Background/Purpose: Achievement of remission or low disease activity with infliximab (IFX) and etanercept (ETN) treatment is currently one of the most important matters in RA treatment. However, there is no method for prediction of remission or low disease activity. Previously, we established and validated SNP algorithms for prediction of remission or non-remission among IFX or ETN-treated RA patients (Matsubara T, et al., The 75th annual meeting of the American College of Rheumatology (ACR).Chicago, IL, USA (2011)). In this study, in order to predict remission or low disease activity with these biologics, we validated a third population sample by using the first and second population algorithms.

Methods: The first population sample included 187 RA patients, the second, 206 patients, and the third, 145 patients, for a total of 538 patients from eleven hospitals in different regions of Japan. Remission criteria and low disease activity were determined by DAS28(CRP) within 24-30 weeks after the initiation of treatment with the biologics. Case-control analyses between 285,548 SNPs and remission or low disease activity was examined by Fisher’s exact test. For each biologic, IFX or ETN, we selected 10 SNPs associated with remission or low disease activity which were common in the analyses of both the first and second population (p < 0.05). We then scored the relationship between each SNP and responsiveness, the estimated total score of 10 SNPs (estimated scoring in each SNP was as follows: homo allele in the majority in remission: +1 point, hetero allele: 0 points, and homo allele in the majority of non-remission: -1 point), and then examined relationship between remission, non-remission, and the total score.

Results: In the validation of the third population sample, accuracy ((true positive+true negative)/total) for prediction of IFX remission or low disease activity using the two combined algorithms was 88.9%. Also, in the validation of the third population sample, accuracy of prediction of ETN remission or low disease activity using two combined algorithms was 76.7%. Therefore, the accuracy of prediction of remission or low disease activity using the two combined algorithms is exponentially better than that of remission or low disease activity algorithms alone.

Conclusion: These highly accurate algorithms using SNP analysis may be useful in the prediction of remission or low disease activity before treatment with IFX or ETN, and in this way can contribute to future tailor-made treatment with biologic agents.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/validation-of-algorithms-using-genome-wide-snp-analysis-for-prediction-of-remission-or-low-disease-activity-for-infliximab-or-etanercept-treated-ra-patients/