High Proportion of Potential Candidates for Immunotherapy in a Chilean Cohort of Gastric Cancer Patients: Results of the FORCE1 Study

Miguel Cordova-Delgado, Mauricio P Pinto, Ignacio N Retamal, Matías Muñoz-Medel, María Loreto Bravo, María F Fernández, Betzabé Cisternas, Sebastián Mondaca, César Sanchez, Hector Galindo, Bruno Nervi, Carolina Ibáñez, Francisco Acevedo, Jorge Madrid, José Peña, Erica Koch, Maria José Maturana, Diego Romero, Nathaly de la Jara, Javiera Torres, Manuel Espinoza, Carlos Balmaceda, Yuwei Liao, Zhiguang Li, Matías Freire, Valentina Gárate-Calderón, Javier Cáceres, Gonzalo Sepúlveda-Hermosilla, Rodrigo Lizana, Liliana Ramos, Rocío Artigas, Enrique Norero, Fernando Crovari, Ricardo Armisén, Alejandro H Corvalán, Gareth I Owen, Marcelo Garrido, Miguel Cordova-Delgado, Mauricio P Pinto, Ignacio N Retamal, Matías Muñoz-Medel, María Loreto Bravo, María F Fernández, Betzabé Cisternas, Sebastián Mondaca, César Sanchez, Hector Galindo, Bruno Nervi, Carolina Ibáñez, Francisco Acevedo, Jorge Madrid, José Peña, Erica Koch, Maria José Maturana, Diego Romero, Nathaly de la Jara, Javiera Torres, Manuel Espinoza, Carlos Balmaceda, Yuwei Liao, Zhiguang Li, Matías Freire, Valentina Gárate-Calderón, Javier Cáceres, Gonzalo Sepúlveda-Hermosilla, Rodrigo Lizana, Liliana Ramos, Rocío Artigas, Enrique Norero, Fernando Crovari, Ricardo Armisén, Alejandro H Corvalán, Gareth I Owen, Marcelo Garrido

Abstract

Gastric cancer (GC) is a heterogeneous disease. This heterogeneity applies not only to morphological and phenotypic features but also to geographical variations in incidence and mortality rates. As Chile has one of the highest mortality rates within South America, we sought to define a molecular profile of Chilean GCs (ClinicalTrials.gov identifier: NCT03158571/(FORCE1)). Solid tumor samples and clinical data were obtained from 224 patients, with subsets analyzed by tissue microarray (TMA; n = 90) and next generation sequencing (NGS; n = 101). Most demographic and clinical data were in line with previous reports. TMA data indicated that 60% of patients displayed potentially actionable alterations. Furthermore, 20.5% were categorized as having a high tumor mutational burden, and 13% possessed micro-satellite instability (MSI). Results also confirmed previous studies reporting high Epstein-Barr virus (EBV) positivity (13%) in Chilean-derived GC samples suggesting a high proportion of patients could benefit from immunotherapy. As expected, TP53 and PIK3CA were the most frequently altered genes. However, NGS demonstrated the presence of TP53, NRAS, and BRAF variants previously unreported in current GC databases. Finally, using the Kendall method, we report a significant correlation between EBV+ status and programmed death ligand-1 (PDL1)+ and an inverse correlation between p53 mutational status and MSI. Our results suggest that in this Chilean cohort, a high proportion of patients are potential candidates for immunotherapy treatment. To the best of our knowledge, this study is the first in South America to assess the prevalence of actionable targets and to examine a molecular profile of GC patients.

Keywords: cancer subtypes; gastric adenocarcinoma; gastric cancer; molecular; prognosis; survival.

Conflict of interest statement

CEMP is affiliated to Pfizer Chile. The authors declare no other conflict of interest.

Figures

Figure 1
Figure 1
Overall survival rates in the FORCE1 cohort. Kaplan–Meier curves indicate overall survival for (A) the entire cohort, (B) by gender, (C) by cancer stage, (D) by histological type.
Figure 2
Figure 2
Profiling of 48 Chilean gastric cancers by next generation sequencing (NGS), clinical, and pathological characteristics. The waterfall plot shows the number of gene alterations per patient (upper section), number of alterations per gene (right). Colored squares indicate the alteration type (SNV, CNV, or fusion drivers/see key). Clinical (age, gender, Lauren classification, signet ring, tumor location), and pathological (PDL1, MSI, HER2, p16, p53, EBV) characteristics for each patient are shown in the lower section.
Figure 3
Figure 3
Kendall correlations between tissue microarray (TMA) and clinical data (a) or between NGS and clinical data (b). Significant correlations (p < 0.05) are indicated by colored squares; positive correlations are in red and negative in blue.

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