A high-quality secretome of A549 cells aided the discovery of C4b-binding protein as a novel serum biomarker for non-small cell lung cancer

Cancer secretomes are a promising source for biomarker discovery. The analysis of cancer secretomes still faces some difficulties mainly related to the intracellular contamination, which hinders the qualification and follow-up validations. This study aimed to establish a high-quality secretome of A549 cells by using the cellular proteome as a reference and to test the merits of this refined secretome for biomarker discovery for non-small cell lung cancer (NSCLC). Using one-dimensional gel electrophoresis followed by liquid-chromatography tandem mass spectrometry, we comprehensively investigated the secretome and the concurrent cellular proteome of A549 cells. A high-quality secretome consisting of 382 proteins was refined from 889 initial secretory proteins. More than 85.3% of proteins were annotated as secreted and 76.8% as extracellular or membrane-bound. The discriminative power of the lung-cancer associated secretome was confirmed by gene expression and serum proteomic data. The elevated level of C4b-binding Protein (C4BP) in NSCLC blood was verified by enzyme-linked immunosorbent assays (ELISA, p = 6.07e-6). Moreover, the serum C4BP level in 89 patients showed a strong association with the clinical staging of NSCLC. Our reference-experiment-driven strategy is simple and widely applicable, and may facilitate the identification of novel promising biomarkers of lung cancer.     

Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer

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.     

Proteomics analysis of human serum of patients with non-small-cell lung cancer reveals proteins as diagnostic biomarker candidates

Non-small-cell lung carcinomas (NSCLC) is the most common type of lung cancer and it has a poor prognosis, because overall survival after 5 years is 20–25% for all stages. Thus, it is extremely important to increase the survival rate in the early stages NSCLC by focusing on novel screening tests of cancer identifying specific biomarkers expression associated with a more accurate tumor staging and patient prognosis. In this study, we focused our attention on quantitative proteomics of three heavily glycosylated serum proteins: AMBP, α2 macroglobulin, and SERPINA1. In particular, we analyzed serum samples from 20 NSCLC lung adenocarcinoma cancer patients in early and advanced stages, and 10 healthy donors to obtain a relative quantification through the MRM analysis of these proteins that have shown to be markers of cancer development and progression. AMBP, α2 macroglobulin, and SERPINA1 were chosen because all of them possess endopeptidase inhibitor activity and play key roles in cancer. We observe a variation in the expression of these proteins linked to the stage of the disease. Therefore, we believe that proteins like α2 macroglobulin, αmicroglobulin/bikunin, and SERPINA1 could be useful biomarkers for early detection of lung cancer and in monitoring its evolution.     

Current progress in protein profiling of complex samples

Accurate cancer biomarkers are needed for early detection, disease classification, prediction of therapeutic response and monitoring treatment. While there appears to be no shortage of candidate biomarker proteins, a major bottleneck in the biomarker pipeline continues to be their verification by enzyme linked immunosorbent assays. Multiple reaction monitoring (MRM), also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation. Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity. This review focuses on recent applications of MRM to the analysis of plasma and serum from cancer patients for biomarker verification. The current status of this approach is discussed along with future directions for targeted mass spectrometry in clinical biomarker validation.     

Protein signature of lung cancer tissues

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.     

Large-scale isotype-specific quantification of Serum amyloid A 1/2 by multiple reaction monitoring in crude sera

Quantification is an essential step in biomarker development. Multiple reaction monitoring (MRM) is a new modified mass spectrometry-based quantification technology that does not require antibody development. Serum amyloid A (SAA) is a positive acute-phase protein identified as a lung cancer biomarker in our previous study. Acute SAA exists in two isoforms with highly similar (92%) amino acid sequences. Until now, studies of SAA have been unable to distinguish between SAA1 and SAA2. To overcome the unavailability of a SAA2-specific antibody, we developed MRM methodology for the verification of SAA1 and SAA2 in clinical crude serum samples from 99 healthy controls and 100 lung adenocarcinoma patients. Differential measurement of SAA1 and SAA2 was made possible for the first time with the developed isotype-specific MRM method. Most healthy control samples had small or no MS/MS peaks of the targeted peptides otherwise, higher peak areas with 10- to 34-fold increase over controls were detected in lung cancer samples. In addition, our SAA1 MRM data demonstrated good agreement with the SAA1 enzyme-linked immunosorbent assay (ELISA) data. Finally, successful quantification of SAA2 in crude serum by MRM, for the first time, shows that SAA2 can be a good biomarker for the detection of lung cancers.     

Lung Cancer: Are we up to the Challenge?

Lung cancer is the leading cause of cancer deaths worldwide among both men and women, with more than 1 million deaths annually. Non-small cell lung cancer (NSCLC) accounts for about 80% of all lung cancers.Although recent advances have been made in diagnosis and treatment strategies, the prognosis of NSCLC patients is poor and it is basically due to a lack of early diagnostic tools.However, in the last years genetic and biochemical studies have provided more information about the protein and gene's mutations involved in lung tumors. Additionally, recent proteomic and microRNA's approaches have been introduced to help biomarker discovery.Here we would like to discuss the most recent discoveries in lung cancer pathways, focusing on the genetic and epigenetic factors that play a crucial role in malignant cell proliferation, and how they could be helpful in diagnosis and targeted therapy.     

Urinary proteome alterations in HER2 enriched breast cancer revealed by multipronged quantitative proteomics

Globally, breast cancer is the second most common cancer among women. Although biomarker discoveries through various proteomic approaches of tissue and serum samples have been studied in breast cancer, urinary proteome alterations in breast cancer are least studied. Urine being a noninvasive biofluid and a significant source of proteins, it has the potential in early diagnosis of breast cancer. This study used complementary quantitative gel‐based and gel‐free proteomic approaches to find a panel of urinary protein markers that could discriminate HER2 enriched (HE) subtype breast cancer from the healthy controls. A total of 183 differentially expressed proteins were identified using three complementary approaches, namely 2D‐DIGE, iTRAQ, and sequential window acquisition of all theoretical mass spectra. The differentially expressed proteins were subjected to various bioinformatics analyses for deciphering the biological context of these proteins using protein analysis through evolutionary relationships, database for annotation, visualization and integrated discovery, and STRING. Multivariate statistical analysis was undertaken to identify the set of most significant proteins, which could discriminate HE breast cancer from healthy controls. Immunoblotting and MRM‐based validation in a separate cohort testified a panel of 21 proteins such as zinc‐alpha2‐glycoprotein, A2GL, retinol‐binding protein 4, annexin A1, SAP3, SRC8, gelsolin, kininogen 1, CO9, clusterin, ceruloplasmin, and α1‐antitrypsin could be a panel of candidate markers that could discriminate HE breast cancer from healthy controls.     

Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10⁻²⁵), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.     

Validation of a blood protein signature for non-small cell lung cancer

Background

CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.

Results

Blood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.

Conclusions

Selecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.     

Comparative profiling of serum glycoproteome by sequential purification of glycoproteins and 2-nitrobenzensulfenyl (NBS) stable isotope labeling: a new approach for the novel biomarker discovery for cancer

The recent progress in various proteomic technologies allows us to screen serum biomarker including carbohydrate antigens. However, only a limited number of proteins could be detected by current conventional methods such as shotgun proteomics, primarily because of the enormous concentration distribution of serum proteins and peptides. To circumvent this difficulty and isolate potential cancer-specific biomarkers for diagnosis and treatment, we established a new screening system consisting of the sequential steps of (1) immunodepletion of 6 high-abundance proteins, (2) targeted enrichment of glycoproteins by lectin column chromatography, and (3) the quantitative proteome analysis using 12C6- or 13C6-NBS (2-nitrobenzenesulfenyl) stable isotope labeling followed by MALDI-QIT-TOF mass spectrometric analysis. Through this systematic analysis for five serum samples derived from patients with lung adenocarcinoma, we identified as candidate biomarkers 34 serum glycoproteins that revealed significant difference in alpha1,6-fucosylation level between lung cancer and healthy control, clearly demonstrating that the carbohydrate-focused proteomics could allow for the detection of serum components with cancer-specific features. In addition, we developed a more simplified and practical technique, mass spectrometry-based glycan structure analysis and lectin blotting, in order to validate glycan structure of candidate biomarkers that could be applicable in clinical use. Our new glycoproteomic strategy will provide highly sensitive and quantitative profiling of specific glycan structures on multiple proteins, which should be useful for serum biomarker discovery.     

A technically detailed and pragmatic protocol for quantitative serum proteomics using iTRAQ

Blood is recognised as a highly important source of disease-related biomarkers, and proteomic approaches for identifying novel blood-borne biomarkers are in demand. The complexity and dynamic protein concentration range of plasma/serum however complicates the analysis process. A number of strategies for simplification of blood prior to proteomic analysis have been developed. In addition, methods for quantifying the levels of proteins in samples, such as isobaric tags for relative and absolute quantification (iTRAQ) are emerging. However, the successful application of these procedures is not always straightforward and technical hurdles must be overcome. Here we provide a technically detailed working protocol for iTRAQ-based quantification of serum proteins following immunodepletion of high abundance proteins. To improve the number of proteins identified and quantified we have introduced several modifications to the standard iTRAQ protocol. We report identifications of 217 proteins (5773 peptides) with a false discovery rate of 1% or 254 proteins with 95% confidence, respectively. Relative quantification data were obtained for 234 (95% confidence) serum proteins, including species present in the concentration range of tissue leakage factors. The samples described here relate to pancreatic cancer; however the protocol can be applied to serum from other control or disease types.     

Up-regulation of the expression of leucine-rich α2-glycoprotein in hepatocytes by the mediators of acute-phase response

Leucine-rich α₂-glycoprotein (LRG) is a plasma protein in which leucine-rich repeats (LRRs) were first discovered. Although the physiological function of LRG is not known, increases in the serum level of LRG have been reported in various diseases. In this study, we found that LRG was induced by recombinant human IL-6 in human hepatoma HepG2 cells. The induction of LRG by IL-6 was up-regulated synergistically with either IL-1β or TNFα in a pattern similar to those for type 1 acute-phase proteins. We also found that lipopolysaccharide (LPS) administered intraperitoneally to mice enhanced dose-dependently the expression of LRG mRNA in the liver as well as those for mouse major acute-phase proteins. These results strongly suggest that LRG was a secretory type 1 acute-phase protein whose expression was up-regulated by the mediator of acute-phase response.     

血清LRG1、GRP78与急诊脓毒症患者继发急性肺损伤的关系研究

摘要 目的：探讨血清富亮氨酸α-2糖蛋白1（LRG1）、葡萄糖调节蛋白78（GRP78）与急诊脓毒症患者继发急性肺损伤（ALI）的关系。方法：选取2021年1月～2022年10月在我院急诊重症监护室（EICU）接受治疗的155例脓毒症患者为观察组,根据是否继发ALI分为ALI组43例和非ALI组112例,另选取同期我院100名体检健康者为对照组。采用酶联免疫吸附试剂盒检测血清LRG1、GRP78水平。通过单因素及多因素Logistic回归分析脓毒症患者继发ALI的影响因素。受试者工作特征（ROC）曲线分析血清LRG1、GRP78水平对脓毒症患者继发ALI的预测价值。结果：与对照组比较,观察组血清LRG1、GRP78水平升高（P＜0.05）。单因素分析显示,急诊脓毒症患者继发ALI与脓毒症分级、EICU时间、机械通气、脓毒症相关器官衰竭评估（SOFA）评分、血乳酸、LRG1、GRP78有关（P<0.05）。多因素Logistic回归分析显示,脓毒性休克、EICU时间延长和SOFO评分、血乳酸、LRG1、GRP78升高为急诊脓毒症患者继发ALI的独立危险因素（P＜0.05）。ROC曲线分析显示,血清LRG1、GRP78水平单独和联合预测急诊脓毒症患者继发ALI的曲线下面积（AUC）分别为0.790、0.782、0.884,二者联合预测的AUC最大。结论：急诊脓毒症患者血清LRG1、GRP78水平升高与继发ALI密切相关,血清LRG1、GRP78水平联合预测急诊脓毒症患者继发ALI的价值较高。     

Development of candidate biomarkers for pancreatic ductal adenocarcinoma using multiple reaction monitoring

Pancreatic ductal adenocarcinoma (PDAC) is the fourth most frequent cause of cancer mortality in the United States. Because CA 19-9 increases not only in PDAC, but also in benign conditions, there is urgent need for an additional PDAC biomarker. Isotope tags for relative and absolute quantification (iTRAQ) were performed using 6 pairs of PDAC and normal tissues from the same patients, to obtain preliminary PDAC-specific proteins; and verification was performed by multiple reactions monitoring (MRM), using 30 PDAC and 20 normal serum, targeting high-abundant serum proteins without any pre-preparation. As a result, 17 candidate proteins from tissue iTRAQ were verified as potential markers (AUC values > 0.7). Multivariate analysis (MA) demonstrated that a 6-marker panel, consisting of alpha-1 antitrypsin, haptoglobin beta chain, hemopexin, transferrin, zinc alpha-2 glycoprotein, and apolipoprotein A4 from the MRM result, had comparable discriminatory power versus CA 19-9. Our study demonstrated that a combination of iTRAQ on PDAC tissue and verification MRM-MA on individual serum was an efficient method for the development of PDAC multimarkers.     

Proteomic analysis of laser-microdissected paraffin-embedded tissues: (1) Stage-related protein candidates upon non-metastatic lung adenocarcinoma

We used formalin-fixed paraffin-embedded (FFPE) materials for biomarker discovery in cases of lung cancer using proteomic analysis. We conducted a retrospective global proteomic study in order to characterize protein expression reflecting clinical stages of individual patients with stage I lung adenocarcinoma without lymph node involvement (n = 7). In addition, we studied more advanced stage IIIA with spread to lymph nodes (n = 6), because the degree of lymph node involvement is the most important factor for staging. FFPE sections of cancerous lesions resected surgically from patients with well-characterized clinical history were subjected to laser microdissection (LMD) followed by Liquid Tissue? solubilization and digestion trypsin. Spectral counting was used to measure the amounts of proteins identified by shotgun liquid chromatography (LC)/tandem mass spectrometry (MS/MS). More than 500 proteins were identified from IA and IIIA cases, and non-parametric statistics showed that 81 proteins correlated significantly with stage IA or IIIA. A subset of those proteins were verified by multiple-reaction monitoring mass spectrometric quantitation (MRM assay), described in other paper in this issue. These results demonstrated the technical feasibility of a global proteomic study using clinically well documented FFPE sections, and its possible utility for detailed retrospective disease analyses in order to improve therapeutic strategy.     

Unique Protein Profiles of Extracellular Vesicles as Diagnostic Biomarkers for Early and Advanced Non‐Small Cell Lung Cancer

Extracellular vesicles (EVs) mediate intercellular communication via transferring proteins and other biological molecules and have been recently investigated as biomarkers of disease. Sensitive and specific biomarkers are required for lung cancer diagnosis and prognosis. The present study screens for abnormal EV proteins in non‐small cell lung cancer (NSCLC) using a quantitative proteomics strategy involving LC‐MS/MS to identify ideal biomarkers for NSCLC diagnosis. EVs are enriched from the sera of early and advanced NSCLC patients and healthy controls and from cell culture supernatants of lung adenocarcinoma and bronchial epithelial cell lines. In the sera and supernatants, 279 and 632 differentially expressed proteins, respectively, are associated with signaling pathways including extracellular membrane–receptor interaction, focal adhesion, and regulation of the actin cytoskeleton. Thirty‐two EV proteins are identified at the intersection of differentially expressed proteins between the NSCLC groups and cell lines. Based on bioinformatics analysis, in silico immunohistochemical, and PRM verification, fibronectin is selected for following in vitro studies and validation with an independent cohort. Fibronectin on EVs is estimated to perform well in the diagnosis of NSCLC patients based on AUC, showing great potential for clinical use and demonstrating the efficacy of this method for EV‐associated biomarker screening.