MS07 - ONCO-05

Immune responses to cancer: from mathematics to clinics

Thursday, July 17 at 3:50pm

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Raluca EFTIMIE (University of Marie & Louis Pasteur, France), Dumitru TRUCU, University of Dundee, UK

Description:

Over the last few decades, immunotherapy approaches have transformed cancer treatment through their focus on the patients’ immune systems. However, since cancer cells develop mechanisms through which they evade immune surveillance, many of the immunotherapies used have shown limited clinical success. Intra-tumour and inter-tumour heterogeneity also has an impact on the efficacy of immunotherapies and on the overall tumour evolution. To improve the clinical success of different immunotherapies, further approaches have been developed, some of which involve combinations therapies: from immunotherapies combined with chemotherapies or with radiation therapies, to bacterial or viral immunotherapies combined with other immunomodulators such as cytokines, etc. Moreover, mathematical and computational approaches have started to be used in combination with such treatment approaches, to pave the way towards personalised treatment strategies: from shedding light on the mechanisms of interactions between different such therapies, or between the immunotherapies and the cancer microenvironment, to the use of individual tumour characteristics to optimise treatment protocols or to improve predictions related to the clinical evolution of cancers. The goal of this mini-symposium is to showcase some state-of-the-art mathematical and computational results in the area, to emphasise the importance of mathematical models in clinical decisions related to cancer immunotherapies.

1. Marom Yosef*, Svetlana Bunimovich

Ariel University

"Mathematical Models to Improve Bladder Cancer Therapies"

Bladder cancer (BC) represents a significant clinical challenge, affecting 549,000 new patients annually, with over 200,000 deaths per year. Despite initial surgical intervention, approximately 70%, of patients experience tumor recurrence, necessitating additional treatment. The current gold standard, Bacillus Calmette-Guérin (BCG) immunotherapy, demonstrates limited efficacy: only 50% of patients achieve complete response, while 80% experience adverse effects ranging from mild discomfort to severe complications requiring treatment discontinuation. In the first part of my talk, I show the mathematical models to improve BCG therapy and have explored various protocols, including six-week induction therapy and extended maintenance treatments. However, these modifications have shown limited success in improving patient outcomes. Recent models including combining BCG with interleukin-2 (IL-2) or Interferon (IFN) immunomodulator therapy have demonstrated promising results, potentially enabling reduced BCG dosages while maintaining therapeutic efficacy. However, the unpredictable nature of immune responses to this combined treatment has hindered its widespread clinical adoption. I present a significant advance in translating mathematical modeling into clinical practice, enabling more precise and personalized treatment protocols while minimizing adverse effects. The framework's ability to provide stable, analytical solutions for combined immunotherapy treatments offers immediate applications for BC treatment optimization and broader implications for other immunotherapy-based cancer treatments. In the second part of the talk, I explain the mathematical model for optimizing Mitomycin-C (MMC) treatment for BC. Current drug dosing strategies rely on general guidelines without precise quantitative justification. Our model revolutionizes this approach by introducing analysis for drug-tumor interactions. While existing methods cannot predict required drug doses theoretically, our framework enables precise calculation of MMC concentrations needed for tumor elimination based on specific tumor characteristics. This innovation transforms MMC dosing from an empirical process to a mathematically guided procedure. These innovations enable a shift from standardized protocols to personalized treatment strategies. Unlike current approaches that modify treatments through trial and error, our models provide a theoretical foundation for optimizing treatment protocols based on individual tumor characteristics, potentially improving outcomes while minimizing unnecessary drug exposure. Keywords: bladder cancer, immunotherapy, BCG treatment, Mitomycin-C, mathematical modeling, tumor-immune interactions, treatment optimization, personalized medicine

Haralampos Hatzikirou:

Khalifa University

"From cell patterns in biopsies to clinical predictions"

Understanding the dynamic role of immune cells in cancer progression is essential for predicting disease outcomes and developing targeted therapies. This talk delves into the transition from cellular patterns observed in biopsies to clinical predictions, with a particular focus on the role of macrophages in the tumor microenvironment (TME). Drawing on advanced computational models and experimental data, we explore how macrophage phenotypic changes, particularly their transition from pro-inflammatory to pro-tumorigenic (M2) states, influence tumor progression and recurrence. By examining macrophage-fibroblast interactions in kidney transplant biopsies, we uncover key insights into macrophage behavior that can be translated to cancer research, particularly in gliomas and other solid tumors. The talk will discuss how macrophage dynamics, observed through transcriptomic profiling and tissue-specific modeling, can be integrated into predictive models of tumor growth and recurrence. This framework has the potential to improve clinical decision-making by enabling earlier interventions and more accurate predictions of treatment outcomes, highlighting the importance of macrophage-driven processes in cancer biology.

Ali Daher

University of Marie & Louis Pasteur

"Integrating High-Throughput Genomic Data with Biologically-Informed Models of Spatiotemporal Dynamics of Skin Lesions: A Computational Parameter Extraction Pipeline"

Skin wound healing typically progresses through three chronologically overlapping stages: inflammation, proliferation, and remodelling [1]. However, in some cases, prolonged or excessive inflammatory and proliferative phases can lead to abnormal wound healing; one such example is keloid scarring. Keloids are benign fibroproliferative tumours characterized by excessive collagen production and extracellular matrix (ECM) deposition by fibroblasts following dermal injury or irritation. Known for their aggressive nature and pathological spread beyond the original wound boundaries, keloids often result in disfiguring scars, have high recurrence rates, and show poor response to current treatment approaches. The rapid advancement of single-cell RNA sequencing (scRNA-seq) techniques has enabled detailed characterization of the cellular landscape, heterogeneity, and intercellular interactions in skin samples from both normal and abnormal wound healing. In the case of keloids, studies have revealed high immune cell infiltration, with a positive correlation between immune cell abundance and keloid recurrence [2]. Additional findings have identified close interactions between immune cells and fibroblasts, whereby immune cells release cytokines and growth factors that drive ECM production and fibroblast proliferation, further fuelling keloid progression [2]. As such, keloids are increasingly regarded as an inflammatory skin disease [2]. Given the limited success of current treatments in resolving or preventing keloid formation and recurrence and the growing evidence of the inflammatory component of keloids, immunotherapy has emerged as a promising novel treatment avenue [3-5], particularly by targeting the communication pathways between immune cells and fibroblasts [2,6]. One prominent communication pathway is mediated by TGF-β, a key effector cytokine secreted by inflammatory cells that promotes fibrotic responses in fibroblasts. Several investigational agents targeting the TGF-β pathway are currently underway in clinical trials for fibrotic and cancer-related diseases [2,7]. However, our current understanding of the immunological underpinnings of keloid pathogenesis remains neither specific nor comprehensive, limiting the effective development of targeted immunotherapies. For instance, persistent inhibition of TGF-β can suppress fibrosis but may also eliminate its anti-inflammatory functions, potentially exacerbating inflammation [8]. The integration of high-throughput genomics technologies, such as scRNA-seq and spatial transcriptomics, has advanced our understanding of the spatial cellular architecture and communication networks in skin wounds, including keloids. Nevertheless, the data generated from these high-resolution technologies alone are insufficient to fully elucidate the inflammatory origins and progression of keloids or to reliably identify optimal immunotherapeutic strategies. In this context, mathematical and computational models, especially spatiotemporal ones, provide powerful complementary tools. They enable the integration and interpretation of experimental data, facilitating in-silico experimentation and hypothesis testing of biological mechanisms underlying normal and abnormal wound healing. These models can simulate the spatiotemporal dynamics of wound healing and keloid progression, offering insights not readily obtainable through conventional experiments. Additionally, they serve as safe and cost-effective testbeds for evaluating immunotherapeutic interventions before clinical application. To this end, we first develop a biologically grounded model capturing the interactions between immune cells (primarily macrophages) and fibroblasts during wound healing. This is achieved through the construction of both particle-based and continuum reaction-diffusion models that describe the production, secretion, diffusion, and uptake of growth factors and ligands mediating these interactions. We then analyse high-throughput genomics data from skin samples of both normal and abnormal wound healing, leveraging matched scRNA-seq and spatial transcriptomics (Visium) data. Through spatial deconvolution, we infer cell type densities across the tissue domain, and we perform intercellular communication analyses to estimate the strength of interactions mediated by specific ligands. Subsequently, we develop a parameter learning framework that combines approximate Bayesian computation with machine learning and backpropagation techniques to infer the parameters of the reaction-diffusion model from the experimental data. By integrating a biologically informed mathematical framework with genomics-derived data, we ensure that our model is both mechanistically and data-driven—an essential requirement for clinical and research relevance. References 1. Liu, Z., et al. “538 Spatiotemporal Single-Cell Roadmap of Human Skin Wound Healing.” Journal of Investigative Dermatology, vol. 144, no. 12, Dec. 2024, p. S321. DOI.org, https://doi.org/10.1016/j.jid.2024.10.551. 2. Zhang, Xiya, et al. “The Communication from Immune Cells to the Fibroblasts in Keloids: Implications for Immunotherapy.” International Journal of Molecular Sciences, vol. 24, no. 20, Oct. 2023, p. 15475. DOI.org, https://doi.org/10.3390/ijms242015475. 3. Zhang, Tao, et al. “Current Potential Therapeutic Strategies Targeting the TGF-β/Smad Signaling Pathway to Attenuate Keloid and Hypertrophic Scar Formation.” Biomedicine & Pharmacotherapy, vol. 129, Sept. 2020, p. 110287. DOI.org, https://doi.org/10.1016/j.biopha.2020.110287. 4. Ekstein, Samuel F., et al. “Keloids: A Review of Therapeutic Management.” International Journal of Dermatology, vol. 60, no. 6, June 2021, pp. 661–71. DOI.org, https://doi.org/10.1111/ijd.15159. 5. Huang, Chenyu, et al. “Managing Keloid Scars: From Radiation Therapy to Actual and Potential Drug Deliveries.” International Wound Journal, vol. 16, no. 3, June 2019, pp. 852–59. DOI.org, https://doi.org/10.1111/iwj.13104. 6. Shan, Mengjie, and Youbin Wang. “Viewing Keloids within the Immune Microenvironment.” American Journal of Translational Research, vol. 14, no. 2, Feb. 2022, pp. 718–27. PubMed Central, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8902558/. 7. Moss, Marcia L., and Dmitry Minond. “Recent Advances in ADAM17 Research: A Promising Target for Cancer and Inflammation.” Mediators of Inflammation, vol. 2017, 2017, pp. 1–21. DOI.org, https://doi.org/10.1155/2017/9673537. 8. Teicher, Beverly A. “TGFβ-Directed Therapeutics: 2020.” Pharmacology & Therapeutics, vol. 217, Jan. 2021, p. 107666. DOI.org, https://doi.org/10.1016/j.pharmthera.2020.107666.

Donggu Lee(*,1), Sunju Oh(2), Sean Lawler(3), and Yangjin Kim (1,3)

(1) Konkuk University, (2) Konkuk University, (3) Brown University

"Bistable dynamics of TAN-NK cells in tumor growth and control of radiotherapy-induced neutropenia in lung cancer treatment"

Neutrophils play a crucial role in the innate immune response as a first line of defense in many diseases, including cancer. Tumor-associated neutrophils (TANs) can either promote or inhibit tumor growth in various steps of cancer progression via mutual interactions with cancer cells in a complex tumor microenvironment (TME). In this study, we developed and analyzed mathematical models to investigate the role of natural killer cells (NK cells) and the dynamic transition between N1 and N2 TAN phenotypes in killing cancer cells through key signaling networks and how adjuvant therapy with radiation can be used in combination to increase anti-tumor efficacy. We examined the complex immune-tumor dynamics among N1/N2 TANs, NK cells, and tumor cells, communicating through key extracellular mediators (Transforming growth factor (TGF-beta), Interferon gamma (IFN-gamma)) and intracellular regulation in the apoptosis signaling network. We developed several tumor prevention strategies to eradicate tumors, including combination (IFN-gamma, exogenous NK, TGF-beta inhibitor) therapy and optimally-controlled ionizing radiation in a complex TME. Using this model, we investigated the fundamental mechanism of radiation-induced changes in the TME and the impact of internal and external immune composition on the tumor cell fate and their response to different treatment schedules.

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Annual Meeting for the Society for Mathematical Biology, 2025.