Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

Autor:	Gallagher K; Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford, United Kingdom.; Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida., Strobl MAR; Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida., Park DS; Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida., Spoendlin FC; Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford, United Kingdom., Gatenby RA; Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida., Maini PK; Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford, United Kingdom., Anderson ARA; Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida.
Jazyk:	angličtina
Zdroj:	Cancer research [Cancer Res] 2024 Jun 04; Vol. 84 (11), pp. 1929-1941.
DOI:	10.1158/0008-5472.CAN-23-2040
Abstrakt:	Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. Here, we applied deep reinforcement learning (DRL) to guide adaptive drug scheduling and demonstrated that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. The DRL strategies were robust to patient variability, including both tumor dynamics and clinical monitoring schedules. The DRL framework could produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. The DRL framework had no knowledge of the underlying mathematical tumor model, demonstrating the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, a proposed five-step pathway, which combined mechanistic modeling with the DRL framework and integrated conventional tools to improve interpretability compared with traditional "black-box" DRL models, could allow translation of this approach to the clinic. Overall, the proposed framework generated personalized treatment schedules that consistently outperformed clinical standard-of-care protocols. Significance: Generation of interpretable and personalized adaptive treatment schedules using a deep reinforcement framework that interacts with a virtual patient model overcomes the limitations of standardized strategies caused by heterogeneous treatment responses. (©2024 The Authors; Published by the American Association for Cancer Research.)
Databáze:	MEDLINE
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