Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies.

Objective Quantitative assessment of disease activity in rheumatoid arthritis (RA) is important for patient management, and additional objective information may aid rheumatologists in clinical decision-making. We validated a recently-developed multi-biomarker disease activity (MBDA) test relative to clinical disease activity in diverse RA cohorts. Methods Serum samples were obtained from the InFoRM, BRASS, and Leiden Early Arthritis Clinic cohorts. Levels of 12 biomarkers were measured and combined according to a pre-specified algorithm to generate the composite MBDA score. The relationship of the MBDA score to clinical disease activity was characterized separately in seropositive and seronegative patients using Pearson correlations and area under the receiver operator characteristic curve (AUROC) to discriminate between patients with low and moderate/high disease activity. Associations between changes in MBDA score and clinical responses 6–12 weeks after initiation of anti-TNF or methotrexate treatment were evaluated by AUROC. Results The MBDA score was significantly associated with DAS28-CRP in both seropositive (AUROC=0.77; P<0.001) and seronegative patients (AUROC=0.70; P<0.001). In subgroups based on age, sex, body-mass index, and treatment, the MBDA score was associated with DAS28-CRP (P<0.05) in all seropositive and most seronegative subgroups. Changes in MBDA score at 6–12 weeks could discriminate both ACR50 responses (P=0.03) and DAS28-CRP improvement (P=0.002). Changes in MBDA score at 2 weeks were also associated with subsequent DAS28-CRP response (P=0.02). Conclusion Our findings establish the criterion and discriminant validity of a novel multi-biomarker test as an objective measure of RA disease activity to aid in the management of RA patients.

BackgroundDisease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment.ObjectivesTo develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis.MethodsCandidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing.Results130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities.ConclusionWe followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels.

ObjectivesTo evaluate the performance of individual biomarkers and a multi-biomarker disease activity (MBDA) score in the early rheumatoid arthritis (RA) patient population from the computer assisted management in early rheumatoid arthritis (CAMERA) study.MethodsTwenty biomarkers were measured in the CAMERA cohort, in which patients were treated with either intensive or conventional methotrexate-based treatment strategies. The MBDA score was calculated using the concentrations of 12 biomarkers (SAA, IL-6, TNF-RI, VEGF-A, MMP-1, YKL-40, MMP-3, EGF, VCAM-1, leptin, resistin and CRP) according to a previously trained algorithm. The performance of the scores was evaluated relative to clinical disease activity assessments. Change in MBDA score over time was assessed by paired Wilcoxon rank sum test. Logistic regression was used to evaluate the ability of disease activity measures to predict radiographic progression.ResultsThe MBDA score had a significant correlation with the disease activity score based on 28 joints-C reactive protein (DAS28-CRP) (r=0.72; p<0.001) and an area under the receiver operating characteristic curve for distinguishing remission/low from moderate/high disease activity of 0.86 (p<0.001) using a DAS28-CRP cut-off of 2.7. In multivariate analysis the MBDA score, but not CRP, was an independent predictor of disease activity measures. Additionally, mean (SD) MBDA score decreased from 53 (18) at baseline to 39 (16) at 6 months in response to study therapy (p<0.0001). Neither MBDA score nor clinical variables were predictive of radiographic progression.ConclusionsThis multi-biomarker test performed well in the assessment of disease activity in RA patients in the CAMERA study. Upon further validation, this test could be used to complement currently available disease activity measures and improve patient care and outcomes.

Rheumatoid arthritis (RA) is a chronic inflammatory disorder that primarily involves the joints. Accurate and frequent assessment of RA disease activity is critical to optimal treatment planning. A novel algorithm has been developed to determine a multi-biomarker disease activity (MBDA) score based upon measurement of the concentrations of 12 serum biomarkers in multiplex format. Biomarker assays from several different platforms were used in feasibility studies to identify biomarkers of potential significance. These assays were adapted to a multiplex platform for training and validation of the algorithm. In this study, the analytical performance of the underlying biomarker assays and the MBDA score was evaluated. Quantification of 12 biomarkers was performed with multiplexed sandwich immunoassays in three panels. Biomarker-specific capture antibodies were bound to specific locations in each well; detection antibodies were labeled with electrochemiluminescent tags. Data were acquired with a Sector Imager 6000, and analyte concentrations were determined. Parallelism, dynamic range, cross-reactivity, and precision were established for each biomarker as well as for the MBDA score. Interference by serum proteins, heterophilic antibodies, and common RA therapies was also assessed. The individual biomarker assays had 3-4 orders of magnitude dynamic ranges, with good reproducibility across time, operators, and reagent lots; the MBDA score had a median coefficient of variation of <2% across the score range. Cross-reactivity as well as interference by serum rheumatoid factor (RF), human anti-mouse antibodies (HAMA), or common RA therapies, including disease-modifying antirheumatic drugs and biologics, was minimal. The same MBDA score was observed in different subjects despite having different biomarker profiles, supporting prior literature reports that multiple pathways contribute to RA.