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.

Serum fibronectin distinguishes the early stages of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death, necessitating the discovery of serum markers for its early detection. In this study, a total of 180 serum samples from liver cirrhosis (LC), hepatocellular carcinoma (HCC) patients and paired samples of HCC patients who recovered (Recovery) were analyzed by multiple reaction monitoring-mass spectrometry (MRM-MS) to verify biomarkers. The three-fold crossvalidation was repeated 100 times in the training and test sets to evaluate statistical significance of 124 candidate proteins. This step resulted in 2 proteins that had an area under the receiver operating curve (AUROC) values ≥0.800 in the training (n = 90) and test sets (n = 90). Specifically, fibronectin (FN1, WCGTTQNYDADQK), distinguished HCC from LC patients, with an AUROC value of 0.926 by logistic regression. A FN1 protein was selected for validation in an independent sample (n = 60) using enzyme-linked immunosorbent assay (ELISA). The combination of alpha-fetoprotein (AFP) and FN1 improved the diagnostic performance and differentiated HCC patients with normal AFP levels. Our study has examined candidate markers for the benign disease state and malignancy and has followed up on the consequent recovery. Thus, improvement in the early detection of HCC by a 2-marker panel (AFP + FN1) might benefit HCC patients.

A quantitative analytical method for PIVKA-II using multiple reaction monitoring-mass spectrometry for early diagnosis of hepatocellular carcinoma.

Prothrombin induced by vitamin K absence-II (PIVKA-II) is an effective tumor marker for hepatocellular carcinoma (HCC). We have developed a novel targeted mass spectrometric (MS) assay for quantifying PIVKA-II in human serum. The ideal signature peptide was selected to measure PIVKA-II concentrations on a triple quadrupole (QqQ) mass spectrometer, and the chromatography gradient was optimized for the peptide separation to minimize elution interference. Using multiple reaction monitoring-mass spectrometry (MRM-MS), good linearity (R 2 = 0.9988) was obtained for PIVKA-II over a range of 3 orders. We achieved a limit of detection (LOD) of 0.45 nM (31.72 ng/mL), a limit of quantification (LOQ) of 0.93 nM (65.31 ng/mL), a lower limit of quantification (LLOQ) of 0.49 nM (34.32 ng/mL), and an upper limit of quantification (ULOQ) of 1000.00 nM (70,037.00 ng/mL). The intra-day and inter-day precisions were within ±14.96%, and the accuracy ranged from 87.66 to 114.29% for QC samples at four concentrations. Compared with an established immunoassay, the correlation (R = 0.8335) was good for the measurements of PIVKA-II concentrations. This method was successfully applied to the analysis of clinical samples for normal control (n = 50), chronic hepatitis (n = 50), liver cirrhosis (n = 50), HCC (n = 50), and recovery (n = 50) serum. Graphical Abstract MRM-MS assay development for determining concentration of PIVKA-II in serum and a comparison between MRM-MS assay and immunoassay with high correlation.

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study.

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.