Wednesday, June 16 at 06:45am (PDT)Wednesday, June 16 at 02:45pm (BST)Wednesday, June 16 10:45pm (KST)
SMB2021 FollowTuesday (Wednesday) during the "CT06" time block.
Linnea C Franssen
Roche, pRED, Basel
"3D atomistic-continuum cancer invasion model: In silico simulations of an in vitro organotypic invasion assay"
We develop a three-dimensional hybrid atomistic-continuum model that describes the invasive growth dynamics of individual cancer cells in tissue. The framework explicitly accounts for phenotypic variation by distinguishing between cancer cells of an epithelial-like and a mesenchymal-like phenotype. It also describes mutations between these cell phenotypes in the form of epithelial-mesenchymal transition (EMT) and its reverse process mesenchymal-epithelial transition (MET). The model consists of a hybrid system of partial and stochastic differential equations that describe the evolution of epithelial-like and mesenchymal-like cancer cells, respectively, under the consideration of matrix-degrading enzyme concentrations and the extracellular matrix density. With the help of inverse parameter estimation and a sensitivity analysis, this three-dimensional model is then calibrated to an in vitro organotypic invasion assay experiment of oral squamous cell carcinoma cells.
"In silico model of evolution in heterogeneous tumors and the influence of the microenvironment"
In heterogeneous tumors, cell types of different properties compete over the available resources, that are nutrients and space. Rapid expansion leads to solid stress in in-vivo tumors that can collapse blood vessels, which together with angiogenesis leads to fluctuations in nutrient availability. Here, we observe the influence of such fluctuations on tumor evolution.We developed a 3D computational model that simulates the evolutionary trajectories of an evolving tumor. Cell motility and cell-cell adhesion are observed as free evolving parameters in tumor cells that grow in a medium of surrounding cells. A nutrient dependent cell cycle is introduced and constant and dynamic nutrient surroundings are compared.We find an evolutionary advantage of low adhesion cells independent of the surrounding. Furthermore we find a dependency between the evolution speed and the frequency of the nutrient fluctuations, with a significant increase of evolutionary speed for a frequency domain.
Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo
"Deconvolution of drug-response heterogeneity in cancer cell populations"
In ex vivo drug-sensitivity assays, cells are treated with varying drug concentrations and viable cells are measured at one or more time points. Viability curves, and their characteristics (e.g. IC50), allow comparing drug sensitivity across multiple drugs and cell samples. However, the interpretation of those curves is confounded by the presence of cellular heterogeneity in each sample. The presence of several subclones with different drug sensitivities results in an aggregated drug-sensitivity profile that does not represent the cell population complexity, and thus hinders the design of precise treatment strategies.Here we show how to infer on the presence of cellular subclones with differential drug response, using standard cell viability data at total population level. We build cell population dynamic models of the evolution of individual subclones over time and dose. We estimate the number of subclones, their mixture frequencies and drug-response profiles. We validate our methodology on data from admixtures of synthetic and actual cancer cells at known frequencies. Finally, we explore the clinical usefulness of the method for multiple myeloma patients.This is joint work with J. Foo, K. Leder, A. Frigessi, E.M. Myklebust, J. Noory, S. Mumenthaler, D.S. Tadele, M. Giliberto, F. Schjesvold, J. Enserink and K. Tasken.
Max Planck Institute for Evolutionary Biology, Germany
"Of slow cells and slower decline – Phenotypic heterogeneity and treatment type in cancer"
It is largely recognized that tumours consist of a diverse population of cancer cells. Treatment exerts selection on the phenotype and may shift the distribution of characteristic functional traits within the population. Taking the underlying phenotypic trait distribution into account, given for example by the growth rate of individual cells, allows to predict and compare the performance of different treatment options. Here, we investigate how treatment that is either growth rate selective or unselective affects a population of cancer cells with diverse growth rates. We find that different treatment types result in different cancer cell population dynamics and trait distributions. Further, we find that accounting for phenotypic diversity allows to select optimal treatment patterns for specific targets. To increase the likelihood of tumour eradication, the maximum mortality should be exerted on the cancer cell population. If tumour eradication is not achievable, maximizing the time until relapse may be achieved by a very different treatment strategy that aims not for maximum cancer cell mortality but rather for a specific, desirable trait distribution. It thus becomes evident that combining a trait-based approach with considering the phenotypic diversity of cancer allows for mechanistic understanding of cancer dynamics and optimization of personalized treatment.