2264 - Prediction of S-1 adjuvant chemotherapy efficacy in Stage II/III gastric cancer treatment based on comprehensive gene expression analysis

Background

Studies to identify predictive biomarkers of adjuvant chemotherapy with S-1 after gastrectomy in Stage II/III gastric cancer patients have been done; however, more clarity and understanding are needed. Our aim in the present study was to identify biomarkers predicting benefit due to S-1 adjuvant chemotherapy using comprehensive gene expression analysis.

Methods

We retrospectively analyzed 102 patients receiving adjuvant chemotherapy with S-1 and 46 patients not receiving S-1 adjuvant chemotherapy after gastrectomy for gastric cancer treatment between January 2014 and December 2016. Hierarchical clustering analysis was performed based on the gene expression data obtained using DNA microarrays. Differentially expressed genes (DEGs) were identified using thresholds of absolute fold changes (FCs) of > 4.0 and a false discovery rate (FDR) P value of < 0.01. Gene Ontology (GO) analysis and GO network visualization were performed using the ClueGO app in Cytoscape.

Results

Hierarchical clustering analysis in patients treated with S-1 adjuvant chemotherapy revealed two clusters with favorable and unfavorable survival outcomes. We identified 147 upregulated DEGs and 192 downregulated DEGs in the favorable outcome group. GO analysis to identify significantly upregulated genes showed enrichment in immune-related genes and GO terms. Upregulation of these immune-related genes was not associated with survival in patients not receiving S-1 adjuvant chemotherapy.

Conclusions

The upregulation and enrichment of immune-related genes and GO terms may be a predictive biomarker in patients who would benefit from adjuvant S-1 chemotherapy to treat Stage II/III gastric cancer.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

M. Terashima: Honoraria (self): Taiho; Honoraria (self): Chugai; Honoraria (self): Yakult; Honoraria (self): ONo; Honoraria (self): BMS; Honoraria (self): Eli-Lilly; Honoraria (self): Takeda; Honoraria (self): Daiichi-Sankyo; Honoraria (self): Kyowa Hakko Kirin; Honoraria (self): Nihon Kayaku; Honoraria (self): Pfizer. All other authors have declared no conflicts of interest.