Stromal-Based Signatures for the Classification of Gastric Cancer

Abstract

Treatment of metastatic gastric cancer typically involves chemotherapy and monoclonal antibodies targeting HER2 (ERBB2) and VEGFR2 (KDR). However, reliable methods to identify patients who would benefit most from a combination of treatment modalities targeting the tumor stroma, including new immunotherapy approaches, are still lacking. Therefore, we integrated a mouse model of stromal activation and gastric cancer genomic information to identify gene expression signatures that may inform treatment strategies. We generated a mouse model in which VEGF-A is expressed via adenovirus, enabling a stromal response marked by immune infiltration and angiogenesis at the injection site, and identified distinct stromal gene expression signatures. With these data, we designed multiplexed IHC assays that were applied to human primary gastric tumors and classified each tumor to a dominant stromal phenotype representative of the vascular and immune diversity found in gastric cancer. We also refined the stromal gene signatures and explored their relation to the dominant patient phenotypes identified by recent large-scale studies of gastric cancer genomics (The Cancer Genome Atlas and Asian Cancer Research Group), revealing four distinct stromal phenotypes. Collectively, these findings suggest that a genomicsbased systems approach focused on the tumor stroma can be used to discover putative predictive biomarkers of treatment response, especially to antiangiogenesis agents and immunotherapy, thus offering an opportunity to improve patient stratification.

Molecular classifications of gastric cancers: Novel insights and possible future applications

“…Fifth, both classifications insist on epithelial cells, but none of them take into account the active, nonmalignant stromal cells. Actually, not only gene expression profiles deriving from stromal tissues may influence assignment to a specific molecular category, thus creating interpretative troubles[8], but also novel stromal-based distinctive signatures have been proposed and related to the predominant cancer phenotype[9]. …”

Anti-Angiogenics: Their Value in Colorectal Cancer Therapy

“…A deeper understanding of the complexity of the tumor milieu and the molecular and cellular dynamics of tumor angiogenesis will be necessary to predict the clinical responsiveness of individual cancers. For the future, predictive angiogenic biomarkers will be indispensable tools for rational treatment allocation and cost containment [46,47].…”

Dissection of gastric cancer heterogeneity for precision oncology

“…In this regard, multiple studies in GC have established the association of stromal content with survival outcomes and/or therapeutic responses. [73][74][75][76][77][78][79] Notably, high stromal gene expression consistently predicted poor clinical outcome in several independent GC cohorts, 73,77 which could, in part, explain the poor clinical prognosis of Lauren's DGC subtype which is typically characterized by high stromal infiltration and close interaction between cancer cells and CAF. 80 Digging deeper into the complex interactions and cross-talk between cancer cells and their associated microenvironment therefore holds the potential to reveal actionable targets in this devastating subtype.…”

“…This was clearly exemplified by Isella et al where the mesenchymal transcriptional subtype of CRC was found to be predominantly driven by stromal components, suggesting a possible superimposition of the EMT process in tumor cells with the inherent mesenchymal traits of stromal cells. In this regard, multiple studies in GC have established the association of stromal content with survival outcomes and/or therapeutic responses . Notably, high stromal gene expression consistently predicted poor clinical outcome in several independent GC cohorts, which could, in part, explain the poor clinical prognosis of Lauren's DGC subtype which is typically characterized by high stromal infiltration and close interaction between cancer cells and CAF .…”