Liu Zhixian, Jiang Zehang, Wu Nan, Zhou Guoren, Wang Xiaosheng
The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Institute of Cancer Research, Jiangsu Cancer Hospital, 42 Baiziting, Nanjing 210009, Jiangsu, China; Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, Jiangsu, China.
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 500040, Guangdong, China; Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, Jiangsu, China.
Transl Oncol. 2021 Jan;14(1):100888. doi: 10.1016/j.tranon.2020.100888. Epub 2020 Oct 20.
Extensive evidence showed that gastric cancer (GC) is heterogeneous, and many studies have been focused on identifying GC subtypes based on genomic profiles. However, few studies have specifically explored the GC classification and predicted the classification accuracy that may help facilitate the optimal stratification of GC patients responsive to immunotherapy.
Using two publicly available GC genomics datasets, we classified GC on the basis of 797 immune related genes. Unsupervised and supervised machine learning methods were used to predict the classification.
We identified two GC subtypes that we named as Immunity-High (IM-H) and Immunity- Low (IM-L), and demonstrated that this classification was duplicable and predictable by analyzing other datasets. IM-H subtype was characterized by greater immune cell infiltration, stronger immune activities, lower tumor purity, as well as worse survival prognosis compared to IM-L subtype. Besides the immune signatures, some cancer-associated pathways were hyperactivated in IM-H, including TGF-beta signaling pathway, Focal adhesion, Cell adhesion molecules (CAMs), Calcium signaling pathway, mTOR signaling pathway, MAPK signaling pathway and Wnt signaling pathway. In contrast, IM-L presented depressed immune signatures and increased activation of base excision repair, DNA replication, homologous recombination, non-homologous end-joining and nucleotide excision repair pathways. Furthermore, we identified subtype-specific genomic or clinical features, and subtype-specific gene ontology and networks in IM-H and IM-L subtype.
We proposed and validated two reproducible immune molecular subtypes of GC, which has potential clinical implications for GC patient selection of immunotherapy.
大量证据表明，胃癌（GC）具有异质性，许多研究致力于基于基因组图谱识别GC亚型。然而，很少有研究专门探讨GC分类并预测有助于促进对免疫治疗有反应的GC患者进行最佳分层的分类准确性。
利用两个公开可用的GC基因组数据集，我们基于797个免疫相关基因对GC进行分类。使用无监督和监督机器学习方法来预测分类。
我们识别出两种GC亚型，命名为免疫高（IM-H）和免疫低（IM-L），并通过分析其他数据集证明这种分类是可重复和可预测的。与IM-L亚型相比，IM-H亚型的特征是免疫细胞浸润更多、免疫活性更强、肿瘤纯度更低以及生存预后更差。除了免疫特征外，一些癌症相关通路在IM-H中过度激活，包括TGF-β信号通路、粘着斑、细胞粘附分子（CAMs）、钙信号通路、mTOR信号通路、MAPK信号通路和Wnt信号通路。相比之下，IM-L呈现出免疫特征降低以及碱基切除修复、DNA复制、同源重组、非同源末端连接和核苷酸切除修复通路的激活增加。此外，我们在IM-H和IM-L亚型中识别出亚型特异性的基因组或临床特征以及亚型特异性的基因本体和网络。
我们提出并验证了两种可重复的GC免疫分子亚型，这对GC患者免疫治疗的选择具有潜在的临床意义。
