Background: Incidence rates for GC have shown to be increased in H vs NHW in studies. With a higher incidence of GC in South Texas compared to the rest of US, South Texas H have more than twice the incidence of GC than NHW with possibly worse survivals. However, these patients are not included in the SEER database, and the etiology of worse survival is unclear. To date, evaluation of a South Texas population with a more homogeneous H population has not been undertaken. Therefore, we chose to evaluate differences in clinical and histopathologic features in H vs NHW with GC at our South Texas NCI-designated cancer center where the homogenous H population is the majority. Methods: Retrospective analysis of GC patients from 2000-2016 at the Cancer Therapy and Research Center, San Antonio, TX. Median overall survival (mOS) was estimated from Kaplan-Meier curves and groups were statistically compared with the log rank test. Results: 168 patients with H 64% (n = 108), NHW 36% (n = 60). Median age 59 years (26-94): H 59.6 vs NHW 58.8 years. Females: H 48% vs NHW 37%. ECOG 0-1: H 48% vs NHW 53% (p = 0.46). Common Locations: H (Antrum 30%, Body 30%, GEJ 16%, Cardia 7%, Fundus 3%) vs NHW (Antrum 23%, Body 40%, GEJ 18%, Cardia 2%, Fundus 2%) (see table). Poorly differentiated: H 58.3% vs NHW 48.3% (P = 1). Stage IV at diagnosis: H 42% vs NHW 23% (P = 0.22). Diffuse-type: H 38% vs NHW 30% (P = 0.41); Intestinal-type: H 9% vs NHW 10% (P = 0.78). No significant difference in regards to Her2, H pylori, location of metastases. mOS: H 11 months (95% CI: 7-14) vs NHW 9.5 months (95% CI 6-n/a), p = 0.66. Conclusions: Despite historical worse prognosis of GC in H, at our majority minority cancer center in South Texas,H did not have a worse prognosis than NHW, and significant differences were not observed in clinicopathologic features. Prospective studies of H patients with GC should investigate differences in epidemiology, pathogenesis, and molecular signatures of GC between both groups to identify variables that correlate with survival and predict efficacy to cancer treatments.