In 2014, The Cancer Genome Atlas (TCGA) project discovered there are four molecular subtypes of gastric cancer: Epstein-Barr virus (EBV), microsatellite instability, genomically stable, and chromosomal instability. However, their clinical significance is unknown.
Now, a study by Bo Hwa Sohn, PhD, of The University of Texas MD Anderson Cancer Center and colleagues investigating the relationship between subtypes and prognosis of patients with gastric cancer has found the molecular classification of the four distinct subtypes of gastric cancer may potentially provide tailored treatment options by helping physicians to predict survival outcomes and patients’ response to chemotherapy. Further development of the prediction model is necessary before it can be implemented into clinical practice, noted the study authors. The study was published in Clinical Cancer Research.
The researchers analyzed gene-expression data of the four subtypes of gastric cancer using genomic data from the TCGA project gastric cancer cohort (n = 262) to develop a subtype prediction model. The association of each subtype with survival and benefit from adjuvant chemotherapy was tested in two other cohorts (n = 267 and 432). An integrated risk-assessment model (TCGA risk score) was also developed.
The researchers found that the EBV subtype was associated with the best prognosis, and the genomically stable subtype was associated with the worst prognosis. Patients with microsatellite instability and chromosomal instable subtypes had poorer overall survival than those with the EBV subtype but better overall survival than those with the genomically stable subtype (P = .004 and .03 in the two cohorts, respectively).
In multivariate Cox regression analysis, the TCGA risk score was an independent prognostic factor (hazard ratio [HR] = 1.5; 95% confidence interval [CI] = 1.2–1.9; P = .001). Patients with the chromosomal instable subtype experienced the greatest benefit from adjuvant chemotherapy (HR = 0.39; 95% CI = 0.16–0.94; P = .03), and those with the genomically stable subtype had the least benefit from adjuvant chemotherapy (HR = 0.83; 95% CI = 0.36–1.89; P = .65).
“The validation of our prediction model in two independent patient cohorts and the fact that the model reflects the biological characteristics associated with each subtype indicate this prediction model could be used to develop rational therapy recommendations. If confirmed in prospective studies, the association between subtype and adjuvant chemotherapy outcomes might improve patient selection for treatment,” concluded the study authors.
“These findings, if confirmed, could provide some information for personalized medicine,” said Ju- Seog Lee, PhD, Associate Professor in the Department of Systems Biology at The University of Texas MD Anderson Cancer Center in Houston, and a coauthor of the study, in a statement. “As we learn more about the biological characteristics associated with each subtype, it will help determine which patients will benefit from immunotherapy, chemotherapy, or other treatment options.” ■