Evaluation of glycomic profiling as a diagnostic biomarker for epithelial ovarian cancer

Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling

BACKGROUND

There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery.

METHODS

We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues.

RESULTS

We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues.

CONCLUSIONS

Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

Advancements in protein glycosylation biomarkers for ovarian cancer through mass spectrometry-based approaches

INTRODUCTION

Ovarian cancer, characterized by metastasis and reduced 5-year survival rates, stands as a substantial factor in the mortality of gynecological malignancies worldwide. The challenge of delayed diagnosis originates from vague early symptoms and the absence of efficient screening and diagnostic biomarkers for early cancer detection. Recent studies have explored the intricate interplay between ovarian cancer and protein glycosylation, unveiling the potential significance of glycosylation-oriented biomarkers.

AREAS COVERED

This review examines the progress in glycosylation biomarker research, with particular emphasis on advances driven by mass spectrometry-based technologies. We document milestones achieved, discuss encountered limitations, and also highlight potential areas for future research and development of protein glycosylation biomarkers for ovarian cancer.

EXPERT OPINION

The association of glycosylation in ovarian cancer is well known, but current research lacks desired sensitivity and specificity for early detection. Notably, investigations into protein-specific and site-specific glycoproteomics have the potential to significantly enhance our understanding of ovarian cancer and facilitate the identification of glycosylation-based biomarkers. Furthermore, the integration of advanced mass spectrometry techniques with AI-driven analysis and glycome databases holds the promise for revolutionizing biomarker discovery for ovarian cancer, ultimately transforming diagnosis and improving patient outcomes.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

Differentiation of Sialyl Linkages Using a Combination of Alkyl Esterification and Phenylhydrazine Derivatization: Application for N-Glycan Profiling in the Sera of Patients with Lung Cancer

Alterations in oligosaccharides and types of sialic acid (SA) attachments have been associated with different pathological states. Matrix-assisted laser desorption mass spectrometry (MS) is commonly used for glycosylation studies. However, native sialylated glycans are suppressed or not detected during MS experiments. Consequently, different approaches have been employed to neutralize the negative charge of the carboxyl group. In this study, we present the advantage of phenylhydrazine (PHN) labeling for the detection and efficient discrimination of SA linkages when this derivatization follows alkyl esterification. As expected, PHN-labeled sialylated oligosaccharides with the 2,6-linkage type can be easily recognized according to the additional shift in mass corresponding to the presence of a methyl or ethyl group. Surprisingly, oligosaccharides with the 2,3-linked SA residue instead of a lactone were detected carrying the second PHN unit. This was beneficial as no further processing after esterification was needed to stabilize the lactone form. Moreover, during tandem mass experiments, all modified glycans produced favorable fragmentation patterns with a coherent recognition of SA linkages. Although both types of esterification, herein called the EST-PHN approach, provided comparable results, methylation exhibited marginally higher linkage specificity than ethyl esterification. The simplicity and effectiveness of the methodology are demonstrated on the model compound, sialyllactose, and its applicability for biological studies is presented on N-glycan profiling in the sera of lung cancer patients.

Specific Analysis of α-2,3-Sialylated N-Glycan Linkage Isomers by Microchip Capillary Electrophoresis-Mass Spectrometry

Sialylated N-glycan isomers with α-2,3 and α-2,6 linkages play crucial and distinctive roles in diverse physiological and pathological processes. Changes of α-2,3-linked sialic acids in sialylated N-glycans are especially important in monitoring the initiation and progression of diseases. However, the specific analysis of α-2,3-sialylated N-glycan linkage isomers remains challenging due to their extremely low abundance and technical limitations in separation and detection. Herein, we designed an integrated strategy that combines linkage-specific derivatization and a charge-sensitive separation method based on microfluidic chip capillary electrophoresis-mass spectrometry (microchip CE-MS) for specific analysis of α-2,3-sialylated N-glycan linkage isomers for the first time. The α-2,6- and α-2,3-sialic acids were selectively labeled with methylamine (MA) and N,N-dimethylethylenediamine (DMEN), respectively, which selectively makes α-2,3-sialylated N-glycans positively charged and realizes online purification, concentration, and discrimination of α-2,3-sialylated N-glycans from other N-glycans in microchip CE-MS. This new approach was demonstrated with standard multisialylated N-glycans, and it was found that only the α-2,3-sialylated N-glycans migrated and were detected in order according to the number of α-2,3-sialic acids. Finally, this strategy was successfully applied in highly sensitive profiling and reproducible quantitation of the serum α-2,3-sialylated N-glycome from ovarian cancer (OC) patients, where 7 of 33 detected α-2,3-sialylated N-glycans significantly changed in the OC group compared with healthy controls.

Glycomic-Based Biomarkers for Ovarian Cancer: Advances and Challenges

Ovarian cancer remains one of the most common causes of death among gynecological malignancies afflicting women worldwide. Among the gynecological cancers, cervical and endometrial cancers confer the greatest burden to the developing and the developed world, respectively; however, the overall survival rates for patients with ovarian cancer are worse than the two aforementioned. The majority of patients with ovarian cancer are diagnosed at an advanced stage when cancer has metastasized to different body sites and the cure rates, including the five-year survival, are significantly diminished. The delay in diagnosis is due to the absence of or unspecific symptoms at the initial stages of cancer as well as a lack of effective screening and diagnostic biomarkers that can detect cancer at the early stages. This, therefore, provides an imperative to prospect for new biomarkers that will provide early diagnostic strategies allowing timely mitigative interventions. Glycosylation is a protein post-translational modification that is modified in cancer patients. In the current review, we document the state-of-the-art of blood-based glycomic biomarkers for early diagnosis of ovarian cancer and the technologies currently used in this endeavor.

Immunoglobulin A N-glycosylation Presents Important Body Fluid-specific Variations in Lactating Mothers

Secretory Immunoglobulin A (SIgA) is central to mucosal immunity: represents one of the main immunological mechanisms of defense against the potential attack of pathogens. During lactation SIgA is produced by plasmablasts in the mammary gland and is present in breast milk, playing a vital role in the passive immunity of the newborn. Interestingly, the different components of SIgA are highly N-glycosylated, and these N-Glycans have an essential role in health maintenance. In this work, we performed a glycomic study to compare N-glycosylation of SIgA purified from mature breast milk and saliva, and plasma IgA from the same lactating participants. Our results revealed a greater diversity than previously reported, with 89 glycan compositions that may correspond to over 250 structures. Among these glycans, 54 glycan compositions were characterized as body-fluid specific. Most of these unique N-Glycan compositions identified in SIgA from mature milk and IgA from plasma were fucosylated and both fucosylated and sialylated species, whereas in salivary SIgA the unique structures were mainly undecorated complex N-Glycans. In addition, we evaluated the effect of delivery mode on (S)IgA glycosylation. Lactating participants who had given birth by vaginal delivery presented an increased proportion of high mannose and fucosylated glycans in salivary SIgA, and selected high mannose, fucosylated, sialylated, and both fucosylated and sialylated glycans in plasma IgA, indicating that the hormonal changes during vaginal delivery could affect plasma and saliva IgA. These results reveal the structural details that provide a new dimension to the roles of (S)IgA N-Glycans in different tissues, and especially in maternal and new-born protection and infant development. The design of optimal recombinant IgA molecules specifically targeted to protect mucosal surfaces will need to include this dimension of structural detail.

Glycoproteomic markers of hepatocellular carcinoma-mass spectrometry based approaches

Hepatocellular carcinoma (HCC) is the third most-common cause of cancer-related death worldwide. Most cases of HCC develop in patients that already have liver cirrhosis and have been recommended for surveillance for an early onset of HCC. Cirrhosis is the final common pathway for several etiologies of liver disease, including hepatitis B and C, alcohol, and increasingly non-alcoholic fatty liver disease. Only 20-30% of patients with HCC are eligible for curative therapy due primarily to inadequate early-detection strategies. Reliable, accurate biomarkers for HCC early detection provide the highest likelihood of curative therapy and survival; however, current early-detection methods that use abdominal ultrasound and serum alpha fetoprotein are inadequate due to poor adherence and limited sensitivity and specificity. There is an urgent need for convenient and highly accurate validated biomarkers for HCC early detection. The theme of this review is the development of new methods to discover glycoprotein-based markers for detection of HCC with mass spectrometry approaches. We outline the non-mass spectrometry based methods that have been used to discover HCC markers including immunoassays, capillary electrophoresis, 2-D gel electrophoresis, and lectin-FLISA assays. We describe the development and results of mass spectrometry-based assays for glycan screening based on either MALDI-MS or ESI analysis. These analyses might be based on the glycan content of serum or on glycan screening for target molecules from serum. We describe some of the specific markers that have been developed as a result, including for proteins such as Haptoglobin, Hemopexin, Kininogen, and others. We discuss the potential role for other technologies, including PGC chromatography and ion mobility, to separate isoforms of glycan markers. Analyses of glycopeptides based on new technologies and innovative softwares are described and also their potential role in discovery of markers of HCC. These technologies include new fragmentation methods such as EThcD and stepped HCD, which can identify large numbers of glycopeptide structures from serum. The key role of lectin extraction in various assays for intact glycopeptides or their truncated versions is also described, where various core-fucosylated and hyperfucosylated glycopeptides have been identified as potential markers of HCC. Finally, we describe the role of LC-MRMs or lectin-FLISA MRMs as a means to validate these glycoprotein markers from patient samples. These technological advancements in mass spectrometry have the potential to lead to novel biomarkers to improve the early detection of HCC.

Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

Mass Spectrometry Approaches to Glycomic and Glycoproteomic Analyses

Glycomic and glycoproteomic analyses involve the characterization of oligosaccharides (glycans) conjugated to proteins. Glycans are produced through a complicated nontemplate driven process involving the competition of enzymes that extend the nascent chain. The large diversity of structures, the variations in polarity of the individual saccharide residues, and the poor ionization efficiencies of glycans all conspire to make the analysis arguably much more difficult than any other biopolymer. Furthermore, the large number of glycoforms associated with a specific protein site makes it more difficult to characterize than any post-translational modification. Nonetheless, there have been significant progress, and advanced separation and mass spectrometry methods have been at its center and the main reason for the progress. While glycomic and glycoproteomic analyses are still typically available only through highly specialized laboratories, new software and workflow is making it more accessible. This review focuses on the role of mass spectrometry and separation methods in advancing glycomic and glycoproteomic analyses. It describes the current state of the field and progress toward making it more available to the larger scientific community.

Developments in Point-of-Care Diagnostic Technology for Cancer Detection

Cancer is the cause of death for one in seven individuals worldwide. It is widely acknowledged that screening and early diagnosis are of vital importance for improving the likelihood of recovery. However, given the costly, time-consuming, and invasive nature of the many methods currently in use, patients often do not take advantage of the services available to them. Consequently, many researchers are exploring the possibility of developing fast, reliable, and non-invasive diagnostic tools that can be used directly or by local physicians at the point-of-care. Herein, we look at the use of established biomarkers in cancer therapy and investigate emerging biomarkers exhibiting future potential. The incorporation of these biomarkers into point-of-care devices could potentially reduce the strain currently experienced by screening programs in hospitals and healthcare systems. Results derived from point-of-care tests should be accurate, sensitive, and generated rapidly to assist in the selection of the best course of treatment for optimal patient care. Essentially, point-of-care diagnostics should enhance the well-being of patients and lead to a reduction in cancer-related deaths.

Serum glycosylation characterization of osteonecrosis of the femoral head by mass spectrometry

Osteonecrosis of the femoral head is a recalcitrant and paralyzing disease often discovered in the end stage at the time of diagnosis, which is often performed by physical examination and diagnostic imaging. Osteonecrosis of the femoral head is typically caused by trauma or long-term steroid use. There are over 30 million patients in the US taking steroids, and roughly 40% will develop osteonecrosis of the femoral head. However, the exact pathophysiological process is not well understood. This study aims to examine the alteration in serum glycosylation of osteonecrosis of the femoral head using the state-of-the-art analytical tools to provide more chemical data for pathophysiology research and possibly biomarker discovery. A training set containing 27 serum samples from steroid-induced osteonecrosis of the femoral head patients and 25 from gender- and age-matched controls was collected and analyzed. Glycosylation of whole serum and site-specific glycosylation of immunoglobulins are characterized using electrospray ionization-Q-time of flight and electrospray ionization-Triple-Quadruple via multiple reaction monitoring, respectively. The whole serum glycosylation analysis yielded 14 N-glycan compositions and multiple reaction monitoring yielded eight glycopeptides that were altered between cases and controls with statistical significance. The increase of nonsialylated, nonfucosylated N-glycans and decrease of fucosylated N-glycans are associated with the development of osteonecrosis of the femoral head. Glycosylation is a posttranslational protein modification and is apparently affected by osteonecrosis of the femoral head. Future studies with a larger cohort and patients from earlier stage will be performed to assess these potential markers' value in disease onset.

Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices

With expanded access to, and decreased costs of, mass spectrometry, investigators are collecting and analyzing multiple biological matrices from the same subject such as serum, plasma, tissue and urine to enhance biomarker discoveries, understanding of disease processes and identification of therapeutic targets. Commonly, each biological matrix is analyzed separately, but multivariate methods such as MANOVAs that combine information from multiple biological matrices are potentially more powerful. However, mass spectrometric data typically contain large amounts of missing values, and imputation is often used to create complete data sets for analysis. The effects of imputation on multiple biological matrix analyses have not been studied. We investigated the effects of seven imputation methods (half minimum substitution, mean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components analysis, singular value decomposition and random forest), on the within-subject correlation of compounds between biological matrices and its consequences on MANOVA results. Through analysis of three real omics data sets and simulation studies, we found the amount of missing data and imputation method to substantially change the between-matrix correlation structure. The magnitude of the correlations was generally reduced in imputed data sets, and this effect increased with the amount of missing data. Significant results from MANOVA testing also were substantially affected. In particular, the number of false positives increased with the level of missing data for all imputation methods. No one imputation method was universally the best, but the simple substitution methods (Half Minimum and Mean) consistently performed poorly.

Tissue glycomics distinguish tumour sites in women with advanced serous adenocarcinoma

In the era of precision medicine, the tailoring of cancer treatment is increasingly important as we transition from organ‐based diagnosis towards a more comprehensive and patient‐centric molecular diagnosis. This is particularly the case for high‐grade serous adenocarcinomas of the ovary and peritoneum, which are commonly diagnosed at an advanced stage, and collectively treated and managed similarly. We characterized the N‐ and O‐glycome of serous ovarian (OC) and peritoneal cancer (PC) tissues using PGC‐LC‐ESI‐IT‐MS/MS profiling and validated the discriminatory glycans and their corresponding glyco‐gene expression levels using cell lines and transcriptomic data from 232 patients. Overall, the N‐ and O‐glycan repertoires of both cancer types were found to comprise mostly of α2,6‐sialylated glycan structures, with the majority of N‐glycans displaying the biantennary mono‐ and disialylation as well as bisecting‐type biantennary glycans. The MS profiling by PGC‐LC also revealed several glycan structural isomers that corresponded to LacdiNAc‐type (GalNAcβ1‐4GlcNAc) motifs that were unique to the serous ovarian cancers and that correlated with elevated gene expression of B4GALNT3 and B4GALNT4 in patients with serous cancer. Statistical evaluation of the discriminatory glycans also revealed 13 N‐ and 3 O‐glycans (P < 0.05) that significantly discriminated tumour‐sampling sites, with LacdiNAc‐type N‐glycans (m/z 1205.0²⁻ and m/z 1059.4²⁻) being associated with ovarian‐derived cancer tissue and bisecting GlcNAc‐type (m/z 994.9²⁻) and branched N‐glycans (m/z 1294.0²⁻ and m/z 1148.4²⁻) upregulated at the metastatic sites. Hence, we demonstrate for the first time that OC and PC display distinct molecular signatures at both their glycomic and transcriptomic levels. These signatures may have potential utility for the development of accurate diagnosis and personalized treatments.

Mass spectrometric profiling reveals association of N-glycan patterns with epithelial ovarian cancer progression

Aberrant changes of N-glycan modifications on proteins have been linked to various diseases including different cancers, suggesting possible avenue for exploring their etiologies based on N-glycomic analysis. Changes in N-glycan patterns during epithelial ovarian cancer development have so far been investigated mainly using serum, plasma, ascites, and cell lines. However, changes in patterns of N-glycans in tumor tissues during epithelial ovarian cancer progression have remained largely undefined. To investigate whether changes in N-glycan patterns correlate with oncogenesis and progression of epithelial ovarian cancer, we profiled N-glycans from formalin-fixed paraffin-embedded tissue slides using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and quantitatively compared among different pathological grades of epithelial ovarian cancer and healthy controls. Our results show that among the 80 compositions of N-glycan detected, expression levels of high-mannose type were higher in epithelial ovarian cancer samples than that observed in healthy controls, accompanied by reduced levels of hybrid-type glycans. By applying receiver operating characteristic analysis, we show that a combined panel composed of four high-mannose and three fucosylated neutral complex N-glycans allows for good discrimination of epithelial ovarian cancer from healthy controls. Furthermore, using a statistical analysis of variance assay, we found that different N-glycan patterns, including 2 high-mannose-type, 2 fucosylated and sialylated complex structures, and 10 fucosylated neutral complex N-glycans, exhibited specific changes in N-glycan abundance across epithelial ovarian cancer grades. Together, our results provide strong evidence that N-glycomic changes are a strong indicator for epithelial ovarian cancer pathological grades and should provide avenues to identify novel biomarkers for epithelial ovarian cancer diagnosis and monitoring.

Mass spectrometry as a tool for biomarkers searching in gynecological oncology

Tumors of the female reproductive tract are an important target for the development of diagnostic, prognostic and therapeutic strategies. Recent research has turned to proteomics based on mass spectrometry techniques, to achieve more effective diagnostic results. Mass spectrometry (MS) enables identification and quantification of multiple molecules simultaneously in a single experiment according to mass to charge ratio (m/z). Several proteomic strategies may be applied to establish the function of a particular protein/peptide or to identify a novel disease and specific biomarkers related to it. Therefore, MS could facilitate treatment in patients with tumors by helping researchers discover new biomarkers and narrowly targeted drugs. This review presents a comprehensive discussion of mass spectrometry as a tool for biomarkers searching that may lead to the discovery of easily available diagnostic tests in gynecological oncology with emphasis on clinical proteomics over the past decade. The article provides an insight into different MS based proteomic approaches.

The ascites N-glycome of epithelial ovarian cancer patients

Epithelial ovarian cancer (EOC) is worldwide the sixth most lethal form of cancer occurring in women. More than one third of ovarian patients have ascites at the time of diagnosis and almost all of them have it when recurrence occurs. Although its effect on tumor cell microenvironment remains poorly understood, its presence is correlated with bad diagnosis. In previous studies, we proposed a novel glycan-based biomarker for the diagnosis of EOC, which showed an improved sensitivity and specificity at any stage of the disease and an improved discrimination between malignant and benign ovarian tumors. In this work, we report for the first time the N-glycome profiles of ascitic fluid from primary serous EOC patients and compare them with the serum N-glycomes of the same patients as well as of healthy controls. N-Glycans were digested from equivalent amount of ascites and serum from 18 EOC patients and from serum of 20 age-matched controls and measured by MALDI-TOF-MS. Ascites N-glycome showed increased antennarity, branching, sialylation and Lewis^X motives compared to healthy serum. In addition, a correlation was established between ascites volume and degree of sialylation.

SIGNIFICANCE

Malignant ascitic fluid is the build-up of large volumes of fluid in the peritoneal cavity secondary to cancer. At least one-third of ovarian cancer patients develop ascites, a generally voluminous fluid containing cells of tumor origin, in the course of cancer and almost all when recurrence occurs. The proteome of ascites is known to be as complex as that of serum and contains high amount of proteins shed from inflammatory cells as well as from tumor cells. Although many attempts have been made to provide molecular insight into the proteomic and peptidomic content of malignant ascites, no data about the N-glycome of the ascitic fluid fraction from cancer patients has been reported to date. In this study, the N-glycosylation profile of ascites from 20 patients suffering from epithelial ovarian cancer (EOC) was analyzed by MALDI-TOF-mass spectrometry and compared to the pathologically modified N-glycan pattern obtained from serum of the same patients as well as to the pattern of serum from healthy individuals. Significant quantitative differences were observed in the ascites of EOC patients when compared to the serum of healthy subjects. The glycome of ascites shows typical features of inflammatory conditions, what was also found in the serum of patients suffering from EOC when compared to healthy serum. In addition, a correlation was established between ascites volume and degree of sialylation, showing that the high-volume ascites contains a higher amount of sialylated structures than the low-volume ascites.

The Use of Multiple Reaction Monitoring on QQQ-MS for the Analysis of Protein- and Site-Specific Glycosylation Patterns in Serum

In recent years, high-throughput glycomics approaches have been developed and applied to either complete biofluids, cell lysates or tissues, or proteins isolated thereof. However, during such analyses the N-glycan are released from the protein backbone and therefore site- and protein-specific information is lost. There exists a need for high-throughput methods that allow quantification of site- and protein-specific glycosylation patterns from complex biological mixtures. We here describe the use of a multiple reaction monitoring mass spectrometry based method for the generation of glycopeptide profiles of the nine high abundance glycoproteins IgG, IgA, IgM, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, alpha-1-acid glycoprotein, transferrin, and complement C3. We show that the sample preparation can be performed at the 96-well level, and using a 17-min gradient on a RP-UPLC-QQQ instrument, 96 samples can be analyzed within 3 days.

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens

MOTIVATION

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results.

RESULTS

We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen.

AVAILABILITY AND IMPLEMENTATION

We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Protein-Specific Differential Glycosylation of Immunoglobulins in Serum of Ovarian Cancer Patients

Previous studies indicated that glycans in serum may serve as biomarkers for diagnosis of ovarian cancer; however, it was unclear to which proteins these glycans belong. We hypothesize that protein-specific glycosylation profiles of the glycans may be more informative of ovarian cancer and can provide insight into biological mechanisms underlying glycan aberration in serum of diseased individuals. Serum samples from women diagnosed with epithelial ovarian cancer (EOC, n = 84) and matched healthy controls (n = 84) were obtained from the Gynecologic Oncology Group. Immunoglobulin (IgG, IgA, and IgM) concentrations and glycosylation profiles were quantified using multiple reaction monitoring mass spectrometry. Differential and classification analyses were performed to identify aberrant protein-specific glycopeptides using a training set. All findings were validated in an independent test set. Multiple glycopeptides from immunoglubins IgA, IgG, and IgM were found to be differentially expressed in serum of EOC patients compared with controls. The protein-specific glycosylation profiles showed their potential in the diagnosis of EOC. In particular, IgG-specific glycosylation profiles are the most powerful in discriminating between EOC case and controls. Additional studies of protein- and site-specific glycosylation profiles of immunoglobulins and other proteins will allow further elaboration on the characteristics of biological functionality and causality of the differential glycosylation in ovarian cancer and thus ultimately lead to increased sensitivity and specificity of diagnosis.

Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics

Proteins have several measurable features in biological fluids that may change under pathological conditions. The current disease biomarker discovery is mostly based on protein concentration in the sample as the measurable feature. Changes in protein structures, such as post-translational modifications and in protein-partner interactions are known to accompany pathological processes. Changes in glycosylation profiles are well-established for many plasma proteins in various types of cancer and other diseases. The solvent interaction analysis method is based on protein partitioning in aqueous two-phase systems and is highly sensitive to changes in protein structure and protein-protein- and protein-partner interactions while independent of the protein concentration in the biological sample. It provides quantitative index: partition coefficient representing changes in protein structure and interactions with partners. The fundamentals of the method are presented with multiple examples of applications of the method to discover and monitor structural protein biomarkers as disease-specific diagnostic indicators.

Differential N-Glycosylation Patterns in Lung Adenocarcinoma Tissue

To decrease the mortality of lung cancer, better screening and diagnostic tools as well as treatment options are needed. Protein glycosylation is one of the major post-translational modifications that is altered in cancer, but it is not exactly clear which glycan structures are affected. A better understanding of the glycan structures that are differentially regulated in lung tumor tissue is highly desirable and will allow us to gain greater insight into the underlying biological mechanisms of aberrant glycosylation in lung cancer. Here, we assess differential glycosylation patterns of lung tumor tissue and nonmalignant tissue at the level of individual glycan structures using nLC-chip-TOF-MS. Using tissue samples from 42 lung adenocarcinoma patients, 29 differentially expressed (FDR < 0.05) glycan structures were identified. The levels of several oligomannose type glycans were upregulated in tumor tissue. Furthermore, levels of fully galactosylated glycans, some of which were of the hybrid type and mostly without fucose, were decreased in cancerous tissue, whereas levels of non- or low-galactosylated glycans mostly with fucose were increased. To further assess the regulation of the altered glycosylation, the glycomics data was compared to publicly available gene expression data from lung adenocarcinoma tissue compared to nonmalignant lung tissue. The results are consistent with the possibility that the observed N-glycan changes have their origin in differentially expressed glycosyltransferases. These results will be used as a starting point for the further development of clinical glycan applications in the fields of imaging, drug targeting, and biomarkers for lung cancer.

Relative Quantification and Higher-Order Modeling of the Plasma Glycan Cancer Burden Ratio in Ovarian Cancer Case-Control Samples

An early-stage, population-wide biomarker for ovarian cancer (OVC) is essential to reverse its high mortality rate. Aberrant glycosylation by OVC has been reported, but studies have yet to identify an N-glycan with sufficiently high specificity. We curated a human biorepository of 82 case-control plasma samples, with 27%, 12%, 46%, and 15% falling across stages I-IV, respectively. For relative quantitation, glycans were analyzed by the individuality normalization when labeling with glycan hydrazide tags (INLIGHT) strategy for enhanced electrospray ionization, MS/MS analysis. Sixty-three glycan cancer burden ratios (GBRs), defined as the log10 ratio of the case-control extracted ion chromatogram abundances, were calculated above the limit of detection. The final GBR models, built using stepwise forward regression, included three significant terms: OVC stage, normalized mean GBR, and tag chemical purity; glycan class, fucosylation, or sialylation were not significant variables. After Bonferroni correction, seven N-glycans were identified as significant (p < 0.05), and after false discovery rate correction, an additional four glycans were determined to be significant (p < 0.05), with one borderline (p = 0.05). For all N-glycans, the vectors of the effects from stages II-IV were sequentially reversed, suggesting potential biological changes in OVC morphology or in host response.

Serum N-glycan profiles in patients with intraductal papillary mucinous neoplasms of the pancreas

BACKGROUND/OBJECTIVES

Diagnosing the invasiveness of intraductal papillary mucinous neoplasms (IPMNs) is difficult, especially by blood test. Alterations in serum glycan profiles have been reported for several cancers, but changes in serum glycan profiles have not been investigated in patients with IPMNs. The objectives of this study were to determine the serum N-glycan profile and to investigate its clinical utility in patients with IPMNs.

METHODS

We measured serum N-glycan profiles in 79 patients with IPMNs, including 13 invasive IPMNs, by performing comprehensive glycome analysis and assessed the relationship between N-glycan changes and clinical parameters.

RESULTS

Seventy glycans were identified and their expression profiles were significantly different depending on the cyst size, the presence of an enhancing solid component, and the histological grade of the IPMN. Nine glycans were highly expressed in patients with invasive IPMNs. The glycan m/z 3195, which is a fucosylated tri-antennary glycan, had the highest diagnostic value for distinguishing invasive IPMNs from non-invasive IPMNs (area under the receiver operating characteristic curve = 0.803). Multivariate analyses revealed high levels of m/z 3195 [odds ratio (OR), 20.5; 95% confidence interval (CI) 2.60-486.4] and the presence of enhancing solid components (OR, 35.8; 95% CI, 5.39-409.6) were significant risk factors for invasive IPMNs.

CONCLUSIONS

We performed a comprehensive evaluation of the changes in serum N-glycan profiles in patients with IPMNs for the first time. We determined that increased expression of fucosylated complex-type glycans, especially m/z 3195, is a potential marker for invasive IPMNs.

N-linked glycan profiling in neuroblastoma cell lines

Although MYCN amplification has been associated with aggressive neuroblastoma, the molecular mechanisms that differentiate low-risk, MYCN-nonamplified neuroblastoma from high-risk, MYCN-amplified disease are largely unknown. Genomic and proteomic studies have been limited in discerning differences in signaling pathways that account for this heterogeneity. N-Linked glycosylation is a common protein modification resulting from the attachment of sugars to protein residues and is important in cell signaling and immune response. Aberrant N-linked glycosylation has been routinely linked to various cancers. In particular, glycomic markers have often proven to be useful in distinguishing cancers from precancerous conditions. Here, we perform a systematic comparison of N-linked glycomic variation between MYCN-nonamplified SY5Y and MYCN-amplified NLF cell lines with the aim of identifying changes in sugar abundance linked to high-risk neuroblastoma. Through a combination of liquid chromatography-mass spectrometry and bioinformatics analysis, we identified 16 glycans that show a statistically significant change in abundance between NLF and SY5Y samples. Closer examination revealed the preference for larger (in terms of total monosaccharide count) and more sialylated glycan structures in the MYCN-amplified samples in comparison to smaller, nonsialylated glycans that are more dominant in the MYCN-nonamplified samples. These results offer clues for deriving marker candidates for accurate neuroblastoma risk diagnosis.

Proteomics in cancer biomarkers discovery: challenges and applications

With the introduction of recent high-throughput technologies to various fields of science and medicine, it is becoming clear that obtaining large amounts of data is no longer a problem in modern research laboratories. However, coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation, in accordance with the evidence-based systems biology, are critical factors in ensuring the emergence of good science out of these recent technologies. This review focuses on the proteomics field and its new perspectives on cancer research. Cornerstone publications that have tremendously helped scientists and clinicians to better understand cancer pathogenesis; to discover novel diagnostic and/or prognostic biomarkers; and to suggest novel therapeutic targets will be presented. The author of this review aims at presenting some of the relevant literature data that helped as a step forward in bridging the gap between bench work results and bedside potentials. Undeniably, this review cannot include all the work that is being produced by expert research groups all over the world.

Clinical Glycomics Employing Graphitized Carbon Liquid Chromatography-Mass Spectrometry

Glycoconjugates and free glycan are involved in a variety of biological processes such as cell-cell interaction and cell trafficking. Alterations in the complex glycosylation machinery have been correlated with various pathological processes including cancer progression and metastasis. Mass Spectrometry (MS) has evolved as one of the most powerful tools in glycomics and glycoproteomics and in combination with porous graphitized carbon-liquid chromatography (PGC-LC) it is a versatile and sensitive technique for the analysis of glycans and to some extent also glycopeptides. PGC-LC-ESI-MS analysis is characterized by a high isomer separation power enabling a specific glycan compound analysis on the level of individual structures. This allows the investigation of the biological relevance of particular glycan structures and glycan features. Consequently, this strategy is a very powerful technique suitable for clinical research, such as cancer biomarker discovery, as well as in-depth analysis of recombinant glycoproteins. In this review, we will focus on how PGC in conjunction with MS detection can deliver specific structural information for clinical research on protein-bound N-glycans and mucin-type O-glycans. In addition, we will briefly review PGC analysis approaches for glycopeptides, glycosaminoglycans (GAGs) and human milk oligosaccharides (HMOs). The presented applications cover systems that vary vastly with regard to complexity such as purified glycoproteins, cells, tissue or body fluids revealing specific glycosylation changes associated with various biological processes including cancer and inflammation.

Applications of Multiple Reaction Monitoring to Clinical Glycomics

Multiple reaction monitoring or MRM is widely acknowledged for its accuracy of quantitation. The applications have mostly been in the analysis of small molecules and proteins, but its utility is expanding. Protein glycosylation was recently identified as a new paradigm in biomarker discovery for health and disease. A number of recent studies have now identified differential glycosylation patterns associated with health and disease states, including aging, pregnancy, rheumatoid arthritis and different types of cancer. While the use of MRM in clinical glycomics is still in its infancy, it can likely play a role in the quantitation of protein glycosylation in the clinical setting. Here, we aim to review the current advances in the nascent application of MRM in the field of glycomics.