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.

Metabolic and immune‐related gene signatures: Predictive stratification and prognostic implications in gastric cancer

“…9 Several studies have demonstrated the potential of immune and metabolic gene-based signatures for predicting GC prognosis and guiding treatment decisions. 10,11 However, challenges remain in the development and validation of these signatures, such as the need for large, high-quality datasets and the identification of robust biomarkers that can be translated into the clinic. In this paper, we will review recent advances in the construction of immune and metabolic gene-based prognosis signatures in GC and discuss their potential clinical applications.…”

“…Several studies have demonstrated the potential of immune and metabolic gene‐based signatures for predicting GC prognosis and guiding treatment decisions 10,11 . However, challenges remain in the development and validation of these signatures, such as the need for large, high‐quality datasets and the identification of robust biomarkers that can be translated into the clinic.…”