Presenter Status
Fellow
Abstract Type
Translational Research
Primary Mentor or Principal Investigator
Laura Ramsey
Presentation Type
Poster-Restricted Access
Start Date
19-5-2026 12:00 PM
End Date
19-5-2026 1:00 PM
Abstract Text
Background:
Methotrexate (MTX) is a slow-acting disease modifying anti-rheumatic drug and one of the first drugs of choice for the treatment of juvenile idiopathic arthritis (JIA). Efficacy is observed within 6 months, and approximately one third of patients are nonresponders. Delays in obtaining early disease control place nonresponders at risk for lifelong disability. Therefore, predicting responsiveness to MTX treatment can prevent long-term consequences in these patients. Methotrexate is a folate agonist and undergoes intracellular processing by addition of up to seven glutamate residues by the cytosolic enzyme folylpolyglutamate synthetase encode by the gene FPGS. It has been reported that patients with higher ratios of long chain MTX polyglutamates (MTXGlu3-5/MTXGlu1+2) respond more effectively to treatment. Previously we reported a wide range in this ratio (0.05 – 26, with a median of 2.54) in a cohort of JIA patients.
Objectives/Goal:
The objective of this project is to identify factors that contribute to the variability of the ratio of long to short chain MTX polyglutamates.
Methods/Design:
A subset of the cohort from an observational multisite clinical study of patients diagnosed with JIA and treated with MTX were genotyped. Participant demographics, treatment course, and blood samples for genomic analysis and MTX polyglutamate (MTXGlun) measurement were collected during their scheduled visits. We performed linear regression analysis using sex, age, weight, dose, and route of administration. We genotyped 104 JIA patients with European ancestry using the Omni 2.5+ exome array. Targeted Genome-Wide Association Analyses (using Additive and Dominant Models) of 247 MTX-related genes were performed using PLINK 2 with a total of 25,575 single nucleotide polymorphisms (SNPs). Only variants passing quality control and with a minor allele frequency greater than 0.05 were included in the analysis.
Results:
Linear regression models identified two drug-related variables that had a statistical significance in the ratio of MTXGlun: dose/m2 and route of administration. Targeted-GWAS results using the Additive Model, identified a highly significant enrichment of P values in the following genes that were in linkage disequilibrium: two SNPs in the folylpolyglutamase synthetase (FPGS) (rs1544105 and rs12379987, p=2.8x10-4 and 6.5x10-4), two SNPs in the neighboring gene endoglin ENG (rs10987746 and rs10760503, p =2.5x10-4 and 5.8x10-4), and two SNPs in the neighboring gene cyclin dependent kinase 9 (CDK9) (rs1002095 and rs913990, p =7.6x10-4 and 8.1x10-4).
Conclusions:
Multivariate analysis using the two drug-related covariates (dose and route of administration) only predicted 29.9% of the variability in MTXGlun ratio suggesting other factors impact interpatient variability. Targeted-GWAS revealed SNPs in the MTX metabolism pathway (FPGS). Incorporating genomic data into MTX‑response prediction will support precision medicine strategies aimed at identifying non‑responders early in treatment.
Pharmacogenetic Associations of Methotrexate Metabolism in a JIA Cohort
Background:
Methotrexate (MTX) is a slow-acting disease modifying anti-rheumatic drug and one of the first drugs of choice for the treatment of juvenile idiopathic arthritis (JIA). Efficacy is observed within 6 months, and approximately one third of patients are nonresponders. Delays in obtaining early disease control place nonresponders at risk for lifelong disability. Therefore, predicting responsiveness to MTX treatment can prevent long-term consequences in these patients. Methotrexate is a folate agonist and undergoes intracellular processing by addition of up to seven glutamate residues by the cytosolic enzyme folylpolyglutamate synthetase encode by the gene FPGS. It has been reported that patients with higher ratios of long chain MTX polyglutamates (MTXGlu3-5/MTXGlu1+2) respond more effectively to treatment. Previously we reported a wide range in this ratio (0.05 – 26, with a median of 2.54) in a cohort of JIA patients.
Objectives/Goal:
The objective of this project is to identify factors that contribute to the variability of the ratio of long to short chain MTX polyglutamates.
Methods/Design:
A subset of the cohort from an observational multisite clinical study of patients diagnosed with JIA and treated with MTX were genotyped. Participant demographics, treatment course, and blood samples for genomic analysis and MTX polyglutamate (MTXGlun) measurement were collected during their scheduled visits. We performed linear regression analysis using sex, age, weight, dose, and route of administration. We genotyped 104 JIA patients with European ancestry using the Omni 2.5+ exome array. Targeted Genome-Wide Association Analyses (using Additive and Dominant Models) of 247 MTX-related genes were performed using PLINK 2 with a total of 25,575 single nucleotide polymorphisms (SNPs). Only variants passing quality control and with a minor allele frequency greater than 0.05 were included in the analysis.
Results:
Linear regression models identified two drug-related variables that had a statistical significance in the ratio of MTXGlun: dose/m2 and route of administration. Targeted-GWAS results using the Additive Model, identified a highly significant enrichment of P values in the following genes that were in linkage disequilibrium: two SNPs in the folylpolyglutamase synthetase (FPGS) (rs1544105 and rs12379987, p=2.8x10-4 and 6.5x10-4), two SNPs in the neighboring gene endoglin ENG (rs10987746 and rs10760503, p =2.5x10-4 and 5.8x10-4), and two SNPs in the neighboring gene cyclin dependent kinase 9 (CDK9) (rs1002095 and rs913990, p =7.6x10-4 and 8.1x10-4).
Conclusions:
Multivariate analysis using the two drug-related covariates (dose and route of administration) only predicted 29.9% of the variability in MTXGlun ratio suggesting other factors impact interpatient variability. Targeted-GWAS revealed SNPs in the MTX metabolism pathway (FPGS). Incorporating genomic data into MTX‑response prediction will support precision medicine strategies aimed at identifying non‑responders early in treatment.
Comments
Poster Board Number: 32