MS07 - ONCO-08

Decoding Drug-Induced Persistence: Experiments, Models, and Optimal Drug Scheduling (Part 2)

Thursday, July 17 at 4:00pm

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Note: this minisymposia has multiple sessions. The other session is: and Part-1.

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.

Jana Gevertz

The College of New Jersey

"Mitigating non-genetic resistance to checkpoint inhibition based on multiple states of exhaustion"

Despite the revolutionary impact of immune checkpoint inhibition (ICI) on cancer therapy, for most indications the majority of patients do not sustain a durable clinical benefit. In this work, we explore the theoretical consequences of the existence of multiple states of immune cell exhaustion on response to ICI therapy. In particular, we consider the emerging understanding that T cells can exist in various states: fully functioning cytotoxic cells, reversibly exhausted cells that are minimally cytotoxic but targetable by ICIs, and terminally exhausted cells that are cytotoxic yet not targetable by ICIs. Under the assumption that tumor-induced inflammation triggers the transition between these T cell phenotypes, we developed a conceptual mathematical model of tumor progression subject to treatment with an ICI that accounts for multi-stage immune cell exhaustion. Simulations of a ‘baseline patient’ without intrinsic resistance to ICI reveal that treatment response (complete responder versus non-responder with non-genetic resistance) sensitively depends on both the dose and frequency of drug administration. A virtual population analysis uncovered that while the standard high-dose, low-frequency protocol is indeed an effective strategy for our baseline patient, it fails a significant fraction of the population. Conversely, a metronomic-like strategy that distributes a fixed amount of drug over many doses given close together is predicted to be effective across the largest proportion of the virtual population. Taken together, our theoretical analyses demonstrate the potential of mitigating resistance to checkpoint inhibitors via dose modulation, and also suggest avenues for selecting combination drug partners.

Raymond Ng

University of Pennsylvania

"The role of tumor gene expression variability in evading CD8+ T cells"

Melanoma cells escape CD8+ T cell killing during tumor progression and development of immunotherapy resistance. While genetic alterations affecting antigen presentation and interferon response pathways are well-established mechanisms of immune escape, melanoma cells display substantial gene expression heterogeneity even prior to acquiring these genetic changes, potentially enabling some cells to survive T cell attack. Here, we investigate how this pre-existing gene expression heterogeneity facilitates melanoma cell evasion of T cell recognition and destruction. Using a model system of ovalbumin-expressing melanoma cells cocultured with OT-I CD8+ T cells, we demonstrate that a subset of melanoma cells consistently survives both acute and prolonged T cell selection. By integrating DNA barcoding and single-cell RNA sequencing with computational approaches, we developed a robust framework to identify survivor versus non-survivor cell lineages. Our bootstrap simulation framework generated empirical null distributions of lineage selection frequencies, enabling robust statistical inference to distinguish “survivor” from “non-survivor” lineages with defined confidence levels. These 'survivor' lineages exhibited elevated expression of oxidative stress response and ferroptosis protection pathways, coupled with reduced expression of epithelial-to-mesenchymal transition (EMT) markers. Through long-term coculture experiments, we generated and characterized T cell-resistant melanoma populations, revealing consistent upregulation of interferon-gamma response pathways while maintaining suppressed EMT-like signatures. Our findings uncover previously unrecognized gene expression programs that enable melanoma immune evasion and suggest potential therapeutic vulnerabilities in pathways controlling oxidative stress responses and cellular plasticity.

Chenyu Wu

University of Minnesota

"A statistical framework for detecting therapy-induced resistance from drug screens"

Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we introduce a robust statistical framework, based on multi-type branching process models of the evolutionary dynamics of tumor cell populations, to detect and quantify therapy-induced resistance phenomena from high throughput drug screening data. Through comprehensive in silico experiments, we show the efficacy of our framework in estimating parameters governing population dynamics and drug responses in a heterogeneous tumor population where cell state transitions are influenced by the drug. Finally, using recent in vitro data from multiple sources, we demonstrate that our framework is effective for analyzing real-world data and generating meaningful predictions.

Michael Cotner

The University of Texas at Austin

"Tracking Resistance to Targeted Therapy in TNBC with Cell Barcodes"

Triple negative breast cancer (TBNC) is marked by fewer standard-of-care treatment options and poorer treatment outcomes than other breast cancer subtypes, with approximately 40% of TNBC patients developing treatment resistance. High intratumoral heterogeneity, a characteristic of TNBC, leads to its difficulty in treatment and rapid acquisition of resistance. To investigate how this heterogeneity influences treatment response and resistance in TNBC, we employ ClonMapper, our DNA barcoding technology that utilizes integrated and heritable unique DNA barcodes, to track clonal cell populations across treatment. ClonMapper barcodes are identifiable in scRNA-seq, which enables tracking of clonal subpopulations and their transcriptomic diversity before and after treatment. We use ClonMapper to follow barcoded heterogenous tumor cell populations through their response to treatment with three clinically-relevant targeted inhibitor chemotherapies, revealing the diverse transcriptomic trajectories taken by different cell subpopulations and how these diverse responses arise from a heterogenous transcriptomic landscape prior to treatment.

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Annual Meeting for the Society for Mathematical Biology, 2025.