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Mutational landscape of gastric adenocarcinoma in Latin America: A genetic approach for precision medicine

Latin-America (LATAM) is the second region in gastric cancer incidence; gastric adenocarcinoma (GA) represents 95% of all cases. We provide a mutational landscape of GA highlighting a) germline pathogenic variants associated with hereditary GA, b) germline risk variants associated with sporadic GA,...

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Detalles Bibliográficos
Autores principales: Cerrato-Izaguirre, Dennis, Chirino, Yolanda I., García-Cuellar, Claudia M., Santibáñez-Andrade, Miguel, Prada, Diddier, Hernández-Guerrero, Angélica, Larraga, Octavio Alonso, Camacho, Javier, Sánchez-Pérez, Yesennia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Chongqing Medical University 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170608/
https://www.ncbi.nlm.nih.gov/pubmed/35685475
http://dx.doi.org/10.1016/j.gendis.2021.04.002
Descripción
Sumario:Latin-America (LATAM) is the second region in gastric cancer incidence; gastric adenocarcinoma (GA) represents 95% of all cases. We provide a mutational landscape of GA highlighting a) germline pathogenic variants associated with hereditary GA, b) germline risk variants associated with sporadic GA, and c) somatic variants present in sporadic GA in LATAM, and analyze how this landscape can be applied for precision medicine. We found that Brazil, Chile, Colombia, Mexico, Peru, and Venezuela are the countries with more published studies from LATAM explicitly related to GA. Our analysis displayed that different germline pathogenic variants for the CDH1 gene have been identified for hereditary GA in Brazilian, Chilean, Colombian, and Mexican populations. An increased risk of developing somatic GA is associated with the following germline risk variants: IL-4, IL-8, TNF-α, PTGS2, NFKB1, RAF1, KRAS and MAPK1 in Brazilian; IL-10 in Chilean; IL-10 in Colombian; EGFR and ERRB2 in Mexican, TCF7L2 and Chr8q24 in Venezuelan population. The path from mutational landscape to precision medicine requires four development levels: 1) Data compilation, 2) Data analysis and integration, 3) Development and approval of clinical approaches, and 4) Population benefits. Generating local genomic information is the initial padlock to overcome to generate and apply precision medicine.