Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, 210009, People's Republic of China.
State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing, 210009, People's Republic of China.
J Cancer Res Clin Oncol. 2018 Sep;144(9):1635-1647. doi: 10.1007/s00432-018-2684-7. Epub 2018 Jun 11.
Traditional classification of melanoma is widely utilized with little apparent results making the development of robust classifiers that can guide therapies an urgency. Successful seminal research on classification has provided a wider understanding of cancer from multiple molecular profiles, respectively. However, it may ignore the complementary nature of the information provided by different types of data, which motivated us to subtype melanoma by aggregating multiple genomic platform data.
Aggregating three omics data of 328 melanoma samples, melanoma subtyping was performed by three clustering methods. Differences across subtypes were extracted by functional enrichment, epigenetically silencing, gene mutations and clinical features. Subtypes were further distinguished by putative biomarkers.
Functional enrichment of the subtype-specific differential expression genes endowed subtypes new designation: immune, melanin and ion, in which the first subtype was enriched for immune system, the second was characterized by melanin and pigmentation, and the third was enriched for ion-involved transmission process. Subtypes also differed in age, Breslow thickness, tumor site, mutation frequency of BRAF, PTGS2, CDKN2A, CDKN2B and incidence of epigenetically silencing for IL15RA, EPSTI1, LXN, CDKN1B genes.
Skin cutaneous melanoma can be robustly divided into three subtypes by SNFCC. Compared with the TCGA classification derived from gene expression, the subtypes we presented share concordance, but new traits are excavated. Such a genomic classification offers insights to further personalize therapeutic decision-making and melanoma management.
传统的黑色素瘤分类方法应用广泛，但效果并不明显，因此开发能够指导治疗的强大分类器已成为当务之急。成功的分类研究分别从多个分子谱提供了对癌症的更广泛理解。然而，它可能忽略了不同类型数据提供的信息的互补性，这促使我们通过聚合多种基因组平台数据对黑色素瘤进行亚型分类。
聚合 328 个黑色素瘤样本的三个组学数据，通过三种聚类方法对黑色素瘤进行亚型分类。通过功能富集、表观遗传沉默、基因突变和临床特征提取亚型间的差异。通过假定的生物标志物进一步区分亚型。
亚型特异性差异表达基因的功能富集赋予了亚型新的命名：免疫、黑色素和离子，其中第一个亚型富含免疫系统，第二个以黑色素和色素沉着为特征，第三个亚型富含涉及离子的传输过程。亚型在年龄、Breslow 厚度、肿瘤部位、BRAF、PTGS2、CDKN2A、CDKN2B 基因突变频率和 IL15RA、EPSTI1、LXN、CDKN1B 基因的表观遗传沉默发生率方面也存在差异。
SNFCC 可以稳健地将皮肤黑色素瘤分为三个亚型。与源自基因表达的 TCGA 分类相比，我们提出的亚型具有一致性，但也挖掘出了新的特征。这种基因组分类为进一步个性化治疗决策和黑色素瘤管理提供了思路。
