Biology subjects

Perez-Garcia, V. M.

Publications and source records attributed to Perez-Garcia, V. M..

7 recordsLinked to original sources

bioRxiv2025

Radiopharmaceutical therapy for metastatic prostate cancer: Insights from mechanistic modeling and in silico trials

Radiopharmaceutical therapy (RPT) has rapidly evolved into a key precision-oncology modality, with radioligands now approved or in late-stage development for multiple solid tumors, including neuroendocrine and prostate cancers. RPT with [177Lu]Lu-PSMA (Prostate-Specific Membrane Antigen) has recently been approved as a life-prolonging treatment for metastatic castration-resistant prostate cancer (mCRPC), but its clinical use still relies on non-personalized, empirically chosen fixed schedules. Here, we develop a mechanistic, patient-personalizable mathematical model simulating mCRPC response and organ-at-risk toxicity during [177Lu]Lu-PSMA RPT. The model integrates tumor growth dynamics, radiobiological response, and organ-resolved pharmacokinetics inferred from mass data and standardized uptake values obtained from positron emission tomography studies. Parameters were derived from the literature, although the framework allows personalization by fitting to patient-specific data such as imaging and prostate-specific antigen levels. Using virtual patient (VP) cohorts generated via stochastic parameter sampling, we conducted in silico trials and validated the model by comparing simulated outcomes with published dosimetry and survival data for [177Lu]Lu-PSMA trials. We then explored dosing and scheduling strategies to optimize efficacy-toxicity trade-offs. Consolidated regimens with fewer, higher-activity injections improved overall survival (OS) in silico but increased toxicity, especially in kidneys. Cycle length had a weaker influence on OS within a 2-9 week window, while it clearly affected toxicity, whereas excessive delays (> 12 weeks) markedly reduced efficacy. Global sensitivity analysis identified tumor growth, uptake, and radiosensitivity parameters as key drivers of interpatient variability, and convergence testing confirmed robustness with respect to VP cohort size. These methodological findings illustrate how mechanistic modeling and in silico trials can inform the design and personalization of RPT regimens. Author summaryStandard radiopharmaceutical therapy regimens for metastatic castration-resistant prostate cancer deliver the same doses at fixed intervals, without accounting for interpatient variability in tumor growth, drug uptake, or organ tolerance. Here, we focus on [177Lu]Lu-PSMA radiopharmaceutical therapy, which is now approved for these patients but still administered using one-size-fits-all protocols. We present a mechanistic mathematical model that simulates tumor and radiopharmaceutical dynamics in individual patients using a virtual patient framework. With appropriate patient-specific data, such as quantitative imaging and prostate-specific antigen levels, this model can be used to generate digital twins and evaluate personalized treatment strategies. By adjusting injection schedules and cycle timing in silico, we explored how standard treatment protocols could be optimized to improve survival while maintaining acceptable toxicity. We found that a 9 week treatment cycle achieved survival outcomes comparable to the standard 6 week protocol, with a significant reduction in toxicity, whereas longer cycle extensions led to loss of therapeutic efficacy. These results provide a quantitative basis for optimizing radiopharmaceutical therapy and highlight the potential of virtual patients and in silico trials to support patient-adapted treatments in modern oncology.

cancer biology↗

bioRxiv2023

Radiation necrosis after radiation therapy treatment of brain metastases: A computational approach

Metastasis is the process through which cancer cells break away from a primary tumor, travel through the blood or lymph system, and form new tumors in distant tissues. One of the preferred sites for metastatic dissemination is the brain, affecting more than 20% of all cancer patients. This figure is increasing steadily due to improvements in treatments of primary tumors. Stereotactic radiosurgery (SRS) is one of the main treatment options for patients with a small or moderate number of brain metastases (BMs). A frequent adverse event of SRS is radiation necrosis (RN), an inflammatory condition caused by late normal tissue cell death. A major diagnostic problem is that RNs are difficult to distinguish from BM recurrences, due to their similarities on standard magnetic resonance images (MRIs). However, this distinction is key to choosing the best therapeutic approach since RNs resolve often without further interventions, while relapsing BMs may require open brain surgery. Recent research has shown that RNs have a faster growth dynamics than recurrent BMs, providing a way to differentiate the two entities, but no mechanistic explanation has been provided for those observations. In this study, computational frameworks were developed based on mathematical models of increasing complexity, providing mechanistic explanations for the differential growth dynamics of BMs relapse versus RN events and explaining the observed clinical phenomenology. Simulated tumor relapses were found to have growth exponents substantially smaller than the group in which there was inflammation due to damage induced by SRS to normal brain tissue adjacent to the BMs, thus leading to RN. ROC curves with the synthetic data had an optimal threshold that maximized the sensitivity and specificity values for a growth exponent {beta}* = 1.05, very close to that observed in patient datasets. Author summaryAfter treatment of brain metastases with radiation therapy, a fraction of patients experience tumor recurrences and others display radiation necrosis (RN). Clinical data shows that the growth of RNs is faster, as measured by the growth exponent, than that of recurrent brain metastases. This reflects the inflammatory nature of the former, and provides a method to distinguish RN in the clinics from relapsing metastatic lesions. A simple mathematical model for the inflammatory response and a more sophisticate discrete stochastic simulator with many biological details were been developed to provide a mechanistic explanation of the differential dynamics of tumor growth versus inflammatory responses after stereotactic radiation surgery of metastatic brain lesions.

systems biology↗

bioRxiv2023

Breaking Down Cell-Free DNA Fragmentation: A Markov Model Approach

Cell-free DNA (cfDNA) is released into the bloodstream from cells in various physiological and pathological conditions. The use of cfDNA as a non-invasive biomarker has attracted attention, but the dynamics of cfDNA fragmentation in the bloodstream are not well understood and have not been previously studied computationally. To address this issue, we present a Markovian model called FRIME (Fragmentation, Immigration, and Exit) that captures three leading mechanisms governing cfDNA fragmentation in the bloodstream. The FRIME model enables the simulation of cfDNA fragment profiles by sampling from the stationary distribution of FRIME processes. By varying the parameters of our model, we generate fragment profiles similar to those observed in liquid biopsies and provide insight into the underlying biological mechanisms driving the fragmentation dynamics. To validate our model, we compare simulated FRIME profiles with mitochondrial and genomic cfDNA fragment profiles. Our simulation results are consistent with experimental and clinical observations and highlight potential physicochemical differences between mitochondrial and genomic cfDNA. The FRIME simulation framework provides an initial step towards an improved computational understanding of DNA fragmentation dynamics in the bloodstream and may aid in the analysis of liquid biopsy data. Author summaryCell-free DNA (cfDNA) released into the bloodstream from cells in different conditions is the basis for liquid biopsies, thus being non-invasive biomarkers of potential interest. However, the dynamics of cfDNA fragmentation in the bloodstream remain poorly understood and are yet to be studied computationally. To address this issue, we developed a Markovian model which captures the three leading mechanisms governing cfDNA fragmentation in the bloodstream: FRagmentation, IMmigration, and Exit (FRIME). The FRIME model enables the simulation of cfDNA fragment profiles by sampling from the stationary distribution of FRIME processes. Simulation results are compared with mitochondrial and genomic cfDNA fragment profiles, which show consistency with experimental and clinical observations. The FRIME simulation framework is a significant step towards understanding DNA fragmentation dynamics in the bloodstream, uncovering potential physicochemical differences between mitochondrial and genomic cfDNA, and aiding the analysis of liquid biopsy data.

bioinformatics↗

bioRxiv2022

On optimal temozolomide scheduling for slowly growing gliomas

BackgroundTemozolomide (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, and is commonly used in low-grade gliomas. In-silico mathematical models can potentially be used to personalize treatments and to accelerate the discovery of optimal drug delivery schemes. MethodsAgent-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 or EGFR vIII in primary progenitors from p16/p19 ko mice and grown in vitro and in vivo in orthotopic allografts, and human glioblastoma 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. ResultsSlow growth virtual murine gliomas 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 glioma growth speed. In-silico trials provided support for methods of implementation in human patients. ConclusionsIn-silico simulations, and 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 gliomas. IMPORTANCE OF THE STUDYIn-vivo evidence is provided of improvements in survival, resistance, and toxicity from TMZ schemes with long rest periods between doses in slowly-growing GBM mouse models. The results match hypotheses generated in silico using a mathematical model incorporating the main biological features and fed with real patient data. An epigenetically-driven change in tumor phenotype was also revealed experimentally, which could explain the reduction in glioma growth speed under the long cycle scheme. To determine the extent to which our results hold for human patients, large sets of simulations were performed on virtual patients. These in-silico trials suggest different ways to bring the benefits observed in experimental models into clinical practice.

cancer biology↗

bioRxiv2021

Stochastic fluctuations drive non-genetic evolution of proliferation in clonal cancer cell populations

Evolutionary dynamics allows us to understand many changes happening in a broad variety of biological systems, ranging from individuals to complete ecosystems. It is also behind a number of remarkable organizational changes that happen during the natural history of cancers. These reflect tumour heterogeneity, which is present at all cellular levels, including the genome, proteome and phenome, shaping its development and interrelation with its environment. An intriguing observation in different cohorts of oncological patients is that tumours exhibit an increased proliferation as the disease progresses, while the timescales involved are apparently too short for the fixation of sufficient driver mutations to promote an explosive growth. Here we discuss how phenotypic plasticity, emerging from a single genotype, may play a key role and provide a ground for a continuous acceleration of the proliferation rate of clonal populations with time. We address this question by combining the analysis of real time growth of non-small-cell lung carcinoma cells (line NCI-H460) together with stochastic and deterministic mathematical models that capture proliferation trait heterogeneity in clonal populations to elucidate the contribution of phenotypic transitions on tumour growth dynamics.

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↗