MS04 - ONCO-08

Decoding Drug-Induced Persistence: Experiments, Models, and Optimal Drug Scheduling (Part 1)

Tuesday, July 15 at 3:50pm

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

Einar Bjarki Gunnarsson (Science Institute, University of Iceland), Maximilian Strobl (Cleveland Clinic, USA, stroblm@ccf.org)

Description:

Drug resistance is a common reason for treatment failure in cancer. While we have learned much about the molecular mechanisms of resistance, less is known about the evolutionary processes by which resistant populations arise and expand, and how anti-cancer drugs influence these processes. Yet, such knowledge is crucial for informing when and how to treat to delay or avert resistance. In this mini-symposium, we are interested in the role of non-genetic mechanisms in resistance evolution, based on mounting evidence that anti-cancer drugs designed to kill cancer cells can simultaneously induce the adoption of non-genetic cell states capable of drug persistence or immune evasion. This evidence challenges the traditional Darwinian view of resistance evolution and confounds conventional high-dose therapy regimens. At the same time, the reversible nature of non-genetic mechanisms creates opportunities for delaying resistance through intermittent or adaptive therapy. We will hear talks from an interdisciplinary panel of experimental, data-driven and theoretical researchers intended to stimulate a conversation around the design of experiments for interrogating drug-induced persistence, the development of mathematical models and associated parameter inference methods, and mathematical model-informed optimal drug scheduling to tackle resistance. The talks will address both targeted therapies and immunotherapies, covering two major types of modern anti-cancer treatment.

Einar Bjarki Gunnarsson

Science Institute, University of Iceland

"Decoding drug-induced persistence: Integrating theory with experiments and optimizing dosing protocols"

Drug resistance is a common reason for treatment failure in cancer. While resistance evolution is usually viewed through the Darwinian lens of random mutation and selection, mounting evidence indicates that anti-cancer drugs designed to kill tumor cells can simultaneously induce the adoption of non-genetic drug-persistent cell states. This confounds conventional high-dose treatment strategies aimed at maximal tumor reduction, since high doses can simultaneously accelerate the evolution of resistance. At the same time, the reversible nature of non-genetic mechanisms creates opportunities for delaying resistance evolution through intermittent or adaptive therapy. In this talk, we discuss mathematical modeling of drug-induced persistence and its role in the evolution of stable drug resistance. We also discuss how mathematical modeling can be used to derive optimal drug schedules aimed at maximally delaying tumor recurrence. In doing so, we introduce the central conversation of the mini-symposium involving the integration of mathematical modeling with experimental data, which we believe is crucial to achieve the long-term goal of personalized model-informed optimal drug scheduling.

Mattia Corigliano

IFOM - The AIRC Institute of Molecular Oncology, Milan, Italy

"Optimal treatment for drug-induced cancer persisters involves release periods and intermediate drug doses"

Targeted cancer therapies often induce a reversible drug-tolerant state in subpopulations of cells, akin to bacterial persistence. Precise characterization of these 'cancer persisters' is crucial for designing more effective treatment strategies. Our recent work demonstrates that, unlike bacterial systems, the transition to persistence in colorectal cancer cell lines exhibits a distinct dependence on drug presence and concentration. In this talk, I will present a mathematical modeling framework that leverages these findings to explore intermittent treatment protocols aimed at reducing the long-term fitness of the treated population. By adapting a bacterial persistence model to colorectal cancer dynamics, we identify success and failure regions within a clinically accessible parameter space. Strikingly, our analysis suggests that optimal treatment outcomes may be achieved with non-zero recovery periods and drug concentrations lower than those typically administered in clinics. Moreover, incorporating patient drug pharmacokinetics into the model reveals that intermittent dosing strategies, currently explored in clinical trials, can be optimized to potentially rival the efficacy of continuous treatment regimens. These results highlight the power of mathematical modeling in optimizing cancer treatment protocols, offering insights into non-trivial trade-offs that could improve patient outcomes.

Irina Kareva

Northeastern University

"Dosing Strategies for Bispecifics with a Bell-Shaped Efficacy Curve: What Looks Like Resistance May Be Corrected Through Schedule Adjustments"

Bispecific T cell engagers (TCEs) can exhibit bell-shaped efficacy curves, where increasing the dose beyond a certain point leads to reduced, not improved, efficacy. This counterintuitive behavior arises when efficacy depends on forming a trimeric complex between drug, tumor target, and T cell receptor, as is the case with teclistamab, a bispecific targeting BCMA and CD3 in multiple myeloma. Using a semi-mechanistic PK/PD model and a virtual patient population, we demonstrate that apparent loss of response may reflect overdosing rather than true resistance. We explore how measurable pre-treatment biomarkers, such as soluble and membrane-bound BCMA, can guide dose optimization and patient stratification. The model supports a shift toward semi-personalized dosing strategies and highlights that lowering the dose may, in some patients, restore efficacy.

Tatiana Miti

Moffitt Cancer Center & Research Institute

"ABM studies on the role of the drug-sheltering effects of stroma on the emergence of resistance"

Lung cancer is the leading cause of cancer-related deaths in the U.S., with non-small cell lung cancer (NSCLC) accounting for 84% of cases and a low 5-year survival rate of 6%. About 20% of NSCLC cases involve abnormal activation of receptor tyrosine kinases, which are treated with tyrosine kinase inhibitors (TKIs). Unfortunately, despite the magnificent initial results, TKIs show modest outcomes as tumors evolve, gain resistance, and eventually relapse. Experimental data suggest that drug-resistant tumor subpopulations emerge after TKI treatment through de novo mutations and epigenetic changes. In vitro studies indicate that this adaptation is supported by microenvironmental factors, particularly cancer-associated fibroblasts (CAFs), which protect tumor cells via secreted paracrine factors, hence access to these pro-survival factors depends on the spatial organization of tumors. However, despite the massive body of studies, the exact contribution of stromal protection to the minimal residual disease and ultimate relapse remains unknown. Based on published data from other teams, as well as published and unpublished data from Dr. Marusyk’s lab we use an Agent-Based Model to assess the stromal effects on the remission-relapse dynamics if (1) CAF mediated stromal protection effects only those tumor cells that are located at close proximity to stroma, (2) both total amount and spatial patterns of stroma within tumors are varied and (3) CAF mediated stromal protection reduces the initial drug induced tumor cell elimination yielding the accumulation of epigenetic mutations, thus contributing to the residual disease and preserving intratumor heterogeneity. Our results show that using mathematical models, we can gain a deeper understanding of the ecological mechanisms that lead to NSCLC relapse under TKI treatment and uncover new therapeutic strategies that account for stromal effects and successfully eradicate tumors.

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Annual Meeting for the Society for Mathematical Biology, 2025.