Each year millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. As the majority 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, 371 protein candidates were identified and a multiple reaction monitoring (MRM) assay was developed 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 cancer matched on 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 high negative predictive value (NPV) of 90%. Validation performance on samples from a non-discovery clinical site showed 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, 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 can provide a powerful complementary tool for physicians in lung cancer diagnosis.

Purpose 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, multi-center, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising 5 diagnostic and 6 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 NSCLC prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% NPV and 26% PPV, 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 COPD 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 non-invasive, diagnostic adjunct for clinical assessments of patients with IPNs.

Background: Misdiagnosis of peanut allergy is a significant clinical challenge. Here, a novel diagnostic blood-based test using a Bead-Based Epitope Assay ("peanut BBEA") has been developed on the LEAP cohort and then independently validated on the CoFAR2 and POISED cohorts.Methods: Development of the peanut BBEA followed the National Academy of Medicine's established guidelines with discovery performed on 133 subjects from the noninterventional arm of the LEAP trial and an independent validation performed on 81 subjects from the CoFAR2 study and 84 subjects from the POISED study. All subject samples were analyzed using the BBEA methodology. The peanut BBEA test measures levels of two Ara h 2 epitopes and compares their combination to a pre=specified threshold. If the combination of the two epitope levels is at or below the threshold, then the subject is ruled "Not Allergic", otherwise the subject is ruled "Allergic". All allergic diagnoses were OFC confirmed and subjects' ages were 7-55 years. Results:In validation on the CoFAR2 and POISED cohorts, the peanut BBEA test had a combined sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio and accuracy of 91%, 95%, 95%, 91%, 18.2, 0.09 and 93%, respectively. Conclusion:The peanut BBEA test performance in validation demonstrated overall high accuracy and compared very favorably with existing diagnostic tests for peanut allergy including skin prick testing, peanut sIgE and peanut component testing..

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.

BackgroundCurrent 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.ResultsWe 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1559-0275-12-3) contains supplementary material, which is available to authorized users.