用AI发现精准时序抑制LOXL2可逆转黑色素瘤免疫耐药
A PBN-RL-XAI Framework for Discovering a "Hit-and-Run" Therapeutic Strategy in Melanoma
- 构建动态概率布尔网络+强化学习，寻找多步治疗策略
- 4步短暂抑制LOXL2可消除耐药分子特征，效果优于持续用药
- 解释性AI揭示'打一枪换一个地方'的治疗机制，适合肿瘤研究者
转移性黑色素瘤对抗PD-1免疫疗法的先天耐药仍是重大临床挑战，其分子机制尚不明确。为此，我们基于患者肿瘤活检的转录组数据构建了动态概率布尔网络，解析治疗反应的调控逻辑。随后利用强化学习代理系统探索最优的多步治疗干预方案，并采用可解释人工智能机制化分析代理的控制策略。分析发现，精确时序的4步短期抑制赖氨酸氧化酶样2蛋白（LOXL2）是最有效的策略。可解释性分析显示，这种‘打一枪换一个地方’的干预足以消除驱动耐药的分子特征，使网络自我修复而无需持续干预。本研究提出一种新颖的时间依赖性治疗假说，以克服免疫疗法耐药，并提供了一个强大的计算框架，用于识别复杂生物系统中非显而易见的干预方案。
原文摘要 · Abstract (English)
Innate resistance to anti-PD-1 immunotherapy remains a major clinical challenge in metastatic melanoma, with the underlying molecular networks being poorly understood. To address this, we constructed a dynamic Probabilistic Boolean Network model using transcriptomic data from patient tumor biopsies to elucidate the regulatory logic governing therapy response. We then employed a reinforcement learning agent to systematically discover optimal, multi-step therapeutic interventions and used explainable artificial intelligence to mechanistically interpret the agent's control policy. The analysis revealed that a precisely timed, 4-step temporary inhibition of the lysyl oxidase like 2 protein (LOXL2) was the most effective strategy. Our explainable analysis showed that this ''hit-and-run" intervention is sufficient to erase the molecular signature driving resistance, allowing the network to self-correct without requiring sustained intervention. This study presents a novel, time-dependent therapeutic hypothesis for overcoming immunotherapy resistance and provides a powerful computational framework for identifying non-obvious intervention protocols in complex biological systems.
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