Classification of gastric cancers based on immunogenomic profiling

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: We proposed and validated two reproducible immune molecular subtypes of GC, which has potential clinical implications for GC patient selection of immunotherapy.