JP7500074B2 - Method for predicting relapse of rheumatoid arthritis and biomarkers used therein - Google Patents
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Description
本発明は、例えば関節リウマチの再燃を予測するバイオマーカー、並びに当該バイオマーカーを利用した関節リウマチの再燃を予測する検査方法及び関節リウマチの再燃を予測する診断キットに関する。 The present invention relates to a biomarker for predicting relapse of rheumatoid arthritis, as well as a testing method for predicting relapse of rheumatoid arthritis using the biomarker and a diagnostic kit for predicting relapse of rheumatoid arthritis.
関節リウマチ（ＲＡ）は、関節炎を主な臨床症状とする全身性の炎症性自己免疫疾患であり、人口の0.5～1％が罹患する。関節リウマチの原因および発症機序は未だ完全には解明されていない。関節リウマチの治療は、現在、根治的な治療法は存在しない。現在の治療アプローチは、基本的に、痛みを軽減させ、炎症を低減させ、関節の損傷および骨の破壊を停止または減速させることを目的にしており、症状の初期に疾患修飾抗リウマチ薬（ＤＭＡＲＤ）を投与することである。近年、関節リウマチの病態生理に関する理解の進展により開発された生物学的ＤＭＡＲＤ（「生物学的製剤」とも呼ぶ）である抗腫瘍壊死因子α（ＴＮＦα）抗体、抗インターロイキン６（ＩＬ－６）受容体抗体、抗インターロイキン１（ＩＬ－１）抗体などの抗体製剤が使用されることにより、症状の寛解に至る割合が顕著に高くなっている。一方で、生物学的製剤の薬価は非常に高く、漫然とした使用は医療費の高騰を招く。また、生物学的製剤の副作用として、使用期間中における結核の再燃などの感染リスク、脱髄性疾患の増悪リスク、間質性肺炎のリスクなどが高まる。従って、生物学的製剤の適切な使用法の開発が必要である。 Rheumatoid arthritis (RA) is a systemic inflammatory autoimmune disease whose main clinical symptom is arthritis, and affects 0.5-1% of the population. The causes and pathogenesis of RA have not yet been fully elucidated. There is currently no cure for RA. The current treatment approach is to administer disease-modifying antirheumatic drugs (DMARDs) at the early stage of symptoms, aiming to relieve pain, reduce inflammation, and stop or slow joint damage and bone destruction. In recent years, the use of antibody preparations such as anti-tumor necrosis factor α (TNFα) antibodies, anti-interleukin 6 (IL-6) receptor antibodies, and anti-interleukin 1 (IL-1) antibodies, which are biological DMARDs (also called "biological agents") developed based on the progress in understanding the pathophysiology of RA, has significantly increased the rate of symptom remission. On the other hand, the drug prices of biological agents are very high, and their careless use leads to soaring medical costs. In addition, side effects of biological agents include an increased risk of infection, such as relapse of tuberculosis, the risk of exacerbation of demyelinating diseases, and the risk of interstitial pneumonia during the period of use. Therefore, it is necessary to develop appropriate methods for using biological agents.
さらに、生物学的製剤は炎症性サイトカイン抑制による対症療法であるため、治療により炎症もなく腫脹もない寛解に至った関節リウマチの患者であっても、一定の確率で再燃する。また、生物学的製剤の１回使用量を減じたり(tapering)、投与間隔を伸ばした(spacing)場合は、再燃の確率が増加する。一方で、臨床的寛解に至った患者での生物学的製剤の使用を中止した場合は、再燃の確率がさらに増加するものの、一定の確率で再燃せず、臨床的寛解の状態が続くといわれている（非特許文献１）。このような臨床的寛解が継続する患者には、生物学的製剤の継続的な投与の必要はない。しかしながら、現在のところ、生物学的製剤の使用中止を考える臨床的寛解の時点で、再燃等の予後を予測できる手段はない。さらに、再燃が予測される患者において、再燃する直前に生物学的製剤の投与を再開できれば再燃させずにすむことができ、関節症状等の悪化を予防することができると推察されるが、再燃の直前を予測する手段もない。 Furthermore, because biological agents are symptomatic treatments that suppress inflammatory cytokines, even patients with rheumatoid arthritis who have reached remission with no inflammation or swelling due to treatment will have a certain probability of relapse. In addition, if the dosage of the biological agent is reduced (tapering) or the administration interval is extended (spacing), the probability of relapse increases. On the other hand, if the use of a biological agent is discontinued in a patient who has reached clinical remission, the probability of relapse increases further, but it is said that there is a certain probability that the state of clinical remission will continue without relapse (Non-Patent Document 1). Such patients who continue to be in clinical remission do not need to be continuously administered with a biological agent. However, at present, there is no means of predicting the prognosis of relapse, etc., at the point of clinical remission when the discontinuation of the use of a biological agent is considered. Furthermore, it is presumed that if the administration of a biological agent can be resumed just before a relapse in patients who are predicted to relapse, relapse can be avoided and the deterioration of joint symptoms can be prevented, but there is no means of predicting just before a relapse.
現在、関節リウマチ患者において、生物学的製剤により臨床的寛解を達成した後、休薬を検討する際に、休薬後に臨床的に再燃するか否かを予測する適切なバイオマーカーは知られていない。 Currently, there are no known suitable biomarkers to predict whether clinical relapse will occur after discontinuation of rheumatoid arthritis treatment after achieving clinical remission with a biologic agent.
以上から、1)休薬後の関節リウマチの再燃を予測するバイオマーカーの探索と、2)その再燃の時期を予測するバイオマーカーの探索が必要である。 Based on the above, it is necessary to 1) search for biomarkers that predict relapse of rheumatoid arthritis after drug discontinuation, and 2) search for biomarkers that predict the timing of relapse.
これまで、関節リウマチのバイオマーカーには、診断に用いられるバイオマーカーや疾患活動性を評価するバイオマーカーが多数報告されている。例えば、ACPA（抗CCP抗体）やリウマチ因子などは診断マーカーであり、疾患活動性の評価にはCRP、MMP-3などがあり、その他S100A8、S100A9、S100A12等も報告されている（非特許文献２,３）。このような疾患活動性を示すバイオマーカーは、症状の寛解時では変化が認められず、また、臨床的な再燃に先立って変動するバイオマーカーではない。生物学的製剤の適切な使用を可能にするには、将来の再燃を判定するマーカー、及び再燃直前を判定するマーカーが必要である。 So far, many biomarkers for rheumatoid arthritis have been reported, including biomarkers used for diagnosis and biomarkers for evaluating disease activity. For example, ACPA (anti-CCP antibody) and rheumatoid factors are diagnostic markers, and CRP and MMP-3 are used to evaluate disease activity, and S100A8, S100A9, S100A12, etc. have also been reported (Non-Patent Documents 2, 3). Such biomarkers indicating disease activity do not change during remission of symptoms, and are not biomarkers that change prior to clinical relapse. To enable the appropriate use of biological agents, markers for determining future relapse and markers for determining immediately before relapse are needed.
本発明は、上述の実情に鑑み、関節リウマチの再燃又は再燃直前を予測するバイオマーカーを提供することを目的とする。 In view of the above-mentioned circumstances, the present invention aims to provide a biomarker that predicts relapse or the immediate pre-relapse of rheumatoid arthritis.
上記課題を解決するため鋭意研究を行った結果、生物学的製剤を投与後寛解し同製剤を休薬した関節リウマチ患者を前向き研究にて再燃までの血液を追跡し、サイトカインの網羅的な解析を実施することで、再燃する患者を予測可能なバイオマーカー群、及び再燃予測群で、当該患者の状態が再燃直前であることを予測可能とするバイオマーカー群を見出し、本発明を完成するに至った。 As a result of intensive research to solve the above problems, a prospective study was conducted in which blood samples of rheumatoid arthritis patients who had gone into remission after receiving a biological agent and then discontinued the drug were tracked until relapse, and a comprehensive analysis of cytokines was performed. This led to the discovery of a group of biomarkers that can predict which patients will relapse, and a group of biomarkers that can predict when a patient is about to relapse in the relapse prediction group, thereby completing the present invention.
すなわち、本発明は、以下を包含する。
（１）生物学的製剤を投与後寛解した関節リウマチ患者由来の血清サンプルにおいて、IL-34、IL-32、CCL1、IL-2、TNFSF12、IFNβ、IL-12p40、IL-26、IL-1β、IL-6、IL-19、CCL8、IFNγ、IFNλ2、CCL26、CCL25、IFNα2、IFNλ1、CCL7、TNFSF14、CCL11、Pentraxin3、CCL21、IL-27p28、CXCL5、IL-35、TSLP、CCL19、IL-4、IL-10、CXCL2、CCL20、Chitinase 3-like 1、CCL27、IL-4、CCL22、CXCL1、IFNβ、IL-20及びそれらの組合せから成る群より選択されるサイトカイン濃度を測定する工程を含む、関節リウマチの再燃を予測する検査方法。
（２）血清サンプルが、関節リウマチ寛解後の関節リウマチ患者由来の血清サンプルである、（１）記載の方法。
（３）血清サンプルが、生物学的製剤投与前及び/又は生物学的製剤投与中の関節リウマチ患者由来の血清サンプルである、（１）記載の方法。
（４）（１）～（３）のいずれか１記載の方法により再燃が予測されると決定された関節リウマチ患者由来の血清サンプルにおいて、sTNFR2、Fractalkine、IL-6、TSLP、MMP3又はそれらの組合せの濃度を測定する工程を含む、関節リウマチの再燃直前を予測する検査方法。
（５）血清サンプルが、関節リウマチ寛解後の関節リウマチ患者由来の血清サンプルである、（４）記載の方法。
（６）IL-34、IL-32、CCL1、IL-2、TNFSF12、IFNβ、IL-12p40、IL-26、IL-1β、IL-6、IL-19、CCL8、IFNγ、IFNλ2、CCL26、CCL25、IFNα2、IFNλ1、CCL7、TNFSF14、CCL11、Pentraxin3、CCL21、IL-27p28、CXCL5、IL-35、TSLP、CCL19、IL-4、IL-10、CXCL2、CCL20、Chitinase 3-like 1、CCL27、IL-4、CCL22、CXCL1、IFNβ、IL-20及びそれらの組合せから成る群より選択されるサイトカインに対する抗体又はその断片を含む、関節リウマチの再燃を予測する診断キット。
（７）sTNFR2、Fractalkine、IL-6、TSLP、MMP3又はそれらの組合せに対する抗体又はその断片を含む、関節リウマチの再燃直前を予測する診断キット。
（８）IL-34、IL-32、CCL1、IL-2、TNFSF12、IFNβ、IL-12p40、IL-26、IL-1β、IL-6、IL-19、CCL8、IFNγ、IFNλ2、CCL26、CCL25、IFNα2、IFNλ1、CCL7、TNFSF14、CCL11、Pentraxin3、CCL21、IL-27p28、CXCL5、IL-35、TSLP、CCL19、IL-4、IL-10、CXCL2、CCL20、Chitinase 3-like 1、CCL27、IL-4、CCL22、CXCL1、IFNβ、IL-20及びそれらの組合せから成る群より選択されるサイトカインを含む、関節リウマチの再燃を予測する診断バイオマーカー。
（９）sTNFR2、Fractalkine、IL-6、TSLP、MMP3又はそれらの組合せを含む、関節リウマチの再燃直前を予測する診断バイオマーカー。
That is, the present invention includes the following.
(1) A method for predicting relapse of rheumatoid arthritis, comprising a step of measuring a concentration of a cytokine selected from the group consisting of IL-34, IL-32, CCL1, IL-2, TNFSF12, IFNβ, IL-12p40, IL-26, IL-1β, IL-6, IL-19, CCL8, IFNγ, IFNλ2, CCL26, CCL25, IFNα2, IFNλ1, CCL7, TNFSF14, CCL11, Pentraxin3, CCL21, IL-27p28, CXCL5, IL-35, TSLP, CCL19, IL-4, IL-10, CXCL2, CCL20, Chitinase 3-like 1, CCL27, IL-4, CCL22, CXCL1, IFNβ, IL-20, and combinations thereof, in a serum sample from a patient with rheumatoid arthritis who has achieved remission after administration of a biological agent.
(2) The method according to (1), wherein the serum sample is a serum sample from a rheumatoid arthritis patient after remission of rheumatoid arthritis.
(3) The method according to (1), wherein the serum sample is a serum sample from a rheumatoid arthritis patient before administration of a biological agent and/or during administration of a biological agent.
(4) A testing method for predicting the imminent relapse of rheumatoid arthritis, comprising a step of measuring the concentrations of sTNFR2, fractalkine, IL-6, TSLP, MMP3, or a combination thereof in a serum sample from a rheumatoid arthritis patient determined to be predicted to experience a relapse by the method according to any one of (1) to (3).
(5) The method according to (4), wherein the serum sample is a serum sample from a patient suffering from rheumatoid arthritis after remission of the disease.
(6) A diagnostic kit for predicting relapse of rheumatoid arthritis, comprising an antibody or a fragment thereof against a cytokine selected from the group consisting of IL-34, IL-32, CCL1, IL-2, TNFSF12, IFNβ, IL-12p40, IL-26, IL-1β, IL-6, IL-19, CCL8, IFNγ, IFNλ2, CCL26, CCL25, IFNα2, IFNλ1, CCL7, TNFSF14, CCL11, Pentraxin3, CCL21, IL-27p28, CXCL5, IL-35, TSLP, CCL19, IL-4, IL-10, CXCL2, CCL20, Chitinase 3-like 1, CCL27, IL-4, CCL22, CXCL1, IFNβ, IL-20, and combinations thereof.
(7) A diagnostic kit for predicting the imminent relapse of rheumatoid arthritis, comprising an antibody or a fragment thereof against sTNFR2, fractalkine, IL-6, TSLP, MMP3, or a combination thereof.
(8) A diagnostic biomarker for predicting relapse of rheumatoid arthritis, comprising a cytokine selected from the group consisting of IL-34, IL-32, CCL1, IL-2, TNFSF12, IFNβ, IL-12p40, IL-26, IL-1β, IL-6, IL-19, CCL8, IFNγ, IFNλ2, CCL26, CCL25, IFNα2, IFNλ1, CCL7, TNFSF14, CCL11, Pentraxin3, CCL21, IL-27p28, CXCL5, IL-35, TSLP, CCL19, IL-4, IL-10, CXCL2, CCL20, Chitinase 3-like 1, CCL27, IL-4, CCL22, CXCL1, IFNβ, IL-20, and combinations thereof.
(9) A diagnostic biomarker for predicting the onset of rheumatoid arthritis relapse, comprising sTNFR2, fractalkine, IL-6, TSLP, MMP3, or a combination thereof.
本発明によれば、関節リウマチの再燃又は再燃直前を予測するバイオマーカーを、関節リウマチ患者の予後予測、生物学的製剤の使用適応などに使用することで、適切な治療プロトコールの開発が可能となる。 According to the present invention, by using a biomarker that predicts relapse or the immediate pre-relapse of rheumatoid arthritis to predict the prognosis of rheumatoid arthritis patients and to determine the indications for use of biological agents, it becomes possible to develop appropriate treatment protocols.
以下、本発明を詳細に説明する。 The present invention is described in detail below.
１．関節リウマチの再燃を予測する診断バイオマーカー
本発明の第１態様は、IL(Interleukin)-34、IL-32、CCL(CC chemokine ligand)1、IL-2、TNFSF12(Tumor necrosis factor ligand superfamily member 12)、IFNβ、IL-12p40、IL-26、IL-1β、IL-6、IL-19、CCL8、IFN(Interferon)γ、IFNλ2、CCL26、CCL25、IFNα2、IFNλ1、CCL7、TNFSF14、CCL11、Pentraxin3、CCL21、IL-27p28、CXCL(CXC chemokine ligand)5、IL-35、TSLP(Thymic stromal lymphopoietin)、CCL19、IL-4、IL-10、CXCL2、CCL20、Chitinase 3-like 1、CCL27、IL-4、CCL22、CXCL1、IFNβ、IL-20及びそれらの組合せから成る群より選択されるサイトカインが、関節リウマチの再燃を予測する診断バイオマーカーであるとの知見に基づくものである。これらサイトカインの組合せとしては、これらサイトカインのうちの2以上、3以上、4以上、5以上、6以上、7以上、8以上、9以上、10以上、11以上、12以上、13以上、14以上、15以上、16以上、17以上、18以上、19以上、20以上、21以上、22以上、23以上、24以上、25以上、26以上、27以上、28以上、29以上、30以上、31以上、32以上、33以上、34以上、35以上、36以上、37以上、38以上、39種全てを含むものであってよい。特に、IL-34、IL-32、TNFSF12、TNFSF14、IFNγ、IL-2、IL-1β、IL-19及びCCL1の組合せによれば、関節リウマチの再燃を高感度に予測することができる。
1. Diagnostic biomarkers for predicting relapse of rheumatoid arthritis The first aspect of the present invention is a diagnostic biomarker for predicting relapse of rheumatoid arthritis, comprising IL (Interleukin)-34, IL-32, CCL (CC chemokine ligand) 1, IL-2, TNFSF12 (Tumor necrosis factor ligand superfamily member 12), IFNβ, IL-12p40, IL-26, IL-1β, IL-6, IL-19, CCL8, IFN (Interferon) γ, IFNλ2, CCL26, CCL25, IFNα2, IFNλ1, CCL7, TNFSF14, CCL11, Pentraxin3, CCL21, IL-27p28, CXCL (CXC chemokine ligand) 5, IL-35, TSLP (Thymic stromal lymphopoietin), CCL19, IL-4, IL-10, CXCL2, CCL20, Chitinase 3-like The present invention is based on the finding that a cytokine selected from the group consisting of 1, CCL27, IL-4, CCL22, CXCL1, IFNβ, IL-20, and a combination thereof is a diagnostic biomarker for predicting relapse of rheumatoid arthritis. The combination of these cytokines may include all of the following cytokines: 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 31 or more, 32 or more, 33 or more, 34 or more, 35 or more, 36 or more, 37 or more, 38 or more, 39 or more. In particular, the combination of IL-34, IL-32, TNFSF12, TNFSF14, IFNγ, IL-2, IL-1β, IL-19 and CCL1 can predict the relapse of rheumatoid arthritis with high sensitivity.
また、生物学的製剤を投与し、関節リウマチ寛解後の関節リウマチ患者においては、CCL1、IL-2、IL-19、IL-34及びIL-1βの組合せによれば、関節リウマチの再燃を高感度に予測することができる。あるいは、生物学的製剤で治療前及び/又は治療中の関節リウマチ患者においては、IFNγ、TNFSF14、TNFSF12、IL-34及びIL-32の組合せによれば、関節リウマチの再燃を高感度に予測することができる。 In addition, in rheumatoid arthritis patients who have been administered a biological agent and have achieved remission, a combination of CCL1, IL-2, IL-19, IL-34, and IL-1β can be used to predict relapse of rheumatoid arthritis with high sensitivity. Alternatively, in rheumatoid arthritis patients before and/or during treatment with a biological agent, a combination of IFNγ, TNFSF14, TNFSF12, IL-34, and IL-32 can be used to predict relapse of rheumatoid arthritis with high sensitivity.
本発明に係る関節リウマチの再燃を予測する検査方法(以下、「第１態様の方法」と称する)は、生物学的製剤を投与後寛解した関節リウマチ患者由来の血清サンプルにおいて、上述のサイトカイン濃度を測定する工程を含むものである。測定されたサイトカインの濃度(又は量若しくはレベル)に基づいて、当該関節リウマチ患者において関節リウマチが再燃するか否かの予測を判定又は評価することができる。第１態様の方法によれば、関節リウマチの再燃の予測を高感度に検査でき、その後の治療を早期に行うことができる。なお、第１態様の方法は、関節リウマチの再燃を予測する方法、関節リウマチの再燃を予測するための評価又は判定方法、関節リウマチの再燃を予測するためのin vitroにおけるデータ収集方法等ということができる。 The test method for predicting relapse of rheumatoid arthritis according to the present invention (hereinafter referred to as "method of the first aspect") includes a step of measuring the above-mentioned cytokine concentration in a serum sample from a rheumatoid arthritis patient who has been administered a biological agent and is in remission. Based on the measured cytokine concentration (or amount or level), it is possible to determine or evaluate the prediction of whether or not rheumatoid arthritis will relapse in the rheumatoid arthritis patient. According to the method of the first aspect, it is possible to test the prediction of relapse of rheumatoid arthritis with high sensitivity, and subsequent treatment can be performed early. The method of the first aspect can be referred to as a method for predicting relapse of rheumatoid arthritis, an evaluation or determination method for predicting relapse of rheumatoid arthritis, an in vitro data collection method for predicting relapse of rheumatoid arthritis, etc.
第１態様の方法において検査対象となる関節リウマチ患者は、生物学的製剤の投与等の治療により関節リウマチが寛解し、関節リウマチ寛解後の休薬中の関節リウマチ患者、関節リウマチを患うものの生物学的製剤の投与等の治療前の関節リウマチ患者、及び/又は生物学的製剤の投与による治療中の関節リウマチ患者であってよい。ここで、生物学的製剤としては、関節リウマチに効果のある治療薬である抗体製剤(例えば、インフリキシマブ、エタネルセプト、アダリムマブ等のTNF阻害薬、トシリズマブ等のIL-6阻害薬)が挙げられる。 The rheumatoid arthritis patient to be tested in the method of the first aspect may be a rheumatoid arthritis patient who has been put into remission by treatment such as administration of a biological agent and is currently on a drug holiday after remission, a rheumatoid arthritis patient who suffers from rheumatoid arthritis but is not yet receiving treatment such as administration of a biological agent, and/or a rheumatoid arthritis patient who is currently undergoing treatment with administration of a biological agent. Here, examples of biological agents include antibody preparations that are effective therapeutic agents for rheumatoid arthritis (for example, TNF inhibitors such as infliximab, etanercept, and adalimumab, and IL-6 inhibitors such as tocilizumab).
第１態様の方法では、サイトカイン濃度を測定すべく、関節リウマチ患者由来の血清サンプル中のサイトカインと抗サイトカイン抗体又はその断片とが結合するように、当該血清サンプルと抗サイトカイン抗体又はその断片とを接触させる。 In the method of the first aspect, in order to measure the cytokine concentration, a serum sample from a patient with rheumatoid arthritis is contacted with an anti-cytokine antibody or a fragment thereof so that the cytokine in the serum sample binds to the anti-cytokine antibody or a fragment thereof.
血清サンプルと抗サイトカイン抗体又はその断片との接触によるサイトカイン測定は、例えば酵素結合免疫吸着検定法(ELISA)等の免疫学的測定方法により行うことができる。例えば、ELISAを使用する場合には、抗サイトカイン抗体又はその断片をプレートのウェルに固定化し、次いで血清サンプルを当該ウェルに加える。さらに、二次抗体として酵素(例えばペルオキシダーゼ)標識した別の抗サイトカイン抗体又はその断片を反応させ、当該酵素の色原性基質を加え、酵素反応による色原性基質からの色素の呈色を分光光度計で吸光度として測定する。 Cytokine measurement by contacting a serum sample with an anti-cytokine antibody or a fragment thereof can be performed by an immunological measurement method such as enzyme-linked immunosorbent assay (ELISA). For example, when using ELISA, an anti-cytokine antibody or a fragment thereof is immobilized in a well of a plate, and then a serum sample is added to the well. Furthermore, another anti-cytokine antibody or a fragment thereof labeled with an enzyme (e.g. peroxidase) is reacted as a secondary antibody, and a chromogenic substrate for the enzyme is added, and the color development from the chromogenic substrate due to the enzyme reaction is measured as absorbance with a spectrophotometer.
一方で、標準品のサイトカインの測定結果から、サイトカイン濃度に対する吸光度の検量線を作成する。当該検量線に基づいて、上述の測定試料の吸光度から血清サンプル中のサイトカイン濃度を決定することができる。 On the other hand, a calibration curve of absorbance versus cytokine concentration is created from the measurement results of the cytokines in the standard samples. Based on this calibration curve, the cytokine concentration in the serum sample can be determined from the absorbance of the above-mentioned measurement sample.
測定した血清サンプル中のサイトカイン濃度に基づいて、例えば各サイトカインのカットオフ値と比較して、血清サンプルが由来する関節リウマチ患者において、関節リウマチが再燃するか否かを予測することができる。例えば、ROC解析における各サイトカインのカットオフ値は、以下の通りである：
生物学的製剤を休薬後の際のカットオフ値：
IL-34： 192 (= 27.58) pg/mL以上
IL-32： 95.0 (= 26.57) pg/mL以上
CCL1： 31.5 (= 24.98) pg/mL以上
IL-1β： 0.840 (= 2-0.252) pg/mL以上
CCL3： 6.01 (= 22.59) pg/mL以上。
生物学的製剤を投薬前および投薬中の際のカットオフ値：
IFNγ: 17.15 (= 24.1) pg/mL以上
IL-34： 137.19 (= 27.1) pg/mL以上
IFNλ2： 48.17 (= 25.59) pg/mL以上
CCL26： 4.32 (= 22.11) pg/mL以上
CCL25： 192.67 (= 27.59) pg/mL以上
IL-2： 44.32 (= 25.47) pg/mL以上
IL-32： 121.10 (= 26.92) pg/mL以上
IFNλ1： 50.56 (= 25.66) pg/mL以上
TNFSF14： 42.52 (= 25.41) pg/mL以下
IL-35： 276.28 (= 28.11) pg/mL以上
CCL1： 34.06 (= 25.09) pg/mL以上
CCL11： 59.30 (= 25.89) pg/mL以上。
Based on the measured cytokine concentration in the serum sample, for example, by comparing with the cutoff value of each cytokine, it is possible to predict whether or not rheumatoid arthritis will relapse in the rheumatoid arthritis patient from which the serum sample is derived. For example, the cutoff value of each cytokine in the ROC analysis is as follows:
Cut-off value after withdrawal of biological agents:
IL-34: 192 (= 27.58 ) pg/mL or higher
IL-32: 95.0 (= 26.57 ) pg/mL or higher
CCL1: 31.5 (= 24.98 ) pg/mL or higher
IL-1β: 0.840 (= 2 -0.252 ) pg/mL or higher
CCL3: ≥ 6.01 (= 22.59 ) pg/mL.
Cut-off values before and during biologic therapy:
IFNγ: 17.15 (= 2 4.1 ) pg/mL or higher
IL-34: 137.19 (= 27.1 ) pg/mL or higher
IFNλ2: 48.17 (= 25.59 ) pg/mL or higher
CCL26: 4.32 (= 22.11 ) pg/mL or higher
CCL25: 192.67 (= 27.59 ) pg/mL or higher
IL-2: 44.32 (= 25.47 ) pg/mL or higher
IL-32: 121.10 (= 26.92 ) pg/mL or higher
IFNλ1: 50.56 (= 25.66 ) pg/mL or higher
TNFSF14: 42.52 (= 25.41 ) pg/mL or less
IL-35: 276.28 (= 2 8.11 ) pg/mL or higher
CCL1: 34.06 (= 25.09 ) pg/mL or higher
CCL11: ≥59.30 (= 25.89 ) pg/mL.
また、生物学的製剤を投与し、関節リウマチ寛解後の関節リウマチ患者において、CCL1、IL-2、IL-19、IL-34及びIL-1βの組合せを用いた場合には、測定した血清サンプル中の各サイトカイン濃度に基づいて、再燃予測指数（RPI, relapse prediction index）スコアを、例えば以下の数式で計算する(Pのcut-off値は0.6である)。
(式) RPIスコア = logit(P) = log(P / (1 - P)) = -21.228 + 0.791*log2（IL-34) + 4.252*log2(CCL1) - 2.419*log2 (IL-2) + 0.465*log2(IL1β) - 0.18*log2(IL-19)
なお、ここに用いられる各バイオマーカーの単位はpg/mLである。
得られたRPIスコアが、閾値以上を示した場合、血清サンプルが由来する関節リウマチ患者において、関節リウマチが再燃すると予測することができる。例えば、閾値はP値のcut-off値を示し、0.6である。
In addition, in patients with rheumatoid arthritis who have been administered a biological agent and have achieved remission from rheumatoid arthritis, when a combination of CCL1, IL-2, IL-19, IL-34 and IL-1β is used, a relapse prediction index (RPI) score is calculated, for example, using the following formula based on the measured concentrations of each cytokine in the serum sample (P cut-off value is 0.6).
(Formula) RPI score = logit(P) = log(P / (1 - P)) = -21.228 + 0.791*log 2 (IL-34) + 4.252*log 2 (CCL1) - 2.419*log 2 (IL-2) + 0.465*log 2 (IL1β) - 0.18*log 2 (IL-19)
The unit of each biomarker used here is pg/mL.
If the obtained RPI score is equal to or greater than the threshold, it can be predicted that the rheumatoid arthritis patient from whom the serum sample was derived will experience a relapse of rheumatoid arthritis. For example, the threshold indicates the cut-off value of the P value, which is 0.6.
さらに、生物学的製剤で治療前及び/又は治療中の関節リウマチ患者において、IFNγ、TNFSF14、TNFSF12、IL-34及びIL-32の組合せを用いた場合には、測定した血清サンプル中の各サイトカイン濃度に基づいて、再燃予測指数（RPI, relapse prediction index）スコアを、例えば以下の数式で計算する(Pのcut-off値は0.27である)。
(式) RPIスコア = logit(P) = log(P / (1 - P)) = -80.983 + 0.75*log2(IL-34) + 3.002*log2(IFNγ) - 1.991*log2(TNFSF14) + 7.139*log2(TNFSF12) + 2.443*log2(IL-32)
なお、ここに用いられる各バイオマーカーの単位はpg/mLである。
得られたRPIスコアが、閾値以上を示した場合、血清サンプルが由来する関節リウマチ患者において、関節リウマチが再燃すると予測することができる。例えば、閾値はP値のcut-off値を示し、0.27である。
Furthermore, in patients with rheumatoid arthritis who are undergoing and/or prior to treatment with a biological agent, when a combination of IFNγ, TNFSF14, TNFSF12, IL-34 and IL-32 is used, a relapse prediction index (RPI) score is calculated based on the measured concentrations of each cytokine in serum samples, for example, using the following formula (P cut-off value is 0.27).
(Formula) RPI score = logit(P) = log(P / (1 - P)) = -80.983 + 0.75* log2 (IL-34) + 3.002* log2 (IFNγ) - 1.991* log2 (TNFSF14) + 7.139* log2 (TNFSF12) + 2.443* log2 (IL-32)
The unit of each biomarker used here is pg/mL.
If the obtained RPI score is equal to or greater than the threshold, it is possible to predict that the rheumatoid arthritis patient from whom the serum sample was derived will experience a relapse of rheumatoid arthritis. For example, the threshold indicates the cut-off value of the P value, which is 0.27.
また、第１態様の方法は、関節リウマチが再燃すると予測された関節リウマチ患者に対して生物学的製剤投与等の治療処置を施す工程をさらに含む、再燃が予測された関節リウマチ患者における再燃予防又は治療方法とすることができる。 The method of the first aspect can also be a method for preventing or treating relapse in a rheumatoid arthritis patient predicted to have a relapse, further comprising a step of administering a therapeutic treatment, such as administering a biological agent, to the rheumatoid arthritis patient predicted to have a relapse of rheumatoid arthritis.
さらに、第１態様の方法は、カットオフ値との比較において、血清サンプルが由来する関節リウマチ患者が再燃するリスクが高いか否かを決定する工程をさらに含む、関節リウマチ患者の再燃群と非再燃群との階層化方法とすることができる。 Furthermore, the method of the first aspect can be a method for stratifying rheumatoid arthritis patients into a relapse group and a non-relapse group, further comprising a step of determining whether the rheumatoid arthritis patient from whom the serum sample was derived is at high risk of relapse by comparison with a cutoff value.
また、本発明は、例えばELISA等の免疫学的測定方法において使用するための、上述のサイトカインに対する抗体又はその断片を含む、関節リウマチの再燃を予測する診断キット(以下、「第１態様のキット」と称する)に関する。当該キットは、検量線作成のための標準品(陽性対照)のサイトカイン、キットの取り扱い説明書、関節リウマチの再燃を予測する診断のための説明書、容器、試薬等も適宜含むことができる。 The present invention also relates to a diagnostic kit for predicting relapse of rheumatoid arthritis (hereinafter referred to as "the kit of the first aspect"), which contains an antibody or a fragment thereof against the above-mentioned cytokine, for use in an immunological measurement method such as ELISA. The kit may also contain a standard (positive control) cytokine for creating a calibration curve, an instruction manual for using the kit, an instruction manual for diagnosis to predict relapse of rheumatoid arthritis, a container, a reagent, etc., as appropriate.
２．関節リウマチの再燃直前を予測する診断バイオマーカー
本発明の第２態様は、第１態様の方法により再燃が予測されると決定された関節リウマチ患者由来の血清サンプルにおいて、サイトカインであるsTNFR2(Soluble Tumor Necrosis Factor Receptor 2)、Fractalkine(CX3CL1(Chemokine (C-X3-C motif) ligand 1)としても知られている)、IL-6、TSLP、MMP(matrix metalloproteinase)3又はそれらの組合せ(これらのうち、2以上、3以上、4以上、好ましくは5つ全ての組合せ)が、関節リウマチの再燃直前を予測する診断バイオマーカーであるとの知見に基づくものである。これらサイトカイン等をバイオマーカーとして使用することで、第１態様の方法により再燃が予測されると決定された関節リウマチ患者(例えば、関節リウマチ寛解後の関節リウマチ患者)において関節リウマチが再燃の直前(例えば、再燃の６時間～９０日前、好ましくは再燃の７２時間～３０日前)にあるか否かの予測を判定又は評価することができる。本態様においては、関節リウマチの再燃直前の予測を高感度に検査でき、早急に生物学的製剤の投与等の治療が必要か否かを判断することができる。
2. Diagnostic biomarkers predicting the onset of relapse of rheumatoid arthritis The second aspect of the present invention is based on the finding that, in a serum sample from a rheumatoid arthritis patient determined to be predictive of relapse by the method of the first aspect, cytokines sTNFR2 (Soluble Tumor Necrosis Factor Receptor 2), fractalkine (also known as CX3CL1 (C-X3-C motif) ligand 1)), IL-6, TSLP, MMP (matrix metalloproteinase) 3, or combinations thereof (a combination of two or more, three or more, four or more, preferably all five of these) are diagnostic biomarkers predicting the onset of relapse of rheumatoid arthritis. By using these cytokines etc. as biomarkers, it is possible to judge or evaluate the prediction of whether or not rheumatoid arthritis is immediately before a relapse (for example, 6 hours to 90 days before a relapse, preferably 72 hours to 30 days before a relapse) in a rheumatoid arthritis patient (for example, a rheumatoid arthritis patient after remission of rheumatoid arthritis) determined to be predicted to have a relapse by the method of the first embodiment. In this embodiment, it is possible to test the prediction of immediately before a relapse of rheumatoid arthritis with high sensitivity, and to judge whether or not treatment such as administration of a biological agent is required immediately.
本態様における関節リウマチの再燃直前を予測する検査方法(以下、「第２態様の方法」と称する)及び関節リウマチの再燃直前を予測する診断キットは、それぞれ第１態様の方法及びキットに準じたものである。 The test method for predicting when rheumatoid arthritis is about to relapse in this embodiment (hereinafter referred to as the "method of the second embodiment") and the diagnostic kit for predicting when rheumatoid arthritis is about to relapse are similar to the method and kit of the first embodiment, respectively.
sTNFR2及びFractalkineのカットオフ値としては、例えば生物学的製剤の休薬直後の寛解時(B)とその後、経時的に測定した時点(C)との間で、実測値のC/B比を求めて得られた比のカットオフ値が挙げられる。この場合、例えばsTNFR2のカットオフ値を<0.8、Fractalkineのカットオフ値を>1.2とすることができる。 The cutoff values for sTNFR2 and fractalkine can be, for example, the cutoff value of the ratio obtained by calculating the C/B ratio of the actual measured values between the time of remission immediately after discontinuing the biological agent (B) and the time points (C) when measurements are taken over time thereafter. In this case, for example, the cutoff value for sTNFR2 can be <0.8, and the cutoff value for fractalkine can be >1.2.
また、IL-6、TSLP及びMMP3の組合せを用いた場合には、測定した血清サンプル中の各サイトカイン等濃度に基づいて、再燃直前予測指数（RPI, relapse prediction index）スコアを、例えば以下の数式で計算する(Pのcut-off値は0.31である)。
(式) RPIスコア = logit(P) = log(P / (1 - P)) = 12.519 + 2.63*log2（IL-6) + 23.393*log2 (TSLP) - 12.014*log2 (MMP3)
なお、ここに用いられる各バイオマーカーの単位はpg/mLである。
得られたRPIスコアが、閾値以上を示した場合、血清サンプルが由来する関節リウマチ患者において、関節リウマチが再燃直前であると予測することができる。例えば、閾値はP値のcut-off値を示し、0.31である。
Furthermore, when a combination of IL-6, TSLP, and MMP3 is used, a relapse prediction index (RPI) score is calculated based on the measured concentrations of each cytokine in the serum sample, for example, using the following formula (P cut-off value is 0.31):
(Formula) RPI score = logit(P) = log(P / (1 - P)) = 12.519 + 2.63*log 2 (IL-6) + 23.393*log 2 (TSLP) - 12.014*log 2 (MMP3)
The unit of each biomarker used here is pg/mL.
When the obtained RPI score is equal to or greater than the threshold, it can be predicted that the rheumatoid arthritis of the patient from whom the serum sample was derived is about to experience a relapse of the rheumatoid arthritis. For example, the threshold indicates the cut-off value of the P value, which is 0.31.
また、第２態様の方法は、関節リウマチが再燃直前であると予測された関節リウマチ患者に対して生物学的製剤投与等の治療処置を施す工程をさらに含む、再燃直前が予測された関節リウマチ患者における関節リウマチの再燃予防又は治療方法とすることができる。 The method of the second aspect can be a method for preventing or treating relapse of rheumatoid arthritis in a patient with rheumatoid arthritis predicted to be on the verge of a relapse, further comprising a step of administering a therapeutic treatment such as administering a biological agent to the patient with rheumatoid arthritis predicted to be on the verge of a relapse.
さらに、第２態様の方法は、生物学的製剤投与により寛解し、休薬中の関節リウマチ患者において、カットオフ値との比較において、血清サンプルが由来する関節リウマチ患者が再燃直前である否かを予測する工程をさらに含む、関節リウマチ患者における再燃予防のための投薬再開時期の予測方法とすることができる。 Furthermore, the method of the second aspect can be a method for predicting the timing of resumption of medication to prevent relapse in a rheumatoid arthritis patient, further comprising a step of predicting whether or not the rheumatoid arthritis patient from whom the serum sample was derived is on the verge of relapse by comparing with a cutoff value in a rheumatoid arthritis patient who has achieved remission through administration of a biological agent and is currently on a drug suspension.
以下、実施例を用いて本発明をより詳細に説明するが、本発明の技術的範囲はこれら実施例に限定されるものではない。 The present invention will be described in more detail below using examples, but the technical scope of the present invention is not limited to these examples.
〔実施例１〕生物学的製剤の休薬後寛解時における将来の関節リウマチの再燃を予測するバイオマーカー
１．材料及び方法
生物学的製剤の休薬後寛解時における将来の関節リウマチの再燃を予測するバイオマーカーを同定する目的で、生物学的製剤を投与後寛解した関節リウマチ患者をコホートとして前向き研究を実施した。具体的には、対象コホートを、生物学的製剤を用いて臨床的寛解が１年以上継続した関節リウマチ患者40名とした。研究デザインとして、図１Aに示す通り、生物学的製剤の投与を中止したのち、再燃までを約２年間原則１ヶ月毎に採血し経過観察した。再燃した患者は再燃の時点で観察を中止した。２年の観察期間の寛解維持率のKaplan-Meier曲線を図１Bに示す。その結果、再燃群（26名／40名）および寛解維持群（非再燃群）（14名／40名）の２群に分類した。２群の患者プロファイルを比較すると、特に有意な差を有するパラメーターは認められなかった（表１）。
[Example 1] Biomarkers predicting future relapse of rheumatoid arthritis during remission after withdrawal of biological agents 1. Materials and methods In order to identify biomarkers predicting future relapse of rheumatoid arthritis during remission after withdrawal of biological agents, a prospective study was conducted with a cohort of rheumatoid arthritis patients who were in remission after administration of biological agents. Specifically, the target cohort was 40 rheumatoid arthritis patients who had been in clinical remission for more than one year using biological agents. As a study design, as shown in Figure 1A, after discontinuing the administration of biological agents, blood samples were taken every month in principle for about two years until relapse and the patient was observed. Patients who relapsed were discontinued at the time of relapse. The Kaplan-Meier curve of the remission maintenance rate over a two-year observation period is shown in Figure 1B. As a result, the patients were classified into two groups: a relapse group (26/40 patients) and a remission maintenance group (non-relapse group) (14/40 patients). When comparing the patient profiles of the two groups, no parameters were found to have significant differences (Table 1).
表１は、コホート患者の基本的特徴を示す。A. 寛解維持群と再燃群の患者の基本的特徴。B. 寛解維持群と再燃群の患者の使用した生物学的製剤。 Table 1 shows the basic characteristics of the cohort patients. A. Basic characteristics of patients in the remission maintenance group and relapse group. B. Biologics used by patients in the remission maintenance group and relapse group.
図２に研究デザインの模式図と患者血清の分類を示す。再燃群において、生物学的製剤の休薬直後をB、再燃直前をC、再燃時をD、BとCの間のサンプルをEとした。また、寛解維持群（非再燃群）においても同様に、生物学的製剤の休薬直後を A、２年間の観察終了時をF、AとFの間のサンプルをGとした。これらの全ての地点における血清を用いて、73種類のサイトカインを、BioRad社のBio-Plex Pro human Inflammation 1, 37-Plex panelおよびBio-Plex Pro human chemokine, 40-Plex panelを用いて網羅的に測定した。測定したサイトカインの項目を表２に示す。 Figure 2 shows a schematic diagram of the study design and the classification of patient sera. In the relapse group, the sample immediately after the withdrawal of the biological agent was classified as B, the sample immediately before the relapse as C, the sample at the time of relapse as D, and the sample between B and C as E. Similarly, in the remission maintenance group (non-relapse group), the sample immediately after the withdrawal of the biological agent was classified as A, the sample at the end of the two-year observation period as F, and the sample between A and F as G. Using the sera from all of these time points, 73 types of cytokines were comprehensively measured using BioRad's Bio-Plex Pro human Inflammation 1, 37-Plex panel and Bio-Plex Pro human chemokine, 40-Plex panel. The cytokines measured are shown in Table 2.
表２は、BioPlexにて測定したサイトカイン一覧を示す。BioPlex IDに対応した、遺伝子名およびUniprot IDを示す。 Table 2 shows a list of cytokines measured by BioPlex. The gene names and Uniprot IDs corresponding to the BioPlex IDs are shown.
統計学的解析：患者由来の血清サンプル数は、特に再燃群においては再燃までの期間に応じて患者毎に異なる。患者の特性に由来するバイアスを除くため、先ず患者におけるサンプル数を固定して解析した。臨床的寛解期間におけるベースラインの変動を考慮するため、時期の異なるポイントを独立したサンプルとして探索的モデル作成の検討に用いた。寛解維持群としてAおよびFのAF群、再燃群としてBおよびCのBC群とし、対数(log2)変換し、さらに平均0、分散1になるように変換したデータを用いて、Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)、相関分析、およびt検定による有意差検定（p値が5%未満を有意な差と判定）を用いて再燃群と寛解維持群の判別に寄与する因子の選択を行った。更に、バイオマーカー解析として対数(log2)変換したデータを用いてROC (Receiver 0perating characteristics)曲線を作製し、各々の因子に対してAUC(area under curve)値とcut-off値を算定した。更に、対数データを平均0、分散1になるように再変換したデータを用いてサポートベクターマシン(SVM)法によるランキング解析(multivariate exploratory ROC解析)を行い、組合せによるバイオマーカーの選定を行った。選択されたバイオマーカーの組合せについて、対数データをもとにしたロジスティック回帰モデルを検討し、10重交差検証法によるROC曲線の作成により組合せの有用性を判定した。統計学的解析に用いたソフトウエアは、RおよびMetaboAnalyst (https://www.metaboanalyst.ca)を用いた。 Statistical analysis: The number of serum samples from patients varies from patient to patient depending on the time to relapse, especially in the relapse group. To eliminate bias derived from patient characteristics, the number of samples from patients was fixed for analysis. To take into account baseline fluctuations during the clinical remission period, independent samples from different time points were used to create an exploratory model. The remission maintenance group was defined as group AF (A and F), and the relapse group was defined as group BC (B and C). The data were logarithmically (log 2 ) transformed and further transformed to have a mean of 0 and a variance of 1. Using these data, factors contributing to the discrimination between the relapse group and the remission maintenance group were selected using sparse partial least squares discriminant analysis (sPLS-DA), correlation analysis, and significance test by t-test (p value of less than 5% was considered significant). Furthermore, as biomarker analysis, ROC (Receiver 0perating characteristics) curves were created using logarithmic (log 2 ) transformed data, and the AUC (area under curve) value and cut-off value were calculated for each factor. Furthermore, the logarithmic data was retransformed to have a mean of 0 and a variance of 1, and a ranking analysis (multivariate exploratory ROC analysis) was performed using the support vector machine (SVM) method to select biomarker combinations. A logistic regression model based on the logarithmic data was examined for the selected biomarker combinations, and the usefulness of the combination was determined by creating an ROC curve using 10-fold cross-validation. The software used for statistical analysis was R and MetaboAnalyst (https://www.metaboanalyst.ca).
２．結果及び考察
生物学的製剤の休薬後寛解時における将来の関節リウマチの再燃を予測するバイオマーカーを同定する目的で、先ず、寛解維持群（AF群）および再燃群（BC群）の２群について、sPLS-DA（図３）、相関分析およびvolcano plot解析（図４及び図５）を実施したところ、バイオマーカーの候補としてIL-34、IL-32、CCL1、IL-2、TNFSF12、IFNβ、IL-12p40、IL-26、IL-1β、IL-6、IL-19およびCCL8の12個が、p値が5％未満の統計学的有意差を有した（図４）。これら12因子のタンパク質-タンパク質間相互作用ネットワーク解析で、これら因子はIL-6、IL-10、IL-1β、IL-2、STAT3を中心としたネットワークで相互に連携しあっていることが判明した（図６）。更に対数変換したデータを用いて単変量ROC解析を行うと、AUC値が0.8以上のものはIL-34のみであり、IL-32、CCL1、IL-1βおよびCCL3は0.7台であった（表３）。それぞれのROC曲線とバイオリンプロットを図７に示す。各因子のcut-off値は、IL-34 192 (= 27.58) pg/mL以上、IL-32 95.0 (= 26.57) pg/mL以上、CCL1 31.5 (= 24.98)pg/mL以上、IL-1β 0.840 (= 2-0.252)pg/mL以上、CCL3 6.01 (= 22.59)pg/mL以上である（表３、図７）。
2. Results and Discussion In order to identify biomarkers that predict future relapse of RA during remission after withdrawal of biologics, we first performed sPLS-DA (Fig. 3), correlation analysis, and volcano plot analysis (Figs. 4 and 5) on the two groups, the remission maintenance group (AF group) and the relapse group (BC group). As a result, 12 candidate biomarkers, IL-34, IL-32, CCL1, IL-2, TNFSF12, IFNβ, IL-12p40, IL-26, IL-1β, IL-6, IL-19, and CCL8, had statistically significant differences with p values of less than 5% (Fig. 4). Protein-protein interaction network analysis of these 12 factors revealed that these factors were mutually linked in a network centered on IL-6, IL-10, IL-1β, IL-2, and STAT3 (Fig. 6). Furthermore, when univariate ROC analysis was performed using logarithmically transformed data, only IL-34 had an AUC value of 0.8 or more, while IL-32, CCL1, IL-1β, and CCL3 had AUC values in the 0.7 range (Table 3). The ROC curves and violin plots for each factor are shown in Figure 7. The cut-off values for each factor were 192 (= 2 7.58 ) pg/mL or more for IL-34, 95.0 (= 2 6.57 ) pg/mL or more for IL-32, 31.5 (= 2 4.98 ) pg/mL or more for CCL1, 0.840 (= 2 -0.252 ) pg/mL or more for IL-1β, and 6.01 (= 2 2.59 ) pg/mL or more for CCL3 (Table 3, Figure 7).
表３は、単変量ROC解析による寛解維持群(N)と再燃群(R)の生物学的製剤投与中止後の寛解状態でのバイオマーカー候補を示す。AとFを寛解維持群(N)、BとCを再燃群(R)とし、この二群を単変量ROC解析で比較し、AUC(Area under the curve)が0.7以上のバイオマーカー候補を表で示す。 Table 3 shows biomarker candidates in the remission state after discontinuation of biologics administration in the remission maintenance group (N) and relapse group (R) based on univariate ROC analysis. A and F represent the remission maintenance group (N), and B and C represent the relapse group (R), and these two groups were compared using univariate ROC analysis, and the table shows biomarker candidates with an AUC (area under the curve) of 0.7 or more.
更に、因子の組合せによる信頼度の高い再燃予測を可能とする最小因子数を同定するために、multivariate exploratory ROC解析を実施した。選択因子数を5因子まで減少させてもAUC = 0.809と再燃予測の高い有用性を示したが、3因子まで減少させるとAUC = 0.741とその予測性能が低下した(図８A)。このことから、信頼度の高い再燃予測には5因子選択が妥当であると判断した。続いて再燃予測判定に適した５因子モデルを検討したところ、IL-34、CCL1、IL-2、IL-1β、IL-19が選択頻度の高い因子として選定された（図８B）。選択された5因子について、寛解維持群（AF群）と再燃群（BC群）でロジスティック回帰モデルによる組合せの有用性を検討したところ、AUC = 0.92の高い有用性を示した（図８C）。また、G群およびE群を追加してサンプル数を増やした寛解維持群（AGF群）および再燃群（BEC群）にてロジスティック回帰モデルによる組合せの解析を実施したところ、AUC = 0.927と依然として高い有用性を示した（図８F）。一方、A群とB群のみの少ないサンプル数で同因子の組み合わせによるロジスティック回帰モデルによる解析を行ったところ、AUC = 0.796とある程度高い有用性を示した（図８G）。さらに、生物学的製剤を休薬直前の時点（Pre-A群およびPre-B群）での同因子の組み合わせによるロジスティック回帰モデルによる解析を行ったところ、AUC = 0.814と比較的高い有用性を示した（図８H）。寛解維持群（AF群）と再燃群（BC群）の再燃予測指数（RPI, relapse prediction index）を検討すると、RPIスコアは、例えば以下の数式で計算され、Pのcut-off値は0.6であった（図８D、図８Eは各因子の回帰係数のまとめ）。
(式) RPIスコア = logit(P) = log(P / (1 - P)) = -21.228 + 0.791*log2（IL-34) + 4.252*log2(CCL1) - 2.419*log2 (IL-2) + 0.465*log2(IL1β) - 0.18*log2(IL-19)
なお、ここに用いられる各バイオマーカーの単位はpg/mLである。
Furthermore, a multivariate exploratory ROC analysis was performed to identify the minimum number of factors that would enable reliable prediction of relapse by factor combination. Even when the number of selected factors was reduced to five, the AUC = 0.809 showed high usefulness in predicting relapse, but when the number of selected factors was reduced to three, the predictive performance decreased to AUC = 0.741 (Figure 8A). From this, it was determined that the selection of five factors is appropriate for reliable prediction of relapse. Next, a five-factor model suitable for predicting relapse was examined, and IL-34, CCL1, IL-2, IL-1β, and IL-19 were selected as the factors with high selection frequency (Figure 8B). When the usefulness of the combination of the selected five factors was examined in the remission maintenance group (AF group) and the relapse group (BC group) using a logistic regression model, it showed high usefulness of AUC = 0.92 (Figure 8C). In addition, a logistic regression model analysis was performed on the remission maintenance group (AGF group) and relapse group (BEC group) with an increased sample size by adding groups G and E, and the combination showed a still high usefulness of AUC = 0.927 (Fig. 8F). On the other hand, a logistic regression model analysis of the same factor combination with a small sample size of only groups A and B showed a relatively high usefulness of AUC = 0.796 (Fig. 8G). Furthermore, a logistic regression model analysis of the same factor combination just before the suspension of biological agents (Pre-A group and Pre-B group) showed a relatively high usefulness of AUC = 0.814 (Fig. 8H). When the relapse prediction index (RPI) of the remission maintenance group (AF group) and the relapse group (BC group) was examined, the RPI score was calculated, for example, by the following formula, and the cut-off value of P was 0.6 (Fig. 8D and Fig. 8E are summaries of the regression coefficients of each factor).
(Formula) RPI score = logit(P) = log(P / (1 - P)) = -21.228 + 0.791*log 2 (IL-34) + 4.252*log 2 (CCL1) - 2.419*log 2 (IL-2) + 0.465*log 2 (IL1β) - 0.18*log 2 (IL-19)
The unit of each biomarker used here is pg/mL.
例えば、上記の式で計算されたスコアをもとに、症状が寛解している時期において、各々の患者で将来再燃が予測されるか否かの判定が可能である。 For example, based on the score calculated using the above formula, it is possible to determine whether or not a future relapse is predicted for each patient during a period of symptom remission.
〔実施例２〕生物学的製剤の治療前及び治療中の解析による関節リウマチの再燃を予測するバイオマーカー
１．材料及び方法
生物学的製剤の治療前および治療中の時点においても、将来の関節リウマチの再燃を予測するバイオマーカーを同定する目的で、実施例１に用いたコホートにおいて、生物学的製剤の治療前および治療中の時点での血清サンプルが存在する患者（再燃群21症例、寛解持続群11症例）を対象コホートとして用いた（図９）。患者血清の分類は、再燃群において、生物学的製剤の投与直前をrP1、投与中で以下のrD2を含まない時点をrD1、投与中で生物学的製剤を休止する直前およびその一つ前の時点をrD2とした。なお、rD1とrD2は各々の複数サンプルの値の平均値をそれぞれの値として用いた。また、寛解維持群（非再燃群）においても同様に、生物学的製剤の投与直前をnP1、投与中で以下のnD2を含まない時点をnD1、投与中で生物学的製剤を休止する直前およびその一つ前の時点をnD2とした。なお、nD1とnD2は各々の複数サンプルの値の平均値をそれぞれの値として用いた。これらの全ての地点における血清を用いて、実施例１と同様の方法で73種類のサイトカインを網羅的に測定した。
[Example 2] Biomarkers predicting relapse of rheumatoid arthritis by analysis before and during treatment with biological agents 1. Materials and methods In order to identify biomarkers predicting future relapse of rheumatoid arthritis even before and during treatment with biological agents, patients with serum samples before and during treatment with biological agents in the cohort used in Example 1 (21 cases in the relapse group, 11 cases in the sustained remission group) were used as the target cohort (Figure 9). In the relapse group, the patient serum was classified as rP1 immediately before the administration of the biological agent, rD1 during the administration when the following rD2 was not included, and rD2 immediately before and the time before the suspension of the biological agent during the administration. Note that the average value of the values of multiple samples for each of rD1 and rD2 was used as the respective values. Similarly, in the remission maintenance group (non-relapse group), the time immediately before the administration of the biological agent was nP1, the time when the following nD2 was not included during the administration was nD1, and the time immediately before and the time before the suspension of the biological agent during the administration was nD2. For nD1 and nD2, the average values of the values obtained from multiple samples were used. Using the serum from all of these time points, 73 types of cytokines were comprehensively measured in the same manner as in Example 1.
統計学的解析：患者サンプルは、先ず生物学的製剤の治療前の時点における寛解維持群（nP1群）および再燃群（rP1群）の２群を、対数(log2)変換し、平均0、分散1になるように変換したデータを用いて、Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)、相関分析、およびt検定による有意差検定（p値が5%未満を有意な差と判定）を用いて再燃群と寛解維持群の判別に寄与する因子の選択を行った。更に、臨床的な活動期、生物学的製剤使用開始後、生物学的製剤使用中における臨床的寛解期などのベースラインの変動を考慮するため、時期の異なるポイントを独立したサンプルとして探索的モデル作成の検討に用いた。治療前のサンプルに治療中のサンプルを加えた寛解維持群のサンプル群（Pre-N群；nP1+nD1+nD2）および再燃群（Pre-R群；rP1+rD1+rD2）の２群を同様に解析した。更に、バイオマーカー解析として対数(log2)変換したデータを用いてROC曲線を作成し、各々の因子に対してAUC値とcut-off値を算定した。対数データを平均0、分散1になるように再変換したデータを用いてSVM法によるランキング解析を行い、組合せによるバイオマーカーの選定を行った。選択されたバイオマーカーの組合せについて、対数変換データをもとにしたロジスティック回帰モデルを検討し、10重交差検証または100重交差検証法によるROC曲線の作成により組合せの有用性を判定した。 Statistical analysis: First, the patient samples were divided into two groups, the remission group (nP1 group) and the relapse group (rP1 group) before biologic treatment, and the data were logarithmically transformed to have a mean of 0 and variance of 1. Using the data, factors contributing to the discrimination between the relapse group and the remission group were selected using sparse partial least squares discriminant analysis (sPLS-DA), correlation analysis, and t-tests (p value of less than 5% was considered significant). Furthermore, in order to consider baseline fluctuations such as the clinical active period, the period after starting biologic treatment, and the clinical remission period during biologic treatment, independent samples from different time points were used to create exploratory models. The remission group (Pre-N group; nP1+nD1+nD2) and the relapse group (Pre-R group; rP1+rD1+rD2), which were samples before treatment and samples during treatment, were analyzed in the same way. Furthermore, for biomarker analysis, ROC curves were created using logarithmic ( log2 ) transformed data, and the AUC and cut-off values were calculated for each factor. Ranking analysis was performed using SVM method using data that had been retransformed to have a mean of 0 and variance of 1, and biomarker combinations were selected. For the selected biomarker combinations, a logistic regression model based on the logarithmic transformed data was examined, and the usefulness of the combinations was assessed by creating ROC curves using 10-fold or 100-fold cross-validation.
２．結果及び考察
生物学的製剤の治療前および治療中の時点においても、将来の関節リウマチの再燃を予測するバイオマーカーを同定する目的で、先ず、生物学的製剤の治療前の時点における寛解維持群（nP1群）および再燃群（rP1群）の２群について、sPLS-DA（図１０A）、相関分析およびt検定（図１１A）を実施したところ、バイオマーカーの候補としてCCL26、IL-34、IFNγ、CCL25、IL-32、IFNλ2、CCL21、IL-2、IFNα2、IFNλ1、CCL1および、IL-27(p28)の１２個がp値5％未満の統計学的有意差を有した（図１１A）。さらに、治療前のサンプルに治療中のサンプルを加えた寛解維持群のサンプル群（Pre-N群；nP1+nD1+nD2）および再燃群（Pre-R群；rP1+rD1+rD2）の２群を比較したところ（図１０B及び図１１B）、バイオマーカーの候補としてIL-34、IFNγ、IFNλ2、CCL26、CCL25、IL-32、IL-2、IFNα2、IFNλ1、CCL1、CCL7、TNFSF14、TNFSF12、CCL11、IL-12(p40)、Pentraxin3、CCL21、IL-26、IL-27(p28)、CXCL5、IL-35、TSLP、CCL19、IL-4、IL-10、CXCL2、IL-12(p70)、CCL20、Chitinase 3-like 1、CXCL1、IFNβおよび、IL-20の３２個が、p値5％未満の統計学的有意差を有した（図１１B）。更に対数変換したデータを用いて単変量ROC解析を行うと、IFNγ、IL-34、IFNλ2、CCL26および、CCL25がAUC値0.8以上を示し、IL-2、IL-32、IFNλ1、TNFSF14、IL-35、CCL1および、CCL11は0.7台を示した（表４）。それぞれのROC曲線とバイオリンプロットを図１２に示す。各因子のcut-off値は、IFNγ 17.15 (= 24.1) pg/mL以上、IL-34 137.19 (= 27.1) pg/mL以上、IFNλ2 48.17 (= 25.59) pg/mL以上、CCL26 4.32 (= 22.11) pg/mL以上、CCL25 192.67 (= 27.59) pg/mL以上、IL-2 44.32 (= 25.47) pg/mL以上、IL-32 121.10 (= 26.92) pg/mL以上、IFNλ1 50.56 (= 25.66) pg/mL以上、TNFSF14 42.52 (= 25.41) pg/mL以下、IL-35 276.28 (= 28.11) pg/mL以上、CCL1 34.06 (= 25.09) pg/mL以上、CCL11 59.30 (= 25.89) pg/mL以上 である（表４）。
2. Results and Discussion In order to identify biomarkers that predict future relapse of rheumatoid arthritis even before and during biologic treatment, we first performed sPLS-DA (Fig. 10A), correlation analysis, and t-test (Fig. 11A) on the two groups, the remission maintenance group (nP1 group) and the relapse group (rP1 group) before biologic treatment. As a result, 12 candidate biomarkers, CCL26, IL-34, IFNγ, CCL25, IL-32, IFNλ2, CCL21, IL-2, IFNα2, IFNλ1, CCL1, and IL-27(p28), had statistically significant differences with p-values of less than 5% (Fig. 11A). Furthermore, when the two groups were compared, the remission maintenance group (Pre-N group; nP1+nD1+nD2) and the relapse group (Pre-R group; rP1+rD1+rD2), which consisted of samples from before treatment and samples from during treatment (Figures 10B and 11B), the following biomarker candidates were identified: IL-34, IFNγ, IFNλ2, CCL26, CCL25, IL-32, IL-2, IFNα2, IFNλ1, CCL1, CCL7, TNFSF14, TNFSF12, CCL11, IL-12(p40), Pentraxin3, CCL21, IL-26, IL-27(p28), CXCL5, IL-35, TSLP, CCL19, IL-4, IL-10, CXCL2, IL-12(p70), CCL20, Chitinase 3-like Thirty-two of the following factors were statistically significant with a p-value of less than 5%: IFNγ, IL-34, IFNλ2, CCL26, and CCL25 (Figure 11B). Univariate ROC analysis using log-transformed data showed that IFNγ, IL-34, IFNλ2, CCL26, and CCL25 showed AUC values of 0.8 or higher, while IL-2, IL-32, IFNλ1, TNFSF14, IL-35, CCL1, and CCL11 showed AUC values in the 0.7 range (Table 4). The ROC curves and violin plots for each are shown in Figure 12. The cut-off values for each factor were: IFNγ ≥ 17.15 (= 2 4.1 ) pg/mL, IL-34 ≥ 137.19 (= 2 7.1 ) pg/mL, IFNλ2 ≥ 48.17 (= 2 5.59 ) pg/mL, CCL26 ≥ 4.32 (= 2 2.11 ) pg/mL, CCL25 ≥ 192.67 (= 2 7.59 ) pg/mL, IL-2 ≥ 44.32 (= 2 5.47 ) pg/mL, IL-32 ≥ 121.10 (= 2 6.92 ) pg/mL, IFNλ1 ≥ 50.56 (= 2 5.66 ) pg/mL, and TNFSF14 ≥ 42.52 (= 2 5.41 ). IL-35 was 276.28 (= 28.11 ) pg/mL or less, CCL1 was 34.06 (= 25.09 ) pg/mL or more, and CCL11 was 59.30 (= 25.89 ) pg/mL or more (Table 4).
表４は、単変量ROC解析による寛解維持群(N)と再燃群(R)の生物学的製剤投与前および投与中でのバイオマーカー候補を示す。Pre-N (nP1、nD1、nD2)を寛解維持群(N)、Pre-R (rP1、rD1、rD2)を再燃群(R)とし、AUC (Area under the curve)が0.7以上のバイオマーカー候補を表で示す。 Table 4 shows biomarker candidates before and during administration of biological agents in the remission group (N) and relapse group (R) based on univariate ROC analysis. Pre-N (nP1, nD1, nD2) represents the remission group (N) and Pre-R (rP1, rD1, rD2) represents the relapse group (R), and the table shows biomarker candidates with an AUC (area under the curve) of 0.7 or more.
先ず、本コホートにおいて、生物学的製剤の使用中や使用前の時点で再燃予測判定が可能か否かを、実施例１で同定したIL-34、CCL1、IL-2、IL-1β、IL-19の５因子を用いて、寛解維持群（Pre-N群）と再燃群（Pre-R群）でロジスティック回帰モデルにてROC解析を実施すると、AUC値は0.794と0.8以下の値となり、実施例１に比べてAUC値は低下した(図１３)。なお前述した様に、生物学的製剤を休薬直前の時点（Pre-A群およびPre-B群）での同因子の組み合わせによるロジスティック回帰モデルによる解析を行ったところ、AUC = 0.814と比較的高い有用性を示した（図８H）。更に信頼度の高い再燃予測を可能にする因子の組合わせを探索するために、multivariate exploratory ROC解析を実施した。選択因子数を5因子まで減少させてもAUC = 0.944と再燃予測の高い有用性を示したが、3因子まで減少させるとAUC = 0.897とその予測性能が低下した(図１４A)。このことから、信頼度の高い再燃予測には5因子選択が妥当であると判断した。続いて再燃予測判定に適した５因子モデルを検討したところ、IFNγ、TNFSF14、TNFSF12、IL-34、IL-32が選択頻度の高い因子として選定された（図１４B）。選択された5因子について、寛解維持群（Pre-N群）と再燃群（Pre-R群）でロジスティック回帰モデルによる組合せの有用性を検討したところ、AUC = 0.976の高い有用性を示した（図１４C）。一方、nP1群とrP1群のみ、またnD2群とrD2群のみの少ないサンプル数で同因子の組み合わせによる100重交差検証法によるROC解析を行ったところ、それぞれAUC = 0.919、およびAUC = 0.857とある程度高い有用性を示した（図１４F, １４G）。寛解維持群（Pre-N群）と再燃群（Pre-R群）での再燃予測指数（RPI, relapse prediction index）を検討すると、RPIスコアは、例えば以下の数式で計算され、Pのcut-off値は0.27であった（図１４D、図１４Eは各因子の回帰係数のまとめ）。
(式) RPIスコア = logit(P) = log(P / (1 - P)) = -80.983 + 0.75*log2(IL-34) + 3.002*log2(IFNγ) - 1.991*log2(TNFSF14) + 7.139*log2(TNFSF12) + 2.443*log2(IL-32)
なお、ここに用いられる各バイオマーカーの単位はpg/mLである。
First, in this cohort, to determine whether or not it is possible to predict relapse during or before the use of biological agents, ROC analysis was performed using a logistic regression model for the remission maintenance group (Pre-N group) and the relapse group (Pre-R group) using the five factors identified in Example 1, IL-34, CCL1, IL-2, IL-1β, and IL-19. The AUC value was 0.794, a value below 0.8, which was lower than that in Example 1 (Figure 13). As mentioned above, when the same factors were combined just before the suspension of biological agents (Pre-A group and Pre-B group), the logistic regression model showed a relatively high usefulness of AUC = 0.814 (Figure 8H). In order to explore a combination of factors that would enable more reliable prediction of relapse, a multivariate exploratory ROC analysis was performed. Even when the number of selected factors was reduced to five, the AUC = 0.944 showed high usefulness in predicting relapse, but when the number of selected factors was reduced to three, the predictive performance decreased to AUC = 0.897 (Figure 14A). From this, it was determined that the selection of five factors is appropriate for highly reliable prediction of relapse. Next, a five-factor model suitable for relapse prediction was examined, and IFNγ, TNFSF14, TNFSF12, IL-34, and IL-32 were selected as the factors with high selection frequency (Figure 14B). When the usefulness of the combination of the selected five factors was examined in the remission maintenance group (Pre-N group) and the relapse group (Pre-R group) using a logistic regression model, it showed high usefulness of AUC = 0.976 (Figure 14C). On the other hand, when ROC analysis was performed using a 100-fold cross-validation method with a small number of samples for the nP1 and rP1 groups, and for the nD2 and rD2 groups, the same factors were combined, and the AUCs were 0.919 and 0.857, respectively, showing a relatively high level of usefulness (Fig. 14F, 14G). When the relapse prediction index (RPI) was examined for the remission maintenance group (Pre-N group) and the relapse group (Pre-R group), the RPI score was calculated, for example, using the following formula, and the cut-off value for P was 0.27 (Fig. 14D and Fig. 14E are summaries of the regression coefficients of each factor).
(Formula) RPI score = logit(P) = log(P / (1 - P)) = -80.983 + 0.75* log2 (IL-34) + 3.002* log2 (IFNγ) - 1.991* log2 (TNFSF14) + 7.139* log2 (TNFSF12) + 2.443* log2 (IL-32)
The unit of each biomarker used here is pg/mL.
例えば、上記の式で計算されたスコアをもとに、症状が活動中である生物学的製剤を使用前の時期や、生物学的製剤を使用中の時期においても、各々の患者で将来再燃が予測されるか否かの予後を検査可能である。特に、生物学的製剤を使用して寛解に至った時期での検査は有用である。 For example, based on the score calculated using the above formula, it is possible to test the prognosis of whether or not future relapse is predicted for each patient before the use of a biological agent when symptoms are active, or even while using a biological agent. In particular, testing at the time when remission has been achieved using a biological agent is useful.
〔実施例３〕関節リウマチの再燃の直前を予測するバイオマーカー
１．材料及び方法
生物学的製剤の休薬後寛解時において、将来関節リウマチの再燃をある程度予測することは可能となったが、臨床的には再燃直前を予測し、生物学的製剤を再開する時期を決定することは非常に重要である。実施例１に用いたコホートの再燃群において、休薬直後の寛解時の群（B群, n=25）と、再燃が生じる直前時の群（C群, n=23）の２群において、網羅的に測定した73種類の血清サイトカインを検討した。用いた値は対数(log2)変換し、平均0、分散1になるように変換したデータを用いて、t検定による２群比較を実施し、heatmapを作成した。次に、実測値のC/B比を求め、sTNFR2のcut-off値を<0.8、Fractalkine/CX3CL1のcut-off値を>1.2として、いずれかが該当する場合を陽性と判定した。また、再燃に至るまでの期間が３ヶ月以下の短い群（R short, n=9）と３ヶ月より長い群（R long, n=14）にサブグループ化し、それぞれのサブグループにおいても実施例１と同様の方法で統計学的解析を行った。
[Example 3] Biomarkers predicting the onset of relapse in rheumatoid arthritis 1. Materials and methods It has become possible to predict the onset of relapse in rheumatoid arthritis to some extent during remission after withdrawal of biological agents, but clinically it is very important to predict the onset of relapse and determine the timing of resumption of biological agents. In the relapse group of the cohort used in Example 1, 73 types of serum cytokines were comprehensively measured in two groups: a group in remission immediately after withdrawal (group B, n = 25) and a group immediately before relapse (group C, n = 23). The values used were logarithmically (log 2 ) converted, and the data converted to have a mean of 0 and a variance of 1 were used to compare the two groups using a t-test to create a heatmap. Next, the C/B ratio of the actual values was calculated, and the cut-off value for sTNFR2 was set to <0.8, and the cut-off value for Fractalkine/CX3CL1 was set to >1.2, and the case where either of the above was applicable was judged to be positive. In addition, the patients were subgrouped into two groups based on the time until relapse: short (≤3 months) group (R short, n=9) and long (>3 months) group (R long, n=14), and statistical analysis was performed on each subgroup in the same manner as in Example 1.
２．結果及び考察
実施例１に用いたコホートの再燃群の生物学的製剤の休薬直後の群（B群）と、再燃が生じる直前時の群（C群）はいずれも臨床的には寛解状態である。この２群において２群比較を行ったところ、t検定において有意な差のあったバイオマーカーの候補因子は、sTNFR2およびFractalkine/CX3CL1の２因子であった（図１５A）。図１５Bにheatmapの図を示す。sTNFR2は、免疫抑制性の因子であり、B群に比べC群ではタンパク量が減少し、Fractalkine/CX3CL1は、免疫賦活性の因子であり、B群に比べC群ではタンパク量が増加する。実測値のC/B比を求め、sTNFR2のcut-off値を<0.8、Fractalkine/CX3CL1のcut-off値を>1.2として、このいずれかの傾向が陽性に出た症例数を確認すると、17/23 (74%)の陽性率であった（図１５C）。
2. Results and Discussion The group of relapsed patients in the cohort used in Example 1, immediately after the suspension of the biological agent (group B) and the group immediately before relapse (group C), were both in a state of clinical remission. When the two groups were compared, the candidate factors for biomarkers that showed significant differences in the t-test were sTNFR2 and Fractalkine/CX3CL1 (Figure 15A). Figure 15B shows a heatmap. sTNFR2 is an immunosuppressive factor, and the protein amount is reduced in group C compared to group B, while Fractalkine/CX3CL1 is an immunostimulatory factor, and the protein amount is increased in group C compared to group B. The C/B ratio of the actual measured values was calculated, and the cut-off value for sTNFR2 was set to <0.8, and the cut-off value for Fractalkine/CX3CL1 was set to >1.2. The number of cases in which either of these trends were positive was confirmed, with a positive rate of 17/23 (74%) (Figure 15C).
以上から、この２因子を検討することにより、ある程度、再燃直前の時期を推測することが可能である。 From the above, by examining these two factors, it is possible to predict to some extent the period immediately prior to relapse.
２年の観察期間の寛解維持率のKaplan-Meier曲線において、休薬後３ヶ月以内の下落曲線が大きくその後なだらかに下落が続く（図１６左）。再燃する患者の約半数（１２人、全体の４６．２％）が3か月以内に再燃しており、サブグループを形成する傾向が観察された（図１６右）。寛解状態と再燃直前状態がより明確化されることを期待して、休薬後３ヶ月以内に再燃した患者群（R short, n=12）と休薬後３ヶ月より後に再燃した患者群（R long, n=14）にサブグループ化し、各サブグループにおいて実施例１と同様の解析を行った。生物学的製剤の休薬直後の群（B群）と、再燃が生じる直前時の群（C群）を比較するvolcano plot解析において、R short（B群とC群の両方に値が存在する患者数n=9）でCXCL13がバイオマーカーの候補因子として抽出された（図１７）。一方で、R long（B群とC群の両方に値が存在する患者数n=14）では、IL-6、TSLP、MMP3が抽出された。これら因子はいずれもC群で増加傾向が認められた（図１８）。更にR long群において再燃直前マーカーの有効性を検証するために対数変換したデータを用いてROC解析を行うと、IL-6、TSLP、MMP3はAUC値が0.7以上を示した（図１９）。このことは、再燃直前予測マーカーとしてこれら３因子の有効性を示唆するものである。続いて再燃直前予測判定に適した3因子モデルついて、生物学的製剤の休薬直後の群（B群）と、再燃が生じる直前時の群（C群）でロジスティック回帰モデルによる組合せの有用性を検討したところ、AUC = 0.811の有用性を示した（図２０）。生物学的製剤の休薬直後の群（B群）と、再燃が生じる直前時の群（C群）の再燃直前予測指数（RPI, relapse prediction index）を検討すると、RPIスコアは、例えば以下の数式で計算され、Pのcut-off値は0.31であった。
(式) RPIスコア = logit(P) = log(P / (1 - P)) = 12.519 + 2.63*log2（IL-6) + 23.393*log2 (TSLP) - 12.014*log2 (MMP3)
なお、ここに用いられる各バイオマーカーの単位はpg/mLである。
In the Kaplan-Meier curve of the remission maintenance rate over a two-year observation period, the decline curve was large within 3 months after drug suspension, and then continued to decline gradually (Figure 16, left). Approximately half of the patients who relapsed (12 patients, 46.2% of the total) relapsed within 3 months, and a tendency to form subgroups was observed (Figure 16, right). In the hope of clarifying the remission state and the state immediately before relapse, the patients were subgrouped into a group of patients who relapsed within 3 months after drug suspension (R short, n = 12) and a group of patients who relapsed more than 3 months after drug suspension (R long, n = 14), and the same analysis as in Example 1 was performed for each subgroup. In a volcano plot analysis comparing the group immediately after drug suspension of the biological agent (group B) and the group immediately before relapse (group C), CXCL13 was extracted as a candidate biomarker factor in R short (number of patients with values in both groups B and C, n = 9) (Figure 17). On the other hand, IL-6, TSLP, and MMP3 were extracted from R long (n=14 patients with values in both groups B and C). All of these factors showed an increasing tendency in group C (Figure 18). Furthermore, when ROC analysis was performed using logarithmically transformed data to verify the effectiveness of the markers just before relapse in the R long group, IL-6, TSLP, and MMP3 showed AUC values of 0.7 or more (Figure 19). This suggests the effectiveness of these three factors as markers just before relapse. Next, the usefulness of the combination of the three-factor model suitable for predicting just before relapse was examined using a logistic regression model in the group immediately after the suspension of biological agents (group B) and the group just before relapse (group C), and the usefulness was shown to be AUC = 0.811 (Figure 20). When examining the relapse prediction index (RPI) for the group immediately after discontinuation of the biological agent (group B) and the group immediately before a relapse occurred (group C), the RPI score was calculated, for example, using the following formula, and the cut-off value for P was 0.31.
(Formula) RPI score = logit(P) = log(P / (1 - P)) = 12.519 + 2.63*log 2 (IL-6) + 23.393*log 2 (TSLP) - 12.014*log 2 (MMP3)
The unit of each biomarker used here is pg/mL.
例えば、上記の式で計算されたスコアをもとに、再燃直前の時期を予測することが可能である。特に将来再燃が予測される患者において、これらの予測スコアは有用である。例えば、生物学的製剤が休薬中の状態で、当該の再燃直前予測スコアを測定することは、生物学的製剤の再使用を行う治療法の選択を判断する大きな指標の一つとなるものである。
より好ましい使用法は、まず実施例１および実施例２における使用法を用いて、まず、生物学的製剤の使用により寛解した患者が、将来再燃するか否かを予測し、生物学的製剤の使用の中止を適切に選択する。生物学的製材を中止した場合、特に再燃予測マーカーの使用により再燃の可能性が高いと判定された場合は、実施例３に示すように、再燃直前予測マーカーを継時的に用いて、再燃直前時期を予測する。再燃直前時期を予測した場合は、再燃が生じる前に生物学的製剤の使用の速やかな再開を選択することが好ましい。以上のような使用により、再燃を事前に防ぎ、関節炎の不可逆的な進行を予防することが可能であり、結果として、関節リウマチ患者のQOLを高めることが可能である。
For example, it is possible to predict the time just before relapse based on the score calculated by the above formula. These prediction scores are particularly useful for patients who are predicted to relapse in the future. For example, measuring the just before relapse prediction score while a biological agent is suspended is one of the major indicators for deciding whether to resume the use of the biological agent.
A more preferred method of use is to first predict whether a patient who has achieved remission by using a biological agent will relapse in the future using the method of use in Example 1 and Example 2, and then appropriately select whether to discontinue the use of the biological agent. When the biological agent is discontinued, especially when it is determined that there is a high possibility of relapse by using the relapse prediction marker, the time immediately before relapse is predicted by continuously using the immediately before relapse prediction marker as shown in Example 3. When the time immediately before relapse is predicted, it is preferable to select to promptly resume the use of the biological agent before relapse occurs. By using the method described above, it is possible to prevent relapse in advance and prevent irreversible progression of arthritis, and as a result, it is possible to improve the QOL of rheumatoid arthritis patients.
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