Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis

ObjectivesPatients with Rheumatoid Arthritis (RA) are increasingly achieving stable disease remission, yet the mechanisms that govern ongoing clinical disease and subsequent risk of future flare are not well understood. We sought to identify serum proteomic alterations that dictate clinically important features of stable RA, and couple broad-based proteomics with machine learning to predict future flare.MethodsWe studied baseline serum samples from a cohort of stable RA patients (RETRO, n = 130) in clinical remission (DAS28<2.6) and quantified 1307 serum proteins using the SOMAscan platform. Unsupervised hierarchical clustering and supervised classification were applied to identify proteomic-driven clusters and model biomarkers that were associated with future disease flare after 12 months of follow-up and RA medication withdrawal. Network analysis was used to define pathways that were enriched in proteomic datasets.ResultsWe defined 4 proteomic clusters, with one cluster (Cluster 4) displaying a lower mean DAS28 score (p = 0.03), with DAS28 associating with humoral immune responses and complement activation. Clustering did not clearly predict future risk of flare, however an XGboost machine learning algorithm classified patients who relapsed with an AUC (area under the receiver operating characteristic curve) of 0.80 using only baseline serum proteomics.ConclusionsThe serum proteome provides a rich dataset to understand stable RA and its clinical heterogeneity. Combining proteomics and machine learning may enable prediction of future RA disease flare in patients with RA who aim to withdrawal therapy.

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FRI0596 NOVEL AUTOANTIBODY BIOMARKERS FOR THE PREDICTION OF THERAPY RESPONSE IN RHEUMATOID ARTHRITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.4184 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 904.1-904

Author(s):

P. Vandormael ◽

A. Pues ◽

E. Sleurs ◽

P. Verschueren ◽

V. Somers

Keyword(s):

Rheumatoid Arthritis ◽

Therapy Response ◽

Autoimmune Disorder ◽

First Line ◽

Research Support ◽

Serum Samples ◽

Glucocorticoid Treatment ◽

Baseline Serum ◽

Disease Remission ◽

Antibody Reactivity

Background:Rheumatoid arthritis (RA) is an autoimmune disorder that is characterized by chronic inflammation of the joint synovium and presence of autoantibodies in most patients. For RA, many treatments are currently available but each treatment will only induce disease remission in a subset of patients. Moreover, finding out which patients respond well to first-line therapy with classical synthetic disease modifying anti-rheumatic drugs (csDMARDs), still largely depends on trial and error.Objectives:In this study, we aim to find novel RA autoantibody biomarkers that predict therapy response to csDMARDs before the initiation of treatment.Methods:In the CareRA trial, a Flemish multicenter study of different treatment regimes, serum samples were collected from RA patients that did or did not show disease remission (DAS28(CRP)<2.6) in response to csDMARDs, combined with a step down glucocorticoid treatment. In our study, baseline samples, collected before the start of treatment, were used to determine predictive antibody reactivity. A cDNA phage display library, representing the antigens from RA synovial tissue, was constructed and screened for antibody reactivity in baseline serum samples of RA patients that failed to reach remission at week 16. Using enzyme-linked immunosorbent assays (ELISA), antibody reactivity against the identified antigens was initially determined in pooled baseline serum samples of RA patients that did (n=50) or did not (n=40) reach disease remission at week 16. Antigenic targets that showed increased antibody reactivity in pools from patients that did not reach disease remission, were further validated in individual serum samples of 69 RA patients that did not reach DAS28(CRP) remission at week 16, and 122 RA patients that did.Results:Screening and validation of antibody reactivity resulted in 41 novel antigens. The retrieved antigenic sequences correspond to (parts of) known proteins and to randomly formed peptides. A panel of 3 of these peptide antigens could be composed, whose baseline antibody reactivity correlated with lack of therapy response at week 16. Presence of antibodies against at least one of these 3 antigens was significantly higher in individual samples of RA patients that did not reach DAS28(CRP) remission (43 vs. 29%, p=0.041), or that failed to reach ACR 70 (42 vs. 26%, p=0.029) response criteria at week 16, compared to RA patients that did reach these respective criteria. In addition, RA patients which were positive for this antibody panel at baseline, also showed less DAS(CRP) remission at week 4 and week 8.Conclusion:We have identified a set of 3 antibody biomarkers that can predict failure of early disease remission after first-line RA therapy, which might contribute to personalized medicine decisions.Disclosure of Interests:Patrick Vandormael: None declared, Astrid Pues: None declared, Ellen Sleurs: None declared, Patrick Verschueren Grant/research support from: Pfizer unrestricted chair of early RA research, Speakers bureau: various companies, Veerle Somers Grant/research support from: Research grant from Pfizer and BMS

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Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01380-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kai-Leun Tsai ◽

Che-Chang Chang ◽

Yu-Sheng Chang ◽

Yi-Ying Lu ◽

I-Jung Tsai ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Machine Learning ◽

Disease Activity ◽

Characteristic Curve ◽

Factor H ◽

Protein Adducts ◽

Complement Factor ◽

Increased Risk ◽

Tandem Mass Spectrometric ◽

Peptide Adducts

Abstract Background Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. Methods Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211–1230, haptoglobin (HPT)78–108, immunoglobulin (Ig) kappa chain C region (IGKC)2–19, and prothrombin (THRB)328–345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. Results Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgM anti-IGKC2−19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgG anti-THRB328−345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78−108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2−19 (OR 12.655, p < 0.001), and IgG anti-THRB328−345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. Conclusions This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgG anti-THRB328−345 may play heavy roles in RA development.

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BAFF Predicts Immunogenicity in Older Patients With Rheumatoid Arthritis Treated With TNF Inhibitors

10.21203/rs.3.rs-74233/v1 ◽

2020 ◽

Author(s):

Borja Hernández-Breijo ◽

Victoria Navarro-Compán ◽

Chamaida Plasencia-Rodríguez ◽

Ioannis Parodis ◽

Johanna E. Gehin ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Older Patients ◽

Operating Characteristic ◽

Certolizumab Pegol ◽

Positive Likelihood Ratio ◽

Characteristic Analysis ◽

Serum Samples ◽

Chi Square ◽

Baseline Serum ◽

Logistic Regression Models

Abstract Background: Immunogenicity related to treatment with TNF inhibitors (TNFi) is one of the causes for the decreased attainment of clinical response in patients with rheumatoid arthritis (RA). The B-cell activating factor (BAFF) may be playing a role in the development of immunogenicity. The objective of this study was to analyse the association of baseline concentration of serum BAFF with immunogenicity after 6 months of TNFi treatment.Methods: A total of 139 patients with RA starting a TNFi (infliximab, adalimumab, certolizumab pegol or golimumab) were followed-up for 6 months. Serum samples were obtained at baseline and at 6 months and anti-drug antibody (ADA) and BAFF concentrations were measured. Logistic regression models were employed in order to analyse the association between BAFF concentrations and immunogenicity. Receiver operating characteristic analysis was performed to determine the BAFF concentrations with a greater likelihood of showing immunogenicity association.Results: At 6 months, 39 patients (28%) developed ADA. A significant interaction between the age and baseline BAFF concentration was found for the development of ADA (Wald chi-square value=5.30; p=0.02); therefore, subsequent results were stratified according to mean age (≤/>55 years). Baseline serum BAFF concentration was independently associated with ADA development only in patients over 55 years (OR=1.55; 95% CI: 1.03-2.12). Baseline serum BAFF≥1034pg/mL predicted the presence of ADA at 6 months (positive likelihood ratio=3.7).Conclusions: Our results suggest that the association of BAFF concentration and immunogenicity depends on the patient’s age. Baseline serum BAFF concentration predicts the presence of ADA within 6 months of TNFi therapy in older patients with RA.

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Antimodified protein antibody response pattern influences the risk for disease relapse in patients with rheumatoid arthritis tapering disease modifying antirheumatic drugs

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2016-209297 ◽

2016 ◽

Vol 76 (2) ◽

pp. 399-407 ◽

Cited By ~ 23

Author(s):

Camille P Figueiredo ◽

Holger Bang ◽

Jayme Fogagnolo Cobra ◽

Matthias Englbrecht ◽

Axel J Hueber ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Antibody Response ◽

Multivariate Logistic Regression Analysis ◽

Sustained Remission ◽

Disease Relapse ◽

Serum Samples ◽

Baseline Serum ◽

Dmard Therapy ◽

Relapse Risk ◽

Disease Modifying

ObjectiveTo perform a detailed analysis of the autoantibody response against post-translationally modified proteins in patients with rheumatoid arthritis (RA) in sustained remission and to explore whether its composition influences the risk for disease relapse when tapering disease modifying antirheumatic drug (DMARD) therapy.MethodsImmune responses against 10 citrullinated, homocitrullinated/carbamylated and acetylated peptides, as well as unmodified vimentin (control) and cyclic citrullinated peptide 2 (CCP2) were tested in baseline serum samples from 94 patients of the RETRO study. Patients were classified according to the number of autoantibody reactivities (0–1/10, 2–5/10 and >5/10) or specificity groups (citrullination, carbamylation and acetylation; 0–3) and tested for their risk to develop relapses after DMARD tapering. Demographic and disease-specific parameters were included in multivariate logistic regression analysis for defining the role of autoantibodies in predicting relapse.ResultsPatients varied in their antimodified protein antibody response with the extremes from recognition of no (0/10) to all antigens (10/10). Antibodies against citrullinated vimentin (51%), acetylated ornithine (46%) and acetylated lysine (37%) were the most frequently observed subspecificities. Relapse risk significantly (p=0.011) increased from 18% (0–1/10 reactivities) to 34% (2–5/10) and 55% (>5/10). With respect to specificity groups (0–3), relapse risk significantly (p=0.021) increased from 18% (no reactivity) to 28%, 36% and finally to 52% with one, two or three antibody specificity groups, respectively.ConclusionsThe data suggest that the pattern of antimodified protein antibody response determines the risk of disease relapse in patients with RA tapering DMARD therapy.Trial registration number2009-015740-42; Results.

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A machine learning approach to predicting short-term mortality risk for patients starting chemotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.6538 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 6538-6538

Author(s):

Ravi Bharat Parikh ◽

Aymen Elfiky ◽

Maximilian J. Pany ◽

Ziad Obermeyer

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Characteristic Curve ◽

Predictive Performance ◽

Cancer Center ◽

Short Term ◽

Model Predictions ◽

Short Term Mortality ◽

Chemotherapy Initiation ◽

Predicted Risk

6538 Background: Patients who die soon after starting chemotherapy incur symptoms and financial costs without survival benefit. Prognostic uncertainty may contribute to increasing chemotherapy use near the end of life, but few prognostic aids exist to guide physicians and patients in the decision to initiate chemotherapy. Methods: We obtained all electronic health record (EHR) data from 2004-14 from a large national cancer center, linked to Social Security data to determine date of death. Using EHR data before treatment initiation, we created a machine learning (ML) model to predict 180-day mortality from the start of chemotherapy. We derived the model using data from 2004-11 and report predictive performance on data from 2012-14. Results: 26,946 patients initiated 51,774 discrete chemotherapy regimens over the study period; 49% received multiple lines of chemotherapy. The most common cancers were breast (23.6%), colorectal (17.6%), and lung (16.6%). 18.4% of patients died within 180 days after chemotherapy initiation. Model predictions were used to rank patients in the validation cohort by predicted risk. Patients in the highest decile of predicted risk had a 180-day mortality of 74.8%, vs. 0.2% in the lowest decile (area under the receiver-operating characteristic curve [AUC] 0.87). Predictions were accurate for patients with metastatic disease (AUC 0.85) and for individual primary cancers and chemotherapy regimens—including experimental regimens not present in the derivation sample. Model predictions were valid for 30- and 90-day mortality (AUC 0.94 and 0.89, respectively). ML predictions outperformed regimen-based mortality estimates from randomized trials (RT) (AUC 0.77 [ML] vs. 0.56 [RT]), and National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) estimates (AUC 0.81 [ML] vs. 0.40 [SEER]). Conclusions: Using EHR data from a single cancer center, we derived a machine learning algorithm that accurately predicted short-term mortality after chemotherapy initiation. Further research is necessary to determine applications of this algorithm in clinical settings and whether this tool can improve shared decision making leading up to chemotherapy initiation.

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Baseline serum angiopoietin-2 and VEGF levels predict the deterioration of the liver functional reserve during lenvatinib treatment for hepatocellular carcinoma

PLoS ONE ◽

10.1371/journal.pone.0247728 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0247728

Author(s):

Taku Shigesawa ◽

Goki Suda ◽

Megumi Kimura ◽

Osamu Maehara ◽

Yoshimasa Tokuchi ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Growth Factor ◽

Clinical Data ◽

Characteristic Curve ◽

Laboratory Data ◽

Complete Response ◽

Serum Samples ◽

Functional Reserve ◽

Baseline Serum ◽

Liver Functional Reserve

A deteriorated liver functional reserve during systemic therapy for unresectable hepatocellular carcinoma (HCC) causes poor patient outcomes. We aimed to identify predictive factors associated with the deterioration of Child-Pugh score at 8 weeks after lenvatinib initiation. Patients with adequate clinical data and baseline preserved serum samples available were included. Baseline fibroblast growth factor (FGF)19 and 21, angiopoietin (ANG)2, and vascular endothelial growth factor (VEGF) levels were evaluated. Thirty-seven patients were included, and 6, 15, 14, and 2 experienced complete response, partial response, stable disease, and progressive disease, respectively. Twenty-four (65%) and 13 (35%) patients showed a maintained/improved and deteriorated Child-Pugh-score, respectively. While baseline clinical data, treatment response, and laboratory data were similar between these two patient groups, baseline ANG2 and VEGF levels were significantly higher (P = 0.0017) and lower (P = 0.0231), respectively, in patients with deteriorated Child-Pugh score than in those without. Based on receiver operating characteristic curve analysis, cut-off values for ANG2 and VEGF were found to be 3,108 pg/mL and 514.9 pg/mL, respectively. Among patients with low VEGF and high ANG2, 89% (8/9) exhibited a deteriorated Child-Pugh score, whereas none of the patients (0/9) with high VEGF and low ANG2 did. The deterioration of the Child-Pugh score in patients with unresectable HCC who are treated with lenvatinib may be predictable based on combined baseline serum ANG2 and VEGF levels.

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Predicting Depression in Community Dwellers Using a Machine Learning Algorithm

Diagnostics ◽

10.3390/diagnostics11081429 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1429

Author(s):

Seo-Eun Cho ◽

Zong Woo Geem ◽

Kyoung-Sae Na

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Characteristic Curve ◽

Feature Reduction ◽

Classification Model ◽

Depression Screening ◽

Test Set ◽

Depression Group ◽

Selection Operator ◽

Community Dwellers

Depression is one of the leading causes of disability worldwide. Given the socioeconomic burden of depression, appropriate depression screening for community dwellers is necessary. We used data from the 2014 and 2016 Korea National Health and Nutrition Examination Surveys. The 2014 dataset was used as a training set, whereas the 2016 dataset was used as the hold-out test set. The synthetic minority oversampling technique (SMOTE) was used to control for class imbalances between the depression and non-depression groups in the 2014 dataset. The least absolute shrinkage and selection operator (LASSO) was used for feature reduction and classifiers in the final model. Data obtained from 9488 participants were used for the machine learning process. The depression group had poorer socioeconomic, health, functional, and biological measures than the non-depression group. From the initial 37 variables, 13 were selected using LASSO. All performance measures were calculated based on the raw 2016 dataset without the SMOTE. The area under the receiver operating characteristic curve and overall accuracy in the hold-out test set were 0.903 and 0.828, respectively. Perceived stress had the strongest influence on the classifying model for depression. LASSO can be practically applied for depression screening of community dwellers with a few variables. Future studies are needed to develop a more efficient and accurate classification model for depression.

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A machine-learning approach to predict postprandial hypoglycemia

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0943-4 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 7

Author(s):

Wonju Seo ◽

You-Bin Lee ◽

Seunghyun Lee ◽

Sang-Man Jin ◽

Sung-Min Park

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Characteristic Curve ◽

Artificial Pancreas ◽

Individual Performance ◽

Machine Learning Algorithms ◽

Prediction Algorithm ◽

Support Vector ◽

K Nearest Neighbor

Abstract Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

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FRI0088 CHANGE IN SERUM BILIRUBIN SUGGESTS TREATMENT RESPONSE IN RHEUMATOID ARTHRITIS PATIENTS TREATED WITH MOLECULAR-TARGETED AGENTS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.2405 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 621.2-622

Author(s):

S. J. Choi ◽

S. H. Nam ◽

J. S. Lee ◽

W. J. Seo ◽

J. S. Oh ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Regression Analysis ◽

Linear Regression ◽

Treatment Response ◽

Serum Bilirubin ◽

Targeted Agents ◽

Das28 Score ◽

Baseline Serum ◽

The Mean ◽

Molecular Targeted Agents

Background:Bilirubin is an antioxidant with anti-inflammatory properties. In previous reports, serum bilirubin levels were correlated with disease activity of autoimmune diseases including rheumatoid arthritis (RA). Various molecular-targeted agents have been developed for RA, and targets, such as IL-6 and TNFα, are associated with liver function. However, the association between serum bilirubin and treatment response in RA patients treated with molecular-targeted agents is still unknown.Objectives:We aimed to evaluate the role of serum bilirubin in the prediction of the early treatment response in RA patients who initiated molecular-targeted agents.Methods:We retrospectively recruited biologic naïve RA patients (n=292) with moderate-to-high disease activity from a tertiary hospital between Jan 2013 and Dec 2019. Patients with viral hepatitis, drug-induced hepatitis, or alcoholic liver disease were excluded. Molecular-targeted agents included tocilizumab (TCZ, n=40), adalimumab (ADA, n=59), etanercept (ETN, n=66), golimumab (GOL, n=60), abatacept (ABA, n=31), and tofacitinib (TOF, n=36). Clinical and laboratory data were collected from electronic medical records. Patients were categorised into an increased bilirubin group (higher serum bilirubin at 3 months than at baseline) and decreased bilirubin group (equal or lower serum bilirubin at 3 months than at baseline). At 6 months of treatment, good response (defined as a DAS28 score ≤3.2) was evaluated. Multivariate logistic regression analysis and multiple linear regression analysis were used to evaluate the association between serum bilirubin and treatment response. The variables included in the multiple logistic and linear regression analyses were age, female sex, rheumatoid factor, prednisolone, DMARDs, baseline liver enzymes, baseline DAS28 score, and components.Results:The mean serum bilirubin level at baseline was 4.7±1.8 mg/L. After 6 months of treatment, 180 (61.6%) patients achieved good responses. The mean serum bilirubin levels at 3 and 6 months were 5.3±2.3 and 5.5±2.2 mg/L, respectively. At 6 months, a good response was more frequent in the increased bilirubin group than in the decreased bilirubin group (71.2% [99/139] vs. 52.9% [81/153], p=0.001). In multivariate logistic regression analysis, the ORs among good responders at 6 months were 1.221 (95% CI 1.014–1.471, p=0.036) for baseline serum bilirubin and 1.377 (95% CI 1.146–1.654, p=0.001) for the change in serum bilirubin at 3 months. According to target agents, the mean changes in serum bilirubin from baseline to 6 months were 1.9±2.5 for TCZ, 1.0±1.5 for ADA, 0.7±1.9 for ETN, 0.6±2.2 for GOL, 0.3±1.2 for ABA, and 0.4±2.2 for TOF (Figure 1). Among the target agents, TCZ showed a significant increase in the mean serum bilirubin level at 3 and 6 months from baseline. In multiple linear regression analysis performed on TCZ, the change in bilirubin at 3 months was associated with the DAS28 score at 6 months (β=−0.349, p=0.020).Figure 1.Change in serum bilirubin during treatment with molecular-targeted agents in rheumatoid arthritis patientsConclusion:High baseline serum bilirubin and an increase in serum bilirubin during treatment are helpful to predict a good response to molecular-targeted agents, especially TCZ.Disclosure of Interests:None declared

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Proteomics Profiling to Distinguish DOCK8 Deficiency From Atopic Dermatitis

Frontiers in Allergy ◽

10.3389/falgy.2021.774902 ◽

2021 ◽

Vol 2 ◽

Author(s):

Minnie Jacob ◽

Afshan Masood ◽

Zakiya Shinwari ◽

Mai Abdel Jabbar ◽

Hamoud Al-Mousa ◽

...

Keyword(s):

Atopic Dermatitis ◽

Pathway Analysis ◽

Serum Proteins ◽

Characteristic Curve ◽

Venn Diagram ◽

Primary Immune Deficiency ◽

Related Protein ◽

Proteomic Profiling ◽

Serum Samples ◽

Dock8 Deficiency

Dedicator of cytokinesis 8 deficiency is an autosomal recessive primary immune deficiency disease belonging to the group of hyperimmunoglobulinemia E syndrome (HIES). The clinical phenotype of dedicator of cytokinesis 8 (DOCK8) deficiency, characterized by allergic manifestations, increased infections, and increased IgE levels, overlaps with the clinical presentation of atopic dermatitis (AD). Despite the identification of metabolomics and cytokine biomarkers, distinguishing between the two conditions remains clinically challenging. The present study used a label-free untargeted proteomics approach using liquid-chromatography mass spectrometry with network pathway analysis to identify the differentially regulated serum proteins and the associated metabolic pathways altered between the groups. Serum samples from DOCK8 (n = 10), AD (n = 9) patients and healthy control (Ctrl) groups (n = 5) were analyzed. Based on the proteomics profile, the PLS-DA score plot between the three groups showed a clear group separation and sample clustering (R2 = 0.957, Q2 = 0.732). Significantly differentially abundant proteins (p < 0.05, FC cut off 2) were identified between DOCK8-deficient and AD groups relative to Ctrl (n = 105, and n = 109) and between DOCK8-deficient and AD groups (n = 85). Venn diagram analysis revealed a differential regulation of 24 distinct proteins from among the 85 between DOCK8-deficient and AD groups, including claspin, haptoglobin-related protein, immunoglobulins, complement proteins, fibulin, and others. Receiver-operating characteristic curve (ROC) analysis identified claspin and haptoglobin-related protein, as potential biomarkers with the highest sensitivity and specificity (AUC = 1), capable of distinguishing between patients with DOCK8 deficiency and AD. Network pathway analysis between DOCK8-deficiency and AD groups revealed that the identified proteins centered around the dysregulation of ERK1/2 signaling pathway. Herein, proteomic profiling of DOCK8-deficiency and AD groups was carried out to determine alterations in the proteomic profiles and identify a panel of the potential proteomics biomarker with possible diagnostic applications. Distinguishing between DOCK8-deficiency and AD will help in the early initiation of treatment and preventing complications.

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