Proteome Multimarker Panel for the Early Detection of Hepatocellular Carcinoma: Multicenter Derivation, Validation, and Comparison.

Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case-control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring-mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both P < 0.05), test (0.894 and 0.893 vs 0.593, both P < 0.05), and confirmation sets (0.961 and 0.937 vs 0.806, both P < 0.05) in detecting small single HCC. The combination and standalone MRM-MS panels had significantly higher AUROC values than the GALAD score (0.945 and 0.931 vs 0.829, both P < 0.05). Our proteome 17-protein multimarker panel distinguished HCC patients from high-risk controls and had high accuracy in the early detection of HCC.

Comparison of Fucose-Specific Lectins to Improve Quantitative AFP-L3 Assay for Diagnosing Hepatocellular Carcinoma Using Mass Spectrometry.

Glycoproteins have many important biological functions. In particular, aberrant glycosylation has been observed in various cancers, such as liver cancer. A well-known glycoprotein biomarker is α-fetoprotein (AFP), a surveillance biomarker for hepatocellular carcinoma (HCC) that contains a glycosylation site at asparagine 251. The low diagnostic sensitivity of AFP led researchers to focus on AFP-L3, which has the same sequence as conventional AFP but contains a fucosylated glycan. AFP-L3 has high affinity for Lens culinaris agglutinin (LCA) lectin, prompting many groups to use it for detecting AFP-L3. However, a few studies have identified more effective lectins for fractionating AFP-L3. In this study, we compared the amounts of enriched AFP-L3 with five fucose-specific lectins─LCA, Lotus tetragonolobus lectin (LTL), Ulex europaeus agglutinin I (UEA I), Aleuria aurantia lectin (AAL), and Aspergillus oryzae lectin (AOL)─to identify better lectins and improve HCC diagnostic assays using mass spectrometry (MS). Our results indicate that LTL was the most effective lectin for capturing AFP-L3 species, yielding approximately 3-fold more AFP-L3 than LCA from the same pool of HCC serum samples. Thus, we recommend the use of LTL for AFP-L3 assays, given its potential to improve the diagnostic sensitivity in patients having limited results by conventional LCA assay. The MS data have been deposited to the PeptideAtlas (PASS01752).

Proteomic profiling of postmortem prefrontal cortex tissue of suicide completers.

Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography-tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.

Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry.

Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring-mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.

Inclusive Quantification Assay of Serum Des-γ-Carboxyprothrombin Proteoforms for Hepatocellular Carcinoma Surveillance by Targeted Mass Spectrometry.

Hepatocellular carcinoma (HCC) is a malignant cancer with one of the highest mortality rates. Des-γ-carboxyprothrombin (DCP) is an HCC serologic surveillance marker that can complement the low sensitivity of alpha-fetoprotein (AFP). DCP exists in the blood as a mixture of proteoforms from an impaired carboxylation process at glutamic acid (Glu) residues within the N-terminal domain. The heterogeneity of DCP may affect the accuracy of measurements because DCP levels are commonly determined using an immunoassay that relies on antibody reactivity to an epitope in the DCP molecule. In this study, we aimed to improve the DCP measurement assay by applying a mass spectrometry (MS)-based approach for a more inclusive quantification of various DCP proteoforms. We developed a multiple-reaction monitoring-MS (MRM-MS) assay to quantify multiple noncarboxylated peptides included in the various des-carboxylation states of DCP. We performed the MRM-MS assay in 300 patients and constructed a robust diagnostic model that simultaneously monitored three noncarboxylated peptides. The MS-based quantitative assay for DCP had reliable surveillance power, which was evident from the area under the receiver operating characteristic curve (AUROC) values of 0.874 and 0.844 for the training and test sets, respectively. It was equivalent to conventional antibody-based quantification, which had AUROC values at the optimal cutoff (40 mAU/mL) of 0.743 and 0.704 for the training and test sets, respectively. The surveillance performance of the MS-based DCP assay was validated using an independent validation set consisting of 318 patients from an external cohort, resulting in an AUROC value of 0.793. Conclusion: Due to cost effectiveness and high reproducibility, the quantitative DCP assay using the MRM-MS method is superior to antibody-based quantification and has equivalent performance.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry.

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Development and Multiple Validation of the Protein Multi-marker Panel for Diagnosis of Pancreatic Cancer.

PURPOSE: To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance. EXPERIMENTAL DESIGN: A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls. RESULTS: A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets. CONCLUSIONS: This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

Clinical Application of Multiple Reaction Monitoring-Mass Spectrometry to Human Epidermal Growth Factor Receptor 2 Measurements as a Potential Diagnostic Tool for Breast Cancer Therapy.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) is often overexpressed in breast cancer and correlates with a worse prognosis. Thus, the accurate detection of HER2 is crucial for providing the appropriate measures for patients. However, the current techniques used to detect HER2 status, immunohistochemistry and fluorescence in situ hybridization (FISH), have limitations. Specifically, FISH, which is mandatory for arbitrating 2+ cases, is time-consuming and costly. To address this shortcoming, we established a multiple reaction monitoring-mass spectrometry (MRM-MS) assay that improves on existing methods for differentiating HER2 status. METHODS: We quantified HER2 expression levels in 210 breast cancer formalin-fixed paraffin-embedded (FFPE) tissue samples by MRM-MS. We aimed to improve the accuracy and precision of HER2 quantification by simplifying the sample preparation through predicting the number of FFPE slides required to ensure an adequate amount of protein and using the expression levels of an epithelial cell-specific protein as a normalization factor when measuring HER2 expression levels. RESULTS: To assess the correlation between MRM-MS and IHC/FISH data, HER2 quantitative data from MRM-MS were divided by the expression levels of junctional adhesion molecule A, an epithelial cell-specific protein, prior to statistical analysis. The normalized HER2 amounts distinguished between HER2 2+/FISH-negative and 2+/FISH-positive groups (AUROC = 0.908), which could not be differentiated by IHC. In addition, all HER2 status were discriminated by MRM-MS. CONCLUSIONS: This MRM-MS assay yields more accurate HER2 expression levels relative to immunohistochemistry and should help to guide clinicians toward the proper treatment for breast cancer patients, based on their HER2 expression.

Web portal for analytical validation of MRM-MS assay abided with integrative multinational guidelines.

Multiple reaction monitoring-mass spectrometry became a mainstream method for quantitative proteomics, which made the validation of a method and the analyzed data important. In this portal for validation of the MRM-MS assay, we developed a website that automatically evaluates uploaded MRM-MS data, based on biomarker assay guidelines from the European Medicines Agency, the US Food & Drug Administration, and the Korea Food & Drug Administration. The portal reads a Skyline output file and produces the following results-calibration curve, specificity, sensitivity, carryover, precision, recovery, matrix effect, recovery, dilution integrity, stability, and QC-according to the standards of each independent agency. The final tables and figures that pertain to the 11 evaluation categories are displayed in an individual page. Spring boot was used as a framework for development of the webpage, which follows MVC Pattern. JSP, HTML, XML, and Java Script were used to develop the webpage. A server was composed of Apache Tomcat, MySQL. Input files were skyline-derived output files (csv file), and each files were organized by specific columns in order. SQL, JAVA were interworked to evaluate all the categories and show the results. Method Validation Portal can be accessed via any kind of explorer from https://pnbvalid.snu.ac.kr.

Proteome Multimarker Panel With Multiple Reaction Monitoring-Mass Spectrometry for Early Detection of Hepatocellular Carcinoma.

There is an urgent need for new biomarkers that address the shortcomings of current screening methods which fail to detect a large proportion of cases with hepatocellular carcinoma (HCC) at early stage. To develop a robust, multiple-biomarker panel based on multiple reaction monitoring-mass spectrometry with high performance in detecting early-stage HCC within at-risk populations. In the discovery set, 150 samples were analyzed to identify candidate biomarkers. The resulting list of candidates was tested in the training set (713 samples) to establish a multimarker panel, which was evaluated in the validation set (305 samples). We identified 385 serum HCC biomarker candidates in the discovery set and developed a multimarker panel consisting of 28 peptides that best differentiated HCC from controls. The area under the receiver operating characteristic curve of multimarker panel was significantly higher than alpha-fetoprotein (AFP) in the training (0.976 vs. 0.804; P < 0.001) and validation (0.898 vs. 0.778; P < 0.001) sets. In the validation set, this multimarker panel, compared with AFP, showed significantly greater sensitivity (81.1% vs. 26.8%; P < 0.001) and lower specificity (84.8% vs. 98.8%; P < 0.001) in detecting HCC cases. Combining AFP with the multimarker panel did not significantly improve the area under the receiver operating characteristic curve compared with the panel alone in the training (0.981 vs. 0.976; P = 0.37) and validation set (0.906 vs. 0.898; P = 0.75). Conclusion: The multiple reaction monitoring-mass spectrometry multimarker panel consisting of 28 peptides discriminates HCC cases from at-risk controls with high performance and may have potential for clinical application in HCC surveillance.

Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics.

Reproducible sample preparation remains a significant challenge in large-scale clinical research using selected reaction monitoring-mass spectrometry (SRM-MS), which enables a highly sensitive multiplexed assay. Although automated liquid-handling platforms have tremendous potential for addressing this issue, the high cost of their consumables is a drawback that renders routine operation expensive. Here we evaluated the performance of a liquid-handling platform in preparing serum samples compared with a standard experiment while reducing the outlay for consumables, such as tips, wasted reagents, and reagent stock plates. A total of 26 multiplex assays were quantified by SRM-MS using four sets of 24 pooled human serum aliquots; the four sets used a fixed number (1, 4, 8, or 24) of tips to dispense digestion reagents. This study demonstrated that the use of 4 or 8 tips is comparable to 24 tips (standard experiment), as evidenced by their coefficients of variation: 13.5% (for 4 and 8 tips) versus 12.0% (24 tips). Thus we can save 37% of the total experimental cost compared with the standard experiment, maintaining nearly equivalent reproducibility. The routine operation of cost-effective liquid-handling platforms can enable researchers to process large-scale samples with high throughput, adding credibility to their findings by minimizing human error.

Clinical Assay for AFP-L3 by Using Multiple Reaction Monitoring-Mass Spectrometry for Diagnosing Hepatocellular Carcinoma.

BACKGROUND: Lens culinaris agglutinin-reactive fraction of α-fetoprotein (AFP-L3) is a serum biomarker for hepatocellular carcinoma (HCC). AFP-L3 is typically measured by liquid-phase binding assay (LiBA). However, LiBA does not always reflect AFP-L3 concentrations because of its low analytical sensitivity. Thus, we aimed to develop an analytically sensitive multiple reaction monitoring-mass spectrometry (MRM-MS) assay to quantify AFP-L3 in serum. METHODS: The assay entailed the addition of a stable isotope-labeled internal standard protein analog, the enrichment of AFP using a monoclonal antibody, the fractionation of AFP-L3 using L. culinaris agglutinin lectin, deglycosylation, trypsin digestion, online desalting, and MRM-MS analysis. The performance of the MRM-MS assay was compared with that of LiBA in 400 human serum samples (100 chronic hepatitis, 100 liver cirrhosis, and 200 HCC). Integrated multinational guidelines were followed to validate the assay for clinical implementation. RESULTS: The lower limit of quantification of the MRM-MS assay (0.051 ng/mL) for AFP-L3 was less than that of LiBA (0.300 ng/mL). Thus, AFP-L3, which was not observed by LiBA in HCC samples (n = 39), was detected by the MRM-MS assay, improving the clinical value of AFP-L3 as a biomarker by switching to a more analytical sensitive platform. The method was validated, meeting all the criteria in integrated multinational guidelines. CONCLUSIONS: Because of the lower incidence of false-negative findings, the MRM-MS assay is more suitable than LiBA for early detection of HCC.

Fully validated SRM-MS-based method for absolute quantification of PIVKA-II in human serum: Clinical applications for patients with HCC.

Protein induced by vitamin K absence or antagonist-II (PIVKA-II), an abnormal form of prothrombin, is used as a serological biomarker that aids in the diagnosis of hepatocellular carcinoma (HCC). PIVKA-II is typically measured by liquid binding assay (LiBA). However, without an internal standard, it is difficult to obtain accurate results. Thus, we aimed to develop a selected reaction monitoring-mass spectrometry (SRM-MS)-based assay to quantify PIVKA-II in serum. Our SRM-MS assay entailed the addition of a protein analog as an internal standard, the enrichment of PIVKA-II using a monoclonal antibody, chymotrypsin digestion, online desalting, and SRM-MS analysis. The performance of the SRM-MS assay was compared with that of LiBA in 400 human serum samples (100 chronic hepatitis, 100 liver cirrhosis, and 200 HCC). Integrated multinational guidelines were followed to validate the assay for clinical implementation. The linearity ranged from 1.28 to 100,000 ng/mL, and the use of a labeled protein analog as an internal standard allowed the error from the sample preparation to be corrected, improving the precision and accuracy. The SRM-MS assay was validated to meet all of the criteria of the compliance with guidelines per the US Food and Drug Administration (FDA), European medicines agency (EMA), Korea FDA (KFDA), and Clinical & Laboratory Standards Institute (CLSI). We have developed and validated a robust and reproducible SRM-MS assay that is superior to the conventional method of distinguishing HCC from noncancer patients, based on PIVKA-II levels, and satisfies clinical standards. This method has potential applications in quantifying other protein biomarkers.

Combination of statistical methods and Fourier transform ion cyclotron resonance mass spectrometry for more comprehensive, molecular-level interpretations of petroleum samples.

Complex petroleum mass spectra obtained by Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS) were successfully interpreted at the molecular level by applying principle component analysis (PCA) and hierarchical clustering analysis (HCA). A total of 40 mass spectra were obtained from 20 crude oil samples using both positive and negative atmospheric pressure photoionization (APPI). Approximately 400,000 peaks were identified at the molecular level. Conventional data analyses would have been impractical with so much data. However, PCA grouped samples into score plots based on their molecular composition. In this way, the overall compositional difference between samples could be easily displayed and identified by comparing score and loading plots. HCA was also performed to group and compare samples based on selected peaks that had been grouped by PCA. Subsequent heat map analyses revealed detailed compositional differences among grouped samples. This study demonstrates a promising new approach for studying multiple, complex petroleum samples at the molecular level.