Identification of IgM as a contaminant in lectin-FLISA assays for HCC detection

Spatial omics-based machine learning algorithms for the early detection of hepatocellular carcinoma

BACKGROUND

Worldwide, hepatocellular carcinoma (HCC) is the second most lethal cancer, although early-stage HCC is amenable to curative treatment and can facilitate long-term survival. Early detection has proved difficult, as proteomics, transcriptomics, and genomics have been unable to discover suitable biomarkers.

METHODS

To find new biomarkers of HCC, we utilized a spatial omics N-glycan imaging method to identify altered glycosylation in cancer tissue (n = 53) and in paired serum of individuals with HCC (n = 23). Glycoproteomics identified the glycoproteins carrying these N-glycan structures, and we utilized an antibody array-based glycan imaging method to examine all the N-glycans associated with the identified glycoproteins. N-glycans from the examined glycoproteins were used to create machine learning algorithms, which were tested in a case-control sample set of 100 patients with cirrhosis and HCC and 101 matched patients with cirrhosis alone.

RESULTS

Spatial glycan imaging identifies thirteen branched, fucosylated, and high mannose glycans as altered in HCC tissue and in matched patient serum. Glycoproteomics identifies over 50 proteins containing these changes, of which sixteen glycoproteins were selected for further testing in an independent patient set. Algorithms using a combination of glycan and glycoproteins accurately differentiate early-stage and all HCC from cirrhosis with AUROC values of 0.88-0.97.

CONCLUSIONS

In conclusion, we present the development and application of a new biomarker platform, which can identify effective biomarkers for the early detection of HCC. This platform may also apply to other diseases, in which changes in N-linked glycosylation are known to occur.

Differential Protease Specificity by Collagenase as a Novel Approach to Serum Proteomics That Includes Identification of Extracellular Matrix Proteins without Enrichment

Circulating extracellular matrix (ECM) proteins are serological biomarkers of interest due to their association with pathologies involving disease processes such as fibrosis and cancers. In this study, we investigate the potential for serum biomarker research using differential protease specificity (DPS), leveraging alternate protease specificity as a targeting mechanism to selectively digest circulating ECM protein serum proteins. A proof-of-concept study is presented using serum from patients with cirrhotic liver or hepatocellular carcinoma. The approach uses collagenase DPS for digestion of deglycosylated serum and liquid-chromatography-trapped ion mobility-tandem mass spectrometry (LC-TIMS-MS/MS) to enhance the detection of ECM proteins in serum. It requires no sample enrichment and minimizes the albumin average precursor intensity readout to less than 1.2%. We further demonstrate the capabilities for using the method as a high-throughput matrix-assisted laser/desorption ionization mass spectrometry (MALDI-MS) assay coupled with reference library searching. A goal is to improve the depth and breadth of biofluid proteomics for noninvasive assays.

Systematic review: Glycomics as diagnostic markers for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer with one of the highest cancer-related mortality rates worldwide. Early diagnosis is crucial for improving the therapeutic options and reducing the disease-related mortality.

AIM

To investigate serum N-glycomics as diagnostic markers for HCC.

METHODS

We performed a comprehensive search in PubMed, EMBASE, Web of Science and Scopus through August 17, 2023. Eligible studies assessed the potential use of serum N-glycomics as diagnostic biomarkers for HCC. Study selection, data extraction and quality assessment were performed by two independent reviewers.

RESULTS

Of the 48 articles included, 11 evaluated the utility of N-glycomics for the diagnosis of HCC in whole serum while the remaining articles focused on specific protein glycoforms or protein levels. Of these specific proteins, haptoglobin, alpha-fetoprotein (AFP), kininogen (Kin), α-1-antitrypsin and Golgi protein 73 (GP73) were the most frequently studied. Increased levels of fucosylation and branching presented as the most prevalent post-translational modifications of glycoproteins in patients with HCC compared to controls. Notably, glycomics-based biomarkers may provide a clinical benefit for the diagnosis of early HCC, as several algorithms achieved AUCs between 0.92-0.97. However, these were based on single studies with limited sample sizes and should therefore be validated.

CONCLUSIONS

Alterations in serum N-glycomics, characterised by increased levels of fucosylation and branching, have potential as diagnostic biomarkers for HCC. Optimisation of study design, patient selection and analysing techniques are needed before clinical implementation will be possible.

A Biomarker Panel Based upon AFP, Fucosylated Kininogen and PEG-Precipitated IgG Is Highly Accurate for the Early Detection Hepatocellular Carcinoma in Patients with Cirrhosis in Phase II and Phase III Biomarker Evaluation

We have previously identified alterations in glycosylation on serum proteins from patients with HCC and developed plate-based assays using lectins to detect the change in glycosylation. However, heterophilic antibodies, which increase with non-malignant liver disease, compromised these assays. To address this, we developed a method of polyethylene glycol (PEG) precipitation that removed the contaminating IgG and IgM but allowed for the lectin detection of the relevant glycoprotein. We found that this PEG-precipitated material itself could differentiate between cirrhosis and HCC. In the analysis of three training cohorts and one validation cohort, consisting of 571 patients, PEG-IgG had AUC values that ranged from 0.713 to 0.810. In the validation cohort, which contained samples from patients at a time of 1-6 months prior to HCC detection or 7+ months prior to detection, the AUC of this marker remained consistent (0.813 and 0.846, respectively). When this marker was incorporated into a biomarker algorithm that also consisted of AFP and fucosylated kininogen, the AUROC increased to 0.816-0.883 in the training cohort and was 0.909 in the external validation cohort. Biomarker performance was also examined though the analysis of partial ROC curves, at false positive values less than 10% (90-ROC), ≤20% (80-ROC) or ≤30% (70-ROC), which highlighted the algorithm's improvement over the individual markers at clinically relevant specificity values.

Biomarkers for the Early Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and the cancer with the fastest increase in mortality in the United States, with more than 39,000 cases and 29,000 deaths in 2018. As with many cancers, survival is significantly improved by early detection. The median survival of patients with early HCC is >60 months but <15 months when detected at an advanced stage. Surveillance of at-risk patients improves outcome, but fewer than 20% of those at risk for HCC receive surveillance, and current surveillance strategies have limited sensitivity and specificity. Ideally, blood-based biomarkers with adequate sensitivity or specificity would be available for early detection of HCC; however, the most commonly used biomarker for HCC, alpha-fetoprotein, has inadequate performance characteristics. There are several candidate serum proteomic, glycomic, and genetic markers that have gone through early stages of biomarker validation and have shown promise for the early detection of HCC, but these markers require validation in well-curated cohorts. Ongoing prospective cohort studies will permit retrospective longitudinal (phase III biomarker study) validation of biomarkers. In this review, we highlight promising candidate biomarkers and biomarker panels that have completed phase II evaluation but require further validation prior to clinical use.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Biomarker analysis of fucosylated kininogen through depletion of lectin reactive heterophilic antibodies in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) accounts for >700,000 deaths worldwide, largely related to poor rates of diagnosis. Our previous work identified glycoproteins with increased levels of fucosylation in HCC. Plate-based assays to measure this change were compromised by increased levels of heterophilic antibodies with glycan lacking terminal galactose residues, which allowed for increased binding to the lectins used in these assays. To address this issue, we developed a multi-step protein A/G incubation and filtration method to remove the contaminating signal. However, this method was time consuming and expensive so alternative methods were desired. Herein, we describe a simple method relying on PEG precipitation that allows for the removal of IgG and IgM but retention of glycoproteins of interest. This method was tested on three sample sets, two internal and one external. PEG depletion of heterophilic IgG and IgM reduced in the coefficient of variation as observed with the protein A/G filtration method from 26.82% to 7.50% and allowed for the measurement of fucosylated protein. This method allowed for the measurement of fucosylated kininogen, which could serve as a biomarker of HCC. In conclusion, a new and simple method for the depletion of heterophilic IgG and IgM was developed and allowed for the analysis of fucosylated kininogen in patients with liver disease.

Changes in the Glycosylation of Kininogen and the Development of a Kininogen-Based Algorithm for the Early Detection of HCC

Background: Hepatocellular carcinoma (HCC) has the greatest increase in mortality among all solids tumors in the United States related to low rates of early tumor detection. Development of noninvasive biomarkers for the early detection of HCC may reduce HCC-related mortality.Methods: We have developed an algorithm that combines routinely observed clinical values into a single equation that in a study of >3,000 patients from 5 independent sites improved detection of HCC as compared with the currently used biomarker, alpha-fetoprotein (AFP), by 4% to 20%. However, this algorithm had limited benefit in those with AFP <20 ng/mL. To that end, we have developed a secondary algorithm that incorporates a marker, fucosylated kininogen, to improve the detection of HCC, especially in those with AFP <20 ng/mL and early-stage disease.Results: The ability to detect early-stage AFP-negative (AFP <20 ng/mL) HCC increased from 0% (AFP alone) to 89% (for the new algorithm). Glycan analysis revealed that kininogen has several glycan modifications that have been associated with HCC, but often not with specific proteins, including increased levels of core and outer-arm fucosylation and increased branching.Conclusions: An algorithm combining fucosylated kininogen, AFP, and clinical characteristics is highly accurate for early HCC detection.Impact: Our biomarker algorithm could significantly improve early HCC detection and curative treatment eligibility in patients with cirrhosis. Cancer Epidemiol Biomarkers Prev; 26(5); 795-803. ©2017 AACR.