MS01 - ONCO-07

Dynamical modeling of cell-state transitions in cancer therapy resistance (Part 1)

Monday, July 14 at 10:20am

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Note: this minisymposia has multiple sessions. The other session is: and Part-2.

Mohit Kumar Jolly (Indian Institute of Science), Sarthak Sahoo (Indian Institute of Science)

Description:

The ability of cancer cells to dynamically alter their cell-state/phenotype in response to stress, including existing therapies, poses a major impediment to effective cancer treatment. Latest experimental advancements have enabled a better characterization of such cell-state transitions and driven the development of mathematical models that can both offer mechanistic understanding and suggest new potent strategies (combinatorial, sequential, adaptive) for clinical management. This proposed mini-symposium brings together 8 experts across diverse academic backgrounds (oncology, mathematics, engineering), geography (Sweden, India, USA, Canada), career stages (from senior PhD students to full Professors) and genders (4 men, 4 women) to present their latest exciting work in this direction. These experts will discuss how an iterative interdisciplinary crosstalk among multi-scale mathematical models and quantitative experimental and clinical data has unraveled diverse regulatory mechanisms (transcriptional, epigenetic, signalling) contribute to cell-state transitions and consequent heterogeneity in a cell population, and its implications in mediating drug tolerance or resistance for multiple existing therapies, and eventually suggesting new therapeutic strategies that can overcome current clinical challenges.

Rebecca A Bekker

University of Southern California

"Modeling Cell-State Dynamics to Unravel and Counteract Immune Suppression in Breast Cancer Immunotherapy"

HER2+ breast cancer is aggressive and has historically had poor outcomes. Despite therapeutic advances, such as the development and approval of HER2 targeted therapies and the associated improved outcomes, resistance invariably develops. However, recent preclinical and clinical evidence suggest patients may benefit from combination therapies that include immunotherapy. The PANACEA trial, which investigated the efficacy of the combination of the HER2 monoclonal antibody trastuzumab and the PD-1 inhibitor pembrolizumab in HER2+ breast cancer, reported a 15% response rate in patients with PD-L1+ tumors. This unexpectedly low response rate may be the result of a highly tumor-engineered immune-suppressive niche containing MDSCs, Tregs, and TAMs. Understanding the interplay between pro- and anti-tumor immune subsets and HER2+ breast cancer cells is crucial for improving responses to immunotherapy and identifying novel therapeutic strategies. To this end, we developed an agent-based model (ABM) of tumor-immune interactions, which is initialized with digitized multiplex immunohistochemistry slides of untreated spontaneous lung metastases from the NT2.5LM HER2+ murine model. The ABM includes tumor evolution via mutations that affect PD-L1 expression, chemotherapy sensitivity, and proliferation speed. We explore how treatment regimens, combining chemotherapy, anti-PD-L1 and anti-CTLA-4, impact immune composition and suppression, and tumor evolution. The model provides a framework to explore how immune plasticity and tumor adaptation may co-evolve under therapy, and to generate hypotheses about potential mechanisms of resistance and strategies to counteract immunosuppression.

James Greene

Clarkson University

"Understanding therapeutic tolerance through a mathematical model of drug-induced resistance"

Resistance to chemotherapy is a major impediment to successful cancer treatment that has been studied over the past three decades. Classically, resistance is thought to arise primarily through random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests this evolution to resistance need not occur randomly, but instead may be induced by the application of the drug. In this work, we present a mathematical model that describes induced resistance. We utilize our mathematical model to study control-theoretic questions with respect to different clinical treatment protocols, and study the effect of different therapies across parameter regimes (e.g. we investigate patient-specific responses). An extended model is then fit to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose; the predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume. These results are reported in J.L Gevertz, J.M Greene, S. Prosperi, N. Comandante-Lou, and E.D. Sontag. Understanding therapeutic tolerance through a mathematical model of drug-induced resistance. npj Systems Biology and Applications, 11, 2025

Sara Hamis

Uppsala University

"Growth rate-driven modelling elucidates phenotypic adaptation in BRAFV600E-mutant melanoma"

Phenotypic adaptation, the ability of cells to change phenotype in response to external pressures, has been identified as a driver of drug resistance in cancer. To quantify phenotypic adaptation in BRAFV600E-mutant melanoma, we develop a theoretical model that emerges from data analysis of WM239A-BRAFV600E cell growth rates in response to drug challenge with the BRAF-inhibitor encorafenib. Our model constitutes a cell population model in which each cell is individually described by one of multiple discrete and plastic phenotype states that are directly linked to drug-dependent net growth rates and, by extension, drug resistance. Data-matched simulations reveal that phenotypic adaptation in the cells is directed towards states of high net growth rates, which enables evasion of drug-effects. The model subsequently provides an explanation for when and why intermittent treatments outperform continuous treatments in vitro, and demonstrates the benefits of not only targeting, but also leveraging, phenotypic adaptation in treatment protocols.

Sarthak Sahoo

Indian Institute of Science

"Mathematical modelling of multi-axis plasticity in ER+ breast cancer"

Resistance of anti-estrogen therapy is a major clinical challenge in treating estrogen receptor positive (ER+) breast cancer. Recent studies highlight the role of non-genetic adaptations in drug tolerance, yet the mechanisms remain unclear. One of the key processes that underlies enhanced drug tolerance as well as metastatic potential is the process of epithelial-mesenchymal plasticity (EMP). Furthermore, lineage switching, and acquisition of immunosuppressive traits are also commonly observed. We investigate the role of genes involved in EMP to elucidate origins of such multi-axis cellular plasticity by mathematical modelling of underlying gene regulatory networks. Specifically, we show that six co-existing phenotypes are enabled by underlying gene regulatory network, with epithelial-sensitive and mesenchymal-resistant being dominant. Population dynamics analysis demonstrates how phenotypic plasticity promotes survival among sensitive and resistant cells, suggesting that mesenchymal-epithelial transition inducers could enhance anti-estrogen therapy effectiveness. Additionally, hybrid epithelial/mesenchymal (E/M) phenotypes, which exhibit high PD-L1 levels, contribute to immune evasion and enhanced metastatic fitness, obviating the need for a full EMT. Finally, multi-modal transcriptomic data analysis shows associations between EMT and luminal-basal plasticity, linking luminal breast cancer with epithelial states and basal breast cancer with hybrid E/M phenotypes and higher heterogeneity. Our mechanistic modelling of ER+ breast cancer recapitulates observed clinical and pre-clinical findings in spatial transcriptomic datasets, thus offering a predictive framework for characterizing intra-tumor heterogeneity and potential therapeutic interventions given a spatially heterogenous tissue sample. These insights underscore the importance of understanding phenotypic plasticity and non-genetic heterogeneity and its integration with spatial transcriptomics to improve treatment strategies for ER+ breast cancer.

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Annual Meeting for the Society for Mathematical Biology, 2025.