Corrigendum to "Analytical validation of protein biomarkers for risk of spontaneous preterm birth" [Clin. Mass Spectrom. 3 (2017) 25-38].

[This corrects the article DOI: 10.1016/j.clinms.2017.06.002.].

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).

Analytical validation of protein biomarkers for risk of spontaneous preterm birth.

Presented are the validation results of a second-generation assay for determining the relative abundances of two protein biomarkers found in maternal serum that predict an individual's risk of spontaneous preterm birth. The sample preparation workflow is complex, consisting of immuno-depletion of high-abundance serum proteins, tryptic digestion of the immuno-depleted fraction to generate surrogate peptide analytes, and detection by tandem mass spectrometry. The method was determined to be robust on observation of the following characteristics: classifier peptide detection precision was excellent; results were accurate when compared to a reference method; results were linear over a clinically relevant range; the limits of quantitation encompassed the range of expected results; and the method demonstrated analytical specificity and resilience to differences in patient serum and common endogenous interferents.

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.

Through the eye of an electrospray needle: mass spectrometric identification of the major peptides and proteins in the milk of the one-humped camel (Camelus dromedarius).

The milk of the one-humped camel (Camelus dromedarius) reportedly offers medicinal benefits, perhaps because of its unique bioactive components. Milk proteins were determined by (1) two-dimensional gel electrophoresis and peptide mass mapping and (2) liquid chromatography-tandem mass spectrometry (LC-MS/MS) following one-dimensional polyacrylamide gel electrophoresis. Over 200 proteins were identified: some known camel proteins including heavy-chain immunoglobulins and others exhibiting regions of exact homology with proteins from other species. Indigenous peptides were also identified following isolation and concentration by two strategies: (1) gel-eluted liquid fraction entrapment electrophoresis and (2) small-scale electrophoretic separation. Extracts were analyzed by LC-MS/MS and peptides identified by matching strategies, by de novo sequencing and by applying a sequence tag tool requiring similarity to the proposed sequence, but not an exact match. A plethora of protein cleavage products including some novel peptides were characterized. These studies demonstrate that camel milk is a rich source of peptides, some of which may serve as nutraceuticals.

Discovery and verification of protein differences between Er positive/Her2/neu negative breast tumor tissue and matched adjacent normal breast tissue.

This study was designed to quantify and identify differences in protein levels between tumor and adjacent normal breast tissue from the same breast in 18 women with stage I/II ER positive/Her2/neu negative invasive breast cancer. Eighteen separate difference gel electrophoresis (DIGE) gels were run (1 gel per patient). Relative quantification was based on DIGE analysis. After excision and tryptic digestion, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and peptide mass mapping were used to identify protein spots. Two hundred and forty-three spots were differentially abundant between normal and cancer tissues. Fifty spots were identified: 41 were over abundant and nine were less abundant in cancers than in normal breast tissue. Western blotting provided independent confirmation for three of the most biologically and statistically interesting proteins. All 18 gels were replicated by another technician and 32% of the differentially abundant proteins were verified by the duplicate analysis. Follow-up studies are now examining these proteins as biomarkers in blood.

Quantitative matrix-assisted laser desorption/ionization mass spectrometry.

This review summarizes the essential characteristics of matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOF MS), especially as they relate to its applications in quantitative analysis. Approaches to quantification by MALDI-TOF MS are presented and published applications are critically reviewed.

Discovery and validation of protein abundance differences between follicular thyroid neoplasms.

Distinguishing between benign follicular thyroid adenoma (FTA) and malignant follicular thyroid carcinoma (FTC) by cytologic features alone is not possible. Molecular markers may aid distinguishing FTA from FTC in patients with indeterminate cytology. The aim of this study is to define protein abundance differences between FTC from FTA through a discovery (proteomics) and validation (immunohistochemistry) approach. Difference gel electrophoresis (DIGE) and peptide mass fingerprinting were performed on protein extracts from five patients with FTC and compared with six patients with FTA. Individual gel comparisons (i.e., each FTC extract versus FTA pool) were also performed for the five FTC patients. Immunohistochemical validation studies were performed on three of the identified proteins. Based on DIGE images, 680 protein spots were matched on individual gels. Of these, 102 spots showed statistically significant differences in abundance between FTC and FTA in the individual gel analyses and were therefore studied further. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to identify 54 of these protein spots. Three candidates involved in protein folding (heat shock protein gp96, protein disulfide isomerase A3, and calreticulin) were studied by immunohistochemistry. Moderate calreticulin immunohistochemical staining was the best single marker with a high negative predictive value (88%); combining all three markers (any marker less than moderate staining) had the best positive predictive value (75%) while still retaining a good negative predictive value (68%). With DIGE, we identified 54 proteins differentially abundant between FTC and FTA. Three of these were validated by immunohistochemistry. These findings provide further insights into the diagnosis, prognosis, and pathophysiology of follicular-derived thyroid neoplasms.

Assessment of post-mortem-induced changes to the mouse brain proteome.

This study was designed to assess the influence of high-energy head-focused microwave irradiation and the post-mortem interval on measurements of the mouse brain proteome. Difference gel electrophoresis was used to compare mouse brain protein levels in animals killed by decapitation, where the tissue was held at 25 degrees C for selected time intervals post-mortem, and by high-energy head-focused microwave irradiation followed by immediate resection. Microwave-mediated killing was used because it comprehensively snap-inactivates enzymes while largely retaining brain cytoarchitecture. Of the 912 protein spots common to at least eight of 10 gels analyzed, 35 (3.8%) showed significant differences in levels (t-test; p < 0.05) depending on whether animals were killed by microwave irradiation or decapitation. When animals were killed by decapitation, 43 protein spots (4.7%) showed changes in levels over the post-mortem interval (anova; p < 0.05). The vast majority of the near 1000 proteins evident on a 2D gel were stable for up to 4 h. These data have important implications for studies of proteins in the brain, whether based on analysis of tissue derived from animal models or from humans.

Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study.

BACKGROUND: Some but not all patients with non-small-cell lung cancer (NSCLC) respond to treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We developed and tested the ability of a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) analysis of pretreatment serum to identify patients who are likely to benefit from treatment with EGFR TKIs. METHODS: Serum collected from NSCLC patients before treatment with gefitinib or erlotinib were analyzed by MALDI MS. Spectra were acquired independently at two institutions. An algorithm to predict outcomes after treatment with EGFR TKIs was developed from a training set of 139 patients from three cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in three control cohorts of patients who were not treated with EGFR TKIs. The clinical outcomes of survival and time to progression were analyzed. RESULTS: An algorithm based on eight distinct m/z features was developed based on outcomes after EGFR TKI therapy in training set patients. Classifications based on spectra acquired at the two institutions had a concordance of 97.1%. For both validation cohorts, the classifier identified patients who showed improved outcomes after EGFR TKI treatment. In one cohort, median survival of patients in the predicted "good" and "poor" groups was 207 and 92 days, respectively (hazard ratio [HR] of death in the good versus poor groups = 0.50, 95% confidence interval [CI] = 0.24 to 0.78). In the other cohort, median survivals were 306 versus 107 days (HR = 0.41, 95% CI = 0.17 to 0.63). The classifier did not predict outcomes in patients who did not receive EGFR TKI treatment. CONCLUSION: This MALDI MS algorithm was not merely prognostic but could classify NSCLC patients for good or poor outcomes after treatment with EGFR TKIs. This algorithm may thus assist in the pretreatment selection of appropriate subgroups of NSCLC patients for treatment with EGFR TKIs.

Quantitative and qualitative differences in protein expression between papillary thyroid carcinoma and normal thyroid tissue.

In order to better understand basic mechanisms of tumor development and identify potential new biomarkers, we have performed difference gel electrophoresis (DIGE) and peptide mass fingerprinting on pooled protein extracts from patients with papillary thyroid carcinoma (PTC) compared with matched normal thyroid tissue. Image analysis of DIGE gels comparing PTC and matched normal thyroid tissue protein indicated that 25% of the protein spots were differentially expressed at a 2.5-fold cutoff and 35% at two-fold. Comparison between two different pools of protein from normal thyroid tissues revealed differential protein expression of only 4% at 2.5-fold and 6% at two-fold cutoff. One hundred ninety-two protein spots were identified by MALDI-TOFMS, representing 90 distinct proteins. Excluding albumin, globins and thyroglobulin, imaging software determined 31 proteins to be differentially expressed at the two-fold (or greater) level. Individual gel comparisons (PTC vs. matched normal) from five patients established that 15/31 (48%) of these proteins exhibited statistically significant differential expression. Previously identified molecular markers in this group of proteins include cathepsin B, cytokeratin 19, and galectin-3. Novel differentially expressed proteins include S100A6, moesin, HSP70 (BiP), peroxiredoxin 2, protein phosphatase 2, selenium binding protein 1, vitamin D binding protein, and proteins involved in mitochondrial function. The use of two-dimensional gel electrophoresis (2DGE) revealed a significantly altered protein mass and/or pI in 10%-15% of proteins, suggesting alternatively spliced forms and other posttranslational modification of proteins revealed by this approach. We confirmed S100A6 as a potentially useful biomarker using immunohistochemical analysis (85% sensitivity and 69% specificity for distinguishing benign from malignant thyroid neoplasms). In summary, proteomic analysis of PTC using DIGE and mass spectrometry has confirmed several known biomarkers, uncovered novel potential biomarkers, and provided insights into global pathophysiologic changes in PTC. Many of the differences observed would not have been detected by genomic or other proteomic approaches.

Is protein overlap in two-dimensional gels a serious practical problem?

In a recently published article, Campostrini et al. [Proteomics 2005, 5, 2385-2395] raised questions regarding the utility of 2-D gels in proteomics research. We believe that the authors have overlooked several key issues including the dynamic range of protein expression and the sensitivity of the analytical methods used to explore a proteome. We argue that 2-D gels have and will continue to provide meaningful quantitative data when applied to proteomic analysis and that the practical significance of spot overlap has been overstated.

Estrogen regulation of the rat anterior pituitary gland proteome.

Estrogen is known to affect the regulation of all six of the established anterior pituitary gland (AP) hormones, but little is known of the specifics of its regulation of the AP hormones, their isoforms, and nonhormonal AP proteins. We used difference gel electrophoresis in conjunction with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and peptide mass fingerprinting to quantify the effects of estrogen on the AP-soluble protein fraction in rats. Two-month-old rats were ovariectomized and used at 6 months of age. They were injected subcutaneously with sesame oil vehicle or 50 mug estradiol valerate in vehicle and studied 48 hrs later, approximately 3 hrs before the time of the anticipated onset of the estrogen-induced surges of gonadotropins in blood. The APs were pooled, and the soluble protein fraction was examined in replicate analyses. After DeCyder software analysis, we identified 26 protein spots that had a 1.5-fold or greater average increase in the experimental group relative to the controls. Nineteen showed a 1.5-fold or greater decrease. Estrogen increased levels of the more acidic isoforms of growth hormone and prolactin and of proteins involved in protein synthesis, folding, and secretion (e.g., eukaryotic translation elongation factor 2, ERp57, ERp29, Hsc70-ps1, calreticulin, coatomer delta subunit, and secretogranin II) and of some metabolic enzymes (e.g., arginosuccinate synthetase, enolase 1, creatine kinase B, phosphoglycerate mutase, malate dehydrogenase, pyruvate kinase, and aldolase A). The majority of the downregulated proteins were involved in RNA or DNA interactions (e.g., five heterogeneous nuclear ribonucleoproteins, DEAD-box proteins 17 and 48, ssDNA binding protein PUR-alpha, PTB-associated splicing factor, and Pigpen protein), but isovaleryl coenzyme A dehydrogenase, mitochondrial aldehyde dehydrogenase, stathmin 1, vinculin, radixin, and secretogranin III were also reduced. Our results indicate that estrogen acts in vivo within 48 hrs to modulate levels of a significant number of AP proteins.

Proteomics as a tool for clinically relevant biomarker discovery and validation.

The excitement associated with clinical applications of proteomics was initially focused on its potential to serve as a vehicle for both biomarker discovery and drug discovery and routine clinical sample analysis. Some approaches were thought to be able to "identify" mass spectral characteristics that distinguished between control and disease samples, and thereafter it was believed that the same tool could be employed to screen samples in a high-throughput clinical setting. However, this has been difficult to achieve, and the early promise is yet to be fully realized. While we see an important place for mass spectrometry in drug and biomarker discovery, we believe that alternative strategies will prove more fruitful for routine analysis. Here we discuss the power and versatility of 2D gels and mass spectrometry in the discovery phase of biomarker work but argue that it is better to rely on immunochemical methods for high-throughput validation and routine assay applications.

A preliminary investigation of the Nostoc punctiforme proteome.

Nostoc punctiforme ATCC 29133 is a filamentous terrestrial cyanobacterium (prokaryote) that expresses several different phenotypes in response to environmental cues. When grown in nitrogen-deficient media the most abundant proteins in addition to phycobiliproteins were superoxide dismutase, ATP synthase, and peptidyl-prolyl cis-trans isomerases. A methylated peptide from an akinete marker protein was also identified, suggesting that methylation could potentially play a regulatory role through signaling. C-phycocyanin alpha-chain was methylated at the C-terminal end of the protein and tandem mass spectrometric data also identified peptides that were deamidated. Since a significant number of putative polyketide/non-ribosomal peptide synthase genes are present in the annotated genome, an analysis of a methanolic extract of whole cells was also performed, and a series of nostopeptolides were identified.