The polygenic implication of clopidogrel responsiveness: Insights from platelet reactivity analysis and next-generation sequencing

Abstract

Clopidogrel is widely used worldwide as an antiplatelet therapy in patients with acute coronary disease. Genetic factors influence interindividual variability in response. Some studies have explored the polygenic contributions in the drug response, generating pharmacogenomic risk scores (PgxPRS). Importantly, these factors are less explored in underrepresented populations, such as Latin-American countries. Identifying patients at risk of high-on-treatment platelet reactivity (HTPR) is highly valuable in translational medicine. In this study we used a custom next-generation sequencing (NGS) panel composed of 91 single nucleotide polymorphisms (SNPs) and 28 genes related to clopidogrel metabolism, to analyze 70 patients with platelet reactivity values, assessed through closure time (CT). Our results demonstrated the association of SNPs with HTPR and non-HTPR, revealing the strongest associations with rs2286823 (OR: 5,0; 95% CI: 1,02-24,48; p: 0,03), rs2032582 (OR: 4,41; 95% CI: 1,20-16,12; p: 0,019), and rs1045642 (OR: 3,38; 95% CI: 0,96-11,9; p: 0,05). Bivariate regression analysis demonstrated the significant association of several SNPs with the CT value, a "surrogate" biomarker of clopidogrel response. Exploratory results from the LASSO regression model showed a high discriminatory capacity between HTPR and non-HTPR patients (AUC: 0,955), and the generated PgxPRS demonstrated a significant negative association between the risk score, CT value, and the condition of HTPR and non-HTPR. To our knowledge, our study addresses for the first time the analysis of the polygenic contribution in platelet reactivity using NGS and establishes PgxPRS derived from the LASSO model. Our results demonstrate the polygenic implication of clopidogrel response and offer insights applicable to the translational medicine of antiplatelet therapy in an understudied population.

Fig 1. Bioinformatic pipeline.

Fig 2. Cross-validation plot for the penalty term λ based on differentially expressed metabolites.

Vertical bars represent acceptable maximum and minimum λ values with corresponding mean-squared error and the number of covariates for each model.

Fig 3. Plots for lasso regression coefficients.

Over different values of the penalty parameter λ for each model.

Fig 4. ROC curves for training datasets for the estimated model.

Fig 5. Boxplot PGxPRS vs HTPR condition.

Boxplot illustrating the relationship between responders and non-responders with respect to PGxPRS. The Mann-Whitney test reveals a statistically significant difference (p<0.0003).

Fig 6. Scatterplot between polygenic risk score (PGxPRS) and obturation time (CT) with the added regression line illustrating the Spearman’s rho correlation (p<0.05).