Objectives: For rheumatoid arthritis (RA) patients failing to achieve treatment targets with conventional synthetic disease-modifying antirheumatic drugs, tumor necrosis factor (TNF)-α inhibitors (anti-TNF therapies) are the primary first-line biologic therapy. In a cross-cohort, cross-platform study, we developed a molecular test that predicts inadequate response to anti-TNF therapies in biologic-naive RA patients.
Materials and Methods: To identify predictive biomarkers, we developed a comprehensive human interactome—a map of pairwise protein/protein interactions—and overlaid RA genomic information to generate a model of disease biology. Using this map of RA and machine learning, a predictive classification algorithm was developed that integrates clinical disease measures, whole-blood gene expression data, and disease-associated transcribed single-nucleotide polymorphisms to identify those individuals who will not achieve an ACR50 improvement in disease activity in response to anti-TNF therapy.
Results: Data from two patient cohorts (n=58 and n=143) were used to build a drug response biomarker panel that predicts nonresponse to anti-TNF therapies in RA patients, before the start of treatment. In a validation cohort (n=175), the drug response biomarker panel identified nonresponders with a positive predictive value of 89.7 and specificity of 86.8.
Conclusions: Across gene expression platforms and patient cohorts, this drug response biomarker panel stratifies biologic-naive RA patients into subgroups based on their probability to respond or not respond to anti-TNF therapies. Clinical implementation of this predictive classification algorithm could direct nonresponder patients to alternative targeted therapies, thus reducing treatment regimens involving multiple trial and error attempts of anti-TNF drugs.