Clinical impact of pharmacogenetic risk variants in a large chinese cohort

Abstract

Incorporating pharmacogenetics into clinical practice promises to improve therapeutic outcomes by optimizing drug selection and dosage based on genetic factors affecting drug response. A key advantage of PGx-guided therapy is to decrease the likelihood of adverse events. To evaluate the clinical impact of PGx risk variants, we performed a retrospective study using genetic and clinical data from the largest Han Chinese cohort, comprising 486,956 individuals, assembled by the Taiwan Precision Medicine Initiative. We found that nearly all participants carried at least one genetic variant that could affect drug response, with many carrying multiple risk variants. Here we show the detailed analyses of four gene-drug pairs, azathioprine (NUDT15/TPMT), clopidogrel (CYP2C19), statins (ABCG2/CYP2C9/SLCO1B1), and NSAIDs (CYP2C9), for which sufficient data exists for statistical power. While the results validate previous findings that PGx risk variants are significantly associated with drug-related adverse events or ineffectiveness, the excess risk of adverse events or lack of efficacy is small compared to that found in those without the PGx risk variants, and most patients with PGx variants do not suffer from adverse events. Our results point to the complexity of implementing PGx in clinical practice and the need for integrative approaches to optimize precision medicine.

Fig. 1. TPMI participants with actionable variants/HLA types in 19 pharmacogenes.

a The fraction of TPMI participants with actionable variants/HLA types in pharmacogenes; (b) The number of individuals carrying actionable PGx risk variants or HLA types.

Fig. 2. The distribution of risk drugs prescription in people carrying actionable PGx variants.

The number of people carrying actionable PGx risk variants or HLA types who took (orange) or did not take (blue) the drug for which they were at risk; number above bar denotes those who took the drug for which they were at risk.

Fig. 3. Impact of CYP2C19 in clopidogrel-related MACE.

Forest plot of the MACE risk in clopidogrel users with different CYP2C19 phenotypes: (a) people who carried at least one CYP2C19 LoF alleles vs non-carriers, (b) CYP2C19 IM vs NM, and (c) CYP2C19 PM vs NM. The case number, OR, 95% CI, and P value were listed in the table. The ORs and 95% CIs were estimated using logistic regression adjusting for covariates (two-sided test). Data points represent odds ratios; error bars represent 95% confidence intervals. Significant associations are shown in red. CI = confident interval; CV_death = cardiovascular death; HF = heart failure; IM = intermediate metabolizer; LoF = loss-of-function; MACE = major adverse cardiovascular events; MI = myocardial infarction; NM = normal metabolizer; OR = odds ratio; PM = poor metabolizer; TLR = target lesion revascularization; UA = unstable angina.

Fig. 4. Influence of NUDT15 and TPMT for AZA discontinuation due to ADE.

Forest plot of the ADE risk in AZA users with different NUDT15 and TPMT phenotypes: (a) people who carried at least one NUDT15 LoF alleles vs non-carriers, (b) NUDT15 IM vs NM, (c) NUDT15 PM vs NM, (d) people who carried at least one TPMT LoF alleles vs non-carriers, (e) TPMT IM vs NM. The case number, OR, 95% CI, and P value were listed in the table. The ORs and 95% CIs were estimated using logistic regression adjusting for covariates (two-sided test). Data points represent odds ratios; error bars represent 95% confidence intervals. Significant associations are shown in red. ADE = adverse events; CI = confident interval; GI = GI discomfort; IM = intermediate metabolizer; LoF = loss-of-function; NM = normal metabolizer; OR = odds ratio; PM = poor metabolizer.

Fig. 5. Influence of ABCG2/SLCO1B1/CYP2C9 in SAMs.

Forest plot of the SAM risk in statin users with different ABCG2, SLCO1B1, or CYP2C9 phenotypes: (a) atorvastatin users who carried two ABCG2 LoF alleles vs non-carriers, (b) fluvastatin users who carried one ABCG2 LoF alleles vs non-carriers, (c) simvastatin users who carried two ABCG2 LoF alleles vs non-carriers, (d) atorvastatin users who carried at least one SLCO1B1 LoF alleles vs non-carriers, (e) simvastatin users who carried at least one SLCO1B1 LoF alleles vs non-carriers, and (f) fluvastatin users who carried at least one CYP2C9 LoF alleles vs non-carriers. The case number, OR, 95% CI, and P value were listed in the table. The ORs and 95% CIs were estimated using logistic regression adjusting for covariates (two-sided test). Data points represent odds ratios; error bars represent 95% confidence intervals. Significant associations are shown in red. CI = confident interval; IM = intermediate metabolizer; LoF = loss-of-function; NM = normal metabolizer; OR = odds ratio; PM = poor metabolizer. SAM = statin-associated muscle events, including myalgia, myositis, and rhabdomyolysis; sSAM = severe forms of statin-associated muscle events, specifically myositis and rhabdomyolysis.

Fig. 6. Influence of CYP2C9 in NSAID-associated adverse events.

Forest plot of the ADE risk in statin users with different CYP2C9 phenotypes: (a) people who carried at least one CYP2C9 LoF alleles vs non-carriers, (b) CYP2C9 IM vs NM, and (c) CYP2C9 PM vs NM. The case number, OR, 95% CI, and P value were listed in the table. The ORs and 95% CIs were estimated using logistic regression adjusting for covariates (two-sided test). Data points represent odds ratios; error bars represent 95% confidence intervals. Significant associations are shown in red. CI = confident interval; GI = GI discomfort; IM = intermediate metabolizer; LoF = loss-of-function; NM = normal metabolizer; OR = odds ratio; PM = poor metabolizer.