Abstract
Increasing evidence has indicated that current tumor-node-metastasis (TNM) stage alone cannot predict prognosis and adjuvant chemotherapy benefits accurately for stages II and III gastric cancer (GC) patients after surgery. In order to improve the predictive ability of survival and adjuvant chemotherapy benefits of GC patients after surgery, this study aimed to establish an immune signature based on the composition of infiltrating immune cells. Twenty-eight types of immune cell fractions were evaluated based on the expression profiles of GC patients from the Gene Expression Omnibus (GEO) database using single-sample gene set enrichment analysis (ssGSEA). The immunoscore (IS) was constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model. Through the LASSO model, an IS classifier consisting of eight immune cells was constructed. Significant difference was found between high-IS and low-IS groups in the training cohort in disease-free survival (DFS, P < 0.0001) and overall survival (OS, P < 0.0001). Multivariate analysis showed that the IS classifier was an independent prognostic indicator. Moreover, a combination of IS and TNM stage exhibited better prognostic value than TNM stage alone. Further analysis demonstrated that low-IS patients who had more tumor-infiltrating lymphocytes had better response to adjuvant chemotherapy. More importantly, we found that patients with high-IS were more likely to benefit from a Xeloda plus cisplatin regimen after surgery. Finally, we established two nomograms to screen the stage II and III GC patients who benefitted from adjuvant chemotherapy after surgery. The combination of IS classifier and TNM stage could predict DFS and OS of GC patients. The IS model has been proven as a promising tool that can be used to identify the patients with stages II and III GC who may benefit from adjuvant chemotherapy.
Highlights
Gastric cancer (GC) is the fifth most common malignant cancer and the third leading cause of cancer-related deaths around the world [1]
We described the landscape of 28 immune cells in gastric cancer (GC) applying singlesample gene set enrichment analysis (ssGSEA) and established a novel immune cellsbased model using least absolute shrinkage and selection operator (LASSO) Cox regression, IS, to predict DFS and OS in patients after surgery
We analyzed the pattern of tumor-infiltrating immune cells (TIICs) by using ssGSEA and constructed an eight-immune cells model through LASSO Cox regression based on DFS
Summary
Gastric cancer (GC) is the fifth most common malignant cancer and the third leading cause of cancer-related deaths around the world [1]. The International Duration Evaluation of Adjuvant Therapy (IDEA) France cohort study showed that 3-year DFS rates of patients who received mFOLFOX6 for 6 months were not dramatically superior to those treated for 3 months (72% vs 76%; HR, 1.44; 95% CI, 1.14–1.82) [16], a follow-up study on IS, consisting of CD3 and CD8, revealed that patients whose tumor had been infiltrated by more lymphocytes (IS-Intermediate+ High) benefited more from 6 months of oxaliplatin-based adjuvant chemotherapy compared with those treated with 3 months of adjuvant chemotherapy (HR, 0.528; 95% CI, 0.372-0.750; P = 0.0004), indicating that tumor-infiltrating immune cells (TIICs) have critical effects on DFS and adjuvant chemosensitivity in cancers [17]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
