Frontiers in pharmacology · 2026
Personalized Boolean network dynamic profiling and machine learning approaches to identify combinatorial treatments for prostate cancer.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kim Se Jin, Salib Sandra, Foster Barbara A, Woloszynska Anna, Mager Donald E
Аннотация
INTRODUCTION: Prostate cancer is the most commonly diagnosed non-cutaneous cancer in males and the second leading cause of male cancer death in the United States. Androgen deprivation therapy to block androgen receptor (AR) signaling is part of the standard of care. However, approximately 30%-40% of patients experience biochemical recurrence (BCR) after primary therapy and progress to a more lethal form of castration-resistant prostate cancer for which there currently is no curative therapy. This study focused on patients at risk of progressing to BCR with the aim of identifying novel combinatorial targets that can be used with AR inhibition to provide greater therapeutic response.
METHODS: Personalized Boolean network models were developed using The Cancer Genome Atlas and in-house data to integrate mutations, copy number alterations, and RNA-seq data. Asynchronous stochastic network simulations in MaBoSS (Markovian Boolean Stochastic Simulator) were used to identify potential drug targets in combination with AR inhibition, which were cross-checked using a machine learning algorithm and tested with a cytotoxicity assay across 4 prostate cancer cell lines.
RESULTS: In silico predictions and in vitro experiments showed that target-specific co-inhibition of E2F1 or Cyclin B with an AR antagonist enhances enzalutamide-associated growth inhibition in all 4 prostate cancer cell lines. Pharmacodynamic modeling suggested that the drug combinations were synergistic with all interaction parameters estimated to be less than 1 (range = 0.156-0.834).
DISCUSSION: Dynamic profiling of personalized Boolean networks in treatment-naïve prostate cancer subjects with and without BCR identified potential drug targets in combination with AR antagonists. Future xenograft studies are needed to confirm in vitro results, and exposure-response modeling could provide guidance for feasibility assessment and clinical trial design.
METHODS: Personalized Boolean network models were developed using The Cancer Genome Atlas and in-house data to integrate mutations, copy number alterations, and RNA-seq data. Asynchronous stochastic network simulations in MaBoSS (Markovian Boolean Stochastic Simulator) were used to identify potential drug targets in combination with AR inhibition, which were cross-checked using a machine learning algorithm and tested with a cytotoxicity assay across 4 prostate cancer cell lines.
RESULTS: In silico predictions and in vitro experiments showed that target-specific co-inhibition of E2F1 or Cyclin B with an AR antagonist enhances enzalutamide-associated growth inhibition in all 4 prostate cancer cell lines. Pharmacodynamic modeling suggested that the drug combinations were synergistic with all interaction parameters estimated to be less than 1 (range = 0.156-0.834).
DISCUSSION: Dynamic profiling of personalized Boolean networks in treatment-naïve prostate cancer subjects with and without BCR identified potential drug targets in combination with AR antagonists. Future xenograft studies are needed to confirm in vitro results, and exposure-response modeling could provide guidance for feasibility assessment and clinical trial design.