MS07 - ONCO-09

Mathematical Modeling of the Tumor-Immune Microenvironment to Advance Immunotherapeutic Strategies

Thursday, July 17 at 3:50pm

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Tyler Simmons (Therapy Modeling and Design Center, University of Minnesota), John Metzcar and Sarah Anderson: Therapy Modeling and Design Center, University of Minnesota

Description:

While immunotherapies have been successful in select cancers, they still fail in many diseases. These failures are often attributed to the tumor-immune microenvironment (TIME). In cell-based therapies, the TIME may limit immune cell penetration, suppress those that infiltrate, drive T cell exhaustion and more. The TIME may also influence the effectiveness of various biologics used in non-cell-based therapies. Overcoming these hurdles is critical to the advancement of immunotherapies, both for optimizing existing treatments and developing new therapies. To address the complexities of the TIME and its role in immunotherapy, mathematical modeling offers a systems-based approach to elucidate the processes behind limited treatment efficacy and help explain clinical and experimental data. Modeling also provides an opportunity to investigate potential immunotherapies. Through system perturbations, we can better understand the mechanisms of action and suggest optimal treatment regimens. Ultimately, the construction, simulation, and analysis of TIME-based mathematical models can help guide therapy development and even predict treatment responses. This minisymposium brings together researchers who leverage mathematical principles and computational approaches to navigate the complex interactions within the TIME and improve immunotherapeutic strategies. We focus on translational research, where models are utilized to investigate therapeutic approaches by addressing the TIME.

Gabriel Côté

Sainte-Justine University Hospital Azrieli Research Centre / Université de Montréal

"The role of chronobiology on immunotherapies to prevent neutrophil infiltration into the tumour microenvironment in lung cancer"

BACKGROUND: Oscillations, particularly circadian rhythms, are ubiquitous in physiology. A sound understanding of these phenomena may have important implications for the administration of treatments targeting oscillatory behavior. For example, neutrophils, the most abundant immune cells, display circadian oscillating properties under the control of CXCR2 and CXCR4 receptors. In lung cancer, neutrophil infiltration in the tumour promotes metastases. CXCR2 inhibitors were suggested to reduce such events. However, experiments in mice showed that their administration must align with circadian rhythms; when timed improperly these inhibitors were found to have little to no effect. Even worse, improper timing could result in the dangerous depletion of neutrophils, leading to worst outcomes. Thus, there is a need for rationalizing CXCR2 inhibitor treatment schedules. METHODS: We developed a mathematical model of neutrophil dynamics, incorporating CXCR2 regulation, and added a PK/PD model of AZD5069, a CXCR2 inhibitor. We adjusted our parameters to murine data and performed global sensitivity analyses to determine main regulation mechanisms. We then simulated therapeutic responses in virtual cohorts to optimize treatment regimens. RESULTS: Our results highlight key circadian mechanisms regulating circulating neutrophil counts. Further, our virtual clinical trial predicted that neutrophil oscillations are determinant for establishing effective yet non-toxic CXCR2 inhibitor treatment schedules. IMPACT: This study underlines the importance of chronobiology to drug and immune responses. Our work may be extended to investigate immunity in shift workers, jet-lagged travelers, and individuals with circadian rhythm sleep disorders.

Jason T. George, MD, PhD

Texas A&M University

"Stochastic modeling of immunomodulation in the tumor-immune microenvironment"

The advent of T cell-based immunotherapy has ushered in a new age of cancer treatment. Cancer immunotherapy – despite durable efficacy in several disease contexts – is still limited in many disease subtypes, often resulting from unfavorable microenvironmental features and subsequent cancer immune-specific adaptation and ultimate evasion. Recent modeling and empirical directions have thus focused on enhancing immunotherapy’s existing anti-tumor effects and activating the immune system in cases that currently lack any therapeutic response. This talk will discuss our recent efforts at understanding cancer immune evasion and our model’s predicted role of the microenvironment on escape dynamics. I will first discuss our group’s development of a stochastic model of ‘variable evasion’ with implications for antigen targeting. Lastly, I will describe how immunomodulation of tumor-specific T cells can impact cancer escape dynamics, which we then use to study clinically observed cancer recurrence times in breast and bladder cancer.

Riley Manning

University of Minnesota

"Agent-based modeling of glioblastoma immunotherapy strategies"

Glioblastoma is an aggressive, highly infiltrative malignant brain tumor with minimal treatment options for patients. Integrated genomic analysis of patient tumors enabled the classification of three molecular subtypes of glioblastoma: proneural, classical, and mesenchymal. Despite distinct alterations in the expression of migration and immune activation-related genes these subtypes are all treated with the same standard of care clinically. Clinical trials investigating T cell based-immunotherapies have had limited success, with many patients quickly developing resistance to treatment. In this work, we use a three-dimensional agent based model of glioblastoma to model the progression of two subtypes: proneural and mesenchymal. Mesenchymal tumors have faster single cell migration speeds and increased infiltration of several immune cell types, including cytotoxic T cells. In contrast, proneural tumors have slower cell migration speeds and are immunologically cold. We model migration, proliferation, and T cell-cancer cell interactions at the single cell level. Cytotoxic T cells deliver sublethal hits to cancer cells, ultimately leading to cancer cell death as damage accumulates. We simulated anti-migratory and T cell-based immunotherapies to identify subtype-specific treatment strategies. We observed differential efficacy between the two tumor subtypes, highlighting the need to account for patient subtype in glioblastoma therapy development. Cytotoxic T cells struggled to eliminate diffuse tumors and escaping tumor cells at the periphery, even at high effector to target ratios. Simulated treatment efficacy was improved with the addition of cancer cell-targeting anti-migratory therapy. This research enhances our understanding of the mechanisms driving therapy failure in glioblastoma and provides a strategy for predicting effective future treatments.

Katherine Owens

Fred Hutchinson Cancer Center, Seattle, WA

"Spatiotemporal dynamics of tumor - CAR T-cell interaction following local administration in solid cancers"

The success of chimeric antigen receptor (CAR) T-cell therapy in treating hematologic malignancies has generated widespread interest in translating this technology to solid cancers. However, issues like tumor infiltration, the immunosuppressive tumor microenvironment, and tumor heterogeneity limit its efficacy in the solid tumor setting. Recent experimental and clinical studies propose local administration directly into the tumor or at the tumor site to increase CAR T-cell infiltration and improve treatment outcomes. Characteristics of the types of solid tumors that may be the most receptive to this treatment approach remain unclear. In this work, we develop a simplified spatiotemporal model for CAR T-cell treatment of solid tumors, and use numerical simulations to compare the effect of introducing CAR T cells via intratumoral injection versus intracavitary administration in diverse cancer types. We demonstrate that the model can reproduce tumor and CAR T-cell data from small imaging studies of local administration of CAR T cells in mouse models. Our results suggest that locally administered CAR T cells will be most successful against slowly proliferating, highly diffusive tumors. In our simulations, assuming equal detectable tumor diameters at the time of treatment, low average tumor cell density is a better predictor of treatment success than total tumor burden or volume doubling time. These findings affirm the clinical observation that CAR T cells will not perform equally across different types of solid tumors, and suggest that measuring tumor density may be helpful when considering the feasibility of CAR T-cell therapy and planning dosages for a particular patient. We additionally find that local delivery of CAR T cells can result in deep tumor responses, provided that the initial CAR T-cell dose does not contain a significant fraction of exhausted cells.

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Annual Meeting for the Society for Mathematical Biology, 2025.