肿瘤细胞治疗研究
英文原题：Emerging biomarkers in melanoma: Bridging molecular discovery and precision oncology.
尽管靶向治疗和免疫治疗取得了重大进展，黑色素瘤仍然是致死率最高的皮肤癌。
黑色素瘤尽管在靶向治疗和免疫治疗方面取得了重大进展，仍是致死率最高的皮肤癌。生物标志物目前在诊断、风险分层、治疗选择和疾病监测中发挥核心作用；然而，其临床整合仍不一致。本综述综合了不断演变的生物标志物格局，涵盖遗传学（如 BRAF、NRAS、KIT、TERT、NF1、CDKN2A）、免疫（PD-L1、LAG-3、TIGIT、TILs、TMB）、蛋白质组学（S100B、MMPs、信号特征）以及数字/影像生物标志物（AI 辅助皮肤镜、空间和多路分析）。我们重点介绍了黏膜、肢端和葡萄膜黑色素瘤的亚型特异性差异，这些亚型的生物标志物模式和治疗反应与皮肤型疾病显著不同。液体活检方法，包括 ctDNA、甲基化特征和细胞外囊泡，被评估用于微小残留病检测和耐药监测。为推进临床转化，我们提出一个标准化、分步的诊断治疗框架，将基于组织和血液的生物标志物与 AI 赋能的影像相结合，以支持辅助治疗和转移性环境中的个体化管理。关键的转化推动因素包括检测方法协调（PD-L1、TMB、ctDNA）、证据分级验证以及纳入生物标志物驱动终点的实用性临床试验。解决成本、可及性和数据伦理问题，对于生物标志物引导的精准肿瘤学在不同卫生系统中成为可持续的临床现实至关重要。
Melanoma remains the most lethal form of skin cancer despite major advances in targeted and immune-based therapies. Biomarkers now play central roles in diagnosis, risk stratification, therapeutic selection, and disease monitoring; however, their clinical integration remains inconsistent. This review synthesizes the evolving biomarker landscape across genetic (e.g., BRAF, NRAS, KIT, TERT, NF1, CDKN2A), immune (PD-L1, LAG-3, TIGIT, TILs, TMB), proteomic (S100B, MMPs, signaling signatures), and digital/imaging biomarkers (AI-assisted dermo copy, spatial and multiplex profiling). We highlight subtype-specific differences in mucosal, acral, and uveal melanoma, where biomarker patterns and therapeutic responses diverge markedly from those of cutaneous disease. Liquid biopsy approaches, including ctDNA, methylation signatures, and extracellular vesicles, are evaluated for minimal residual disease detection and resistance monitoring. To advance clinical translation, we propose a standardized, stepwise diagnostic therapeutic framework integrating tissue- and blood-based biomarkers with AI-enabled imaging to support personalized management in both adjuvant and metastatic settings. Key translational enablers include assay harmonization (PD-L1, TMB, ctDNA), evidence-tiered validation, and pragmatic clinical trials incorporating biomarker-driven endpoints. Addressing cost, accessibility, and data ethics will be essential for biomarker-guided precision oncology to become a sustainable clinical reality across diverse health systems.
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