Li Changchang, Lin Xiaoqiong, Wang Jinhui, Zhou Qiaochu, Feng Fangfang, Xu Jie
Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, Zhejiang, China.
Front Immunol. 2025 Jun 12;16:1601243. doi: 10.3389/fimmu.2025.1601243. eCollection 2025.
Melanoma is a highly heterogeneous malignancy with diverse molecular and clinical behaviors. A precise molecular classification is critical for improving prognostic assessment and guiding personalized therapy.
We performed an integrative multi-omics analysis of skin cutaneous melanoma using data from The Cancer Genome Atlas (TCGA) and validated our findings in independent cohorts. Multi-layered data, including transcriptomic, genomic, epigenetic, and immune landscape profiles, were analyzed using unsupervised clustering and machine learning approaches to define molecular subtypes. Functional assays and in silico drug screening were employed to explore subtype-specific vulnerabilities.
Three robust molecular subtypes (CS1, CS2, CS3) were identified, each with distinct genomic alterations, tumor microenvironment characteristics, and clinical outcomes. The CS2 subtype was immunologically "hot," characterized by high tumor mutational burden (TMB), elevated neoantigen load, strong immune infiltration, and activated IFN-γ signaling. CS2 tumors showed significant enrichment of immune checkpoint gene expression and were associated with favorable response to anti-PD-1 therapy in external validation cohorts. In contrast, CS1 and CS3 were immunologically "cold" with immune exclusion, high chromosomal instability, and activation of oncogenic pathways linked to immune evasion. Transcriptomic drug sensitivity modeling suggested that CS1 and CS3 may benefit from HSP90 or MEK inhibitors. Moreover, COL11A2 was identified as a subtype-enriched oncogenic driver predominantly expressed in CS1/CS3, and its silencing impaired tumor cell proliferation, invasion, and epithelial-mesenchymal transition (EMT) features.
This study presents a refined multi-omics classification of melanoma that reveals biologically and clinically distinct subtypes with divergent immune and therapeutic profiles. It offers a framework for subtype-specific treatment strategies, and identifies COL11A2 as a potential target in immune-cold melanomas.
黑色素瘤是一种具有高度异质性的恶性肿瘤，具有多种分子和临床行为。精确的分子分类对于改善预后评估和指导个性化治疗至关重要。
我们使用来自癌症基因组图谱（TCGA）的数据对皮肤黑色素瘤进行了综合多组学分析，并在独立队列中验证了我们的发现。使用无监督聚类和机器学习方法分析了包括转录组、基因组、表观遗传和免疫景观图谱在内的多层数据，以定义分子亚型。采用功能测定和计算机药物筛选来探索亚型特异性的脆弱性。
确定了三种稳定的分子亚型（CS1、CS2、CS3），每种亚型都有独特的基因组改变、肿瘤微环境特征和临床结果。CS2亚型在免疫方面是“热”型，其特征是肿瘤突变负荷（TMB）高、新抗原负荷升高、强烈的免疫浸润和激活的IFN-γ信号传导。CS2肿瘤显示免疫检查点基因表达显著富集，并且在外部验证队列中与对抗PD-1治疗的良好反应相关。相比之下，CS1和CS3在免疫方面是“冷”型，具有免疫排斥、高染色体不稳定性以及与免疫逃逸相关的致癌途径激活。转录组药物敏感性建模表明，CS1和CS3可能受益于HSP90或MEK抑制剂。此外，COL11A2被确定为主要在CS1/CS3中表达的亚型富集致癌驱动因子，其沉默会损害肿瘤细胞的增殖、侵袭和上皮-间质转化（EMT）特征。
本研究提出了一种精细的黑色素瘤多组学分类，揭示了具有不同免疫和治疗特征的生物学和临床不同亚型。它为亚型特异性治疗策略提供了一个框架，并将COL11A
