外周免疫耐受作为肿瘤免疫逃逸与治疗耐药的驱动因素。
完整摘要 · 中文译文
外周免疫耐受对于预防自身免疫至关重要，而肿瘤正越来越多地利用这一机制来驱动疾病进展、转移播散和免疫治疗耐药。中枢耐受可清除自身反应性克隆，而外周耐受则仍受抗原持续存在、细胞因子梯度、代谢信号和基质结构的动态调控，这种可塑性正是肿瘤所利用以构建多层免疫抑制生态系统的机制。本叙述性综述综合了多组学、空间、细胞和分子证据，重点涵盖2021至2025年的文献及基础性研究，提出肿瘤诱导的外周耐受（TIPT）作为免疫逃逸和治疗失败的统一框架。TIPT源于肿瘤驻留调节性T细胞、髓源性抑制细胞、调节性B细胞、M2样巨噬细胞、耐受性树突状细胞和耗竭性CD8⁺T细胞的协同抑制，并由抑制性细胞因子网络、代谢剥夺、功能异常状态的表观遗传固定以及基质-血管排斥屏障所维持。空间上组织化的耐受微环境与免疫逃逸、转移定植和检查点阻断耐药相关。针对抑制性细胞群体、代谢通路、染色质调控因子和基质屏障的治疗策略正处于临床开发阶段，但仅有少数有限亚群证实了临床获益，且抗TIGIT抗体在肺癌三期试验中未达到主要终点。机器学习模型在预测耐受方面展现出前景。 状态并指导联合方案设计，但仍有待前瞻性临床验证。TIPT可能代表肿瘤演化及免疫治疗反应中的一个重要且潜在可干预的决定因素，而持久的肿瘤控制很可能需要联合策略来瓦解这一耐受生态系统。
完整摘要 · 英文原文
Peripheral immune tolerance, essential for preventing autoimmunity, is increasingly co-opted by tumors to drive progression, metastatic dissemination, and immunotherapy resistance. Whereas central tolerance eliminates autoreactive clones, peripheral tolerance remains dynamically regulated by antigen persistence, cytokine gradients, metabolic cues, and stromal architecture, a plasticity that tumors exploit to build a multilayered immunosuppressive ecosystem. This narrative review synthesizes multi-omic, spatial, cellular, and molecular evidence, emphasizing 2021 to 2025 publications alongside foundational studies, to propose tumor-induced peripheral tolerance (TIPT) as a unifying framework for immune escape and treatment failure. TIPT emerges from coordinated suppression by tumor-resident regulatory T cells, myeloid-derived suppressor cells, regulatory B cells, M2-like macrophages, tolerogenic dendritic cells, and exhausted CD8? T cells, sustained by suppressive cytokine networks, metabolic deprivation, epigenetic fixation of dysfunctional states, and stromal-vascular exclusion barriers. Spatially organized tolerance niches are associated with immune evasion, metastatic seeding, and resistance to checkpoint blockade. Strategies targeting suppressive cell populations, metabolic pathways, chromatin regulators, and stromal barriers are in clinical development, but clinical benefit has been established for only a limited subset, and anti-TIGIT antibodies failed to meet primary endpoints in phase 3 lung cancer trials. Machine-learning models show promise for predicting tolerance states and guiding combination design, but prospective clinical validation is still needed. TIPT may represent an important, potentially actionable determinant of tumor evolution and immunotherapy response, and durable cancer control is likely to require combined strategies that dismantle this tolerance ecosystem.