Biology subjects

Fassoni, A. C.

Publications and source records attributed to Fassoni, A. C..

6 recordsLinked to original sources

bioRxiv2026

Leukemic fitness predicts treatment-free remission and guides adaptive therapy in chronic myeloid leukemia

Chronic myeloid leukemia is controlled by tyrosine kinase inhibitors, yet only about half of well-responding patients who stop therapy remain in long-term remission, and currently it cannot be predicted who will relapse. Mechanistic models reproduce both outcomes but rely on many parameters and longitudinal immune measurements, limiting clinical use. Here we derive a minimal two-equation model in which the immune compartment is eliminated by a quasi-steady-state argument and retained implicitly as a tumor load-dependent kill rate. This reduction lowers the parameter count from fourteen to eight, requires no direct immune measurements, and preserves the two key features of immune-leukemia interaction: a switch-like per-capita kill rate and a bell-shaped immune window peaking at deep remission. A complete analytical treatment shows that a patient's long-term outcome depends on a single quantity, the leukemic fitness, defined as the ratio of leukemic proliferation to maximal immune-mediated killing. Values above one render the cure steady state unstable, whereas values below one make it stable and therefore attainable; the rate-limiting step is depletion of the quiescent stem cell reservoir below a separatrix, reproducing the clinical link between treatment duration and remission level with durable remission after cessation. Fitted to 75 patients of the DESTINY trial, the minimal model matches the fit quality of its more detailed predecessors. Leukemic fitness estimated from time courses up to treatment cessation, including the dose-reduction period, is associated with outcome, with discrimination improving further when six months of post-cessation data are added. Guided by this single parameter, we design two personalized strategies: extended full-dose therapy for low-fitness patients who have not yet crossed the separatrix, and maintenance dosing for high-fitness patients. Leukemic fitness is thus a clinical predictor that can be estimated from routine monitoring data, and a minimally parametrized model is enough to turn patient trajectories into personalized treatment decisions.

systems biology↗

bioRxiv2024

Predicting treatment-free remission outcomes in Chronic Myeloid Leukemia patients using an integrated model of tumor-immune dynamics

The interactions between tumor and the immune system are main factors in determining cancer treatment outcomes. In Chronic Myeloid Leukemia (CML), considerable evidence shows that the dynamics between residual leukemia and the patients immune system can result in either sustained disease control, leading to treatment-free remission (TFR), or disease recurrence. The question remains how to integrate mechanistic and data-driven models to support prediction of treatment outcomes. Starting from classical ecological modeling concepts, which allow to explicitly account for immune interactions at the cellular level, we incorporate time-course data on natural killer (NK) cell number, function, and their tumor-induced suppression into our general model of CML treatment. We identify relevant time scales governing treatment and immune response, enabling refined model calibration using tumor and NK cell time courses from different datasets. While the model successfully describes patient-specific response dynamics, critical parameters for predicting treatment outcome remain uncertain. However, by explicitly incorporating tumor load changes in response to TKI dose alterations, these parameters can be estimated and used to derive model predictions for treatment cessation. Further exploring dynamic changes in the number of functional immune cells, we suggest specific measurement strategies of immune effector cell populations to enhance prediction accuracy for CML recurrence following treatment cessation. The generalizability and flexibility of our approach represent a significant step towards quantitative, personalized medicine that integrates tumor-immune dynamics to guide clinical decisions and optimize dynamic cancer therapies.

systems biology↗

bioRxiv2024

On the design and stability of cancer adaptive therapy cycles: deterministic and stochastic models

Adaptive therapy is a promising paradigm for treating cancers, that exploits competitive interactions between drug-sensitive and drug-resistant cells, thereby avoiding or delaying treatment failure due to evolution of drug resistance within the tumor. Previous studies have shown the mathematical possibility of building cyclic schemes of drug administration which restore tumor composition to its exact initial value in deterministic models. However, algorithms for cycle design, the conditions on which such algorithms are certain to work, as well as conditions for cycle stability remain elusive. Here, we state biologically motivated hypotheses that guarantee existence of such cycles in two deterministic classes of mathematical models already considered in the literature: Lotka-Volterra and adjusted replicator dynamics. We stress that not only existence of cyclic schemes, but also stability of such cycles is a relevant feature for applications in real clinical scenarios. We also analyze stochastic versions of the above deterministic models, a necessary step if we want to take into account that real tumors are composed by a finite population of cells subject to randomness, a relevant feature in the context of low tumor burden. We argue that the stability of the deterministic cycles is also relevant for the stochastic version of the models. In fact, Dua, Ma and Newton [Cancers (2021)] and Park and Newton [Phys. Rev. E (2023)] observed breakdown of deterministic cycles in a stochastic model (Moran process) for a tumor. Our findings indicate that the breakdown phenomenon is not due to stochasticity itself, but to the deterministic instability inherent in the cycles of the referenced papers. We then illustrate how stable deterministic cycles avoid for very large times the breakdown of cyclic treatments in stochastic tumor models.

cancer biology↗

bioRxiv2024

Mathematical modeling unveils the timeline of CAR-T cell therapy and macrophage-mediated cytokine release syndrome

Chimeric antigen receptor (CAR)-T cell therapy holds significant potential for cancer treatment, although disease relapse and cytokine release syndrome (CRS) remain as frequent clinical challenges. To better understand the mechanisms underlying the temporal dynamics of CAR-T cell therapy response and CRS, we developed a novel multi-layer mathematical model incorporating antigen-mediated CAR-T cell expansion, antigen-negative resistance, and macrophage-associated cytokine release. Three key mechanisms of macrophage activation are considered: release of damage-associated molecular patterns, antigen-binding mediated activation, and CD40-CD40L contact. The model accurately describes 25 patient time courses with different responses and IL-6 cytokine kinetics. We successfully link the dynamic shape of the response to interpretable model parameters and investigate the influence of CAR-T cell dose and initial tumor burden on the occurrence of CRS and treatment outcome. By disentangling the timeline of macrophage activation, the model identified distinct contributions of each activation mechanism, suggesting the CD40-CD40L axis as a major driver of CRS and a clinically feasible target to control the activation process and modulate cytokine peak height. Our multi-layer model provides a comprehensive framework for understanding the complex interactions between CAR-T cells, tumor cells, and macrophages during therapy.

systems biology↗

bioRxiv2023

Mechanisms of Resistance to CAR-T cell Immunotherapy: Insights from a Mathematical Model

Chimeric Antigen Receptor (CAR)-T cell therapy long-term follow-up studies revealed non-durable remissions in a significant number of patients. Some of the mechanisms underlying these relapses include poor CAR T cell cytotoxicity or persistence, as well as antigen loss or lineage switching in tumor cells. In order to investigate how antigen-mediated resistance mechanisms affect therapy outcomes, we develop a mathematical model based on a set of integral-partial differential equations. Using a continuous variable to describe the level of antigen expression of tumor cells, we recapitulated important cellular mechanisms across patients with different therapeutic responses. Fitted with clinical data, the model successfully captured the dynamics of tumor and CAR-T cells for several hematological cancers. Furthermore, the role played by these mechanisms are explored with regard to different biological scenarios, such as pre-existing or acquired mutations, providing a deeper understanding of key factors underlying resistance to CAR-T cell immunotherapy. Statement of significanceOur study introduces the first mathematical model to characterize the influence of a continuous level of antigen expression on the interplay between Chimeric Antigen Receptor (CAR)-T cells and cancer cells. We examine various cellular mechanisms across different hematologic cancers, taking into account both antigen-positive and antigen-negative relapses. Our findings shed light on the role of antigen density in CAR-T cell therapies and provide a valuable framework to investigate resistance with potential to improve patients outcomes.

cancer biology↗

bioRxiv2019

Inferring immunological control mechanisms from TKI dose alterations in CML patients

Recent clinical findings in chronic myeloid leukemia (CML) patients suggest that the risk of molecular recurrence after stopping tyrosine kinase inhibitors (TKI) treatment substantially depend on an individual, leukemia-specific immune response. However, it is still not possible to prospectively identify patients that will most likely remain in a long-term treatment free remission (TFR). Here, we use a mathematical model for CML, which explicitly includes an anti-leukemic (presumably immunological) effect and apply it to a set of patients (n=60) for whom BCR-ABL/ABL time courses had been quantified before and after TKI stop. We demonstrate that such a feedback control is conceptually necessary to explain long-term remission as observed in about half of the patients. Based on simulation results we classify the patient data sets into three different groups according to their predicted immune system configuration. While one class of patients requires a complete CML eradication to achieve TFR, other patients are able to control the leukemia after treatment cessation. Among them, we identified a third class of patients, which only maintains TFR if an optimal balance between leukemia abundance and immunological activation is achieved before treatment cessation. Further, we demonstrate that the immune response classification of the patients cannot be obtained solely from BCR-ABL measurements before treatment cessation. However, our results strongly suggest that changes in the BCR-ABL dynamics arising after system perturbations, such as TKI dose reduction, holds the information to predict the individual outcome after treatment cessation.

systems biology↗