Arthritis & Rheumatism, Volume 60,
October 2009 Abstract Supplement

The 2009 ACR/ARHP Annual Scientific Meeting
Philadelphia October 16-21, 2009.


An Algorithm Using Genome-Wide SNP Analysis for Prediction of Responders and Non-Responders, and Adverse Events in Tocilizumab-Treated RA Patients

Matsubara1,  Tsukasa, Koyano2,  Satoru, Funahashi2,  Keiko, Toriyama3,  Sayumi, Nakahara3,  Kunihiko, Hagiwara1,  Takafumi, Miura1,  Takako

Matsubara Mayflower Hospital, Kato, Japan,
Research Institute of Joint Diseases, Kobe, Japan,
HuBit genomix, Inc., Tokyo, Japan,
Sagawa Akira Rheumatology Clinic, Sapporo, Japan,
Inoue Hospital, Takasaki, Japan,
Matsuno Clinic for Rheumatic Diseases, Toyama, Japan,
Izumihara Rheumatic and Medical Clinic. Kagoshima, Japan,
Shono Rheumatology Clinic, Fukuoka, Japan

Purpose:

Tocilizumab, a human anti-IL-6 receptor antibody, is an efficient biologic agent for inflammatory diseases such as RA. However, there is no method for prediction of responders, non-responders, and adverse events which can occur during the treatment. We established an SNP algorithm for prediction of responders or non-responders, and adverse events among tocilizumab-treated RA patients.

Methods and Patients:

One hundred RA patients treated with tocilizumab were included in this study. The efficacy was determined by Clinical Disease Activity Index (CDAI) within 24–30 weeks after the initial treatment. The efficacy of tocilizumab was judged by the scores of CDAI (remission and low disease activity group—'responders', moderate and high disease activity group—'nonresponders'). Adverse events such as leukopenia, high total cholesterol, fever, and skin manifestations were documented. Genome-wide SNP genotyping was performed by Illumina HumanHap300K chip technology. Case-control analyses between 285,548 SNPs and CDAI were examined by chi-square tests. We selected 10 SNPs strongly associated with tocilizumab-responsiveness, or adverse events (p < 0.001).

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 tocilizumab ranged 92–97%. For adverse events, accuracy, specificity and sensitivity of the algorithm ranged 90–97%. It is, therefore, suggested that the SNP algorithm predict responders and adverse events prior to the initiation of treatment with this biologic agent.

Conclusion:

The highly accurate algorithm using SNP analysis may be useful in the prediction of responsiveness and adverse events before treatment of tocilizumab, and in this way can contribute to future tailor-made treatment with biologic agents.

To cite this abstract, please use the following information:
Matsubara, Tsukasa, Koyano, Satoru, Funahashi, Keiko, Toriyama, Sayumi, Nakahara, Kunihiko, Hagiwara, Takafumi, et al; An Algorithm Using Genome-Wide SNP Analysis for Prediction of Responders and Non-Responders, and Adverse Events in Tocilizumab-Treated RA Patients [abstract]. Arthritis Rheum 2009;60 Suppl 10 :2014
DOI: 10.1002/art.27086

Abstract Supplement

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