Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection.

Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients' survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening.

Identification of beta-arrestin-1 as a diagnostic biomarker in lung cancer.

BACKGROUND: Distinguishing lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) has a tremendous therapeutic implication. Sometimes, the commonly used immunohistochemistry (IHC) markers fail to discriminate between them, urging for the identification of new diagnostic biomarkers. METHODS: We performed IHC on tissue microarrays from two cohorts of lung cancer patients to analyse the expression of beta-arrestin-1, beta-arrestin-2 and clinically used diagnostic markers in ADC and SCC samples. Logistic regression models were applied for tumour subtype prediction. Parallel reaction monitoring (PRM)-based mass spectrometry was used to quantify beta-arrestin-1 in plasma from cancer patients and healthy donors. RESULTS: Beta-arrestin-1 expression was significantly higher in ADC versus SCC samples. Beta-arrestin-1 displayed high sensitivity, specificity and negative predictive value. Its usefulness in an IHC panel was also shown. Plasma beta-arrestin-1 levels were considerably higher in lung cancer patients than in healthy donors and were higher in patients who later experienced a progressive disease than in patients showing complete/partial response following EGFR inhibitor therapy. CONCLUSIONS: Our data identify beta-arrestin-1 as a diagnostic marker to differentiate ADC from SCC and indicate its potential as a plasma biomarker for non-invasive diagnosis of lung cancer. Its utility to predict response to EGFR inhibitors is yet to be confirmed.

Quantification of SAA1 and SAA2 in lung cancer plasma using the isotype-specific PRM assays.

The quantification of plasma proteins using the high resolution and accurate mass (HR/AM)-based parallel reaction monitoring (PRM) method provides an immediate benefit over the conventional SRM-based method in terms of selectivity. In this study, multiplexed PRM assays were developed to analyze isotypes of serum amyloid A (SAA) proteins in human plasma with a focus on SAA1 and SAA2. Elevated plasma levels of these proteins in patients diagnosed with lung cancer have been reported in previous studies. Since SAA1 and SAA2 are highly homologous, the available immunoassays tend to overestimate their concentrations due to cross-reactivity. On the other hand, when mass spectrometry (MS)-based assays are used, the presence of the several allelic variants may result in a problem of underestimation. In the present study, eight peptides that represent the target proteins at three different levels: isotype-specific (SAA1α,  SAA 1ß,  SAA1γ,  SAA2α,  SAA2ß), protein-specific (SAA1 or SAA2), and pan SAA (SAA1 and SAA2) were chosen to differentiate SAAs in lung cancer plasma samples using a panel of PRM assays. The measurement of specific isotypes, leveraging the analytical performance of PRM, allowed to quantify the allelic variants of both target proteins. The isotypes detected were corroborated with the genetic information obtained from the same samples. The combination of SAA2α and SAA2ß assays representing the total SAA2 concentration demonstrated a superior analytical outcome than the previously used assay on the common peptide when applied to the detection of lung cancer.

Verification of the biomarker candidates for non-small-cell lung cancer using a targeted proteomics approach.

Lung cancer, with its high metastatic potential and high mortality rate, is the worldwide leading cause of cancer-related deaths. High-throughput "omics"-based platforms have accelerated the discovery of biomarkers for lung cancer, and the resulting candidates are to be evaluated for their diagnostic potential as noninvasive biomarkers. The evaluation of the biomarker candidates involves the quantitative measurement of large numbers of proteins in bodily fluids using advanced mass spectrometric techniques. In this study, a robust pipeline based on targeted proteomics was developed for biomarker verification in plasma samples and applied to verifying lung cancer biomarker candidates. Highly multiplexed liquid chromatrography-selected reaction monitoring (LC-SRM) assays for 95 potential tumor markers for non-small-cell lung cancer (NSCLC) were generated to screen plasma samples obtained from 72, early to late stage, patients. A total of 17 proteins were verified as potent tumor markers detectable in plasma and, where available, verified by enzyme-linked immunosorbent assays (ELISAs). A novel plasma-based biomarker, zyxin, fulfilled the criteria for a potential early diagnostic marker for NSCLC.