A serum proteomic approach to gauging the state of remission in Wegener's granulomatosis

John H. Stone*, Vinodh N. Rajapakse, Gary S. Hoffman, Ulrich Specks, Peter A. Merkel, Robert F. Spiera, John C. Davis, E. William St. Clair, Joseph McCune, Sally Ross, Ben A. Hitt, Timothy D. Veenstra, Thomas P. Conrads, Lance A. Liotta, Emanuel F. Petricoin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Objective. To identify serum ion patterns that distinguish remission from active disease in patients with Wegener's granulomatosis (WG). Methods. Using sera collected in the WG Etanercept Trial, we selected samples from patients who either were undergoing a period of extended disease remission or had recent flares of active WG. Unfractionated samples were randomized into sets for training and testing, such that remission sera and active disease sera could be analyzed without batch bias. Molecular species within the sera were ionized by high-resolution, matrix-assisted laser desorption ionization time-of-flight mass spectrometry. We then used a bioinformatics pattern-recognition tool to identify optimal combinations of ions. During the training stage, the clinical data (remission versus active disease) were provided in association with the spectral data from each sample. In the testing stage, we performed blinded testing on a previously unexamined set of samples. Results. The most robust model, trained on a total of 82 samples (42 remission, 40 active disease), included 7 key ions with mass:charge ratios of 803.239, 2,171.672, 2,790.574, 3,085.237, 5,051.726, 5,833.989, and 6,630.465. The combined relative amplitudes of these 7 ions identified 5 distinct clusters of either remission or active disease samples during the training stage. In the testing stage, this model segregated 72 samples into the same 5 clusters, including 1 large remission cluster (n = 28) and another large active disease cluster (n = 32). Three smaller clusters of active disease or remission samples were also identified, with remission clusters populated by 2 samples in one cluster and 8 in another, and an active disease cluster populated by 2 samples. The model categorized 35 of 37 remission samples correctly (sensitivity 95%, 95% confidence interval [95% CI] 82.1-99.4) and 32 of 35 active disease samples correctly (specificity 91%, 95% CI 78.1-98.1). Conclusion. This serum proteomic profiling approach appears to be useful in distinguishing between states of stable clinical remission and active disease. Further validation and refinement of this strategy may help clinicians apply immunosuppressive therapies more judiciously among their patients, thereby avoiding morbidity and mortality from excessive treatment. Identification of the most robust and clinically useful combinations of ions will permit the rational selection of molecules for sequencing and analysis.

Original languageEnglish
Pages (from-to)902-910
Number of pages9
JournalArthritis and Rheumatism
Issue number3
StatePublished - Mar 2005
Externally publishedYes


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