Gene expression profiling points to methotrexate nonresponders
medwireNews: Rheumatoid arthritis (RA) patients who are unlikely to respond to methotrexate could be identified based on their gene expression profiles before and 4 weeks after initiating treatment, say researchers.
They explain that although low-dose methotrexate is a key treatment option for the majority of patients with RA, around 30–40% of treated patients do not achieve adequate control of disease activity, a fact that may not become evident until the 6-month mark.
Therefore, to identify an early biomarker of methotrexate response, the investigators used whole blood samples from 85 participants of the UK Rheumatoid Arthritis Medication Study (RAMS), of whom 42 were classified as good responders at the 6-month mark as per the DAS28-CRP score and EULAR response criteria, while 43 were considered nonresponders.
Gene expression profiles from pretreatment and 4-week on-treatment samples were generated using the Illumina HumanHT-12-v4 Expression BeadChips array (Illumina Inc, San Diego, California, USA), and a model based on the ratio of gene expression at these timepoints could discriminate between responders and nonresponders on 78% of occasions.
This was better than the performance of models based on clinical factors – such as sex, age at RA onset, smoking status, and the number of swollen and tender joints – measured at baseline or 3 months, which correctly identified responders and nonresponders 65% and 70% of the time, respectively.
Anne Barton, from the University of Manchester in the UK, and team summarize: “Testing for changes in gene expression between pre-treatment and 4-week post-treatment may provide an early classifier for patients who are unlikely to benefit from [methotrexate] by 6-months and who should, therefore, have their treatment escalated more rapidly, thus potentially impacting treatment pathways.”
They add however that the gene expression classifier “now requires validation, not only in independent samples but also using independent technology,” by, for instance, using RNA sequencing instead of array-based datasets.
“If predictive utility of gene expression data can be confirmed, this could pave the way for a paradigm shift in treatment outcomes from clinically based treat-to-target approaches to biologically driven precision medicine,” the researchers conclude in Arthritis & Rheumatology.
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