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Background: Malignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improve surveillance and detection of MM in the high-risk population, we completed a series of clinical studies to develop a noninvasive test for early detection. Methodology/Principal Findings: We conducted multi-center case-control studies in serum from 117 MM cases and 142 asbestos-exposed control individuals. Biomarker discovery, verification, and validation were performed using SOMAmer proteomic technology, which simultaneously measures over 1000 proteins in unfractionated biologic samples. Using univariate and multivariate approaches we discovered 64 candidate protein biomarkers and derived a 13-marker random forest classifier with an AUC of 0.99±0.01 in training, 0.98±0.04 in independent blinded verification and 0.9560.04 in blinded validation studies. Sensitivity and specificity at our pre-specified decision threshold were 97%/92% in training and 90%/95% in blinded verification. This classifier accuracy was maintained in a second blinded validation set with a sensitivity/ specificity of 90%/89% and combined accuracy of 92%. Sensitivity correlated with pathologic stage; 77% of Stage I, 93% of Stage II, 96% of Stage III and 96% of Stage IV cases were detected. An alternative decision threshold in the validation study yielding 98% specificity would still detect 60% of MM cases. In a paired sample set the classifier AUC of 0.99 and 91%/94% sensitivity/specificity was superior to that of mesothelin with an AUC of 0.82 and 66%/88% sensitivity/specificity. The candidate biomarker panel consists of both inflammatory and proliferative proteins, processes strongly associated with asbestos-induced malignancy. Significance: The SOMAmer biomarker panel discovered and validated in these studies provides a solid foundation for surveillance and diagnosis of MM in those at highest risk for this disease. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the leading cause of cancer deaths worldwide. New diagnostics are needed to detect early stage lung cancer because it may be cured with surgery. However, most cases are diagnosed too late for curative surgery. Here we present a comprehensive clinical biomarker study of lung cancer and the first large-scale clinical application of a new aptamer-based proteomic technology to discover blood protein biomarkers in disease. Methodology/Principal Findings: We conducted a multi-center case-control study in archived serum samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. Sera were collected and processed under uniform protocols. Case sera were collected from 291 patients within 8 weeks of the first biopsy-proven lung cancer and prior to tumor removal by surgery. Control sera were collected from 1,035 asymptomatic study participants with ≥10 pack-years of cigarette smoking. We measured 813 proteins in each sample with a new aptamer-based proteomic technology, identified 44 candidate biomarkers, and developed a 12-protein panel (cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin, and YES) that discriminates NSCLC from controls with 91% sensitivity and 84% specificity in cross-validated training and 89% sensitivity and 83% specificity in a separate verification set, with similar performance for early and late stage NSCLC. Conclusions/Significance: This study is a significant advance in clinical proteomics in an area of high unmet clinical need. Our analysis exceeds the breadth and dynamic range of proteome interrogated of previously published clinical studies of broad serum proteome profiling platforms including mass spectrometry, antibody arrays, and autoantibody arrays. The sensitivity and specificity of our 12-biomarker panel improves upon published protein and gene expression panels. Separate verification of classifier performance provides evidence against over-fitting and is encouraging for the next development phase, independent validation. This careful study provides a solid foundation to develop tests sorely needed to identify early stage lung cancer. [ABSTRACT FROM AUTHOR]

Abstract: Blood-based protein biomarkers hold great promise to advance medicine with applications that detect and diagnose diseases and aid in their treatment. We are developing such applications with our proteomics technology that combines high-content with low limits of detection. Biomarker discovery relies heavily on archived blood sample collections. Blood is dynamic and changes with different sampling procedures potentially confounding biomarker studies. In order to better understand the effects of sampling procedures on the circulating proteome, we studied three sample collection variables commonly encountered in archived sample sets. These variables included (1) three different sample tube types, PPT plasma, SST serum, and Red Top serum, (2) the time from venipuncture to centrifugation, and (3) the time from centrifugation to freezing. We profiled 498 proteins for each of 240 samples and compared the results by ANOVA. The results found no significant variation in the measurements for most proteins (~99%) when the two sample processing times tested were 2h or less, regardless of sample tube type. Even at the longest timepoints, 20h, ~82% of the proteins, on average for the three collection tube types, showed no significant change. These results are encouraging for proteomic biomarker discovery. [Copyright &y& Elsevier]

Human rhinoviruses (HRV) are the main cause of the common cold. Viral replication utilizes the activity of the HRV3C protease (3CP) enzyme [Antimicrob. Agents Chemother. 43 (1999) 2444; Antimicrob. Agents Chemother. 44 (2000) 1236]. Therefore, 3CP is an attractive target for antiviral drug development, and a new class of orally bioavailable irreversible 3CP inhibitors has been designed [J. Med. Chem. 45 (2002) 1607]. We have used related inhibitors to develop a rapid test for rhinovirus. The optical immuno assay (OIA®) thin film detection technology utilizes an optically coated silicon surface to convert specific molecular binding events into visual color changes by altering the reflective properties of light through molecular thin films. The purpose of this study was to develop a rapid assay for the determination of 3CP combining the Thermo Electron Bio Star® OIA technology and the newly designed inhibitor compounds. The advantage of this assay was in its approach, in which therapeutic and diagnostic targets are the same thus allowing patients with detected rhinoviruses to receive optimal treatment.Three different biotinylated inhibitor compounds were synthesized. The length of the spacer between the inhibitor and biotin core was 5, 10, and 15 atoms. These compounds were incorporated into the OIA format for the HRV assay development. A rapid (20 min) OIA test was developed using a 15 atom spacer biotinylated inhibitor (4). Forty different HRV serotypes were studied and thirty three serotypes of these 40 were detected (80%). [Copyright &y& Elsevier]

Despite recent progress, non-invasive tests for the diagnostic assessment and monitoring of non-alcoholic fatty liver disease (NAFLD) remain an unmet need. Herein, we aimed to identify diagnostic signatures of the key histological features of NAFLD. Using modified-aptamer proteomics, we assayed 5,220 proteins in each of 2,852 single serum samples from 636 individuals with histologically confirmed NAFLD. We developed and validated dichotomized protein-phenotype models to identify clinically relevant severities of steatosis (grade 0 vs. 1-3), hepatocellular ballooning (0 vs. 1 or 2), lobular inflammation (0-1 vs. 2-3) and fibrosis (stages 0-1 vs. 2-4). The AUCs of the four protein models, based on 37 analytes (18 not previously linked to NAFLD), for the diagnosis of their respective components (at a clinically relevant severity) in training/paired validation sets were: fibrosis (AUC 0.92/0.85); steatosis (AUC 0.95/0.79), inflammation (AUC 0.83/0.72), and ballooning (AUC 0.87/0.83). An additional outcome, at-risk NASH, defined as steatohepatitis with NAFLD activity score ≥4 (with a score of at least 1 for each of its components) and fibrosis stage ≥2, was predicted by multiplying the outputs of each individual component model (AUC 0.93/0.85). We further evaluated their ability to detect change in histology following treatment with placebo, pioglitazone, vitamin E or obeticholic acid. Component model scores significantly improved in the active therapies vs. placebo, and differential effects of vitamin E, pioglitazone, and obeticholic acid were identified. Serum protein scanning identified signatures corresponding to the key components of liver biopsy in NAFLD. The models developed were sufficiently sensitive to characterize the longitudinal change for three different drug interventions. These data support continued validation of these proteomic models to enable a "liquid biopsy"-based assessment of NAFLD. Not applicable. An aptamer-based protein scan of serum proteins was performed to identify diagnostic signatures of the key histological features of non-alcoholic fatty liver disease (NAFLD), for which no approved non-invasive diagnostic tools are currently available. We also identified specific protein signatures related to the presence and severity of NAFLD and its histological components that were also sensitive to change over time. These are fundamental initial steps in establishing a serum proteome-based diagnostic signature of NASH and provide the rationale for using these signatures to test treatment response and to identify several novel targets for evaluation in the pathogenesis of NAFLD. [Display omitted] • Aptamer proteomics and machine learning generated blood-based NASH models. • Serum models of liver steatosis, inflammation, ballooning and fibrosis were validated. • Models accurately reflect liver biopsy results and NASH severity. • Models allow for non-invasive longitudinal monitoring of treatment response. [ABSTRACT FROM AUTHOR]

Objective: To assess the effects of empagliflozin, a selective sodium-glucose cotransporter 2 (SGLT2) inhibitor, on broad biological systems through proteomics.Research Design and Methods: Aptamer-based proteomics was used to quantify 3,713 proteins in 144 paired plasma samples obtained from 72 participants across the spectrum of glucose tolerance before and after 4 weeks of empagliflozin 25 mg/day. The biology of the plasma proteins significantly changed by empagliflozin (at false discovery rate-corrected P < 0.05) was discerned through Ingenuity Pathway Analysis.Results: Empagliflozin significantly affected levels of 43 proteins, 6 related to cardiomyocyte function (fatty acid-binding protein 3 and 4 [FABPA], neurotrophic receptor tyrosine kinase, renin, thrombospondin 4, and leptin receptor), 5 to iron handling (ferritin heavy chain 1, transferrin receptor protein 1, neogenin, growth differentiation factor 2 [GDF2], and β2-microglobulin), and 1 to sphingosine/ceramide metabolism (neutral ceramidase), a known pathway of cardiovascular disease. Among the protein changes achieving the strongest statistical significance, insulin-like binding factor protein-1 (IGFBP-1), transgelin-2, FABPA, GDF15, and sulphydryl oxidase 2 precursor were increased, while ferritin, thrombospondin 3, and Rearranged during Transfection (RET) were decreased by empagliflozin administration.Conclusions: SGLT2 inhibition is associated, directly or indirectly, with multiple biological effects, including changes in markers of cardiomyocyte contraction/relaxation, iron handling, and other metabolic and renal targets. The most significant differences were detected in protein species (GDF15, ferritin, IGFBP-1, and FABP) potentially related to the clinical and metabolic changes that were actually measured in the same patients. These novel results may inform further studies using targeted proteomics and a prospective design. [ABSTRACT FROM AUTHOR]

Objective: Coronary artery disease (CAD) is a major challenge in patients with type 2 diabetes (T2D). Coronary computed tomography angiography (CCTA) provides a detailed anatomic map of the coronary circulation. Proteomics are increasingly used to improve diagnostic and therapeutic algorithms. We hypothesized that the protein panel is differentially associated with T2D and CAD.Research Design and Methods: In CAPIRE (Coronary Atherosclerosis in Outlier Subjects: Protective and Novel Individual Risk Factors Evaluation-a cohort of 528 individuals with no previous cardiovascular event undergoing CCTA), participants were grouped into CAD- (clean coronaries) and CAD+ (diffuse lumen narrowing or plaques). Plasma proteins were screened by aptamer analysis. Two-way partial least squares was used to simultaneously rank proteins by diabetes status and CAD.Results: Though CAD+ was more prevalent among participants with T2D (HbA1c 6.7 ± 1.1%) than those without diabetes (56 vs. 30%, P < 0.0001), CCTA-based atherosclerosis burden did not differ. Of the 20 top-ranking proteins, 15 were associated with both T2D and CAD, and 3 (osteomodulin, cartilage intermediate-layer protein 15, and HTRA1) were selectively associated with T2D only and 2 (epidermal growth factor receptor and contactin-1) with CAD only. Elevated renin and GDF15, and lower adiponectin, were independently associated with both T2D and CAD. In multivariate analysis adjusting for the Framingham risk panel, patients with T2D were "protected" from CAD if female (P = 0.007), younger (P = 0.021), and with lower renin levels (P = 0.02).Conclusions: We concluded that 1) CAD severity and quality do not differ between participants with T2D and without diabetes; 2) renin, GDF15, and adiponectin are shared markers by T2D and CAD; 3) several proteins are specifically associated with T2D or CAD; and 4) in T2D, lower renin levels may protect against CAD. [ABSTRACT FROM AUTHOR]

BACKGROUND: Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics. METHODS: A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death. RESULTS: Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk. CONCLUSIONS: Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients. [ABSTRACT FROM AUTHOR]

Abstract: Using modified nucleotides and selecting for slow off-rates in the SELEX procedure, we have evolved a special class of aptamers, called SOMAmers (slow off‐rate modified aptamers), which bind tightly and specifically to proteins in body fluids. We use these in a novel assay that yields 1:1 complexes of the SOMAmers with their cognate proteins in body fluids. Measuring the SOMAmer concentrations of the resultant complexes reflects the concentration of the proteins in the fluids. This is simply done by hybridization to complementary sequences on solid supports, but it can also be done by any other DNA quantification technology (including NexGen sequencing). We use measurements of over 1000 proteins in under 100μL of serum or plasma to answer important medical questions, two of which are reviewed here. A number of bioinformatics methods have guided our discoveries, including principal component analysis. We use various methods to evaluate sample handling procedures in our clinical samples and can identify many parameters that corrupt proteomics analysis. [Copyright &y& Elsevier]

Lung cancer remains the most common cause of cancer-related mortality. We applied a highly multiplexed proteomic technology (SOMAscan) to compare protein expression signatures of non small-cell lung cancer (NSCLC) tissues with healthy adjacent and distant tissues from surgical resections. In this first report of SOMAscan applied to tissues, we highlight 36 proteins that exhibit the largest expression differences between matched tumor and non-tumor tissues. The concentrations of twenty proteins increased and sixteen decreased in tumor tissue, thirteen of which are novel for NSCLC. NSCLC tissue biomarkers identified here overlap with a core set identified in a large serum-based NSCLC study with SOMAscan. We show that large-scale comparative analysis of protein expression can be used to develop novel histochemical probes. As expected, relative differences in protein expression are greater in tissues than in serum. The combined results from tissue and serum present the most extensive view to date of the complex changes in NSCLC protein expression and provide important implications for diagnosis and treatment. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "Coronary Artery Disease and Type 2 Diabetes: A Proteomic Study" which appeared in the previous issue.

Introduction: Chronic kidney disease (CKD) confers increased risk of cardiovascular (CV) events. Traditional risk factors, such as hypertension and diabetes contribute, but do not fully explain the excess CV risk in patients with CKD. CKD is associated with changes in concentrations of circulating proteins, which can impact the interpretation of CV biomarkers (i.e. troponin and natriuretic peptides), but can also lead to the presence of new markers of CV risk that are unique to patients with renal disease. To further assess the relationship between CV risk and CKD, as well as investigate potential biomarkers in patients with CKD, we applied proteomic analysis to a cohort of patients with stable coronary artery disease. Methods: Using plasma samples from 938 patients from the Heart and Soul cohort, we used modified aptamers (SOMAscan) to quantify 1054 proteins. Protein levels were correlated to eGFR using the CKD-EPI 2009 equation (based on serum creatinine), using Spearman's rank correlation coefficients. Cox proportional hazard models were used to estimate the association between individual protein levels and the risk of the primary outcome (first event of myocardial infarction, stroke, heart failure hospitalization, or all-cause mortality). Bonferroni-corrected significance levels were reported, adjusting for all 1054 proteins measured. To determine prognostic proteins separately in patients with normal renal function and CKD, we divided the cohort according to eGFR ≥ 60 ml/min/1.73 m2 and eGFR < 60 ml/min/1.73 m2. Results: Of the 1054 proteins analyzed, 664 (63%) proteins had significant correlation with eGFR (p < 0.05) and 217 (20.5%) had at least moderate correlation (rho ≥ 0.2). We identified 196 proteins that were prognostic of CV risk. After correction for eGFR, 87 proteins remained prognostic. Among the subgroup of patients with CKD, there were 8 prognostic proteins unique to these patients, and heretofore unknown. Conclusion: Using large scale proteomic analysis, we demonstrate that CKD has a profound impact on the circulating proteome of patients with stable coronary artery disease. We also describe protein biomarkers that predict CV risk in CKD, independently of glomerular filtration, as well as those that are unique to the CKD population. [ABSTRACT FROM AUTHOR]

Background: Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics.Methods: A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death.Results: Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk.Conclusions: Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients.Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264. [ABSTRACT FROM AUTHOR]