Ma, Kexin, Wang, Ningjing, Yang, Zai, Cheke, Robert A. and Tang, Biao (2026) Single-threshold–guided adaptive cancer therapy with partial-cycle treatment: a mechanistic and reinforcement learning analysis. PLoS Computational Biology, 22 (6):e1014457. pp. 1-24. ISSN 1553-734X (Print), 1553-7358 (Online) (doi:10.1371/journal.pcbi.1014457)

Preview

PDF (Open Access Preproof) 53918 CHEKE_Single-Threshold–Guided_Adaptive_Cancer_Therapy_(OA PROOF)_2026.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

Adaptive cancer therapy seeks to modulate aggressive treatment to preserve drug-sensitive tumor cells that suppress resistant populations, but existing strategies often rely on frequent treatment decisions enabled by intensive surveillance, limiting clinical feasibility. Here, we propose a clinically motivated alternative that shortens the treatment window within a fixed and relatively long surveillance cycle, thereby avoiding the need for frequent monitoring. Based on this idea, we develop a mechanistic modeling framework for single-threshold-guided adaptive therapy with partial surveillance-cycle treatment (AT-PSC) and benchmark its performance using reinforcement learning. Using clinically calibrated parameters from an individual patient, simulations show that AT-PSC prolongs the time to progression (TTP) by 402 days compared with adaptive therapy using full surveillance-cycle treatment, while substantially reducing treatment exposure (dose reduced by 10.1%). Consequently, AT-PSC achieves significantly larger TTP gains than continuous therapy (1891 days) and two-threshold-guided adaptive therapy AT50 (1123 days). Simulations using data from six additional patients and sensitivity analyses further demonstrate that these benefits are robust across heterogeneous tumor growth profiles, while individual-based treatment should be considered to maximize TTP. Reinforcement learning yields comparable outcomes under the same fixed treatment window and can further extend TTP when the treatment window is adaptively adjusted. Together, these results support AT-PSC as a clinically feasible strategy to improve disease control while reducing treatment burden, and suggest that a practical regimen, such as a 14-day treatment window within a 30-day surveillance cycle, can provide sustained benefits for a broad patient population.

Item Type:	Article
Additional Information:	Copyright: © 2026 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The parameters for the Lotka–Volterra model were adopted from a previous study [26], which calibrated the model using clinical data originally reported in [45]. These clinical data are publicly available at https://www.nicholasbruchovsky.com/clinicalResearch.html. The specific parameter values used in our study are listed in Table S1 of the Supporting information. The code is publicly available at https://github.com/Kexin0902/AT-PSC. Funding: This work was supported by the National Key R&D Program of China (2023YFA1008600 to BT), the National Natural Science Foundation of China (12522124 and 12371502 to BT), and the Young Talent Support Plan of Xi’an Jiaotong University (to BT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Uncontrolled Keywords:	cancer, prostate, adaptive therapy, Time to Progression, Threshold, Reinforcement Learning
Subjects:	R Medicine > R Medicine (General) R Medicine > RC Internal medicine R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Centre for Sustainable Agriculture 4 One Health Faculty of Engineering & Science > Natural Resources Institute > Centre for Sustainable Agriculture 4 One Health > Behavioural Ecology
Last Modified:	06 Jul 2026 10:40
URI:	https://gala.gre.ac.uk/id/eprint/53918

Actions (login required)

View Item

Downloads