Hispanic/Latino Patients with Gastric Adenocarcinoma Have Distinct Molecular Profiles Including a High Rate of Germline CDH1 Variants.
Sam C WangYunku YeuSuntrea T G HammerShu XiaoHao ZhuChangjin HongJean R ClemenceauLynn Y YoonIbrahim NassourJeanne ShenDeepak AgarwalScott I ReznikJohn C MansourAdam C YoppTae Hyun HwangMatthew R PorembkaPublished in: Cancer research (2020)
Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary. SIGNIFICANCE: Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.
Keyphrases
- end stage renal disease
- chronic kidney disease
- ejection fraction
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- healthcare
- randomized controlled trial
- machine learning
- type diabetes
- insulin resistance
- single cell
- hepatitis c virus
- metabolic syndrome
- radiation therapy
- oxidative stress
- risk factors
- copy number
- dna damage
- single molecule
- artificial intelligence
- human immunodeficiency virus
- deep learning
- data analysis
- biofilm formation
- glycemic control
- hiv testing