Biology subjects

Rezaei, J.

Publications and source records attributed to Rezaei, J..

4 recordsLinked to original sources

bioRxiv2026

Persister cells and their effects on cancer stabilization therapy

Some cancers have the ability to persist under treatment. Persister cancer cells can be a small subpopulation of cancer cells that enter a dormant or quiescent state, thereby evading the effects of chemotherapy, targeted therapy, or immunotherapy. Motivated by these observations, we develop and investigate an evolutionary game theory model that captures the interactions between three types of cancer cells: treatment-sensitive, drug-tolerant persister, and treatment-resistant cells. In our model, persister cells transition into resistant cells at a rate that can depend on the treatment intensity. We analyze the model's equilibria and derive conditions for their existence and stability. In particular, we show that an equilibrium in which sensitive and resistant cells coexist is stable when intra-type competition is stronger than inter-type competition. We also show that equilibria in which only one cell type persists require the transition rate to exceed an explicit threshold determined by the treatment dose and competition parameters. Our findings reveal that persister cells can both stabilize and destabilize tumor dynamics, depending on treatment and cell type-transition rates, highlighting the importance of accounting for persister behavior in designing effective evolutionary therapies.

cancer biology↗

bioRxiv2026

Initial tumor composition shapes resistance evolution and treatment outcomes in non-small cell lung cancer

Drug resistance is a leading cause of treatment failure in non-small cell lung cancer (NSCLC), yet how resistance evolves during treatment and whether its fitness consequences depend on tumor composition remains poorly understood. Using a game-theoretic mathematical model fitted to longitudinal in-vitro data from alectinib-sensitive and alectinib-resistant H3122 NSCLC cells grown under different treatment and microenvironmental conditions, we found that the fitness effect of evolving resistance depended critically on the initial proportion of resistant cells in the tumor. When resistant cells were initially rare, resistance evolved faster and increasing resistance was associated with a growth advantage. When resistant cells were initially frequent, increasing resistance was associated with a fitness cost. In both cases, increasing resistance eroded treatment efficacy. In the gain-of-resistance regime, stabilization therapy could maintain a stable tumor equilibrium only if resistant cells were excluded. Maximum tolerated dosing was not always optimal for maximizing time to progression; intermediate doses performed better when they kept the initial tumor growth rate close to zero. These results suggest that evolutionary therapy for NSCLC should account not only for the abundance of resistant cells, but also for how resistance is evolving and what fitness consequences it currently carries in individual patients.

cancer biology↗

bioRxiv2026

Assessing the Operational Feasibility of Evolutionary Therapy in Metastatic Non-Small Cell Lung Cancer

Evolutionary cancer therapy (ECT) applies principles of evolutionary game theory to prolong the effectiveness of cancer treatment by curbing the development of treatment resistance. It was shown to increase time to progression while decreasing the cumulative drug dose. ECT individually tailors treatment schedules for patients based on their cancer dynamics and, thus, requires regular follow-up and precise measurements of the cancer burden. The current literature on ECT often overlooks clinical realities, such as rather long intervals between tests, possible appointment delays and measurement errors, in the development of the treatment protocols. In this study, we assess the clinical feasibility of ECT for metastatic non-small cell lung cancer (NSCLC). We create virtual patients with cancer dynamics described by the polymorphic Gompertzian model, based on data from the START-TKI clinical trial. We assess the effects of longer test intervals, measurement error and appointment delays on the expected time to progression under the evolutionary therapy protocols. We show that a higher containment level, although it increases time to progression in the models predictions, may lead to premature treatment failure in the presence of measurement error and appointment delay. Further, we show that the ECT protocol with a single containment bound is more robust to the clinical realities than the protocol with two bounds. Finally, we show that a dynamically adjusted treatment protocol can be beneficial for individual patients, but requires a thorough follow-up. This study contributes to the design of a clinical trial and the future clinical implementation of evolutionary therapy for NSCLC.

cancer biology↗

bioRxiv2021

On stability of the Darwinian dynamics

Here we analyze Darwinian dynamics of cancer introduced in [1], extended by including a competition matrix, and evaluate (i) when the eco-evolutionary equilibrium is positive and (ii) when the eco-evolutionary equilibrium is asymptotically stable.

cancer biology↗