Breast cancer diagnosis by analysis of serum N-glycans using MALDI-TOF mass spectroscopy

N-glycan signatures identified in the serum from biliary tract cancer patients: Association with clinical diagnosis and prognosis

BACKGROUND

Changes in the expression of genes related to glycosyltransferases may lead to alterations in N-glycan structure abundance, potentially acting as markers for diagnosis and prognosis in biliary tract cancer (BTC).

METHODS

This study was divided into cross-sectional and longitudinal approaches. The cross-sectional study included 316 BTC and 301 non-BTC. Propensity score matching was applied to adjust for sex and age differences between BTC and non-BTC. Univariate and multivariate logistic regression identified independent risk factors for BTC and constructed the BTC-G model. The ROC curve was used to validate the diagnostic performance of BTC-G. Longitudinal follow-up studies included postoperative (N = 50) and immunotherapy (N = 43) follow-up cohorts. Cox regression analysis identified N-glycan structures impacting BTC prognosis postoperative and immunotherapy, with further confirmation through Kaplan-Meier curves.

RESULTS

Univariate and multivariate analyses identified Peak3 (OR: 0.790, 95% CI: 0.658-0.949), Peak9 (OR: 1.646, 95% CI: 1.409-1.922), and Peak9p (OR: 2.467, 95% CI: 1.267-4.804) as independent BTC risk factors, leading to the creation of the BTC-G. The ROC curve confirmed that BTC-G performed well in training (AUC: 0.753, 95% CI: 0.703-0.799), validation (AUC: 0.811, 95% CI: 0.740-0.870), and CA19-9 negative cohorts (AUC: 0.717, 95% CI: 0.664-0.767). Cox regression analysis and Kaplan-Meier curves established that Peak12 (HR: 5.578, 95% CI: 1.145-27.170) and Peak11 (HR: 1.104, 95% CI: 0.611-1.994) are independent risk factors for BTC prognosis following surgery and immunotherapy, respectively.

CONCLUSIONS

Our NGFP technology supplements BTC diagnostics, distinguishing survival and recurrence subtypes for postoperative and immunotherapy, thereby supporting the development of treatment strategies.

Serum N-glycomic profiling identifies candidate biomarker panels for assessing coronary artery stenosis severity

Stenosis severity may escalate over the course of coronary artery disease (CAD), increasing the risk of death for the patient. Conventionally, the assessment of stenosis degree relies on invasive coronary angiography (ICA), an invasive examination unsuitable for patients in poor physical condition or those with contrast allergies and one that imposes a psychological burden on patients. Although abnormal serum N-glycan profiles have exhibited robust associations with various cardiovascular diseases, including CAD, their potential in diagnosing CAD stenosis remains to be determined. In this study, we performed a comprehensive analysis of serum N-glycome from 132 patients who underwent ICA and 27 healthy controls using MALDI-TOF-mass spectrometry. The patients who underwent ICA examination were categorized into four groups based on stenosis severity: no/mild/moderate/severe stenosis. Twenty-seven N-glycans were directly quantified, and 47 derived glycan traits were obtained. Notably, among these 74 glycan features, 18 exhibited variations across the study groups. Using a combination of least absolute shrinkage and selection operator and logistic regression analyses, we developed five diagnostic models for recognizing stenosis degree. Our results suggested that alterations in serum N-glycosylation modifications might be valuable for identifying stenosis degree and monitoring disease progression in individuals with CAD. It is expected to offer a noninvasive alternative for those who could not undergo ICA because of various reasons. However, the diagnostic potential of serum N-glycan panels as biomarkers requires multicenter, large cohort validation in the future.

Applications and continued evolution of glycan imaging mass spectrometry

Glycosylation is an important posttranslational modifier of proteins and lipid conjugates critical for the stability and function of these macromolecules. Particularly important are N-linked glycans attached to asparagine residues in proteins. N-glycans have well-defined roles in protein folding, cellular trafficking and signal transduction, and alterations to them are implicated in a variety of diseases. However, the non-template driven biosynthesis of these N-glycans leads to significant structural diversity, making it challenging to identify the most biologically and clinically relevant species using conventional analyses. Advances in mass spectrometry instrumentation and data acquisition, as well as in enzymatic and chemical sample preparation strategies, have positioned mass spectrometry approaches as powerful analytical tools for the characterization of glycosylation in health and disease. Imaging mass spectrometry expands upon these strategies by capturing the spatial component of a glycan's distribution in-situ, lending additional insight into the organization and function of these molecules. Herein we review the ongoing evolution of glycan imaging mass spectrometry beginning with widely adopted tissue imaging approaches and expanding to other matrices and sample types with potential research and clinical implications. Adaptations of these techniques, along with their applications to various states of disease, are discussed. Collectively, glycan imaging mass spectrometry analyses broaden our understanding of the biological and clinical relevance of N-glycosylation to human disease.

Glycosylation Alterations in Cancer Cells, Prognostic Value of Glycan Biomarkers and Their Potential as Novel Therapeutic Targets in Breast Cancer

Although we are lately witnessing major improvements in breast cancer treatment and patient outcomes, there is still a significant proportion of patients not receiving efficient therapy. More precisely, patients with triple-negative breast cancer or any type of metastatic disease. Currently available prognostic and therapeutic biomarkers are not always applicable and oftentimes lack precision. The science of glycans is a relatively new scientific approach to better characterize malignant transformation and tumor progression. In this review, we summarize the most important information about glycosylation characteristics in breast cancer cells and how different glycoproteins and enzymes involved in glycosylation could serve as more precise biomarkers, as well as new therapeutic targets.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.

Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints

Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction (P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

Breast cancer glycan biomarkers: their link to tumour cell metabolism and their perspectives in clinical practice

INTRODUCTION

Breast cancer (BCa) is the most common cancer type diagnosed in women and 5^th most common cause of deaths among all cancer deaths despite the fact that screening program is at place. This is why novel diagnostics approaches are needed in order to decrease number of BCa cases and disease mortality.

AREAS COVERED

In this review paper, we aim to cover some basic aspects regarding cellular metabolism and signalling in BCa behind altered glycosylation. We also discuss novel exciting discoveries regarding glycan-based analysis, which can provide useful information for better understanding of the disease. The final part deals with clinical usefulness of glycan-based biomarkers and the clinical performance of such biomarkers is compared to already approved BCa biomarkers and diagnostic tools based on imaging.

EXPERT OPINION

Recent discoveries suggest that glycan-based biomarkers offer high accuracy for possible BCa diagnostics in blood, but also for better monitoring and management of BCa patients. The review article was written using Web of Science search engine to include articles published between 2019 and 2021.

Apo-H (beta-2-glycoprotein) intact N-glycan analysis by MALDI-TOF-MS using sialic acid derivatization

Apo-H is a plasma glycoprotein. Nearly 19% of the molecular weight of this protein is composed of glycans. Up- and down-regulation and structural changes in protein glycans provide diagnostic value for disease detection. Here, an efficient, sensitive, and optimized method is developed for Apo-H N-glycans analysis by MALDI-TOF-MS in positive mode. This bioanalytical method includes sample preparation, sample purification, and detection. An Apo-H enrichment method is developed using standard proteins by anti-Apo-H beads followed by enrichment from plasma samples. SDS-PAGE confirms the Apo-H protein enrichment, which is further verified by LC-MS/MS analysis. The lower ionization efficiency of sialylated glycan hampers their analysis by MALDI-MS. For this, stabilization of sialic acids is done by selective derivatization of carboxyl groups to differentiate between α(2,3)- and α(2,6)-linked sialic acids. Glycans are further purified by HILIC-SPE and analyzed by MALDI-MS. Several branched bi- and tri-antennary glycans with fucosylation and sialylation are identified. The reproducibility of the developed method is tested by analyzing multiple replicates of human plasma, where the same glycans are consistently identified. This method could be applied for the Apo-H glycan profiling of large clinical cohorts for diagnostic purposes.

Insights into Bioinformatic Applications for Glycosylation: Instigating an Awakening towards Applying Glycoinformatic Resources for Cancer Diagnosis and Therapy

Glycosylation plays a crucial role in various diseases and their etiology. This has led to a clear understanding on the functions of carbohydrates in cell communication, which eventually will result in novel therapeutic approaches for treatment of various disease. Glycomics has now become one among the top ten technologies that will change the future. The direct implication of glycosylation as a hallmark of cancer and for cancer therapy is well established. As in proteomics, where bioinformatics tools have led to revolutionary achievements, bioinformatics resources for glycosylation have improved its practical implication. Bioinformatics tools, algorithms and databases are a mandatory requirement to manage and successfully analyze large amount of glycobiological data generated from glycosylation studies. This review consolidates all the available tools and their applications in glycosylation research. The achievements made through the use of bioinformatics into glycosylation studies are also presented. The importance of glycosylation in cancer diagnosis and therapy is discussed and the gap in the application of widely available glyco-informatic tools for cancer research is highlighted. This review is expected to bring an awakening amongst glyco-informaticians as well as cancer biologists to bridge this gap, to exploit the available glyco-informatic tools for cancer.

Potential of MALDI-TOF-based serum N-glycan analysis for the diagnosis and surveillance of breast cancer

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based serum N-glycan analysis has gained acknowledgment for the diagnosis of breast cancer in recent years. In this study, the possibilities of expanding its application for breast cancer management and surveillance were discovered and evaluated. First, a novel MALDI-TOF platform, IDsys RT, was confirmed to be effective for breast cancer analysis, showing a maximum area under the curve of 0.91. Multiple N-glycan markers were identified and validated using this process, and they were found to be applicable for differentiating recurring breast cancer samples from healthy control or ordinary breast cancer samples. Recurrence samples were especially distinct from non-recurrence samples when N-glycan signatures were sampled in multiple time points and monitored via MALDI-TOF, throughout the therapy. These results suggested the feasibility of MALDI-TOF-based N-glycan analysis for tracking the molecular signatures of breast cancer and predicting recurrence.

Mass spectrometry in clinical glycomics: The path from biomarker identification to clinical implementation

Over the past decades, the genome and proteome have been widely explored for biomarker discovery and personalized medicine. However, there is still a large need for improved diagnostics and stratification strategies for a wide range of diseases. Post-translational modification of proteins by glycosylation affects protein structure and function, and glycosylation has been implicated in many prevalent human diseases. Numerous proteins for which the plasma levels are nowadays evaluated in clinical practice are glycoproteins. While the glycosylation of these proteins often changes with disease, their glycosylation status is largely ignored in the clinical setting. Hence, the implementation of glycomic markers in the clinic is still in its infancy. This is for a large part caused by the high complexity of protein glycosylation itself and of the analytical techniques required for their robust quantification. Mass spectrometry-based workflows are particularly suitable for the quantification of glycans and glycoproteins, but still require advances for their transformation from a biomedical research setting to a clinical laboratory. In this review, we describe why and how glycomics is expected to find its role in clinical tests and the status of current mass spectrometry-based methods for clinical glycomics.