(Image Above) Tumour growth was modelled using three different microenvironments: (a) uniform extracellular matrix (ECM), (b) grainy ECM and (c) low nutrient availability. The upper row shows the resulting tumour cell distributions obtained after 3 months of simulated growth: we can see that the three different microenvironments have produced distinct tumour morphologies.
The process of using mechanistic models to assist with understanding experimental results and informing clinical decisions depends on a suitable framework for developing, analyzing, implementing, adjusting and presenting the models and results. Furthermore, models must be calibrated so that they recapitulate experimental and clinical observations both in terms of their range of behavior as well as the intrinsic variability of outcomes before they can be used to specifically impact one or more clinical decisions (e.g. predict novel treatment strategies). There is also a practical aspect of turning theoretical predictions into actionable clinical decisions that requires tools to generate patient cohorts, mimic clinical trials and present results in a visual and easy to use manner such that clinicians can immediately understand and manipulate them. The central focus of the Bench-to-Bedside Core is therefore to integrate experiments, clinical data, and models to assist with hypothesis generation, testing, and validation (Project 1), and translate successes into a clinical setting using clinically-relevant tools (Project 2). This will be accomplished through: Aim 1, core models that explore unifying principles of evolution, heterogeneity, and response to treatment; Aim 2, Radiomics, Pathomics, and a generalized set of analysis tools for integrating experimental and clinical data to facilitate mathematical model development and calibration; and Aim 3, decision support tools including Phase “i" trials and dynamically optimized therapy for use in clinical trials. The tools and methods we develop here are generalized and therefore suitable to a wider class of experimental and clinical data and will serve as a key legacy from this project to drive bench-to-beside science.
Project Leadership: Robert Gatenby, M.D.; Robert Gillies, Ph.D.; Alexander Anderson, Ph.D.; Ariosto Silva, Ph.D.
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