The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy

Proceedings of the Royal Society B Biological Sciences, Год журнала: 2021, Номер 288(1947)

Опубликована: Март 24, 2021

Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR cells, after lymphodepletion (LD) by chemotherapy. The quantitative laws underlying this and subsequent tumour eradication remain unknown. We develop mathematical model cell–tumour interactions demonstrate can be explained immune reconstitution dynamics LD competition among cells. cells rapidly grow engage but experience an emerging growth rate disadvantage compared to normal Since deterministically unstable our model, we define cure as stochastic event, which, even when likely, occur at variable times. However, show variability timing largely determined patient variability. While events impacted these fluctuations early are narrowly distributed, progression late more widely distributed time. parameterized using population-level data over time compare predictions with progression-free survival rates. find could improved optimizing the tumour-killing cells' ability adapt, quantified their carrying capacity. Our extinction leveraged examine why works some patients not others, better understand interplay deterministic effects outcomes. For example, implies before second injection necessary.

Язык: Английский

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Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Cancers, Год журнала: 2021, Номер 13(12), С. 3008 - 3008

Опубликована: Июнь 16, 2021

Tumor-associated vasculature is responsible for the delivery of nutrients, removal waste, and allowing growth beyond 2–3 mm3. Additionally, vascular network, which changing in both space time, fundamentally influences tumor response to systemic radiation therapy. Thus, a robust understanding dynamics necessary accurately predict growth, as well establish optimal treatment protocols achieve control. Such goal requires intimate integration theory experiment. Quantitative time-resolved imaging methods have emerged technologies able visualize characterize properties before during therapy at tissue cell scale. Parallel to, but separate from those developments, mathematical modeling techniques been developed enable silico investigations into theoretical dynamics. In particular, recent efforts sought integrate experiment data-driven modeling. models are calibrated by data obtained individual tumor-vascular systems future agents, radiotherapy. this review, we discuss experimental visualizing quantifying including magnetic resonance imaging, microfluidic devices, confocal microscopy. We then focus on these measures with biologically based generate testable predictions.

Язык: Английский

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Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system

Neoplasia, Год журнала: 2022, Номер 28, С. 100796 - 100796

Опубликована: Апрель 19, 2022

Radiotherapy is a primary therapeutic modality widely utilized with curative intent. Traditionally tumor response was hypothesized to be due high levels of cell death induced by irreparable DNA damage. However, the immunomodulatory aspect radiation now accepted. As such, interest into combination radiotherapy and immunotherapy increasing, synergy which has potential improve regression beyond that observed after either treatment alone. questions regarding timing (sequential vs concurrent) dose fractionation (hyper-, standard-, or hypo-fractionation) result in improved anti-tumor immune responses, thus potentially enhanced inhibition, remain. Here we discuss biological its properties before giving an overview pre-clinical data clinical trials concerned answering these questions. Finally, review published mathematical models impact on tumor-immune interactions. Ranging from considering microenvironment induction choice site setting metastatic disease, all have underlying feature common: push towards personalized therapy.

Язык: Английский

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Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Advanced Drug Delivery Reviews, Год журнала: 2022, Номер 187, С. 114367 - 114367

Опубликована: Май 30, 2022

Язык: Английский

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An Overview of Mathematical Modelling in Cancer Research: Fractional Calculus as Modelling Tool

Fractal and Fractional, Год журнала: 2023, Номер 7(8), С. 595 - 595

Опубликована: Авг. 1, 2023

Cancer is a complex disease, responsible for significant portion of global deaths. The increasing prioritisation know-why over know-how approaches in biological research has favoured the rising use both white- and black-box mathematical techniques cancer modelling, seeking to better grasp multi-scale mechanistic workings its phenomena (such as tumour-immune interactions, drug resistance, tumour growth diffusion, etc.). In light this wide-ranging mathematics unique memory non-local properties Fractional Calculus (FC) have been sought after last decade replace ordinary differentiation hypothesising FC’s superior modelling oncological phenomena, which shown possess an accumulated knowledge past states. As such, review aims present thorough structured survey about main guiding trends categories research, emphasising field oncology employment whole. most pivotal questions, challenges future perspectives are also outlined.

Язык: Английский

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Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling

Bulletin of Mathematical Biology, Год журнала: 2024, Номер 86(2)

Опубликована: Янв. 18, 2024

Abstract Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed predict treatment outcome in this context, have potential guide clinical decision-making inform personalised protocols. Hindering effective use context is sparsity measurements juxtaposed with model complexity required produce full range possible patient responses. In work, we present a compartment composition, which, despite relative simplicity, capable producing wide We then develop novel statistical methodology leverage cohort existing predictive both progression associated level uncertainty evolves throughout patient’s course treatment. To capture inter-patient variability, all parameters specific, bootstrap particle filter-like Bayesian approach developed set training as prior knowledge. validate our against subset unseen data, demonstrate ability trained its limitations.

Язык: Английский

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Validation of polymorphic Gompertzian model of cancer through in vitro and in vivo data

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0310844 - e0310844

Опубликована: Янв. 9, 2025

Mathematical modeling plays an important role in our understanding and targeting therapy resistance mechanisms cancer. The polymorphic Gompertzian model, analyzed theoretically numerically by Viossat Noble to demonstrate the benefits of adaptive metastatic cancer, describes a heterogeneous cancer population consisting therapy-sensitive therapy-resistant cells. In this study, we that model successfully captures trends both vitro vivo data on non-small cell lung (NSCLC) dynamics under treatment. Additionally, for tumor patients undergoing treatment, compare goodness fit classical oncologic models, which were previously identified as models best. We show can capture U-shape trend size during relapse, not be fitted with models. general, corresponds well real-world data, suggesting it candidate improving efficacy therapy, example, through evolutionary/adaptive therapies.

Язык: Английский

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Proceedings of the National Cancer Institute Workshop on combining immunotherapy with radiotherapy: challenges and opportunities for clinical translation

The Lancet Oncology, Год журнала: 2025, Номер 26(3), С. e152 - e170

Опубликована: Март 1, 2025

Язык: Английский

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Modeling CAR T-Cell Therapy with Patient Preconditioning

Bulletin of Mathematical Biology, Год журнала: 2021, Номер 83(5)

Опубликована: Март 19, 2021

Язык: Английский

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Predicting patient-specific response to adaptive therapy in metastatic castration-resistant prostate cancer using prostate-specific antigen dynamics

Neoplasia, Год журнала: 2021, Номер 23(9), С. 851 - 858

Опубликована: Июль 20, 2021

Abiraterone acetate (AA) has been proven effective for metastatic castration-resistant prostate cancer (mCRPC), and it proposed that adaptive AA may reduce toxicity prolong time to progression, when compared continuous AA. We developed a simple quantitative model of prostate-specific antigen (PSA) dynamics evaluate (PCa) stem cell enrichment as plausible driver treatment resistance. The incorporated PCa cells, non-stem cells PSA during therapy. A leave-one-out analysis was used calibrate validate the against longitudinal data from 16 mCRPC patients receiving in pilot clinical study. Early response were predict patient subsequent treatment. extended incorporate burden also investigated survival benefit adding concurrent chemotherapy predicted become resistant. Model simulations demonstrated self-renewal resistance Evolutionary individual cycles combined with measurements 81% accuracy (specificity=92%, sensitivity=50%). In those progress, addition suggest between 1% 11% reduction probability progression alone. This study first patient-specific mathematical use early responses AA, demonstrating putative value integrating modeling into decision making.

Язык: Английский

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