Theodore Mellors, Johanna B. Withers, Asher Ameli, Alex Jones, Mengran Wang, Lixia Zhang, Helia N. Sanchez, Marc Santolini, Italo Do Valle, Michael Sebek, Feixiong Cheng, Dimitrios A. Pappas, Joel M. Kremer, Jeffery R. Curtis, Keith J. Johnson, Alif Saleh, Susan D. Ghiassian, and Viatcheslav R. Akmaev
Network and Systems Medicine Volume 3.1, 2020 DOI: 10.1089/nsm.2020.0007 Accepted June 18, 2020
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.