Background/Purpose:  Abatacept (ABT), a CTLA4-Ig fusion protein agent targeted to T-cells, is a relatively new biological agent for RA treatment in Japan. However, there is no method for prediction of responders, non-responders, or adverse events which can occur during treatment. We established SNP algorithms for prediction of responsiveness, remission and adverse events in ABT-treated patients by using multiple medical cohorts.

Methods:  The first population sample included 46 RA patients treated with ABT and the second, 52 patients; a total of 98 patients from 13 hospitals in different regions of Japan. Efficacy was assessed by DAS28 (CRP) at 48 weeks after the initiation of treatment. Any adverse events that may have been related to ABT administration and observed at 48 weeks of this long-term administration and during phase II were considered to be side effects. Genome-wide SNP genotyping was performed by Illumina HumanHap 300K chip technology. Case-controlled analyses between 302,814 SNPs and responsiveness, remission or occurrence of adverse events were examined by Fisher’s exact test. We selected 10 SNPs associated with ABT-responsiveness, remission, and adverse events which were common in both analyses of the first and second populations (p < 0.05). We 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 responders: +1 point, hetero allele: 0 points, and homo allele in the majority of non-responders: -1 point), and then examined relationships between responders and non-responders, remission and non-remission, and occurrence of adverse events, plus or minus, and the total score.

Results:  Accuracy ((true positive+true negative)/total), specificity (true negative/(false positive+true negative)), and sensitivity (true positive/(true positive+false negative)) of the algorithm for responsiveness of abatacept ranged from 87-92%. For remission, accuracy, specificity and sensitivity of the algorithm ranged from 81-87%. For adverse events, accuracy, specificity and sensitivity of the algorithm ranged from 83-96%. It is therefore suggested that the SNP algorithms can predict responders and adverse events prior to the initiation of treatment with abatacept.

Conclusion:  These highly accurate algorithms using SNP analysis may be useful in the prediction of responsiveness and adverse events before treatment with abatacept, 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/algorithms-using-genome-wide-snp-analysis-for-prediction-of-efficacy-and-adverse-events-of-abatacept-using-two-population-samples-from-multiple-medical-cohorts/