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The risk factors for each recurrence pattern and timing of gastric cancer can be predicted by the clinicopathological features of the primary tumour. Since the results of treatment remain dismal, studies of perioperative adjuvant therapy in an attempt to reduce recurrence are warranted.

Purpose The Cancer Genome Atlas (TCGA) project recently uncovered four molecular subtypes of gastric cancer: Epstein-Barr virus (EBV), microsatellite instability (MSI), genomically stable (GS), and chromosomal instability (CIN). However, their clinical significances are currently unknown. We aimed to investigate the relationship between subtypes and prognosis of patients with gastric cancer. Experimental Design Gene expression data from a TCGA cohort (n = 262) were used to develop a subtype prediction model, and the association of each subtype with survival and benefit from adjuvant chemotherapy was tested in 2 other cohorts (n = 267 and 432). An integrated risk assessment model (TCGA risk score) was also developed. Results EBV subtype was associated with the best prognosis and GS subtype was associated with the worst prognosis. Patients with MSI and CIN subtypes had poorer overall survival than those with EBV subtype but better overall survival than those with GS subtype (P = 0.004 and 0.03 in two cohorts respectively). In multivariate Cox regression analyses, TCGA risk score was an independent prognostic factor (hazard ratio [HR] = 1.5; 95% confidence interval [CI] = 1.2–1.9; P = 0.001). Patients with the CIN subtype experienced the greatest benefit from adjuvant chemotherapy (HR = 0.39; 95% CI = 0.16–0.94; P = 0.03) and those with the GS subtype had the least benefit from adjuvant chemotherapy (HR = 0.83; 95% CI = 0.36–1.89; P = 0.65). Conclusion Our prediction model successfully stratified patients by survival and adjuvant chemotherapy outcomes. Further development of the prediction model is warranted.

Purpose: Despite continual efforts to develop a prognostic model of gastric cancer by using clinical and pathologic parameters, a clinical test that can discriminate patients with good outcomes from those with poor outcomes after gastric cancer surgery has not been established. We aim to develop practical biomarker-based risk score that can predict relapse of gastric cancer after surgical treatment.Experimental Design: Microarray technologies were used to generate and analyze gene expression profiling data from 65 gastric cancer patients to identify biomarker genes associated with relapse. The association of expression patterns of identified genes with relapse and overall survival was validated in independent gastric cancer patients.Results: We uncovered two subgroups of gastric cancer that were strongly associated with the prognosis. For the easy translation of our findings into practice, we developed a scoring system based on the expression of six genes that predicted the likelihood of relapse after curative resection. In multivariate analysis, the risk score was an independent predictor of relapse in a cohort of 96 patients. We were able to validate the robustness of the six-gene signature in an additional independent cohort.Conclusions: The risk score derived from the six-gene set successfully prognosticated the relapse of gastric cancer patients after gastrectomy.

BACKGROUND & AIMS Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that each have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences patient survival times and responses to various standard-of-care cytotoxic drugs. METHODS We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-FU, cisplatin, oxaliplatin) was also assessed. RESULTS Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF), that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren’s histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-FU and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-FU based therapy. CONCLUSIONS Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.

PURPOSE In the CLASSIC and MAGIC trials, microsatellite instability (MSI)–high status was a favorable prognostic and potential negative predictive factor for neoadjuvant/adjuvant chemotherapy in resectable gastric cancer (GC). Given the low prevalence of MSI-high status in GC and its association with other positive prognostic variables, large data sets are needed to draw robust evidence of its prognostic/predictive value. PATIENTS AND METHODS We performed a multinational, individual-patient-data meta-analysis of the prognostic/predictive role of MSI in patients with resectable GC enrolled in the MAGIC, CLASSIC, ARTIST, and ITACA-S trials. Prognostic analyses used multivariable Cox models (MVM). The predictive role of MSI was assessed both in an all-comer population and in MAGIC and CLASSIC trials by MVM testing of the interaction of treatment (chemotherapy plus surgery v surgery) with MSI. RESULTS MSI status was available for 1,556 patients: 121 (7.8%) had MSI-high status; 576 were European, and 980 were Asian. In MSI-high versus MSI-low/microsatellite stable (MSS) comparisons, the 5-year disease-free survival (DFS) was 71.8% (95% CI, 63.8% to 80.7%) versus 52.3% (95% CI, 49.7% to 55.1%); the 5-year overall survival (OS) was 77.5% (95% CI, 70.0% to 85.8%) versus 59.3% (95% CI, 56.6% to 62.1%). In MVM, MSI was associated with longer DFS (hazard ratio [HR], 1.88; 95% CI, 1.28 to 2.76; P < .001) and OS (HR, 1.78; 95% CI, 1.17 to 2.73; P = .008), as were pT, pN, ethnicity, and treatment. Patients with MSI-low/MSS GC benefitted from chemotherapy plus surgery: the 5-year DFS compared with surgery only was 57% versus 41% (HR, 0.65; 95% CI, 0.53 to 0.79), and the 5-year OS was 62% versus 53% (HR, 0.75; 95% CI, 0.60 to 0.94). Conversely, those with MSI-high GC did not: the 5-year DFS was 70% versus 77% (HR, 1.27; 95% CI, 0.53 to 3.04), and the 5-year OS was 75% versus 83% (HR, 1.50; 95% CI, 0.55 to 4.12). CONCLUSION In patients with resectable primary GC, MSI is a robust prognostic marker that should be adopted as a stratification factor by clinical trials. Chemotherapy omission and/or immune checkpoint blockade should be investigated prospectively in MSI-high GCs according to clinically and pathologically defined risk of relapse.

Gastric cancer is a heterogeneous cancer, making treatment responses difficult to predict. Here we show that we identify two distinct molecular subtypes, mesenchymal phenotype (MP) and epithelial phenotype (EP), by analyzing genomic and proteomic data. Molecularly, MP subtype tumors show high genomic integrity characterized by low mutation rates and microsatellite stability, whereas EP subtype tumors show low genomic integrity. Clinically, the MP subtype is associated with markedly poor survival and resistance to standard chemotherapy, whereas the EP subtype is associated with better survival rates and sensitivity to chemotherapy. Integrative analysis shows that signaling pathways driving epithelial-to-mesenchymal transition and insulin-like growth factor 1 (IGF1)/IGF1 receptor (IGF1R) pathway are highly activated in MP subtype tumors. Importantly, MP subtype cancer cells are more sensitive to inhibition of IGF1/IGF1R pathway than EP subtype. Detailed characterization of these two subtypes could identify novel therapeutic targets and useful biomarkers for prognosis and therapy response.