Identification of gastric cancer subtypes based on pathway clustering
Gastric cancer (GC) is one of the leading causes of cancer deaths in the world and particularly prevails in East Asia. Abundant evidence had shown that GC is highly heterogeneous. A classification of GC based on various different features may provide new insights into the heterogeneity in GC.
This study proposed a new classification method based on the activities of four types of pathways, including immune pathways (natural killer cell-mediated cytotoxicity, antigen processing and presentation, T cell receptor signaling, B cell receptor signaling, and Fc gamma R-mediated phagocytosis), stromal pathways (ECM–receptor interaction, focal adhesion, and tight junction), DNA damage repair pathways (p53 signaling, mismatch repair, and homologous recombination), and oncogenic pathways (PI3K-Akt signaling, Wnt signaling, TGF-β signaling, and cell cycle). We identified three GC subtypes: Immunity-deprived (ImD), Stroma-enriched (StE), and Immunity-enriched (ImE). ImD showed low immune infiltration, high DNA damage repair activity, large-scale genome instability (aneuploidy), high intratumor heterogeneity, and frequent TP53 mutations. StE displayed high stromal signatures, low DNA damage repair activity, genomic stability, low intratumor heterogeneity, and poor prognosis. ImE had strong immune infiltration, high DNA damage repair activity, small-scale genome instability with high tumor mutation burden (TMB), the prevalence of microsatellite instability, frequent ARID1A mutations, elevated PD-L1 expression, and favorable prognosis. Based on the expression levels of four genes in the four types of pathways, we developed a prognostic model (IDOScore), which was an adverse prognostic factor and correlated inversely with immunotherapy response in cancer.
This study is interesting for several reasons. First, for the first time, we proposed a pathway-based classification method for GC. We demonstrated that this classification method was stable and reproducible using multiple datasets. Second, our classification method captures the comprehensive heterogeneity of GC in tumor immune and stromal microenvironment, DNA damage repair activity, genomic integrity, intratumor heterogeneity, profiles of somatic mutation, somatic copy number alteration, DNA methylation, and protein expression, response to chemotherapy and immunotherapy, and clinical outcomes. Third, we showed that the high TMB was not necessarily correlated with an active anti-tumor immune response in GC. Finally, the linear model IDOScore has potential clinical utility for predicting cancer prognosis and immunotherapy response.
The identification of new GC subtypes provides novel insights into tumor biology and has potential clinical implications for the management of GCs.