Sensitive New Assay System for Serum Wisteria floribunda Agglutinin-Reactive Ceruloplasmin That Distinguishes Ovarian Clear Cell Carcinoma from Endometrioma

GRable Version 1.0: A Software Tool for Site-Specific Glycoform Analysis With Improved MS1-Based Glycopeptide Detection With Parallel Clustering and Confidence Evaluation With MS2 Information

High-throughput intact glycopeptide analysis is crucial for elucidating the physiological and pathological status of the glycans attached to each glycoprotein. Mass spectrometry-based glycoproteomic methods are challenging because of the diversity and heterogeneity of glycan structures. Therefore, we developed an MS1-based site-specific glycoform analysis method named "Glycan heterogeneity-based Relational IDentification of Glycopeptide signals on Elution profile (Glyco-RIDGE)" for a more comprehensive analysis. This method detects glycopeptide signals as a cluster based on the mass and chromatographic properties of glycopeptides and then searches for each combination of core peptides and glycan compositions by matching their mass and retention time differences. Here, we developed a novel browser-based software named GRable for semi-automated Glyco-RIDGE analysis with significant improvements in glycopeptide detection algorithms, including "parallel clustering." This unique function improved the comprehensiveness of glycopeptide detection and allowed the analysis to focus on specific glycan structures, such as pauci-mannose. The other notable improvement is evaluating the "confidence level" of the GRable results, especially using MS2 information. This function facilitated reduced misassignment of the core peptide and glycan composition and improved the interpretation of the results. Additional improved points of the algorithms are "correction function" for accurate monoisotopic peak picking; one-to-one correspondence of clusters and core peptides even for multiply sialylated glycopeptides; and "inter-cluster analysis" function for understanding the reason for detected but unmatched clusters. The significance of these improvements was demonstrated using purified and crude glycoprotein samples, showing that GRable allowed site-specific glycoform analysis of intact sialylated glycoproteins on a large-scale and in-depth. Therefore, this software will help us analyze the status and changes in glycans to obtain biological and clinical insights into protein glycosylation by complementing the comprehensiveness of MS2-based glycoproteomics. GRable can be freely run online using a web browser via the GlyCosmos Portal (https://glycosmos.org/grable).

Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is widely recognized as the most lethal gynecological malignancy; however, its early-stage detection remains a considerable clinical challenge. To address this, we have introduced a new method, named Comprehensive Serum Glycopeptide Spectral Analysis (CSGSA), which detects early-stage cancer by combining glycan alterations in serum glycoproteins with tumor markers. We detected 1712 glycopeptides using liquid chromatography-mass spectrometry from the sera obtained from 564 patients with EOC and 1149 controls across 13 institutions. Furthermore, we used a convolutional neural network to analyze the expression patterns of the glycopeptides and tumor markers. Using this approach, we successfully differentiated early-stage EOC (Stage I) from non-EOC, with an area under the curve (AUC) of 0.924 in receiver operating characteristic (ROC) analysis. This method markedly outperforms conventional tumor markers, including cancer antigen 125 (CA125, 0.842) and human epididymis protein 4 (HE4, 0.717). Notably, our method exhibited remarkable efficacy in differentiating early-stage ovarian clear cell carcinoma from endometrioma, achieving a ROC-AUC of 0.808, outperforming CA125 (0.538) and HE4 (0.557). Our study presents a promising breakthrough in the early detection of EOC through the innovative CSGSA method. The integration of glycan alterations with cancer-related tumor markers has demonstrated exceptional diagnostic potential.

Determination of copper and other trace elements in serum samples from patients with biliary tract cancers: prospective noninterventional nonrandomized clinical study protocol

BACKGROUND

Biliary tract cancers (BTCs) are usually diagnosed at an advanced stage, when the disease is incurable. Currently used tumor biomarkers have limited diagnostic value for BTCs, so there is an urgent need for sensitive and specific biomarkers for their earlier diagnosis. Deregulation of the homeostasis of trace elements is involved in the carcinogenesis of different cancers, including BTCs. The objective of the study is to determine/compare the total concentrations of copper (Cu), zinc (Zn) and iron (Fe) and the proportions of free Cu and Cu bound to ceruloplasmin (Cp) and the isotopic ratio of 65Cu/63Cu in serum samples from healthy volunteers and cancer patients using inductively coupled plasma-mass spectrometry-based methods (ICP-MS).

PATIENTS AND METHODS

In this prospective, noninterventional, nonrandomized study 20 patients and 20 healthy volunteers will be enrolled to identify serum Cu, Zn and Fe levels, Cu isotopic fractionation as a predictive biomarker of response to systemic therapy of BTCs, which will be evaluated by computed tomography. Newly developed analytical methods based on ICP-MS will be applied to metal-based biomarker research in oncology.

CONCLUSIONS

In the study the comparison of the total concentration of selected trace elements, the proportion of free Cu and Cu bound to Cp and the isotopic ratio of 65Cu/63Cu in serum samples from healthy volunteers and cancer patients will be conducted to provide the foundation for the development of a BTC cancer screening methodology and the data on their usability as a potential predictive biomarker for BTCs of response to systemic therapy.

Development and Evaluation of a Robust Sandwich Immunoassay System Detecting Serum WFA-Reactive IgA1 for Diagnosis of IgA Nephropathy

Aberrant glycosylation of IgA1 is involved in the development of IgA nephropathy (IgAN). There are many reports of IgAN markers focusing on the glycoform of IgA1. None have been clinically applied as a routine test. In this study, we established an automated sandwich immunoassay system for detecting aberrant glycosylated IgA1, using Wisteria floribunda agglutinin (WFA) and anti-IgA1 monoclonal antibody. The diagnostic performance as an IgAN marker was evaluated. The usefulness of WFA for immunoassays was investigated by lectin microarray. A reliable standard for quantitative immunoassay measurements was designed by modifying a purified IgA1 substrate. A validation study using multiple serum specimens was performed using the established WFA-antibody sandwich automated immunoassay. Lectin microarray results showed that WFA specifically recognized N-glycans of agglutinated IgA1 in IgAN patients. The constructed IgA1 standard exhibited a wide dynamic range and high reactivity. In the validation study, serum WFA-reactive IgA1 (WFA+-IgA1) differed significantly between healthy control subjects and IgAN patients. The findings indicate that WFA is a suitable lectin that specifically targets abnormal agglutinated IgA1 in serum. We also describe an automated immunoassay system for detecting WFA+-IgA1, focusing on N-glycans.