone receptor-positive breast cancer, we developed a simple mathematical model of two unique estrogen receptor positive (ER+) breast cancer cell types and their response to combination treatment with palbociclib and fulvestrant. We used this model to understand how the initial fraction of either cell type may impact the fraction remaining after treatment and examined how heterogeneity in pharmacokinetics and pharmacodynamics result in a large distribution of outcomes. Our results suggest that the pharmacokinetics and pharmacodynamics of fulvestrant were the major drivers of final tumour size and composition. We then leveraged our model to guide therapeutic scheduling of combination palbociclib and fulvestrant, demonstrating the use of mathematical modelling to improve our understanding of cancer biology and treatments.

cancer biology↗