Significance: Through the use of different in silico modeling approaches capturing tumor heterogeneity, we postulated that areas of high metabolic activity would shift toward the periphery as tumors become more aggressive. To confirm the hypothesis and provide clinical value for the finding, we collected ¹⁸ F-FDG PET images of breast cancers and non–small-cell lung cancers, where we measured the distance from the point of maximum activity to the tumor centroid, normalizing it by a surrogate of the volume. The metric, NHOC, showed higher prognostic value than other classical PET-based metabolic biomarkers used in oncology, evidencing that the shift of the hotspot of activity from the center of the tumor to its periphery correlates with a poor prognosis.

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.

Temozolomide (TMZ) is an oral alkylating agent active against gliomas with a favorable toxicity profile. It is part of the standard of care in the management of glioblastoma (GBM), and is commonly used in low-grade gliomas (LGG). In-silico mathematical models can potentially be used to personalize treatments and to accelerate the discovery of optimal drug delivery schemes. Agent-based mathematical models fed with either mouse or patient data were developed for the in-silico studies. The experimental test beds used to confirm the results were: mouse glioma models obtained by retroviral expression of EGFR-wt/EGFR-vIII in primary progenitors from p16/p19 ko mice and grown in-vitro and in-vivo in orthotopic allografts, and human GBM U251 cells immobilized in alginate microfibers. The patient data used to parametrize the model were obtained from the TCGA/TCIA databases and the TOG clinical study. Slow-growth “virtual” murine GBMs benefited from increasing TMZ dose separation in-silico. In line with the simulation results, improved survival, reduced toxicity, lower expression of resistance factors, and reduction of the tumor mesenchymal component were observed in experimental models subject to long-cycle treatment, particularly in slowly growing tumors. Tissue analysis after long-cycle TMZ treatments revealed epigenetically driven changes in tumor phenotype, which could explain the reduction in GBM growth speed. In-silico trials provided support for implementation methods in human patients. In-silico simulations, in-vitro and in-vivo studies show that TMZ administration schedules with increased time between doses may reduce toxicity, delay the appearance of resistances and lead to survival benefits mediated by changes in the tumor phenotype in slowly-growing GBMs.

We have developed a 3D biosphere model using patient-derived cells (PDCs) from glioblastoma (GBM), the major form of primary brain tumors in adult, plus cancer-activated fibroblasts (CAFs), obtained by culturing mesenchymal stem cells with GBM conditioned media. The effect of MSC/CAFs on the proliferation, cell-cell interactions, and response to treatment of PDCs was evaluated. Proliferation in the presence of CAFs was statistically lower but the spheroids formed within the 3D-biosphere were larger. A treatment for 5 days with Temozolomide (TMZ) and irradiation, the standard therapy for GBM, had a marked effect on cell number in monocultures compared to co-cultures and influenced cancer stem cells composition, similar to that observed in GBM patients. Mathematical analyses of spheroids growth and morphology confirm the similarity with GBM patients. We, thus, provide a simple and reproducible method to obtain 3D cultures from patient-derived biopsies and co-cultures with MSC with a near 100% success. This method provides the basis for relevant in vitro functional models for a better comprehension of the role of tumor microenvironment and, for precision and/or personalized medicine, potentially to predict the response to treatments for each GBM patient.

Neuroblastoma is a significant health concern in children, as it is one of the most common types of cancer among this age group and is associated with poor survival rates. Currently, there are no effective therapies that significantly improve outcomes for these patients. This study explores the efficacy of Celyvir - an advanced therapy comprising mesenchymal stem cells (MSCs) carrying the oncolytic virus ICOVIR 5 - against neuroblastoma, by means of an individual-based model. A probabilistic cellular automaton was developed to implement the dynamic interactions between neuroblastoma cells, T lymphocytes, and the therapeutic agent Celyvir. The model examines various sizes, shapes, and positions of the tumour within a lattice, along with different infection probabilities associated with the action of Celyvir and various treatment schedules. This analysis identifies the most influential infection probabilities according to the cellular automaton model, and demonstrates that different treatment regimens can effectively eradicate the tumour, in contrast to standard clinical approaches. Additionally, Kaplan-Meier curves have been generated to assess different treatment schedules under specific tumour scenarios, highlighting the importance of precise treatment scheduling to optimise therapeutic outcomes. This study provides insights into the potential of Celyvir in neuroblastoma treatment, emphasising the need to understand tumour dynamics and strategically implement treatment schemes to improve clinical outcomes.