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

Abstract

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, matrixassisted laser desorption ionization time-of-flight mass spectrometry. We then used a bioinformatics patternrecognition 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 Conclusion. This serum proteomic profiling approach appears to be useful in distinguishing between states of stable clinical remission and active disease.

A Comparison of Biomarker and Fingerprint-Based Classifiers of Disease

“…The medoid classification algorithm is a best attempt at reproducing the algorithm used in many of the studies conducted in the laboratories of Emmanuel Petricoin and Lance Liotta [Browers et al, 2005;Conrads et al, 2004;Ornstein et al, 2004;Petricoin et al, 2004; www.intechopen.com Srinivasan et al, 2006;Stone et al, 2005]. While these authors stated that their algorithm was quite similar to a Self-Organizing Map (SOM), their algorithm, as they described it, has virtually nothing in common with a SOM.…”

“…While the algorithm described by Petricoin and Liotta [Browers et al, 2005;Conrads et al, 2004;Ornstein et al, 2004;Petricoin et al, 2004;Srinivasan et al, 2006;Stone et al, 2005] used a genetic algorithm driver to search for an optimum set of features, allowing for different putative solutions to use different numbers of features (5-20 features), our algorithm uses a mEP feature selection algorithm and all putative solutions have the same number of features n. For a given value of n, n features were selected and the intensities of these features were rescaled for each individual using the following formula [Browers et al, 2005;Conrads et al, 2004;Ornstein et al, 2004;Petricoin et al, 2004;Srinivasan et al, 2006;Stone et al, 2005]:…”

Future perspective for diagnosis in autoimmune diseases

“…As an example, the proteomic analysis of plasma may provide specific profiles for certain kinds of clinical conditions, such as early breast and prostate cancer (Ornstein et al 2004, Martin 2006. A similar approach has been applied to the definition of proteomic signature of the inflammatory status in autoimmune conditions, such as SLE and Wegener's granulomatosis (Bennett et al 2003, Stone et al 2005). …”

“…In SLE, microarray analysis of peripheral blood mononuclear cells has allowed the definition of a characteristic expression signature associated with genes regulated by Type I IFN, and this pattern is associated with disease activity (Belmont and Gibbs 2004). Proteomic analysis of biological samples has been also applied to the detection of several types of cancer (Ornstein et al 2004, Liotta andPetricoin 2006) and to the assessment of the activity status of certain inflammatory diseases (Stone et al 2005).…”

An array of possibilities for the study of autoimmunity

“…In addition, Stone and colleagues analysed, by mass spectrometry, serum from patients with Wegener's granulomatosis who were enrolled in a trial of the drug etanercept (a TNF inhibitor). They showed that this technique could distinguish between patients in stable clinical remission and those with the active disease 34 . It is important to note that the proteins or peptides identified using unbiased technologies are simply correlated with disease activity, and that their role in disease causation can only be determined through loss-or gain-of-function experiments.…”