Biology subjects

Belmonte-Beitia, J.

Publications and source records attributed to Belmonte-Beitia, J..

4 recordsLinked to original sources

bioRxiv2025

EGFR-Driven Phenotypes Dictate Differential Therapeutic Response to Radiotherapy and Temozolomide in Glioblastoma.

Despite the established Stupp regimen, glioblastoma (GBM) remains a highly lethal cancer with a 5-year survival rate below 10%. The epidermal growth factor receptor (EGFR) is frequently amplified or mutated in GBM, and we have previously shown that different EGFR statuses correlate with distinct tumor phenotypes and responses to temozolomide (TMZ). In this study, we investigated the differential response of two mouse GBM models, one overexpressing EGFRwt (EGFRwt/amp) and the other with overexpressing the EGFRvIII variant, to radiotherapy (RT) alone and in combination with TMZ. While both tumor models were sensitive to RT in vitro, in vivo experiments showed no significant survival benefit from RT for mice carrying EGFRwt/amp GBM, regardless of the RT schedule. Moreover, for these tumors, different combinations of RT with TMZ were not significantly better than spaced TMZ treatment alone. In contrast, EGFRvIII tumors responded well to RT alone, and spacing out the RT doses offered no additional benefit. Although a Stupp-like protocol provided a small benefit compared to RT alone in this model, the combinatorial treatment also induced the expression of several resistance markers, such as MGMT or NF-{kappa}b phosphorylation. Our retrospective analysis of patient data supports these findings, suggesting that RT alone may not improve survival for patients with EGFRwt/amp GBM, whereas for GBM with EGFRvIII mutations, adding RT or TMZ and RT, does provide a clear survival benefit to surgery or to surgery and RT, respectively. These results suggest that EGFR status could serve as a crucial biomarker to predict tumor response and guide personalized treatment decisions, particularly in cases where minimizing toxicity is a priority.

cancer biology↗

bioRxiv2025

Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies

Recent biological research has highlighted the relevance of myeloid-cell populations in glioma growth, with a particular role played by tumor-associated macrophages (TAMs), which comprise resident microglia and monocyte-derived macrophages. Additionally, radiation therapy, the most common treatment for gliomas, significantly alters the tumor microenvironment, affecting TAMs and contributing to tumor recurrence. Promising preclinical studies have identified and developed drugs targeting TAMs. The development and combined deployment of these therapies require in silico techniques that enable us to optimize their outcomes. To do so, we need mathematical models of glioma growth and therapy response that explicitly incorporate TAMs--an often overlooked component in existing models. Here, we present a dynamical model of glioma growth driven by tumor-immune interactions. The model was parametrized using published data from mice experiments, including responses to ionizing radiation. We used this model to investigate glioma progression under radiotherapy combined with three treatments targeting distinct aspects of TAM biology. Simulations revealed that anti-CD47 enhanced the otherwise weak phagocytic activity, extending the upper tail of the survival curve. -CD49d, which limits monocyte trafficking after irradiation, offered consistent survival benefits across digital twins of mice. Finally, CSF-1R inhibitors, which block the primary growth factor regulating TAM function, resulted in the largest overall survival improvement in silico. Our results aligned well with experimental evidence, suggesting that the model may help inform the optimization of myeloid cell-targeted immunotherapies, including their timing, dosage, and combination with radiation therapy, with potential relevance for improving glioma treatment strategies.

cancer biology↗

bioRxiv2020

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

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. Author summaryComputer simulation based on mathematical models provides a way to improve the understanding of complex processes in oncology. In this paper we develop a stochastic mesoscopic simulation approach that incorporates key cellular processes while keeping computational costs to a minimum. Our methodology captures the development of tumor heterogeneity and the underlying evolutionary dynamics. The physical scale considered allows microscopic information to be included, tracking the spatio-temporal evolution of tumor heterogeneity and reconstructing clinically sized tumors from high-resolution medical imaging data, with a low computational cost. We illustrate the functionality of the modeling approach for the case of glioblastoma, an epitome of heterogeneity in tumors.

systems biology↗

bioRxiv2019

Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas

The use of mathematical models for personalization of cancer therapies and raising hypothesis of potential clinical impact is an emerging topic in the interface between mathematics and oncology. Here we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model described the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutical strategies were tried computationally on the in-silico twins of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large virtual clinical trial including 2000 patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances.\n\nOn the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. This scheme could be useful for defining a standardized TMZ treatment for LGO patients with survival benefits.\n\nAuthor summaryA mathematical model described the longitudinal volumetric growth data of grade II oligodendrogliomas patients and their response to temozolomide. The model was used to explore alternative therapeutical protocols for the in-silico twins of the patients and in virtual clinical trials. The simulations show that enlarging the time interval between chemotherapy cycles would maintain the therapeutical efficacy, while limiting toxicity and defering the development of resistances. This may allow for improved drug-exposure by administering a larger number of cycles for longer treatment periods. A scheme based on this idea consisting of an induction phase (5 consecutive cycles, 1 per month) and a maintenance phase (12 cycles given in three-months intervals) led to substantial survival benefits in-silico.

cancer biology↗