Predicting probability of tolerating discrete amounts of peanut protein in allergic children using epitope-specific IgE antibody profiling.

BACKGROUND: IgE-epitope profiling can accurately diagnose clinical peanut allergy. OBJECTIVE: We sought to determine whether sequential (linear) epitope-specific IgE (ses-IgE) profiling can provide probabilities of tolerating discrete doses of peanut protein in allergic subjects undergoing double-blind, placebo-controlled food challenges utilizing PRACTALL dosing. METHODS: Sixty four ses-IgE antibodies were quantified in blood samples using a bead-based epitope assay. A pair of ses-IgEs that predicts Cumulative Tolerated Dose (CTD) was determined using regression in 75 subjects from the discovery cohort. This epitope-based predictor was validated on 331 subjects from five independent cohorts (ages 4-25 years). Subjects were grouped based on their predicted values and probabilities of reactions at each CTD threshold were calculated. RESULTS: In discovery, an algorithm using two ses-IgE antibodies was correlated with CTDs (rho = 0.61, p < .05); this correlation was 0.51 (p < .05) in validation. Using the ses-IgE-based predictor, subjects were assigned into "high," "moderate," or "low" dose-reactivity groups. On average, subjects in the "high" group were four times more likely to tolerate a specific dose, compared with the "low" group. For example, predicted probabilities of tolerating 4, 14, 44, and 144 or 444 mg in the "low" group were 92%, 77%, 53%, 29%, and 10% compared with 98%, 95%, 94%, 88%, and 73% in the "high" group. CONCLUSIONS: Accurate predictions of food challenge thresholds are complex due to factors including limited responder sample sizes at each dose and variations in study-specific challenge protocols. Despite these limitations, an epitope-based predictor was able to accurately identify CTDs and may provide a useful surrogate for peanut challenges.

Accurate and reproducible diagnosis of peanut allergy using epitope mapping.

BACKGROUND: Accurate diagnosis of peanut allergy is a significant clinical challenge. Here, a novel diagnostic blood test using the peanut bead-based epitope assay ("peanut BBEA") was developed utilizing the LEAP cohort and then validated using two independent cohorts. METHODS: The development of the peanut BBEA diagnostic test followed the National Academy of Medicine's established guidelines with discovery performed on 133 subjects from the non-interventional arm of the LEAP trial and an independent validation performed on 82 subjects from the CoFAR2 and 84 subjects from the POISED study. All samples were analyzed using the peanut BBEA methodology, which measures levels of IgE to two Ara h 2 sequential (linear) epitopes and compares their combination to a threshold pre-specified in the model development phase. When a patient has an inconclusive outcome by skin prick testing (or sIgE), IgE antibody levels to this combination of two epitopes can distinguish whether the patient is "Allergic" or "Not Allergic." Diagnoses of peanut allergy in all subjects were confirmed by double-blind placebo-controlled food challenge and subjects' ages were 7-55 years. RESULTS: In the validation using CoFAR2 and POISED cohorts, the peanut BBEA diagnostic test correctly diagnosed 93% of the subjects, with a sensitivity of 92%, specificity of 94%, a positive predictive value of 91%, and negative predictive value of 95%. CONCLUSIONS: In validation of the peanut BBEA diagnostic test, the overall accuracy was found to be superior to existing diagnostic tests for peanut allergy including skin prick testing, peanut sIgE, and peanut component sIgE testing.

An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples.

BACKGROUND: Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing. RESULTS: We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low µg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples. CONCLUSIONS: We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.

Validation of a multiprotein plasma classifier to identify benign lung nodules.

INTRODUCTION: Indeterminate pulmonary nodules (IPNs) lack clinical or radiographic features of benign etiologies and often undergo invasive procedures unnecessarily, suggesting potential roles for diagnostic adjuncts using molecular biomarkers. The primary objective was to validate a multivariate classifier that identifies likely benign lung nodules by assaying plasma protein expression levels, yielding a range of probability estimates based on high negative predictive values (NPVs) for patients with 8 to 30 mm IPNs. METHODS: A retrospective, multicenter, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising five diagnostic and six normalization proteins, and blinded analysis of an independent validation set of plasma samples. RESULTS: The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based nonsmall-cell lung cancer prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, as shown in our prior work, at 92% sensitivity and 20% specificity, with the lower bound of the classifier's performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model. CONCLUSIONS: This proteomic classifier provides a range of probability estimates for the likelihood of a benign etiology that may serve as a noninvasive, diagnostic adjunct for clinical assessments of patients with IPNs.

A blood-based proteomic classifier for the molecular characterization of pulmonary nodules.

Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis.

Peptide Shifter: enhancing separation reproducibility using correlated expression profiles.

Chromatographic protein and peptide separation technologies enable comprehensive proteomic analysis of plasma and other complex biological samples by mass spectrometry. However, as the number of separations and/or fractions increases, so does the number of peptides split across fraction boundaries. Irreproducibility of peptide chromatographic separation results in peptides on or near the boundary moving partially or entirely into adjacent fractions. Peptide shifting across fraction boundaries increases the variability of measured peptide abundance, and so there is a trade-off between proteomic comprehensiveness using separation technologies and accurate quantitative proteomic measurements. In this paper, a method for detecting and correcting split peptides, called Peptide Shifter, is introduced and evaluated. An essential component of Peptide Shifter is a global peptide expression profile analysis that allows the inference of the underlying peptide shift pattern without the use of peptide labeling or internal standards. A controlled proteomic analysis of plasma samples demonstrates a 34% decrease in peptide intensity variability after the application of Peptide Shifter.

Organ messenger ribonucleic acid and plasma proteome changes in the adjuvant-induced arthritis model: responses to disease induction and therapy with the estrogen receptor-beta selective agonist ERB-041.

Two receptors [estrogen receptor (ER)alpha and ERbeta] mediate the manifold effects of estrogens throughout the body. Although a clear role has been established for ERalpha in the classical effects of estrogen activity, the physiological role of ERbeta is less well understood. A small-molecule ERbeta selective agonist, ERB-041, has potent antiinflammatory activity in the Lewis rat model of adjuvant-induced arthritis. To characterize the response of target organs and pathways responsible for this antiinflammatory effect, mRNA expression profiling of the spleen, lymph node, and liver was performed, in conjunction with a global analysis of the plasma proteome. We find that the expression of a large number of genes and proteins are altered in the disease model and the majority of these are partially or fully reversed by ERB-041 treatment. Regulated pathways include the acute-phase response, eicosanoid synthesis, fatty acid metabolism, and iron metabolism. In addition, many of the regulated genes and proteins are known to be dysregulated in human rheumatoid arthritis, providing further evidence that the manifestations of the Lewis rat adjuvant-induced arthritis model bear similarity to the human disease.