Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score

Figures & data

Figure 1. Overview of statistical analyses. The pathways denoted by black–dotted and grey–straight lines represent the analyses performed in the development and validation steps, respectively. AUC-ROC, area under the receiving operating characteristic curve; GEE, generalized estimation equations; NPV, negative predictive value; NRI, net reclassification index; PLS, partial least squares; par., parameters; sel., selection.

Table 1. Baseline characteristics of development (n = 65) and validation cohort (n = 185) before multiple imputation.

Clinical parameter	Development (n = 65)	Validation (n = 185)	p
Gender, female	47 (72.3)	138 (74.6)	0.744
Age (years), mean ± SD	57.0 ± 10.9	54.1 ± 12.3	0.094
Disease duration (years), median (IQR)	6.0 (2.0–14.5)	6.0 (2.0–12.0)	0.817
Smoking currently	23 (35.4)	44 (23.8)	0.075
RF, positive	36 (55.4)	122 (66.7)	0.133
ACPA, positive	45 (69.2)	131 (72.4)	0.634
CRP (mg/L), median (IQR)	4.0 (1.1–11.0)	6.0 (3.0–13.0)	0.011
ESR (mm/h), median (IQR)	14.0 (7.0–33.5)	19.5 (10.0–33.8)	0.145
Baseline DAS28, mean ± SD	4.1 ± 1.2	4.5 ± 1.2	0.019
No. of previously used bDMARDs			0.520
0 (naïve)	35 (53.8)	118 (63.8)
1	22 (33.8)	53 (28.6)
≥ 2	8 (12.3)	14 (7.6)
Initiating treatment with			0.058
Adalimumab	17 (26.2)	73 (39.5)
Etanercept	33 (50.8)	65 (35.1)
Golimumab	5 (7.7)	26 (14.1)
Infliximab	3 (4.6)	11 (5.9)
Certolizumab	7 (10.8)	10 (5.4)
Concomitant treatment
MTX	64 (98.5)	133 (71.9)	< 0.001
GCs	24 (36.9)	67 (36.2)	1.000

Data are shown as n (%) unless otherwise indicated.

IQR, interquartile range; RF, rheumatoid factor; ACPA, anti-citrullinated protein antibody; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; DAS28, Disease Activity Score based on 28-joint count; bDMARD, biological disease-modifying anti-rheumatic drug; MTX, methotrexate; GCs, glucocorticoids.

Statistically significant p values are marked in bold (< 0.05).

Table 2. Models for (baseline) prediction of response in development cohort (n = 65).

	Clinical model		Combined model
Item	OR (95% CI)	p	OR (95% CI)	p
Protein score			0.24 (0.07–0.83)	0.024
DAS28 at baseline	1.64 (0.89–3.03)	0.112	3.18 (1.30–7.77)	0.011
CRP, ln-transformed	1.32 (0.76–2.28)	0.321	1.04 (0.56–1.93)	0.894
HAQ	0.30 (0.08–1.13)	0.075	0.33 (0.08–1.43)	0.138

Two models predicting response were developed: one clinical model containing the Disease Activity Score based on 28-joint count (DAS28), C-reactive protein (CRP), and Health Assessment Questionnaire (HAQ), and a combined model including the same clinical parameters as well as the protein score (containing a combination of the 12 best predicting proteins out of all 53 proteins). Shown are the odds ratios (ORs) and statistical significance of all parameters when entered in the multivariable generalized estimating equation model with response to treatment as outcome. The two models were compared for their predicting performance: the clinical model showed an area under the receiver operating characteristics curve (AUC-ROC) of 0.72 [95% confidence interval (CI) 0.59–0.86] and the combined model showed a higher predictive ability, with an AUC-ROC of 0.77 (95% CI 0.64–0.90).

Table 3. Risk categories for probability on European League Against Rheumatism (EULAR) good response in the development cohort (n = 65).

Category for probability of good response	Clinical model	Combined model
Low (< 10%), estimated size (%)	21.5	28.2
Normal (> 10%), estimated size (%)	79.5	71.8
NNT		15

The implementation of a predictive tool for treatment response requires guidance on how to use the result of this tool in clinical practice. Therefore, a cut-off was determined where patients had such a low chance of a good response to tumour necrosis factor-α inhibitor (TNFi) that they could rather be advised not to start treatment. This cut-off was defined as a probability < 10% of a good response. The proportion of patients with this low chance of response, as well as those with a normal risk, is shown for all 65 patients, as predicted by the clinical model and by the combined model. As the implementation of the better performing combined model requires expensive protein measurements, we calculated the number needed to test (NNT) to gain some insight into the cost–benefit ratio. The NNT represents the number of patients who require protein measurements, so that one extra patient can be classified as having a low chance of response and thus can be advised not to start TNFi treatment.

Table 4. Risk categories for European League Against Rheumatism (EULAR) good response in the validation cohort (n = 185).

Category for probability of good response	Clinical model	Combined model
Low (< 10%), estimated size (%)	17.3	6.1
Normal (> 10%), estimated size (%)	82.7	93.9
NNT		−9

A probability < 10% of a good response was used to determine patients with such a low chance of a good response to tumour necrosis factor-α inhibitor (TNFi) that they could rather be advised not to start treatment. The proportion of patients with this low chance of response, as well as those with a normal risk, is shown for all 185 patients in the validation cohort, as predicted by both the clinical model and the combined model. The number needed to test (NNT) represents the number of patients who require protein measurements, so that one extra patient can be classified as having a low chance of response and thus can be advised not to start TNFi treatment. The negative NNT implies that the addition of protein measurement results in fewer patients being correctly classified by the new model.

Table 5. Non-exhaustive overview of studies with a proteomic approach to identify baseline predictive proteins regarding response to tumour necrosis factor-α inhibitor (TNFi) treatment.

Study	Treatment	No. of patients	Material	Method	No. of proteins	Associations found	Technical replication*	Validation in new patients
Fabre et al (14)	ETN	33	Serum	Protein Biochip Array Technology (Investigator Evidence; Randox, Mauguio, France)	12	High EGF and MCP-1† predicts good response	No	No
Hueber et al (15)	ETN	93	Serum	12-plex human cytokine FLEX® kit (Millipore) and ELISA (targets preselected by autoantigen microarrays)	26	24 protein signature predicts response [GM-CSF-, IL-6, IL-1α, IL-1β, eotaxin, IP-10, FGF-2, MCP-1†, IL-12 p40, IL-12 p70, IL-15, fibrinogen protein, FibA (616–635)cit, fibromodulin, clusterin (2x), ApoE, H2B/e, HSP58, COMP, acetyl-calpastatin, biglycin, osteoglycin, serine protease-11, unknown direction)	No	No
Lequerre et al (16)	IFX	76	Serum	ELISA, HPLC, latex agglutination, and Waaler–Rose tests	22	No predictors	No	No
Ortea et al (17)	IFX	8	Serum	LC-MS/MS	287 confidently quantified	Low concentrations of CP, GC, apoA-II, apoB-100, apoM, ITIH1, ITIH2, GSN, CFHR4, APMAP, MASP2, thrombospondin-1, and complement C4-B α-chain predict response	No	No
Trocmé et al (18)	IFX	60	Plasma	SELDI-TOF MS	N/A (infinite)	High concentration of six biomarkers predicts response, of which two proteins identified: apoA-I and CXCL4 (PF4)	No	No
Uno et al (19)	ETN	43	Serum	Bio-Plex Human Cytokine 27-Plex panel (Bio-Rad), and Milliplex MAP (EMD Millipore)	31	High IL-9 and low TNF-α predict high DAS28-CRP	No	No
Visvanathan et al (20)	GLM	100	Serum	Luminex, ELISA, and HumanMAP (Myriad RBM)	107	Low concentrations of vWF and pyridinoline predict ACR20 response	No	Yes, although these markers could not be validated in 236 consecutively enrolled patients
Wright et al (21)	IFX, ADA, ETN	22	Synovial fluid	Human cytokine Luminex multiplex assay (Biosource, Paisley, UK)	12	High IL-2 and G-CSF and low IL-6 predict response	No	No

*After final selection/developed model.

†This protein overlaps with another study.

ETN, etanercept; IFX, infliximab; GLM, golimumab; ADA, adalimumab; ELISA, enzyme-linked immunosorbent assay; HPLC, high-performance liquid chromatography; LC-MS/MS, liquid chromatography–tandem mass spectrometry; SELDI-TOF-MS, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry; MAP, multi-analyte profiling; N/A, not applicable; EGF, epidermal growth factor; MCP, monocyte chemoattractant protein; GM-CSF, granulocyte–macrophage colony-stimulating factor; IL, interleukin; IP, interferon-inducible protein; FGF, fibroblast growth factor; FibA, fibrinogen A; apoE, apolipoprotein E; H2B/e, histone 2B/e; HSP, heat shock protein; COMP, cartilage oligomeric matrix protein; CP, Ceruloplasin ??; GC, glucocorticoids; ITIH, inter-α-trypsin inhibitor heavy chain; GSN, gelsolin; CFHR, complement factor H-related protein; APMAP, adipocyte plasma membrane-associated protein; MASP, mannan-binding lectin serine protease; CXCL, chemokine (C-X-C motif) ligand; PF, platelet factor; TNF-α, tumour necrosis factor-α; DAS28-CRP, Disease Activity Score based on 28-joint count–C-reactive protein; vWF, von Willebrand factor; ACR, American College of Rheumatology; G-CSF, granulocyte colony-stimulating factor.

Fabre S, Dupuy AM, Dossat N, Guisset C, Cohen JD, Cristol JP, et al. Protein biochip array technology for cytokine profiling predicts etanercept responsiveness in rheumatoid arthritis. Clin Exp Immunol 2008;153:188–95.

 PubMed Web of Science ®Google Scholar

Hueber W, Tomooka BH, Batliwalla F, Li W, Monach PA, Tibshirani RJ, et al. Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis. Arthritis Res Ther 2009;11:R76.

 PubMed Web of Science ®Google Scholar

Lequerre T, Jouen F, Brazier M, Clayssens S, Klemmer N, Menard JF, et al. Autoantibodies, metalloproteinases and bone markers in rheumatoid arthritis patients are unable to predict their responses to infliximab. Rheumatology (Oxford) 2007;46:446–53.

 PubMed Web of Science ®Google Scholar

Ortea I, Roschitzki B, Ovalles JG, Longo JL, De Lt I, Gonzalez I, et al. Discovery of serum proteomic biomarkers for prediction of response to infliximab (a monoclonal anti-TNF antibody) treatment in rheumatoid arthritis: an exploratory analysis. J Proteomics 2012;77:372–82.

 PubMed Web of Science ®Google Scholar

Trocme C, Marotte H, Baillet A, Pallot-Prades B, Garin J, Grange L, et al. Apolipoprotein A-I and platelet factor 4 are biomarkers for infliximab response in rheumatoid arthritis. Ann Rheum Dis 2009;68:1328–33.

 PubMed Web of Science ®Google Scholar

Uno K, Yoshizaki K, Iwahashi M, Yamana J, Yamana S, Tanigawa M, et al. Pretreatment prediction of individual rheumatoid arthritis patients’ response to anti-cytokine therapy using serum cytokine/chemokine/soluble receptor biomarkers. Plos One 2015;10:e0132055.

 PubMed Web of Science ®Google Scholar

Visvanathan S, Rahman MU, Keystone E, Genovese M, Klareskog L, Hsia E, et al. Association of serum markers with improvement in clinical response measures after treatment with golimumab in patients with active rheumatoid arthritis despite receiving methotrexate: results from the GO-FORWARD study. Arthritis Res Ther 2010;12:R211.

 PubMed Web of Science ®Google Scholar

Wright HL, Bucknall RC, Moots RJ, Edwards SW. Analysis of SF and plasma cytokines provides insights into the mechanisms of inflammatory arthritis and may predict response to therapy. Rheumatology (Oxford) 2012;51:451–9.

 PubMed Web of Science ®Google Scholar