A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Abstract

Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.

The Growth Laws of Brain Metastases

“…A stochastic mesoscale BM growth simulator was developed based on Ref. [20]. SRS and the main biological processes occurring in response to treatment were incorporated into the model as described in Supplementary Information Section 2.…”

“…This was done by adapting the mesoscopic model of Ref. [20] to two competing populations sharing space and resources as described in 'Methods'. The initially most abundant population proliferated and migrated at fixed rates, while the least abundant population had an advantage in both processes, and hence was assumed to be more aggressive, due to either mutational changes or irreversible phenotype changes, providing evolutionary benefits.…”

On optimal temozolomide scheduling for slowly growing gliomas

“…Human and murine parameters were estimated from previous studies (see Table 1) and our own datasets. Virtual human simulations were fed with real patient data 35,36 to generate realistic tumors in silico .…”

“…An on-lattice agent-based mesoscopic model 35 The methodology was first used to run simulations to explore the influence of the parameters on outcome, and to test the efficacy of the use of different dose spacings using murine parameters. Murine tumors were simulated without treatment (control), and treated with 3 TMZ doses, separated by 1, 4, 7 and 13 days.…”

Super-Linear Growth Reveals the Allee Effect in Tumors

“…On such a large scale, continuum models that use PDEs are preferred. These two approaches can be integrated to generate hybrid models that simulate larger cell numbers with more biological details [39,40]. With similar assumptions for population dynamics, IBMs and PDEs may not lead to the same results [41] and the connection between the two approaches is not necessarily straightforward.…”