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Threshold-awareness in adaptive cancer therapy.
Wang, MingYi; Scott, Jacob G; Vladimirsky, Alexander.
Afiliación
  • Wang M; Center for Applied Mathematics, Cornell University, Ithaca, New York, United States of America.
  • Scott JG; Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, Ohio, United States of America.
  • Vladimirsky A; Department of Mathematics and Center for Applied Mathematics, Cornell University, Ithaca, New York, United States of America.
PLoS Comput Biol ; 20(6): e1012165, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38875286
ABSTRACT
Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Procesos Estocásticos / Biología Computacional / Neoplasias Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Procesos Estocásticos / Biología Computacional / Neoplasias Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos