Analysis of MALDI-TOF mass spectrometry data for discovery of peptide and glycan biomarkers of hepatocellular carcinoma

This paper presents computational methods to analyze MALDI-TOF mass spectrometry data for quantitative comparison of peptides and glycans in serum. The methods are applied to identify candidate biomarkers in serum samples of 203 participants from Egypt; 73 hepatocellular carcinoma (HCC) cases, 52 patients with chronic liver disease (CLD) consisting of cirrhosis and fibrosis cases, and 78 population controls. Two complementary sample preparation methods were applied prior to generating mass spectra: (1) low molecular weight (LMW) enrichment of each serum sample was carried out for MALDI-TOF quantification of peptides, and (2) glycans were enzymatically released from proteins in each serum sample and permethylated for MALDI-TOF quantification of glycans. A peak selection algorithm was applied to identify the most useful peptide and glycan peaks for accurate detection of HCC cases from high-risk population of patients with CLD. In addition to global peaks selected by the whole population based approach, where identically labeled patients are treated as a single group, subgroup-specific peaks were identified by searching for peaks that are differentially abundant in a subgroup of patients only. The peak selection process was preceded by peak screening, where we eliminated peaks that have significant association with covariates such as age, gender, and viral infection based on the peptide and glycan spectra from population controls. The performance of the selected peptide and glycan peaks was evaluated in terms of their ability in detecting HCC cases from patients with CLD in a blinded validation set and through the cross-validation method. Finally, we investigated the possibility of using both peptides and glycans in a panel to enhance the diagnostic capability of these candidate markers. Further evaluation is needed to examine the potential clinical utility of the candidate peptide and glycan markers identified in this study.     

Natural glycan microarrays

Glycan microarrays are emerging as increasingly used screening tools with a high potential for unraveling protein–carbohydrate interactions: probing hundreds or even thousands of glycans in parallel, they provide the researcher with a vast amount of data in a short time-frame, while using relatively small amounts of analytes. Natural glycan microarrays focus on the glycans’ repertoire of natural sources, including both well-defined structures as well as still-unknown ones. This article compares different natural glycan microarray strategies. Glycan probes may comprise oligosaccharides from glycoproteins as well as glycolipids and polysaccharides. Oligosaccharides may be purified from scarce biological samples that are of particular relevance for the carbohydrate-binding protein to be studied. We give an overview of strategies for glycan isolation, derivatization, fractionation, immobilization and structural characterization. Detection methods such as fluorescence analysis and surface plasmon resonance are summarized. The importance of glycan density and multivalency is discussed. Furthermore, some applications of natural glycan microarrays for studying lectin and antibody binding are presented.     

A strategy to reveal potential glycan markers from serum glycoproteins associated with breast cancer progression

Aberrant glycosylation on glycoproteins that are either presented on the surface or secreted by cancer cells is a potential source of disease biomarkers and provides insights into disease pathogenesis. N-Glycans of the total serum glycoproteins from advanced breast cancer patients and healthy individuals were sequenced by HPLC with fluorescence detection coupled with exoglycosidase digestions and mass spectrometry. We observed a significant increase in a trisialylated triantennary glycan containing alpha1,3-linked fucose which forms part of the sialyl Lewis x epitope. Following digestion of the total glycan pool with a combination of sialidase and beta-galactosidase, we segregated and quantified a digestion product, a monogalactosylated triantennary structure containing alpha1,3-linked fucose. We compared breast cancer patients and controls and detected a 2-fold increase in this glycan marker in patients. In 10 patients monitored longitudinally, we showed a positive correlation between this glycan marker and disease progression and also demonstrated its potential as a better indicator of metastasis compared to the currently used biomarkers, CA 15-3 and carcinoembryonic antigen (CEA). A pilot glycoproteomic study of advanced breast cancer serum highlighted acute-phase proteins alpha1-acid glycoprotein, alpha1-antichymotrypsin, and haptoglobin beta-chain as contributors to the increase in the glycan marker which, when quantified from each of these proteins, marked the onset of metastasis in advance of the CA 15-3 marker. These preliminary findings suggest that specific glycans and glycoforms of proteins may be candidates for improved markers in the monitoring of breast cancer progression.     

Lectin microarrays identify cell-specific and functionally significant cell surface glycan markers

Glycosylation is among the most complex posttranslational modifications with an extremely high level of diversity that has made it refractory to high-throughput analyses. Despite its resistance to high-throughput techniques, glycosylation is important in many critical cellular processes that necessitate a productive approach to their analysis. To facilitate studies in glycosylation, we developed a high-throughput lectin microarray for defining mammalian cell surface glycan signatures. Using the lectin microarray we established a binary analysis of cell binding and hierarchical organization of 24 mammalian cell lines. The array was also used to document changes in cell surface glycosylation during cell development and differentiation of primary murine immune system cells. To establish the biological and clinical importance of glycan signatures, the lectin microarray was applied in two systems. First, we analyzed the cell surface glycan signatures and were able to predict mannose-dependent tropism using a model pathogen. Second, we used the glycan signatures to identify novel lectin biomarkers for cancer stem-like cells in a murine model. Thus, lectin microarrays are an effective tool for analyzing diverse cell processes including cell development and differentiation, cell-cell communication, pathogen-host recognition, and cell surface biomarker identification.     

Extraction of leukemia specific glycan motifs in humans by computational glycomics

There have been almost no standard methods for conducting computational analyses on glycan structures in comparison to DNA and proteins. In this paper, we present a novel method for extracting functional motifs from glycan structures using the KEGG/GLYCAN database. First, we developed a new similarity measure for comparing glycan structures taking into account the characteristic mechanisms of glycan biosynthesis, and we tested its ability to classify glycans of different blood components in the framework of support vector machines (SVMs). The results show that our method can successfully classify glycans from four types of human blood components: leukemic cells, erythrocyte, serum, and plasma. Next, we extracted characteristic functional motifs of glycans considered to be specific to each blood component. We predicted the substructure alpha-D-Neup5Ac-(2-->3)-beta-D-Galp-(1-->4)-D-GlcpNAc as a leukemia specific glycan motif. Based on the fact that the Agrocybe cylindracea galectin (ACG) specifically binds to the same substructure, we conducted an experiment using cell agglutination assay and confirmed that this fungal lectin specifically recognized human leukemic cells.     

Novel glycan biomarkers for the detection of lung cancer

Lung cancer has a poor prognosis and a 5-year survival rate of 15%. Therefore, early detection is vital. Diagnostic testing of serum for cancer-associated biomarkers is a noninvasive detection method. Glycosylation is the most frequent post-translational modification of proteins and it has been shown to be altered in cancer. In this paper, high-throughput HILIC technology was applied to serum samples from 100 lung cancer patients, alongside 84 age-matched controls and significant alterations in N-linked glycosylation were identified. Increases were detected in glycans containing Sialyl Lewis X, monoantennary glycans, highly sialylated glycans and decreases were observed in core-fucosylated biantennary glycans, with some being detectable as early as in Stage I. The N-linked glycan profile of haptoglobin demonstrated similar alterations to those elucidated in the total serum glycome. The most significantly altered HILIC peak in lung cancer samples includes predominantly disialylated and tri- and tetra-antennary glycans. This potential disease marker is significantly increased across all disease groups compared to controls and a strong disease effect is visible even after the effect of smoking is accounted for. The combination of all glyco-biomarkers had the highest sensitivity and specificity. This study identifies candidates for further study as potential biomarkers for the disease.     

The development of retrosynthetic glycan libraries to profile and classify the human serum N‐linked glycome

Annotation of the human serum N‐linked glycome is a formidable challenge but is necessary for disease marker discovery. A new theoretical glycan library was constructed and proposed to provide all possible glycan compositions in serum. It was developed based on established glycobiology and retrosynthetic state‐transition networks. We find that at least 331 compositions are possible in the serum N‐linked glycome. By pairing the theoretical glycan mass library with a high mass accuracy and high‐resolution MS, human serum glycans were effectively profiled. Correct isotopic envelope deconvolution to monoisotopic masses and the high mass accuracy instruments drastically reduced the amount of false composition assignments. The high throughput capacity enabled by this library permitted the rapid glycan profiling of large control populations. With the use of the library, a human serum glycan mass profile was developed from 46 healthy individuals. This paper presents a theoretical N‐linked glycan mass library that was used for accurate high‐throughput human serum glycan profiling. Rapid methods for evaluating a patient's glycome are instrumental for studying glycan‐based markers.     

Natural glycan microarrays

Glycan microarrays are emerging as increasingly used screening tools with a high potential for unraveling protein-carbohydrate interactions: probing hundreds or even thousands of glycans in parallel, they provide the researcher with a vast amount of data in a short time-frame, while using relatively small amounts of analytes. Natural glycan microarrays focus on the glycans' repertoire of natural sources, including both well-defined structures as well as still-unknown ones. This article compares different natural glycan microarray strategies. Glycan probes may comprise oligosaccharides from glycoproteins as well as glycolipids and polysaccharides. Oligosaccharides may be purified from scarce biological samples that are of particular relevance for the carbohydrate-binding protein to be studied. We give an overview of strategies for glycan isolation, derivatization, fractionation, immobilization and structural characterization. Detection methods such as fluorescence analysis and surface plasmon resonance are summarized. The importance of glycan density and multivalency is discussed. Furthermore, some applications of natural glycan microarrays for studying lectin and antibody binding are presented.     

On the analysis of glycomics mass spectrometry data via the regularized area under the ROC curve

Background  

Novel molecular and statistical methods are in rising demand for disease diagnosis and prognosis with the help of recent advanced biotechnology. High-resolution mass spectrometry (MS) is one of those biotechnologies that are highly promising to improve health outcome. Previous literatures have identified some proteomics biomarkers that can distinguish healthy patients from cancer patients using MS data. In this paper, an MS study is demonstrated which uses glycomics to identify ovarian cancer. Glycomics is the study of glycans and glycoproteins. The glycans on the proteins may deviate between a cancer cell and a normal cell and may be visible in the blood. High-resolution MS has been applied to measure relative abundances of potential glycan biomarkers in human serum. Multiple potential glycan biomarkers are measured in MS spectra. With the objection of maximizing the empirical area under the ROC curve (AUC), an analysis method was considered which combines potential glycan biomarkers for the diagnosis of cancer.     

An alpha-numeric code for representing N-linked glycan structures in secreted glycoproteins

Advances in high-throughput techniques have led to the creation of increasing amounts of glycome data. The storage and analysis of this data would benefit greatly from a compact notation for describing glycan structures that can be easily stored and interpreted by computers. Towards this end, we propose a fixed-length alpha-numeric code for representing N-linked glycan structures commonly found in secreted glycoproteins from mammalian cell cultures. This code, GlycoDigit, employs a pre-assigned alpha-numeric index to represent the monosaccharides attached in different branches to the core glycan structure. The present branch-centric representation allows us to visualize the structure while the numerical nature of the code makes it machine readable. In addition, a difference operator can be defined to quantitatively differentiate between glycan structures for further analysis. The usefulness and applicability of GlycoDigit were demonstrated by constructing and visualizing an N-linked glycosylation network.     

Declining plasma fibrinogen alpha fragment identifies HER2-positive breast cancer patients and reverts to normal levels after surgery

Breast cancer is the most common nonskin malignancy affecting women. Currently, no simple, blood-based diagnostic test exists to complement radiological screening and increase sensitivity of detection. To screen plasma specimens and identify biomarkers that detect HER2-positive breast cancer, automated robotic sample processing followed by surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectroscopy was used. Multiple statistical algorithms were used to select biomarkers that segregate cancer patients versus controls and produced average CV rates ranging from 20% to 29%. A set of seven biomarkers were validated on an independent test data set and achieved the best error rate of 19.1%. A permutation test indicated a p-value for CV error less than 0.002. Moreover, a ROC curve using these biomarkers achieved an area-under-the-curve value of 0.95 on an independent test data set. The marker responsible for most of the resolving power was identified as a fragment of Fibrinogen Alpha (FGA) encompassing residues 605-629. This marker was present at lower levels in cancer patients as compared to controls. The importance of this biomarker was validated in a longitudinal study comparing pre- and post-operative levels and was shown to revert to normal levels after surgery. This fragment may serve as a useful diagnostic and treatment-monitoring marker.     

A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins

Advances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array.     

High throughput screening of co-expressed gene pairs with controlled false discovery rate (FDR) and minimum acceptable strength (MAS).

Many exploratory microarray data analysis tools such as gene clustering and relevance networks rely on detecting pairwise gene co-expression. Traditional screening of pairwise co-expression either controls biological significance or statistical significance, but not both. The former approach does not provide stochastic error control, and the later approach screens many co-expressions with excessively low correlation. We have designed and implemented a statistically sound two-stage co-expression detection algorithm that controls both statistical significance (false discovery rate, FDR) and biological significance (minimum acceptable strength, MAS) of the discovered co-expressions. Based on estimation of pairwise gene correlation, the algorithm provides an initial co-expression discovery that controls only FDR, which is then followed by a second stage co-expression discovery which controls both FDR and MAS. It also computes and thresholds the set of FDR p-values for each correlation that satisfied the MAS criterion. Using simulated data, we validated asymptotic null distributions of the Pearson and Kendall correlation coefficients and the two-stage error-control procedure; we also compared our two-stage test procedure with another two-stage test procedure using the receiver operating characteristic (ROC) curve. We then used yeast galactose metabolism data to illustrate the advantage of our method for clustering genes and constructing a relevance network. The method has been implemented in an R package "GeneNT" that is freely available from the Comprehensive R Archive Network (CRAN): www.cran.r-project.org/.     

GLYDE-an expressive XML standard for the representation of glycan structure

The amount of glycomics data being generated is rapidly increasing as a result of improvements in analytical and computational methods. Correlation and analysis of this large, distributed data set requires an extensible and flexible representational standard that is also ‘understood’ by a wide range of software applications. An XML-based data representation standard that faithfully captures essential structural details of a glycan moiety along with additional information (such as data provenance) to aid the interpretation and usage of glycan data, will facilitate the exchange of glycomics data across the scientific community. To meet this need, we introduce GLYcan Data Exchange (GLYDE) standard as an XML-based representation format to enable interoperability and exchange of glycomics data. An online tool (http://128.192.9.86/stargate/formatIndex.jsp) for the conversion of other representations to GLYDE format has been developed.     

A Pilot Study Identifying a Set of microRNAs As Precise Diagnostic Biomarkers of Acute Kidney Injury

In the last decade, Acute Kidney Injury (AKI) diagnosis and therapy have not notably improved probably due to delay in the diagnosis, among other issues. Precocity and accuracy should be critical parameters in novel AKI biomarker discovery. microRNAs are key regulators of cell responses to many stimuli and they can be secreted to the extracellular environment. Therefore, they can be detected in body fluids and are emerging as novel disease biomarkers. We aimed to identify and validate serum miRNAs useful for AKI diagnosis and management. Using qRT-PCR arrays in serum samples, we determined miRNAs differentially expressed between AKI patients and healthy controls. Statistical and target prediction analysis allowed us to identify a panel of 10 serum miRNAs. This set was further validated, by qRT-PCR, in two independent cohorts of patients with relevant morbi-mortality related to AKI: Intensive Care Units (ICU) and Cardiac Surgery (CS). Statistical correlations with patient clinical parameter were performed. Our results demonstrated that the 10 selected miRNAs (miR-101-3p, miR-127-3p, miR-210-3p, miR-126-3p, miR-26b-5p, miR-29a-3p, miR-146a-5p, miR-27a-3p, miR-93-3p and miR-10a-5p) were diagnostic biomarkers of AKI in ICU patients, exhibiting areas under the curve close to 1 in ROC analysis. Outstandingly, serum miRNAs estimated before CS predicted AKI development later on, thus becoming biomarkers to predict AKI predisposition. Moreover, after surgery, the expression of the miRNAs was modulated days before serum creatinine increased, demonstrating early diagnostic value. In summary, we have identified a set of serum miRNAs as AKI biomarkers useful in clinical practice, since they demonstrate early detection and high diagnostic value and they recognize patients at risk.     

Focused differential glycan analysis with the platform antibody-assisted lectin profiling for glycan-related biomarker verification

Protein glycosylation is a critical subject attracting increasing attention in the field of proteomics as it is expected to play a key role in the investigation of histological and diagnostic biomarkers. In this context, an enormous number of glycoproteins have now been nominated as disease-related biomarkers. However, there is no appropriate strategy in the current proteome platform to qualify such marker candidate molecules, which relates their specific expression to particular diseases. Here, we present a new practical system for focused differential glycan analysis in terms of antibody-assisted lectin profiling (ALP). In the developed procedure, (i) a target protein is enriched from clinic samples (e.g. tissue extracts, cell supernatants, or sera) by immunoprecipitation with a specific antibody recognizing a core protein moiety; (ii) the target glycoprotein is quantified by immunoblotting using the same antibody used in (i); and (iii) glycosylation difference is analyzed by means of antibody-overlay lectin microarray, an application technique of an emerging glycan profiling microarray. As model glycoproteins having either N-linked or O-linked glycans, prostate-specific antigen or podoplanin, respectively, were subjected to systematic ALP analysis. As a result, specific signals corresponding to the target glycoprotein glycans were obtained at a sub-picomole level with the aid of specific antibodies, whereby disease-specific or tissue-specific glycosylation changes could be observed in a rapid, reproducible, and high-throughput manner. Thus, the established system should provide a powerful pipeline in support of on-going efforts in glyco-biomarker discovery.     

Extracting glycan motifs using a biochemicallyweighted kernel

Carbohydrates, or glycans, are one of the most abundant and structurally diverse biopolymers constitute the third major class of biomolecules, following DNA and proteins. However, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins, mainly due to their inherent structural complexity. However, their analysis is important because they serve various important roles in biological processes, including signaling transduction and cellular recognition. In order to glean some light into glycan function based on carbohydrate structure, kernel methods have been developed in the past, in particular to extract potential glycan biomarkers by classifying glycan structures found in different tissue samples. The recently developed weighted qgram method (LK-method) exhibits good performance on glycan structure classification while having limitations in feature selection. That is, it was unable to extract biologically meaningful features from the data. Therefore, we propose a biochemicallyweighted tree kernel (BioLK-method) which is based on a glycan similarity matrix and also incorporates biochemical information of individual q-grams in constructing the kernel matrix. We further applied our new method for the classification and recognition of motifs on publicly available glycan data. Our novel tree kernel (BioLK-method) using a Support Vector Machine (SVM) is capable of detecting biologically important motifs accurately while LK-method failed to do so. It was tested on three glycan data sets from the Consortium for Functional Glycomics (CFG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) GLYCAN and showed that the results are consistent with the literature. The newly developed BioLK-method also maintains comparable classification performance with the LK-method. Our results obtained here indicate that the incorporation of biochemical information of q-grams further shows the flexibility and capability of the novel kernel in feature extraction, which may aid in the prediction of glycan biomarkers.     

Serum microRNA biomarkers for detection of non-small cell lung cancer

Non small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality world-wide and the majority of cases are diagnosed at late stages of disease. There is currently no cost-effective screening test for NSCLC, and the development of such a test is a public health imperative. Recent studies have suggested that chest computed tomography screening of patients at high risk of lung cancer can increase survival from disease, however, the cost effectiveness of such screening has not been established. In this Phase I/II biomarker study we examined the feasibility of using serum miRNA as biomarkers of NSCLC using RT-qPCR to examine the expression of 180 miRNAs in sera from 30 treatment naive NSCLC patients and 20 healthy controls. Receiver operating characteristic curves (ROC) and area under the curve were used to identify differentially expressed miRNA pairs that could distinguish NSCLC from healthy controls. Selected miRNA candidates were further validated in sera from an additional 55 NSCLC patients and 75 healthy controls. Examination of miRNA expression levels in serum from a multi-institutional cohort of 50 subjects (30 NSCLC patients and 20 healthy controls) identified differentially expressed miRNAs. A combination of two differentially expressed miRNAs miR-15b and miR-27b, was able to discriminate NSCLC from healthy controls with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100% in the training set. Upon further testing on additional 130 subjects (55 NSCLC and 75 healthy controls), this miRNA pair predicted NSCLC with a specificity of 84% (95% CI 0.73-0.91), sensitivity of 100% (95% CI; 0.93-1.0), NPV of 100%, and PPV of 82%. These data provide evidence that serum miRNAs have the potential to be sensitive, cost-effective biomarkers for the early detection of NSCLC. Further testing in a Phase III biomarker study in is necessary for validation of these results.