Combining clinical and candidate gene data into a risk score for azathioprine-associated leukopenia in routine clinical practice

Association Between Genetically Predicted Expression of TPMT and Azathioprine Adverse Events

Polymorphisms thiopurine-S-methyltransferase (TPMT) and nudix hydrolase 15 (NUDT15) can increase the risk of azathioprine myelotoxicity, but little is known about other genetic factors that increase risk for azathioprine-associated side effects. PrediXcan is a gene-based association method that estimates the expression of individuals' genes and examines their correlation to specified phenotypes. As proof of concept for using PrediXcan as a tool to define the association between genetic factors and azathioprine side effects, we aimed to determine whether the genetically predicted expression of TPMT or NUDT15 was associated with leukopenia or other known side effects. In a retrospective cohort of 1364 new users of azathioprine with EHR-reported White race, we used PrediXcan to impute expression in liver tissue, tested its association with pre-specified phecodes representing known side effects (e.g., skin cancer), and completed chart review to confirm cases. Among confirmed cases, patients in the lowest tertile (i.e., lowest predicted) of TPMT expression had significantly higher odds of developing leukopenia (OR=3.30, 95%CI: 1.07-10.20, p=0.04) versus those in the highest tertile; no other side effects were significant. The results suggest that this methodology could be deployed on a larger scale to uncover associations between genetic factors and drug side effects for more personalized care.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

A case-control study of a combination of single nucleotide polymorphisms and clinical parameters to predict clinically relevant toxicity associated with fluoropyrimidine and platinum-based chemotherapy in gastric cancer

Background

Fluoropyrimidine plus platinum chemotherapy remains the standard first line treatment for gastric cancer (GC). Guidelines exist for the clinical interpretation of four DPYD genotypes related to severe fluoropyrimidine toxicity within European populations. However, the frequency of these single nucleotide polymorphisms (SNPs) in the Latin American population is low (< 0.7%). No guidelines have been development for platinum. Herein, we present association between clinical factors and common SNPs in the development of grade 3–4 toxicity.

Methods

Retrospectively, 224 clinical records of GC patient were screened, of which 93 patients were incorporated into the study. Eleven SNPs with minor allelic frequency above 5% in GSTP1, ERCC2, ERCC1, TP53, UMPS, SHMT1, MTHFR, ABCC2 and DPYD were assessed. Association between patient clinical characteristics and toxicity was estimated using logistic regression models and classification algorithms.

Results

Reported grade ≤ 2 and 3–4 toxicities were 64.6% (61/93) and 34.4% (32/93) respectively. Selected DPYD SNPs were associated with higher toxicity (rs1801265; OR = 4.20; 95% CI = 1.70–10.95, p = 0.002), while others displayed a trend towards lower toxicity (rs1801159; OR = 0.45; 95% CI = 0.19–1.08; p = 0.071). Combination of paired SNPs demonstrated significant associations in DPYD (rs1801265), UMPS (rs1801019), ABCC2 (rs717620) and SHMT1 (rs1979277). Using multivariate logistic regression that combined age, sex, peri-operative chemotherapy, 5-FU regimen, the binary combination of the SNPs DPYD (rs1801265) + ABCC2 (rs717620), and DPYD (rs1801159) displayed the best predictive performance. A nomogram was constructed to assess the risk of developing overall toxicity.

Conclusion

Pending further validation, this model could predict chemotherapy associated toxicity and improve GC patient quality of life.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08745-0.

Pharmacogenetics of inflammatory bowel disease

Patients with inflammatory bowel disease (IBD) show large variability in disease course, and also treatment response. The variability in treatment response has led to many initiatives in search of genetic markers to optimize treatment and avoid severe side effects. This has been very successful for thiopurines, one of the drugs used to induce and maintain remission in IBD. However, for the newer treatment options for IBD, like biologicals, the search for genetic predictors has not yielded any candidate biomarkers with clinical utility. In this review, a summary of recent advances in pharmacogenetics focusing on thiopurines and anti-TNF agents is given.

Precision medicine for rheumatologists: lessons from the pharmacogenomics of azathioprine

Precision medicine aims to personalize treatment for both effectiveness and safety. As a critical component of this emerging initiative, pharmacogenomics seeks to guide drug treatment based on genetics. In this review article, we give an overview of pharmacogenomics in the setting of an immunosuppressant frequently prescribed by rheumatologists, azathioprine. Azathioprine has a narrow therapeutic index and a high risk of adverse events. By applying candidate gene analysis and unbiased approaches, researchers have identified multiple variants associated with an increased risk for adverse events associated with azathioprine, particularly bone marrow suppression. Variants in two genes, TPMT and NUDT15, are widely recognized, leading drug regulatory agencies and professional organizations to adopt recommendations for testing before initiation of azathioprine therapy. As more gene-drug interactions are discovered, our field will continue to face the challenge of balancing benefits and costs associated with genetic testing. However, novel approaches in genomics and the integration of clinical and genetic factors into risk scores offer unprecedented opportunities for the application of pharmacogenomics in routine practice. Key Points • Pharmacogenomics can help us understand how individuals' genetics may impact their response to medications. • Azathioprine is a success story for the clinical implementation of pharmacogenomics, particularly the effects of TPMT and NUDT15 variants on myelosuppression. • As our knowledge advances, testing and dosing recommendations will continue to evolve, with our field striving to balance costs and benefits to patients. • As we aim toward the goals of precision medicine, future research may integrate increasingly individualized traits-including clinical and genetic characteristics-to predict the safety and efficacy of particular medications for individual patients.