Glycoproteomic markers of hepatocellular carcinoma‐mass spectrometry based approaches

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149557/1/mas21583_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149557/2/mas21583.pd

Establiment d’un mètode d’anàlisi de glicans biomarcadors de càncer de pàncrees per cromatografia de líquids acoblada a l’espectrometria de masses

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2023, Tutora: Estela Giménez LópezGlycoproteins are proteins that have oligosaccharides covalently attached to the peptide backbone. These carbohydrates are also known as glycans. Protein glycans play a significant role in various cellular processes, including cell-cell recognition, signaling, and adhesion on cell surfaces. However, glycans undergo structural changes in many diseases, such as cancer. Therefore, there is a need to establish new analytical methods that aid in identifying and quantifying glycans to detect the presence of diseases. In the present study, a reference method for the separation and identification of labelled glycans using capillary liquid chromatography (capLC) coupled with ultraviolet (UV) and mass spectrometry (MS) detection will be developed. First, standard glycans selected as model will be derivatized with aniline (AN) and procainamide (ProA) labels. To assess their degree of derivatization, they will be analysed by matrix assisted laser desorption ionization mass spectrometry (MALDI-MS). Finally, these labelled glycans will serve for the development of a reference analytical method by capLC-UV and capLC-MS. This reference method will be used in the future to test the status of the chromatographic system in glycoprotein biomarker research. For instance, for the analysis of the N-glycans of human alpha-1-acid glycoprotein (hAGP) under study as a potential biomarker of pancreatic cancer

Characterization of Cell Glycocalyx with Mass Spectrometry Methods.

The cell membrane plays an important role in protecting the cell from its extracellular environment. As such, extensive work has been devoted to studying its structure and function. Crucial intercellular processes, such as signal transduction and immune protection, are mediated by cell surface glycosylation, which is comprised of large biomolecules, including glycoproteins and glycosphingolipids. Because perturbations in glycosylation could result in dysfunction of cells and are related to diseases, the analysis of surface glycosylation is critical for understanding pathogenic mechanisms and can further lead to biomarker discovery. Different mass spectrometry-based techniques have been developed for glycan analysis, ranging from highly specific, targeted approaches to more comprehensive profiling studies. In this review, we summarized the work conducted for extensive analysis of cell membrane glycosylation, particularly those employing liquid chromatography with mass spectrometry (LC-MS) in combination with various sample preparation techniques

Glycoproteomics-Based Identification of Cancer Biomarkers

Protein glycosylation is one of the most common posttranslational modifications in mammalian cells. It is involved in many biological pathways and molecular functions and is well suited for proteomics-based disease investigations. Aberrant protein glycosylation may be associated with disease processes. Specific glycoforms of glycoproteins may serve as potential biomarkers for the early detection of disease or as biomarkers for the evaluation of therapeutic efficacy for treatment of cancer, diabetes, and other diseases. Recent technological developments, including lectin affinity chromatography and mass spectrometry, have provided researchers the ability to obtain detailed information concerning protein glycosylation. These in-depth investigations, including profiling and quantifying glycoprotein expression, as well as comprehensive glycan structural analyses may provide important information leading to the development of disease-related biomarkers. This paper describes methodologies for the detection of cancer-related glycoprotein and glycan structural alterations and briefly summarizes several current cancer-related findings

Utilizing Mass Spectrometry Imaging to Correlate N-Glycosylation of Hepatocellular Carcinoma with Tumor Subtypes for Biomarker Discovery

Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths globally and is a growing clinical problem with poor survival outcomes beyond early-stage disease. Surveillance for HCC has primarily relied on ultrasound and serum α-fetoprotein (AFP), but combined they only have a sensitivity of 63% for early-stage HCC tumors, suggesting a need for improved diagnostic strategies. Alterations to N-glycan expression are relevant to the progression of cancer, and there a multitude of N-glycan-based cancer biomarkers that have been identified with sensitivity for various cancer types including HCC. Spatial HCC tissue profiling of N-linked glycosylation by matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) serves as a new method to evaluate tumor-correlated N-glycosylation and thereby identify potential HCC biomarkers. Previous work has identified significant changes in the N-linked glycosylation of HCC tumors, but has not accounted for the heterogeneous genetic and molecular nature of HCC, which has led to inadequate sensitivity of N-glycan biomarkers. Therefore, we hypothesized that the incorporation of genetic/molecular information into N-glycan-based biomarker development would result in improved sensitivity for HCC. To determine the correlation between HCC-specific N-glycosylation and genetic/molecular tumor features, we profiled HCC tissue samples with MALDI-IMS and correlated the spatial N-glycosylation with a widely used HCC molecular classification that utilizes histological, genetic, and clinical tumor features (Hoshida subtypes). MALDI-IMS data displayed trends that could approximately distinguish between subtypes, with Subtype 1 demonstrating significantly dysregulated N-glycosylation compared to Subtypes 2 and 3, particularly in regard to fucosylation. In order to further the clinical relevance of subtype-dependent N-glycosylation, we analyzed patient-matching HCC tumor tissue, background liver tissue and serum samples through MALDI-IMS. Results showed a N-glycan based model capable of differentiating tumor tissue from background liver tissue with an AUC of 0.9842. When analyzing the associated serum, 24.7% of detected N-glycans were significantly positively correlated between tumor tissue and serum, suggesting that N-glycosylation trends translate from tissue to serum. Additionally, a serum N-glycan-based model was capable of distinguishing Subtype 1/Subtype 2 tumors from Subtype 3 tumors with an AUC of 0.881. Through the utilization of MALDI-IMS, subtype-dependent N-glycosylation trends were identified in both tissue and serum, which can significantly further the development of HCC biomarkers for clinical application

Analysis of N-Linked Oligosaccharides of Prostate-Specific Antigen and Prostatic Acid Phosphatase in Prostatic Fluids

Presently, prostate cancer is the most common cancer afflicting men in the United States, with serum PSA being the gold standard protein biomarker used in the clinic for detecting and diagnosing prostate cancer. Nonetheless, serum PSA levels can also be elevated in non-cancerous conditions as well, such as benign prostatic hyperplasia (BPH). Due to this overlap, many unnecessary biopsies and radical prostatectomies occur, leading to patient distress. Despite recent advances to clinical assays which consider other clinical parameters, there is still a great need for improved clinical detection methodologies for prostate cancer, including improved biomarkers. Therefore, this research project aims to examine the N-glycosylation patterns of prostate-specific antigen (PSA) and prostatic acid phosphatase (PAP) in prostate proximal fluids, as well as to examine the total glycan profile for prostate proximal fluids with the intent of discovering carbohydrate-based biomarkers for the detection of early prostate carcinomas. To this end, preliminary glycomic and proteomic studies were completed using seminal plasma samples, based on the disease cohorts normal, BPH, and prostate cancer. These samples resulted in sufficient protein levels of both PSA and PAP for glycopeptide and glycomic analyses. Furthermore, these studies led to additional knowledge of PSA and PAP glycosylation. In addition to this sample set, pools of disease-defined expressed prostatic secretions (EPS) were generated and subsequently analyzed for detection of both protein levels and carbohydrate structures of PSA and PAP, as well as examined for their total glycomic profile. We succeeded in characterizing EPS fluids for both protein and carbohydrate content, as well as identified potential carbohydrate targets for the generation of new clinical assays for the detection of early prostate carcinomas. These targets are fucosylated, complex sub-type glycans which we found to be under-expressed in prostate carcinoma samples as compared to their non-cancerous counterparts. We believe EPS fluids have utility not only for discovery of cancer biomarkers, but also for use in future clinical assays

Analysis and characterisation of human chorionic gonadotropin glycoforms in pregnancy and trophoblastic disorders

Human chorionic gonadotropin (hCG) is a heterogeneous glycoprotein hormone with a varying degree of carbohydrate moieties. Its glycosylation arrangements are increasingly recognized as a common and important element of disease pathophysiology and are associated with many disorders including gestational trophoblastic diseases (GTDs). This study aimed to optimise methodologies to permit the characterisation of hCG N-linked glycans from urine samples collected throughout normal pregnancy and GTD using matrix-assisted laser desorption ionisation time of flight mass spectrometry (MALDI-TOF MS). hCG isolated from pooled pregnancy urine was used in this study. All the stages in pregnancy urine preparation were optimised; including conditions for hCG immunopurification, deglycosylation, solid-phase extraction of resulting glycan:protein mixture and application of N-glycans for MALDI-TOF MS analysis. GlycoQuest software was used to characterise specific N-glycans configurations from the resulting MALDI-TOF MS spectra. This methodology was then applied to urine samples collected throughout normal singleton pregnancy and urine collected from patients with choriocarcinoma. The relative abundance of glycans of different molecular weights and specific types (i.e. fucosylated, sialylated, bisected and sulphated) at each stage of normal pregnancy and in GTD were compared. Each stage of optimisation increased the number of N-glycans detected such that we were ultimately able to detect 50 different glycans in normal pregnancy urine. In these samples, advancing gestation was associated with an increase in the proportion of branched N-glycans and multi-fucosylated N-glycans. Also, a significant increase in the proportion of high molecular weight glycans (>2100 Da) between choriocarcinoma and first-trimester normal pregnancy was observed. Further striking differences in the repertoire of glycan expression were also seen in choriocarcinoma urine compared with first-trimester pregnancy urine. The proportion of multi-fucosylated and tri-and tetra antennal, glycans was increased 3 and 2 fold respectively. In addition, 14 unique N-linked glycan structures were identified in choriocarcinoma samples. These included hyperfucosylated (7 fucose groups) and hypersialylated (4 sialic acid groups) glycans. A feature of this unique set of glycans was that they contained a combination of multiple branching, fucosylation, sialylation, sulphation and glycans with Lewis X terminal epitopes. In summary, we have successfully developed a methodology for the detection of a diverse range of N-linked glycans from hCG. These results suggest that this approach can be successfully used for the detection of novel glyco-biomarkers for the early detection of choriocarcinoma and may be applied to other GTDs associated with a dysregulation of hCG expression

An Imaging Mass Spectrometry Investigation Into the N-linked Glycosylation Landscape of Pancreatic Ductal Adenocarcinoma and the Development of Associated Tools for Enhanced Glycan Separation and Characterization

The severity of pancreatic ductal adenocarcinoma (PDAC) is largely attributed to a failure to detect the disease before metastatic spread has occurred. CA19-9, a carbohydrate biomarker, is used clinically to surveille disease progression, but due to specificity challenges is not suitable for early discovery. As CA19-9 and other prospective markers are glycan epitopes, there is great clinical interest in understanding the glycobiology of pancreatic cancer. Unfortunately, few studies have been able to link glycosylation changes directly to pancreatic tumors and instead have focused on peripheral glycan alterations in the serum of PDAC patients. To address this gap in our understanding, we applied an imaging mass spectrometry (IMS) approach with complementary enzymatic and chemical isomer separation techniques to spatially assess the PDAC N-glycome in a cohort of pancreatic cancer patients. Orthogonally, we characterized the expression of CA19-9 and a new biomarker, sTRA, by multi-round immunofluorescence (IF) in the same cohort. These analyses revealed increased sialylation, fucosylation and branching amongst other structural themes in areas of PDAC tumor tissue. CA19-9 expressing tumors were defined by multiply branched, fucosylated bisecting N-glycans while sTRA expressing tumors favored tetraantennary N-glycans with polylactosamine extensions. IMS and IF-derived glycan and biomarker features were used to build classification models that detected PDAC tissue with an AUC of 0.939, outperforming models using either dataset individually. While studying sialylation isomers in our PDAC cohort, we saw an opportunity to enhance the chemical derivatization protocol we were using to address its shortcomings and expand its functionality. Subsequently, we developed a set of novel amidation-amidation strategies to stabilize and differentially label 2,3 and 2,6-linked sialic acids. In our alkyne-based approach, the differential mass shifts induced by the reactions allow for isomeric discrimination in imaging mass spectrometry experiments. This scheme, termed AAXL, was further characterized in clinical tissue specimens, biofluids and cultured cells. Our azide-based approach, termed AAN3, was more suitable for bioorthogonal applications, where the azide tag installed on 2,3 and 2,8-sialic acids could be reacted by click chemistry with a biotin-alkyne for subsequent streptavidin-peroxidase staining. Furthering the use of AAN3, we developed two additional techniques to fluorescently label (SAFER) and preferentially enrich (SABER) 2,3 and 2,8-linked sialic acids for more advanced glycomic applications. Initial experiments with these novel approaches have shown successful fluorescent staining and the identification of over 100 sialylated glycoproteins by LC-MS/MS. These four bioorthogonal strategies provide a new glycomic tool set for the characterization of sialic acid isomers in pancreatic and other cancers. Overall, this work furthers our collective understanding of the glycobiology underpinning pancreatic cancer and potentiates the discovery of novel carbohydrate biomarkers for the early detection of PDAC

Developing and Utilizing Novel Biofluid N-Glycan Profiling Methods for the Classification of Suspicious Mammogram Findings

Biofluids are a great source of biomarkers because they reflect the immune and metabolic status of cells throughout the body and can be collected non-invasively. Changes in the levels and compositions of N-glycans released from serum and plasma glycoproteins have been assessed in many diseases across many large clinical sample cohorts. Assays used for N-glycan profiling in these fluids currently require multiple lengthy processing steps, thus diminishing their potential for use as standard clinical diagnostic assays. In response to this need for rapid, simple, and high-throughput biofluid analysis, a novel slide-based biofluid profiling platform was developed for the detection of N-glycan alterations that can function as biomarkers of disease. This platform was initially validated for the analysis of serum and plasma N-glycan profiles but was also adapted for the evaluation of urine and prostatic fluid N-glycan profiles. Key to these workflows was the immobilization of the fluid glycoproteins to a slide that was washed of all contaminants, followed by the rapid and highly sensitive detection of enzymatically released N-glycans by mass spectrometry. The enzyme used to release the N-glycans can be changed to target and increase the sensitivity for specific N-glycan classes. To demonstrate the utility and feasibility of applying this platform, serum samples from 199 women with breast cancer and 99 women with a benign lesion in their breast were analyzed in order to identify differences in the N-glycan profiles. The overall N-glycan profiles of the two patient groups had no differences, but there were several individual N-glycans with significant differences in intensities between patients with benign lesions and ductal carcinoma in situ (DCIS). For women aged 50 - 74 with a body mass index of 18.5 - 24.9, a model including the intensities of two N-glycans, 1850.666 m/z and 2163.743 m/z, age, and BMI were able to clearly distinguish the breast cancer patients from the patients with benign lesions with an AUROC of 0.899 and an optimal cutoff with 82% sensitivity and 84% specificity. This study indicates that serum N-glycan profiling is a promising approach for providing clarity for breast cancer screening, especially within the subset of healthy weight women in the age group recommended for mammograms. Overall, the workflows described in this dissertation have displayed the sensitivity, adaptability, and throughput to be utilized as a biomarker discovery platform with significant clinical utility

The Use of Proteomic Technologies to Identify Serum Glycoproteins for the Early Detection of Liver and Prostate Cancers

The application of proteomic technologies to identify serum glycoproteins is an emerging technique to identify new biomarkers indicative of disease severity. Many of these newly evolving protein-profiling methodologies have evolved from previous global protein expression profiling studies such as those involving SELDI-TOF-MS technologies. Though the SELDI approach could distinguish disease from normal by utilizing protein patterns as shown herein with the HCC study of chapter II, it was unable to offer sequence information on the selected peaks, and did not have the ability to analyze the entire dynamic range of the serum/plasma proteome. To address these deficiencies, new strategies that incorporate the use of differential lectin-based glycoprotein capture and targeted immuno-based assays have been developed. The carbohydrate binding specificities of different lectins offers a biological affinity approach that both complements existing mass spectrometer capabilities and retains automated throughput options. A prostate cancer study using disease stratified samples is utilized herein to determine whether lectin capture can identify glycoproteins, which are indicative of different stages of prostate disease. By utilizing upfront lectin fractionation we show here evidence of glycoproteins and glycoprotein isoforms, which are specific to cancer progression. In addition, the incorporation of lectin fractionation followed by albumin depletion allows for a more in depth analysis of the entire dynamic range of the human serum and plasma proteome. Taken together we believe this approach is an attractive strategy for the discovery of proteins indicative of the early detection of liver and prostate cancers