Optimising Combination Cancer Therapies: Searching the Massive Solution Space of a Mechanistic Model
Автор: Medical Physics UWA
Загружено: 2025-05-28
Просмотров: 70
Optimising Combination Cancer Therapies: Searching the Massive Solution Space of a Mechanistic Model
By: Allison Mei Yun Ng
This study explores the synergistic effects of radiotherapy (RT) and immune checkpoint inhibitors (ICIs)—specifically anti-PD-1 and anti-CTLA-4—using a mechanistic model fitted to murine mesothelioma data. The model simulates tumour microenvironment dynamics, including immune responses and tumour hypoxia, and was used to evaluate various RT and ICI treatment schedules. Additionally, reinforcement learning (RL) algorithms were trained to optimise RT dosing per fraction using two reward strategies: one focused on tumour cell kill and another penalising higher total RT doses. The results affirmed the benefit of combining RT and ICIs, with anti-PD-1 plus anti-CTLA-4 being more effective than monotherapy. While RL-informed schedules yielded tumour control probabilities (TCPs) above 0.55, a baseline 2Gy-per-fraction schedule performed best overall. The findings inform pre-clinical optimisation of combined RT-ICI therapies.
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