High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

Abstract

Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous systems. Therapies act on this cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic models could help identify the factors driving a treatment's success or failure, but exploring mechanistic models over highdimensional parameter spaces is computationally challenging. In this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in adapting PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where thou-sands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization. We close by discussing novel applications to synthetic multicellular systems for cancer therapy.

Learning-accelerated discovery of immune-tumour interactions

“…The model presented in this paper was implemented using PhysiCell Version 1.4.1, and modified the model from Ghaffarizadeh et al 15 and Ozik et al 16 to allow selection of 2-D or 3-D simulations. The full source code is available at GitHub; see the link in the ESI †…”

“…Numerous agent-based models have been developed to study cancer-immune interactions and immunotherapy (see the excellent recent review by Norton et al 2019,14 and our own recent work by Ghaffarizadeh et al 15 and Ozik et al 16). By adjusting model parameters and simulation rules, we can explore the characteristics of successful and unsuccessful treatments, and learn how the “best policies” vary with a patient's tumour characteristics 16–18…”

Nested Active Learning for Efficient Model Contextualization and Parameterization: Pathway to generating simulated populations using multi-scale computational models

“…Our ML models are trained and integrated using the Extreme-scale Model Exploration With Swift (EMEWS) framework (21)(22)(23). EMEWS enables the creation of high-performance computing (HPC) workflows for implementing large-scale model exploration studies.…”

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

“…Less biased migration would increase mixing of cancer and immune cells and increase the efficacy of the immune attack. In [20,21], we used high-throughput computing to further expand this investigation to explore the impact of stochastic migration and tumor-immune cell adhesion dynamics on the treatment efficacy. A full User Guide (see S2 Text) is included in the documentation directory of every PhysiCell download.…”

“…PhysiCell's design has allowed straightforward deployment on supercomputers for high-throughput simulation studies. We recently explored over 250 instances of a 3-D cancer-immune model (see Additional PhysiCell examples) to study the impact of immune cell motility and cancer-immune adhesion dynamics on a cancer immune response, thus reducing 1.5 years' worth of simulations to 2 days on a Cray supercomputer [20,21]. We anticipate that users can similarly use PhysiCell to efficiently explore large spaces of parameter values and hypotheses (model rules) with biophysically realistic, 3-D models.…”