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.

翻译：抗PD-1免疫疗法的先天耐药性仍然是转移性黑色素瘤面临的主要临床挑战，其背后的分子网络机制尚不明确。为此，我们利用患者肿瘤活检的转录组数据构建了动态概率布尔网络模型，以阐明调控治疗响应的逻辑规则。随后，我们采用强化学习智能体系统性地发现最优的多步治疗干预方案，并运用可解释人工智能对智能体的控制策略进行机制性解读。分析表明，对赖氨酰氧化酶样2蛋白（LOXL2）实施精确计时的四步临时抑制是最有效的策略。我们的可解释性分析显示，这种"一击即退"的干预足以消除驱动耐药性的分子特征，使网络能够自我修正而无需持续干预。本研究为克服免疫治疗耐药性提出了一种新颖的时序依赖性治疗假说，并为识别复杂生物系统中非显而易见的干预方案提供了强大的计算框架。