[EULAR文摘] 利用蛋白组学技术开发一项蛋白评分用于预测TNFi疗效

利用蛋白组学技术开发一项蛋白评分与临床参数联用可以增强对TNF拮抗剂对RA疗效的预测效能

Cuppen BV, et al. EULAR 2015. Present ID: OP0130.

背景: 对类风湿关节炎(RA)而言, 为了避免延迟有效治疗、潜在副作用和不必要的健康支出, 在治疗之前区分出TNFi无效者是很重要的。本研究在一大组炎性蛋白中搜索能预测生物制剂疗效的生物标记。

目的: 开发一项能预测TNFi疗效的蛋白评分, 并测试它与临床参数联用的预测效果。

方法: 本研究顺序纳入适合应用TNFi的RA患者, 这些患者也为BiOCURA患者注册登记所记录。在给予治疗之前采集血清, 利用蛋白组学平台xMP对57种炎性蛋白进行分析。治疗3个月后评估EULAR反应。采用一种监督聚类分析, 部分最小二乘法(partial least squares, PLS), 筛选出蛋白组合, 并交叉验证以得到一种可重复的蛋白评分。采用多种罚分处理基线临床缺失值。通过单变量和多变量回溯筛选方法(p<0.1)，甄别与EULAR优良应答有关的临床参数。 通过评估接收者操作特征曲线(ROC)的曲线下面积(AUC-ROC)、阴性预测值(NPV)和重分类指数(NRI), 比较联用或不联用蛋白评分的最终模型的预测能力。

结果: 共有192例患者接受治疗, 其中171例有临床反应数据。除了基线CRP和DAS28, 最小二乘法(PLS)还揭示了9种重要炎性蛋白, 包括sCD14、IFN-γ、MCP1、MIP1b、MIP3b、TARC、 sTNFRI、sTNFRII和TSLP。这些标记物可以解释治疗3个月后DAS28发生31.5%的变化。用于预测TNFi疗效的临床模型所含参数包括基线DAS28、未曾用过生物制剂、HAQ、RF阳性、同时使用MTX和糖皮质激素。联用蛋白评分并未改善预测模型的AUC-ROC的界值(0.80(0.73-0.87), 然而, 若将NPV预设界值定为≥0.9, 联用蛋白评分的预测模型可以将原归为低概率分类中的30例患者重新分类(参见表)。由此, 有应答者的分类改善了24.2%, 无应答者的分类改善了-4.8%, NRI=19.4%。

结论: 本研究显示结合了蛋白评分和临床参数的模型可以预测哪些患者可能对TNFi治疗无反应, 相较于单用临床参数, 该联合模型可以将更多患者分别归于不同风险类别。因此, 蛋白评分有助于个体化治疗, 从而优化医疗资源的使用。近期, 我们将进行外部验证。

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原文链接或参见以下信息。

Ann Rheum Dis 2015;74:117 doi:10.1136/annrheumdis-2015-eular.4843

• Oral Presentations

OP0130 A Proteomics Approach to Predict the TNF-Alpha Inhibitor Response in RA: The Added Clinical Value of a Protein Score

1. B.V. Cuppen¹, 
2. P.M. Welsing¹, 
3. W. de Jager², 
4. R.D. Fritsch¹, 
5. A.C. Marijnissen¹, 
6. J.W. Bijlsma¹,
7. M.J. van der Veen³, 
8. J.M. van Laar¹, 
9. F.P. Lafeber¹ 
10. on behalf of Investigators of the Society for Rheumatology Research Utrecht (SRU)

+Author Affiliations

1. ¹Rheumatology & Clinical Immunology
2. ²Pediatric Immunology, University Medical Center Utrecht, Utrecht
3. ³Rheumatology, st Jansdal Hospital, Harderwijk, Netherlands

Abstract

Background In rheumatoid arthritis (RA) it is of major importance to distinguish non-responders to TNF-alpha inhibitor (TNFi) treatment before start to prevent a delay in effective treatment, potential side-effects and unnecessary healthcare costs. We investigated the ability of al large set of inflammatory proteins to predict (absence of) response to biological treatment.

Objectives To develop a protein score predictive for response to TNFi treatment in RA and investigate its added predictive value over clinical parameters alone.

Methods In consecutive RA patients eligible for TNFi treatment as included in the BiOCURA registry, serum was collected before start of treatment and analyzed on 57 inflammatory proteins using xMAP technology. EULAR response was determined after three months. A supervised cluster analysis method, partial least squares (PLS) was used to select the best combination of proteins and cross-validation to gain a reproducible protein score. Multiple imputation was used to account for missing data of baseline clinical parameters. Relevant clinical parameters for EULAR good response were selected by performing a univariate (p<0.2) and multivariable backward selection (p<0.1). The predictive ability of the final model with and without the protein score was assessed using the area under the receiving operater curve (AUC-ROC), negative predictive values (NPV) and the reclassification index (NRI).

Results Response was determined for 171 of the 192 cases starting treatment. On top of CRP and DAS28 at baseline, PLS revealed 9 important proteins: sCD14, IFNγ, MCP1, MIP1b, MIP3b, TARC, sTNFRI, sTNFRII and TSLP. These markers were able to explain 31.5% of the variance in DAS28 at 3 months.

Final models for prediction of TNFi response included baseline DAS28, naivety for bDMARDs, HAQ, RF positivity, concomitant MTX and GC use. The protein score did not improve the AUC-ROC of 0.80 (0.73-0.87), however, when the predefined cut-off for a NPV≥0.9 was set, the addition of the protein score resulted in the classification of 30 extra patients in the low probability category (table). An improved classification was observed of 24.2% and -4.8% for patients with and without a response respectively (NRI=19.42%).

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Conclusions We showed that a combination of a protein score and clinical variables is able to predict absence of EULAR good response to TNF inhibiting treatment and can classify more patients at baseline in the appropriate risk category than clinical variable alone. This protein score may therefore contribute to a more patients tailored treatment, leading to a better usage of the available resources. In the near future these findings will be validated externally.