Paul Kearney

A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules

Systems biology and targeted mass spectrometry were combined to develop and validate a blood-based protein classifier for pulmonary nodules. Avoiding Unnecessary Surgery Physicians have difficulty distinguishing early-stage malignant lung cancers from benign lung nodules. As a result, many patients with benign lung nodules undergo unnecessary, invasive, and costly medical procedures such as biopsy and surgery. Li et al. have developed and validated a noninvasive blood-based protein panel to identify benign lung nodules. They applied an advanced molecular technology, called multiple reaction monitoring mass spectrometry, to systematically evaluate the potential roles of 371 blood proteins in lung cancer. The authors discovered a panel of 13 proteins that was able to distinguish benign lung nodules from early-stage lung cancers in a clinical study. They then validated the performance of the panel in a second clinical study with new patients. The protein panel provided insightful information on the disease status of lung nodules beyond the clinical risk factors currently used by physicians. By measuring protein concentrations in blood samples, the protein panel can be used to rescue patients with benign lung nodules from unnecessary invasive procedures. 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.

Modulation of the phagosome proteome by interferon-gamma.

Macrophages are immune cells that function in the clearance of infectious particles. This process involves the engulfment of microbes into phagosomes where these particles are lysed and degraded. In the current study, we used a large scale quantitative proteomics approach to analyze the changes in protein abundance induced on phagosomes by interferon-γ (IFN-γ), an inflammatory cytokine that activates macrophages. Our analysis identified 167 IFN-γ-modulated proteins on phagosomes of which more than 90% were up-regulated. The list of phagosomal proteins regulated by IFN-γ includes proteins expected to alter phagosome maturation, enhance microbe degradation, trigger the macrophage immune response, and promote antigen loading on major histocompatibility complex (MHC) class I molecules. A dynamic analysis of IFN-γ-sensitive proteins by Western blot indicated that newly formed phagosomes display a delayed proteolytic activity coupled to an increased recruitment of the MHC class I peptide-loading complex. These phagosomal conditions may favor antigen presentation by MHC class I molecules on IFN-γ-activated macrophages.

Management of Pulmonary Nodules by Community Pulmonologists: A Multicenter Observational Study

BACKGROUND: Pulmonary nodules (PNs) are a common reason for referral to pulmonologists. The majority of data for the evaluation and management of PNs is derived from studies performed in academic medical centers. Little is known about the prevalence and diagnosis of PNs, the use of diagnostic testing, or the management of PNs by community pulmonologists. METHODS: This multicenter observational record review evaluated 377 patients aged 40 to 89 years referred to 18 geographically diverse community pulmonary practices for intermediate PNs (8-20 mm). Study measures included the prevalence of malignancy, procedure/test use, and nodule pretest probability of malignancy as calculated by two previously validated models. The relationship between calculated pretest probability and management decisions was evaluated. RESULTS: The prevalence of malignancy was 25% (n = 94). Nearly one-half of the patients (46%, n = 175) had surveillance alone. Biopsy was performed on 125 patients (33.2%). A total of 77 patients (20.4%) underwent surgery, of whom 35% (n = 27) had benign disease. PET scan was used in 141 patients (37%). The false-positive rate for PET scan was 39% (95% CI, 27.1%-52.1%). Pretest probability of malignancy calculations showed that 9.5% (n = 36) were at a low risk, 79.6% (n = 300) were at a moderate risk, and 10.8% (n = 41) were at a high risk of malignancy. The rate of surgical resection was similar among the three groups (17%, 21%, 17%, respectively; P = .69). CONCLUSIONS: A substantial fraction of intermediate-sized nodules referred to pulmonologists ultimately prove to be lung cancer. Despite advances in imaging and nonsurgical biopsy techniques, invasive sampling of low-risk nodules and surgical resection of benign nodules remain common, suggesting a lack of adherence to guidelines for the management of PNs.

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.

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

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.

Factors that influence physician decision making for indeterminate pulmonary nodules.

RATIONALE Pulmonologists frequently encounter indeterminate pulmonary nodules in practice, but it is unclear what clinical factors they rely on to guide the diagnostic evaluation. OBJECTIVES To assess the current approach to the management of indeterminate pulmonary nodules and to determine the extent to which the addition of a hypothetical diagnostic blood test will influence clinical decision making. METHODS Selected pulmonologists practicing in the United States were invited to participate in a conjoint exercise based on 20 randomly generated cases of varying age, smoking history, and nodule size. Some cases included the result of a hypothetical blood test. Each respondent chose from among three diagnostic options for a patient: noninvasive monitoring (i.e., serial CT or positron emission tomography scan), a minor procedure (i.e., biopsy or bronchoscopy), or a major procedure (i.e., video-assisted thorascopic surgery or thoracotomy). Multivariate logistic regression was used to assess the impact of the three risk factors and the diagnostic blood test on decision making. MEASUREMENTS AND MAIN RESULTS Four hundred nineteen physicians participated (response rate, 10%). One hundred fifty-three physician surveys met predetermined criteria and were analyzed (4% of all invitees). A diagnostic procedure was recommended for 23% of 6-mm nodules, versus 54, 66, 77, and 84% of nodules 10, 14, 18, and 22 mm, respectively (P < 0.001). Older age limited recommendations for invasive testing: 54% of 80-year-olds versus 61, 64, 63, and 61% of patients 71, 62, 53, and 44 years of age, respectively (P < 0.001). In multivariate analyses, nodule size, smoking history, age, and the blood test each influenced decision making (P < 0.001). CONCLUSIONS The pulmonologists who participated in this survey were more likely to proceed with invasive testing, instead of observation or additional imaging, as the size of the nodule increased. The use of a hypothetical blood test resulted in significant alterations in the decision to pursue invasive testing.

Assessment of Plasma Proteomics Biomarker’s Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial

BACKGROUND: Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%. METHODS: A prospective, multicenter observational trial of 685 patients with 8‐ to 30‐mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made. RESULTS: A subgroup of 178 patients with a clinician‐assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82‐100), a specificity of 44% (CI, 36‐52), and a negative predictive value of 98% (CI, 92‐100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified. CONCLUSIONS: When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).

An evaluation of multidimensional fingerprinting in the context of clinical proteomics

Multidimensional fingerprinting (MDF) utilizes measurable peptide characteristics to identify proteins. In this study, 3‐D fingerprinting, namely, parent protein molecular weight, peptide mass, and peptide retention time on RPLC, is used to identify 331 differentially expressed proteins between normal and human colon cancer plasma membrane samples. A false discovery rate (FDR) procedure is introduced to evaluate the performance of MDF on the colon cancer dataset. This evaluation establishes a false protein identification rate below 15% for this dataset. Western blot analysis is performed to validate the differential expression of the MDF‐identified protein VDAC1 on the original tissue samples. The limits of MDF are further assessed by a simulation study where key parameters such as database size, query size, and mass accuracy are varied. The results of this simulation study demonstrate that fingerprinting with three dimensions yields low FDR values even for large queries on the complete human proteome without the need for prior peptide sequencing by tandem mass spectrometry. Specifically, when mass accuracy is 10 ppm or lower, full human proteome searches can achieve FDR values of 10% or less.

An integrated risk predictor for pulmonary nodules

It is estimated that over 1.5 million lung nodules are detected annually in the United States. Most of these are benign but frequently undergo invasive and costly procedures to rule out malignancy. A risk predictor that can accurately differentiate benign and malignant lung nodules could be used to more efficiently route benign lung nodules to non-invasive observation by CT surveillance and route malignant lung nodules to invasive procedures. The majority of risk predictors developed to date are based exclusively on clinical risk factors, imaging technology or molecular markers. Assessed here are the relative performances of previously reported clinical risk factors and proteomic molecular markers for assessing cancer risk in lung nodules. From this analysis an integrated model incorporating clinical risk factors and proteomic molecular markers is developed and its performance assessed on a subset of 222 lung nodules, between 8mm and 20mm in diameter, collected in a previously reported prospective study. In this analysis it is found that the molecular marker is most predictive. However, the integration of clinical and molecular markers is superior to both clinical and molecular markers separately. Clinical Trial Registration: Registered at ClinicalTrials.gov (NCT01752101).

Protein Catalyzed Capture Agents with Tailored Performance for In Vitro and In Vivo Applications

We report on peptide‐based ligands matured through the protein catalyzed capture (PCC) agent method to tailor molecular binders for in vitro sensing/diagnostics and in vivo pharmacokinetics parameters. A vascular endothelial growth factor (VEGF) binding peptide and a peptide against the protective antigen (PA) protein of Bacillus anthracis discovered through phage and bacterial display panning technologies, respectively, were modified with click handles and subjected to iterative in situ click chemistry screens using synthetic peptide libraries. Each azide‐alkyne cycloaddition iteration, promoted by the respective target proteins, yielded improvements in metrics for the application of interest. The anti‐VEGF PCC was explored as a stable in vivo imaging probe. It exhibited excellent stability against proteases and a mean elimination in vivo half‐life (T1/2) of 36 min. Intraperitoneal injection of the reagent results in slow clearance from the peritoneal cavity and kidney retention at extended times, while intravenous injection translates to rapid renal clearance. The ligand competed with the commercial antibody for binding to VEGF in vivo. The anti‐PA ligand was developed for detection assays that perform in demanding physical environments. The matured anti‐PA PCC exhibited no solution aggregation, no fragmentation when heated to 100°C, and  > 81% binding activity for PA after heating at 90°C for 1 h. We discuss the potential of the PCC agent screening process for the discovery and enrichment of next generation antibody alternatives.