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A Brownian dynamics tumor progression simulator with application to glioblastoma.

ABSTRACT: Tumor progression modeling offers the potential to predict tumor-spreading behavior to improve prognostic accuracy and guide therapy development. Common simulation methods include continuous reaction-diffusion (RD) approaches that capture mean spatio-temporal tumor spreading behavior and discrete agent-based (AB) approaches which capture individual cell events such as proliferation or migration. The brain cancer glioblastoma (GBM) is especially appropriate for such proliferation-migration modeling approaches because tumor cells seldom metastasize outside of the central nervous system and cells are both highly proliferative and migratory. In glioblastoma research, current RD estimates of proliferation and migration parameters are derived from computed tomography or magnetic resonance images. However, these estimates of glioblastoma cell migration rates, modeled as a diffusion coefficient, are approximately 1-2 orders of magnitude larger than single-cell measurements in animal models of this disease. To identify possible sources for this discrepancy, we evaluated the fundamental RD simulation assumptions that cells are point-like structures that can overlap. To give cells physical size (~10 ?m), we used a Brownian dynamics approach that simulates individual single-cell diffusive migration, growth, and proliferation activity via a gridless, off-lattice, AB method where cells can be prohibited from overlapping each other. We found that for realistic single-cell parameter growth and migration rates, a non-overlapping model gives rise to a jammed configuration in the center of the tumor and a biased outward diffusion of cells in the tumor periphery, creating a quasi-ballistic advancing tumor front. The simulations demonstrate that a fast-progressing tumor can result from minimally diffusive cells, but at a rate that is still dependent on single-cell diffusive migration rates. Thus, modeling with the assumption of physically-grounded volume conservation can account for the apparent discrepancy between estimated and measured diffusion of GBM cells and provide a new theoretical framework that naturally links single-cell growth and migration dynamics to tumor-level progression.

SUBMITTER: Klank RL

PROVIDER: S-EPMC6322960 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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A Brownian dynamics tumor progression simulator with application to glioblastoma.

Klank Rebecca L RL Rosenfeld Steven S SS Odde David J DJ

Convergent science physical oncology 20180103 1

Tumor progression modeling offers the potential to predict tumor-spreading behavior to improve prognostic accuracy and guide therapy development. Common simulation methods include continuous reaction-diffusion (RD) approaches that capture mean spatio-temporal tumor spreading behavior and discrete agent-based (AB) approaches which capture individual cell events such as proliferation or migration. The brain cancer glioblastoma (GBM) is especially appropriate for such proliferation-migration modeli ...[more]

PMID: 30627438

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Project description:Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. Here, we applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3D to identify three principal physical parameters that govern cell migration: motor number - describing myosin II activity, clutch number - describing adhesion level, and F-actin polymerization rate. Experimentally, we found that glioblastoma patient-derived (xenograft) (PD(X)) cell lines across mesenchymal (MES), proneural (PN), classical (CL) subtypes and two institutions (N=13 patients) had optimal motility and traction force on stiffnesses around 9.3kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. By contrast, with the CMS parameterization, we found glioblastoma cells consistently had balanced motor/clutch ratios to enable effective migration, and that MES cells had higher actin polymerization rates resulting in higher motility. The CMS also predicted differential sensitivity to cytoskeletal drugs between patients. Finally, we identified 11 genes that correlated with the physical parameters, suggesting that transcriptomic data alone could potentially predict the mechanics and speed of glioblastoma cell migration. Overall, we describe a general physics-based framework for parameterizing individual glioblastoma patients and connecting to clinical transcriptomic data, that can potentially be used to develop patient-specific anti-migratory therapeutic strategies generally.Significance statementSuccessful precision medicine requires biomarkers to define patient states and identify personalized treatments. While biomarkers are generally based on expression levels of protein and/or RNA, we ultimately seek to alter fundamental cell behaviors such as cell migration, which drives tumor invasion and metastasis. Our study defines a new approach for using biophysics-based models to define mechanical biomarkers that can be used to identify patient-specific anti-migratory therapeutic strategies.

Dataset Information

A Brownian dynamics tumor progression simulator with application to glioblastoma.

Publications

A Brownian dynamics tumor progression simulator with application to glioblastoma.

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