Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.06.26.661628

A Mathematical Model of Persister Cell Plasticity and Its Impact on Adaptive Cancer Therapy

Abstract

The emergence of resistance to therapy remains a significant obstacle to successful treatment in cancer that is driven by somatic evolution. Adaptive therapies represent a novel approach to manage the emergence of resistance, by leveraging the competition between different cell phenotypes to control tumor burden rather than aiming for complete eradication. However, the emergence of phenotypica ly plastic persister cells, exhibiting transient epigenetic resistance, poses a significant challenge to the efficacy of these approaches, and their specific impact is often overlooked in preclinical and mathematical models. This study investigates the role of persisters within adaptive therapy using a spatial agent-based model simulating sensitive, persistent, and genetically resistant cell populations. Our simulations reveal that persisters critically undermine treatment efficacy, significantly reducing progression-free survival (PFS) by approximately 40% (average 207 vs. 344 days in simulations) as they provide a reservoir for acquiring genetic resistance. Furthermore, tumors with higher levels of epigenetic resistance (more robust persisters) showed accelerated evolution towards resistance dominance phenotypes. The model showed treatment was most effective when sensitive cells initially dominated the tumor microenvironment. These findings highlight that persister dynamics are crucial determinants of adaptive therapy outcomes, suggesting that future strategies must account for epigenetic resistance, potentially informing approaches to assess tumor composition and sensitivity to better tailor treatments and improve patient outcomes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mathews, A., Basanta, D.. 2025-06-27. A Mathematical Model of Persister Cell Plasticity and Its Impact on Adaptive Cancer Therapy. https://doi.org/10.1101/2025.06.26.661628

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗

A thermoregulatory design principle for transitions into hypometabolism

Mammals entering torpor or hibernation undergo an abrupt transition from normothermia to hypothermia, yet how thermoregulation enables this switch remains poorly understood. Here, we identify dynamical signatures that precede these transitions and a mathematical principle that can generate them. In fasting-induced torpor in mice, body-temperature fluctuations increased before torpor onset, providing an early-warning signal that tracked proximity to the transition better than temperature decline alone. A heat-balance model showed that reducing how strongly the effective heat-loss coefficient depends on body temperature reorganizes thermoregulatory stability, allowing a low-temperature equilibrium to emerge while the normothermic state remains stable. This organization is consistent with a symmetry-broken pitchfork involving a saddle-node. Similar increases in temperature fluctuations preceded hibernation onset in hamsters. These findings link pre-transition temperature dynamics to changes in the underlying thermoregulatory landscape and provide a framework for detecting and understanding transitions from normothermia to hypothermia.

systems biology↗