Abstract
Prostate cancer is the most common cancer in men worldwide. Despite its prevalence, there is a critical knowledge gap in understanding factors driving disparities in survival among different cohorts of prostate cancer patients. Identifying molecular features separating disparate populations is an important first step in prostate cancer research that could lead fundamental hypotheses in prostate biology, predictive biomarker discovery, and personalized therapy. N-linked glycosylation is a co-translational event during protein folding that modulates a myriad of cellular processes. Recently, aberrant N-linked glycosylation has been reported in prostate cancers. However, the full clinical implications of dysregulated glycosylation in prostate cancer has yet to be explored. Herein, we performed direct on-tissue analysis of N-linked glycans using matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) from tissue microarrays of over 100 patient tumors with over 10 years of follow-up metadata. We successfully identified a panel of N-glycans that are unique between benign and prostate tumor tissue. Specifically, high-mannose as well as tri-and tetra-antennary N-glycans were more abundant in tumor tissue and increase proportionally with tumor grade. Further, we expanded our analyses to examine the N-glycan profiles of Black and Appalachian patients and have identified unique glycan signatures that correlate with recurrence in each population. Our study highlights the potential applications of MALDI-MSI for digital pathology and biomarker discovery for prostate cancer.
Introduction
Prostate cancer is the most common cancer in men and is the second leading cause of cancer-related mortality in men worldwide (1). Many factors contribute to the development and progression of prostate cancer including age, family history, ethnicity, and diet or lifestyle (2). Patient prognosis largely depends on tumor grade, more specifically referred to as the “grade group”, which is determined by microscopic histopathologic examination (3, 4). While patients diagnosed with low-grade prostate tumors have a 99% 5-year survival rate, patients with higher grade tumors and those who present with distant metastasis have significantly poorer survival (3). Standard treatment for prostate cancer includes active surveillance for patients with low-grade tumors, localized therapy (radical prostatectomy and/or radiation) for intermediate and selected high-grade tumors, and hormone therapy for patients with recurrence or metastatic disease (2). Despite the prognostic correlation with tumor grade group, health disparities further contribute to prostate cancer patient outcomes. For example, Black males and men from rural Appalachia have a poorer prognosis even when diagnosed with low-grade prostate tumors (5–8). Further, preliminary genomic profiling studies have revealed distinct differences between prostate cancer patients of African descent compared to European descent (9). One critical knowledge gap in prostate cancer biology is the molecular events underlying higher incidence and mortality rates within the Black and Appalachian patient populations, which could lead to better understanding of prostate cancer biology, predictive biomarkers, and personalized therapy.
N-linked glycosylation is a co-translational event necessary for cell surface, secreted, and circulating proteins, wherein glycoconjugates containing N-acetylglucosamine (GlcNAc) are covalently attached to asparagine residues on the nascent carrier protein, followed by sequential addition of monosaccharides such as mannose, fucose, sialic acid, or GlcNAc (10). Several biological processes are regulated by N-linked glycosylation including cell adhesion, immune modulation, cell-matrix interactions, and cell proliferation (11–15). Recent glycomic and proteomic studies have revealed extensive alterations in both the N-glycan profile and glycosyltransferase expression of several human cancers, including breast, lung, and prostate (16). Moreover, aberrant N-glycosylation has been shown to directly facilitate epithelial-to-mesenchymal transition (EMT) and subsequent metastatic protentional of cancer cells by directly altering the activity of extracellular matrix proteins and growth factor signaling (17). Given the role of N-glycosylation during EMT and metastasis, defining the N-glycome of prostate tumors could provide insight into the molecular mechanisms driving prostate cancer progression and could be used to discover new biomarkers or potential novel therapies.
Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is a new and innovative technique in glycobiology that can be used to profile N-glycans with spatial distribution in formalin-fixed paraffin-embedded (FFPE) samples and high throughput analysis of tissue microarrays (TMAs) (18, 19). This novel approach uniquely utilizes 1) the enzyme peptide-N-glycosidase F (PNGase F) that directly releases in situ N-linked glycans from glycoproteins, and 2) α-cyano-4-hydroxycinnamic acid (CHCA) ionization matrix for detection of N-linked glycans by MALDI-MSI (20). Previous studies have revealed distinct alterations in the N-glycan distribution between normal and prostate tumor tissue (19, 21), with several of the N-glycan species elevated in prostate cancer being linked to EMT and metastasis (22, 23). Current MALDI-MSI analyses of prostate cancer tissues have utilized large prostate tissue sections, and elegantly describe the N-glycan spatial differences between tumor and nontumor regions (19, 21). Given these recent findings and the role of N-glycans in EMT, we hypothesized that N-glycan profiling may have the potential to both define tumor grade and predict overall patient outcome in prostate cancer. We performed MALDI-MSI analysis on FFPE prostate cancer TMAs constructed from archived human prostate tissues from over 100 patients treated at the Markey Cancer Center. This patient data set included both cancer and matched normal tissue from racially and geographically diverse patients with over 10 years of follow-up metadata, allowing us to evaluate N-glycans as prognostic indicators for the long-term clinical course of prostate cancer progression.
We observed significant N-glycan dysregulation between benign prostate tissue and tumor prostate tissue with several glycans tracking either positively or negatively with tumor grade group. Specifically, high mannose as well as tri- and tetra-antennary branched N-glycans were more abundant within tumor tissue and correlated with increasing tumor grade. Further, we expanded our analyses to access glycosylation patterns in patient populations disproportionally affected by prostate cancer. We found statistically significant differences in the N-glycan profiles of low-grade group prostate cancer tumors between our cohort of Black and White patients. Moreover, we have identified a glycan signature that separates Appalachian patients who developed disease recurrence compared to those who remained disease-free. This striking data highlights fundamental differences in carbohydrate metabolism may represent a novel research strategy for the treatment of prostate cancer. Overall, our data suggest that aberrant N-linked glycosylation correlates with the clinical course of prostate cancer, which highlights the clinical potential of MALDI-MSI analysis for novel biomarker discovery, and emphasizes the need for personalized medicine for prostate cancer patients.
Methods
Chemicals and Reagents
High-performance liquid chromatography-grade acetonitrile, ethanol, methanol, water, α-cyano-4-hydroxycinnamic acid (CHCA), and trifluoroacetic acid (TFA) were purchased from Sigma-Aldrich. Histological-grade xylenes was purchased from Spectrum Chemical. Citraconic anhydride for antigen retrieval was obtained from Thermo Fisher Scientific. Recombinant PNGaseF Prime was obtained from Bulldog Bio, Inc. (Portsmith, NH, USA).
Clinical Prostate Cancer FFPE Tissue Microarrays
Tissue microarrays (TMAs) were created from residual FFPE radical prostatectomy samples by the Biospecimen Procurement and Translation Pathology Shared Resource Facility (BPTP SRF) of the Markey Cancer Center MCC with approval from the institutional review board (IRB). These specimens were coupled with de-identified demographic and clinical data provided by the Cancer Research Informatics (CRI) SRF and the MCC with approval from the IRB. The TMAs contained prostate tumor tissue (n=108 samples) and benign prostate tissue (n=30 samples) from 138 patients. Clinical and demographic data are summarized in Table 1. All tissues were de-identified to the investigators.
Table 1. Summary table of clinicopathological patient parameters for prostate cancer TMA samples.
Clinicopathological parameters assessed include tumor grade group, disease recurrence, race, and geographic region.
| Variable | ||
|---|---|---|
| Clinical Grade Group | Patients (n) | Avg Age at Diagnosis |
| 1 (Total) | 19 | 57.4 (±7.8) |
| 2 (Total) | 50 | 56.7 (±5.9) |
| 3 (Total) | 21 | 61.0 (±6.8) |
| 4 (Total) | 3 | 60.3 (±7.4) |
| 5 (Total) | 15 | 60.7 (±6.1) |
| Race | Patients (n) | Avg Age at Diagnosis |
| White (Total) | 81 | 58.3 (±6.8) |
| Grade Group 1 | 15 | 57.7 (±7.3) |
| Grade Group 2 | 32 | 56.0 (±6.1) |
| Grade Group 3 | 19 | 60.9 (±7.1) |
| Grade Group 5 | 15 | 60.7 (±6.1) |
| Black (Total) | 21 | 57.5 (±6.6) |
| Grade Group 1 | 4 | 56.5 (±10.8) |
| Grade Group 2 | 15 | 57.3 (±5.9) |
| Grade Group 3 | 2 | 61 (±2.8) |
| Asian (Total) | 3 | 61 (±1.7) |
| Geographic Region | Patients (n) | Avg Age at Diagnosis |
| Appalachian County | 37 | 57.3 (±6.9) |
| Grade Group 1 | 6 | 57.1 (±9.5) |
| Grade Group 2 | 19 | 56.1 (±3.7) |
| Grade Group 3 | 5 | 59.2 (±13.0) |
| Grade Group 5 | 6 | 59.7 (±6.3) |
| Non-Appalachian County | 66 | 58.7 (±6.5) |
| Grade Group 1 | 12 | 57.6 (±7.1) |
| Grade Group 2 | 30 | 57.1 (±7.1) |
| Grade Group 3 | 15 | 61.2 (±3.8) |
| Grade Group 5 | 9 | 61.3 (±6.3) |
| Recurrence | Patients (n) | Avg Age at Diagnosis |
| White | ||
| None | 27 | 59.7 (±7.0) |
| Recurrence | 53 | 57.5 (±6.6) |
| Black | ||
| None | 4 | 59.3 (±5.2) |
| Recurrence | 17 | 57.1 (±7.0) |
| Appalachian County | ||
| None | 12 | 59.9 (±7.8) |
| Recurrence | 25 | 56.0 (±6.3) |
| Non-Appalachian Country | ||
| None | 20 | 59.7 (±6.0) |
| Recurrence | 46 | 58.3 (±6.8) |
Tissue Preparation and Enzyme Digestion
FFPE TMA slides were sectioned at 4 μm on positively charged glass slides and processed as previously described (20, 24). In brief, tissues were dewaxed and rehydrated followed by antigen retrieval in citraconic anhydride buffer (25μl citraconic anhydride, 2μl 12 M HCl, and 50ml HPLC-grade water, pH 3.0–3.5). Recombinant PNGase F (0.1μg/μl) was applied using an M5 TMSprayer Robot (HTX Technologies LLC, Chapel Hill, NC). Enzyme was sprayed onto the slide at a rate of 25μl/min with a 0mm offset and a velocity of 1200mm/min at 45°C and 10psi for 15 passes, followed by a 2-hour incubation at 37°C in a prewarmed humidity chamber. After incubation, slides were desiccated and 7mg/ml CHCA matrix in 50% acetonitrile with 0.1% TFA was applied at 100 μl/min with a 2.5mm offset and a velocity of 1300mm/min at 79°C and 10psi for 10 passes using the M5 Sprayer. Slides were stored in a desiccator or immediately used for MALDI-MSI analysis.
N-Glycan MALDI-MSI Analysis
A Waters Synapt G2-Si mass spectrometer (Waters Corporation, Milford, MA) equipped with an Nd:YAG UV laser with a spot size of 50μm was used to detect released N-glycans at X and Y coordinates of 75μm. Spectra acquired were uploaded to High Definition Imaging (HDI) Software (Waters Corporation) for mass range analysis from 750 to 3500 m/z. For N-glycan quantification, regions of interest (ROI) were defined for the entire patient sample core on the TMAs using HDI image ROI drawing tool. For all pixels defined within each ROI, peak intensities were averaged and normalized by total ion current. For inter-sample normalization, the relative abundance for all N-glycans within each patient sample were normalized to the average total ion intensity as described in (25). Representative glycan structures were generated in GlycoWorkbench.
Statistics
Statistical analyses were carried out using GraphPad Prism 9.0 and Metaboanalyst 5.0. For Metaboanalyst multivariate analyses of all glycans, log transformation and auto scaling were used for normalization. Heatmaps were generated based on the Euclidean distance measure and the Ward clustering algorithm. Glycans with variable importance in projection (VIP) scores>1.5 based on partial least squares discriminant analysis (PLS-DA) were selected for further analysis. For biomarker analysis, areas under the curve (AUC) were obtained using multivariate receiver operating characteristic (ROC) analysis based on the linear SVM classification method and SVM feature ranking method. For univariate analysis of individual glycans, all numerical data were analyzed using GraphPad Prism 9.0 and are presented as individual data points ± S.E.M. Column analyses were performed using Student’s t-test with Welch’s correction when appropriate. A p-value less than 0.05 was considered statistically significant.
Study Approval
TMAs containing human prostate tissue were created by the BPTP SRF of the Markey Cancer Center with approval from the IRB. Samples were coupled with de-identified demographic and clinical data provided by the CRI SRF of the Markey Cancer Center with approval from the IRB. Use of the tissue and de-identified information for the purpose of this study was given an exempt status from the IRB.
Results
Utilizing TMAs for high-throughput analysis of prostate tumors N-glycans by MALDI-MSI
Previous MALDI-MSI analyses of prostate cancer tissue sections have revealed distinct differences in the spatial distribution of several species of N-glycans between tumor and nontumor regions (19, 21). We aimed to expand on these observations and utilized MALDI-MSI analysis to define the N-glycome of over 100 prostate cancer patients with demographical information and clinical course. We obtained FFPE prostate TMAs containing both benign prostate tissue and prostate tumor tissue constructed from patients who underwent radical prostatectomy. This unique patient cohort with over 10 years of patient follow-up metadata allowed survival analysis against various clinical parameters (Supplemental Figure 1). The TMAs analyzed included patient samples of prostate cancer grade groups 1 through 5, and clinicopathological parameters included race, geographic location, as well as disease recurrence and patient survival (Table 1). Utilizing the modern grading system, few patients are diagnosed with grade group 4 prostate cancer at radical prostatectomy (26), and this fact is reflected in our cohort of patients with only three grade group 4 patient samples. Therefore, these samples were omitted from the analysis due to a low statistical power. TMA slides were prepared using a previously established MALDI-MSI workflow (19, 20). First, bound N-glycans were cleaved from glycoproteins by the addition of PNGaseF; then, the CHCA ionization matrix was applied uniformly using an HTX high velocity dry-spraying robot (27). Released N-glycans were analyzed using a Waters Synapt G2 ion-mobility enabled mass spectrometer equipped with an Nd:YAG UV laser (Figure 1A). Ion mobility improved glycan detection by separating N-glycans from ionization matrix based on differential collision cross section (Figure 1B). Using this method, we detected 46 N-glycans across all tissue samples (Figure 1C and Table 2). Imaging capability allows the direction visualization of all patient tumor cores on the same scale. Representative HDI software images of four distinct classes of N-glycans: core fucose (1809 m/z), high-mannose (1743 m/z), bisecting (1704 m/z), and sialylated (1976 m/z) are shown in Figure 1D–G, and exhibit a wide range of abundance across the TMA.
Figure 1. Overview of MALDI-MSI workflow for N-glycan detection in prostate cancer TMAs.
(A) Schematic workflow of N-glycan imaging using MALDI coupled to high resolution mass spectrometry. In brief, FFPE TMAs were sectioned to 4μm followed by dewaxing and antigen retrieval. Slides were treated with PNGase F to release N-glycans followed by CHCA (ionization matrix) application using an HTX high velocity sprayer. Glycans and matrix were ionized using a Nd:YAG UV laser and were separated by ion mobility. Glycan mass spectra were acquired by Waters Synapt G2-Si high resolution mass spectrometer. (B) Scatter plot of monoisotopic mass versus drift time in the ion mobility cell for N-glycans and the MALDI matrix. (C) Extracted ion chromatogram of released N-glycans based on ion mobility separation. Structures of several detected glycans are shown on the plot. (D) HDI images of 1809 m/z, (E) 1743 m/z, (F) 1704 m/z, and (G) 1976 m/z. Glycan structures are placed on the right side of their corresponding image. Structure key: blue square-N-acetylglucosamine, green circle-mannose, yellow circle-galactose, purple diamond-sialic acid, and red triangle-fucose. Intensity gradient from blue (least abundant) to yellow (most abundant), and scale bar are located beneath image. Scale bar – 5 mm.
Table 2. Summary table of composition, m/z values, and class type for N-glycans detected in tumor and benign tissue from prostate cancer TMAs.
Cumulative list of 46 glycans detected in tissues analyzed in Figures 1–6. This includes the composition, average detected mass m/z, and N-glycan class.
| Composition | Actual mass m/z | N-glycan type |
|---|---|---|
| Hex2HexNAc2 | 771.2650 | Pauci mannose |
| Hex3HexNAc2 | 933.3152 | Pacui mannose |
| Hex3dHex1HexNAc2 | 1079.3717 | Pauci mannose |
| Hex4HexNAc2 | 1095.3668 | Pauci mannose |
| Hex3HexNAc3 | 1136.3923 | Pauci mannose |
| Hex5HexNAc2 | 1257.4135 | High mannose |
| Hex3dHex1HexNAc3 | 1282.4407 | Pauci mannose |
| Hex4HexNAc3 | 1298.4361 | Complex |
| Hex3HexNAc4 | 1339.4385 | Complex |
| Hex6HexNAc2 | 1419.4488 | High mannose |
| Hex4dHex1HexNAc3 | 1444.4759 | Complex |
| Hex5HexNAc3 | 1460.4656 | Hybrid |
| Hex3dHex1HexNAc4 | 1485.4943 | Complex |
| Hex4HexNAc4 | 1501.4809 | Complex |
| Hex3HexNAc5 | 1542.4761 | Complex bisecting |
| Hex7HexNAc2 | 1581.4683 | High mannose |
| Hex6HexNAc3 | 1622.4835 | Hybrid |
| Hex4dHex1HexNAc4 | 1647.5090 | Complex |
| Hex5HexNAc4 | 1663.5004 | Complex |
| Hex3dHex1HexNAc5 | 1688.5169 | Complex bisecting |
| Hex4HexNAc5 | 1704.5035 | Complex bisecting |
| Hex8HexNAc2 | 1743.4685 | High mannose |
| Hex4HexNAc4NeuAc1 | 1791.4917 | Complex |
| Hex4dHex2HexNAc4 | 1793.4945 | Complex |
| Hex5dHex1HexNAc4 | 1809.5004 | Complex |
| Hex4dHex1HexNAc5 | 1850.5032 | Complex bisecting |
| Hex5HexNAc5 | 1866.4827 | Complex bisecting |
| Hex3dHex1HexNAc6 | 1891.4668 | Complex |
| Hex9HexNAc2 | 1905.4371 | High mannose |
| Hex5dHex1HexNAc4 + 2Na + SO4 | 1911.4492 | Complex |
| Hex5HexNAc4NeuAc1 | 1954.4389 | Complex |
| Hex5dHex2HexNAc4 | 1955.4591 | Complex |
| Hex5HexNAc4NeuAc1 + 2Na | 1976.4181 | Complex |
| Hex5dHex1HexNAc5 | 2012.4494 | Complex bisecting |
| Hex6HexNAc5 | 2028.4315 | Complex bisecting |
| Hex5dHex2HexNAc4 + 2Na + SO4 | 2057.4372 | Complex |
| Hex5dHex1HexNAc4NeuAc1 | 2100.3839 | Complex |
| Hex5dHex3HexNAc4 | 2101.3887 | Complex |
| Hex5dHex1HexNAc4NeuAc1 + 2Na | 2122.3506 | Complex |
| Hex4dHex1HexNAc5NeuAc1 + 2Na | 2163.3204 | Complex |
| Hex6dHex1HexNAc5 | 2174.3516 | Complex bisecting |
| Hex5HexNAc4NeuAc2 + 3Na | 2289.1741 | Complex |
| Hex6dHex2HexNAc5 | 2320.2197 | Complex |
| Hex6HexNAc5NeuAc1 + 2Na | 2341.1551 | Complex |
| Hex7HexNAc6 | 2393.1311 | Complex |
| Hex7dHex1HexNAc6 | 2539.9269 | Complex |
To investigate the unique N-glycan profile of prostate tumors, we first compared the N-glycome of benign and prostate tumor tissue. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue was moderately separated from benign by partial least squares discriminant analysis (PLS-DA) (Figure 2A). The top 15 most discriminant N-glycans revealed by variable importance in projection (VIP) analysis after PLS-DA are listed in Figure 2B. Consistent with previous studies, we observed increases in both high-mannose (Figure 2C–E) (19, 21) as well as branched (Figure 2F–H) N-glycans in prostate tumor tissue compared to benign. VIP analysis identified several additional discriminant N-glycan structures between benign and prostate tumor tissue including biantenarry complex, core fucosylated, bisecting, and sialylated, which were further analyzed by student’s t-test. We observed a significant decrease in both 1663 m/z and 1976 m/z, a bianetarry complex and sialylated N-glycan, respectively (Supplemental Figure 2A,B). Additionally, 1501 m/z (bianetarry complex), 1704 m/z (bisecting), and 1809 m/z (core fucosylated) trend towards decreased abundance in prostate tumor tissue, however this was not found to be statistically significant (Supplemental Figure 2C–E). We next utilized multivariate receiver operating characteristic (ROC) analysis to investigate the utility of N-glycan profiling in distinguishing between benign and prostate tumor tissue. ROC analysis of the top 5 most discriminant N-glycans identified by VIP analysis yielded an accuracy of 0.612 (95% CI: 0.167–0.784) (Figure 2I); however, ROC analysis using a panel of only high-mannose and branched N-glycans improved this to 0.676 (95% CI: 0.546–0.832) (Figure 2J). These findings reaffirm that aberrant N-linked glycans are clinical features of prostate cancers (19, 21).
Figure 2. Prostate tumor tissue exhibits increased abundance of high-mannose and branched N-glycans.
(A) Multivariate analysis of N-glycans found in benign tissue and grouped prostate tumor tissue by PLS-DA displaying 95% confidence intervals. (B) VIP analysis showing top 5 most discriminant N-glycans revealed by PLS-DA. (C-H) N-glycan relative abundance for benign versus grouped prostate tumor tissue (left) and representative structure (right) for high mannose N-glycans: (C) 1581 m/z, (D) 1743 m/z, (E) 1905 m/z, and branched N-glycans: (F) 1891 m/z, (G) 2320 m/z, (H) 2539 m/z. Values represent individual patient samples ± S.E.M, analyzed by Student’s t-test with Welch’s correction. *p<0.05 and **p<0.01. (I) Multivariate ROC analysis of top 15 discriminant glycans revealed by PLS-DA between benign and grouped prostate tumors displaying 95% confidence interval. (J) Multivariate ROC analysis of high-mannose and branched glycans between benign and grouped prostate tumors displaying 95% confidence interval. Structure key: blue square-N-acetylglucosamine, green circle-mannose, yellow circle-galactose, purple diamond-sialic acid, and red triangle-fucose.
Prostate tumors exhibit increased high-mannose and branched complex N-glycans in a grade group-dependent manner
Building on our initial observation between the benign and tumor N-linked glycan profile, we hypothesize that certain glycans will track with prostate grade groups. To test this hypothesis, we expanded our analysis to tumor grade group to identify specific N-glycan differences. Unsupervised clustering heatmap analysis demonstrated distinct N-glycan patterns between benign and grade group-specific tumors (Supplemental Figure 2F). Notably, benign and grade group 1 tumors cluster together while grade group 3 and 5 exhibit similar N-glycan profiles. We observed an increase in high-mannose (Figure 3A–C) and branched (Figure 3D–F) N-glycans that positively correlated with tumor grade group. Further, we observed a grade group dependent decrease in both non-fucosylated (1663 and 1501 m/z) (Supplemental Figure 2G–H) and core fucosylated (1809 m/z) (Supplemental Figure 2I) structures of several biantennary N-glycans in prostate tumor tissue compared to benign.
Figure 3. Prostate tumor tissue exhibits increased abundance of high mannose and branched N-glycans proportional to tumor grade group.
N-glycan relative abundance stratified by tumor grade group (left), representative structure (right), and representative TMA cores for each grade group (below): (A) 1581 m/z, (B) 1743 m/z, (C) 1905 m/z, (D) 1891 m/z, (E) 2320 m/z, and (F) 2539 m/z. Values represent mean ± S.E.M., analyzed by a simple linear regression. p-values are listed for each glycan. Structure key: blue square-N-acetylglucosamine, green circle-mannose, yellow circle-galactose, and red triangle-fucose. Intensity gradient from blue (least abundant) to yellow (most abundant), and size scale bar are located beneath the image. Scale bar – 2 mm.
High-mannose glycans are produced early in the N-glycan biosynthetic pathway, wherein mannose residues are sequentially added to the growing glycan chain in the golgi, followed by further processing by mannosidases and glycotransferases into more structurally diverse complex and hybrid glycans (28, 29). Excess levels of high-mannose glycans are routinely detected in cancer tissues and have been implicated in the progression of several human cancers including liver, lung, and breast (30–32). Further, tri- and tetra-antennary branched glycan structures have been linked to many aspects of tumorigenesis including neoplastic transformation, cell proliferation, and abnormal cell morphology (33). Moreover, it has been demonstrated that the addition of tri- and tetra- antennary branched glycans to E-cadherin impairs cell adhesion and promotes tumor cell invasion (34). Our findings demonstrate accumulation of these N-glycan structures correlate with tumor progression. In addition to being less abundant in prostate tumor tissue compared to benign, we found the abundance of the sialylated N-glycan 1976 m/z decreases with tumor grade group (Supplemental Figure 2J). Consistent with this observation, we found abundance of the bisecting N-glycan, 1704 m/z decreases proportionally to tumor grade group (Supplemental Figure 2K). Together, these changes represent a diverse classes of N-linked glycans that have been previously attributed to participate in resistance to cell death (35), evasion of immune surveillance (36), and alterations of surface receptor ligand binding, dimerization, and signaling capacities (37–39). Collectively, our data suggest re-organization of the N-linked glycan profile within the prostate cancer tissues that correlates to the aggressive nature of higher grade group prostate cancer.
Elevated 2320 m/z is a potential biomarker for disease progression across all patient populations
Higher tumor grade groups are typically associated with poorer patient outcomes and an increased likelihood of developing disease recurrence (3). Therefore, we hypothesized that specific alterations in N-linked glycosylation may predict the clinical course of prostate cancer progression. To assess this hypothesis, we took advantage of the 10 year follow-up metadata linked to the our patient cohort for survival analysis. First, we compared the N-glycan profile of prostate tumors between patients who did or did not have disease recurrence. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue from patients with disease recurrence significantly overlaps with patients who did not have disease progression by PLS-DA (Figure 4A). The top 15 most discriminant N-glycans revealed by VIP analysis after PLS-DA are listed in Figure 4B. Multivariate ROC analysis of the top 5 VIP scored glycans yielded an accuracy of 0.590 (95% CI: 0.456–0.697) (Figure 4C). These glycan signatures could not robustly predict patients with recurrence with high accuracy; however, we identified one branched N-glycan (2320 m/z) that was significantly more abundant in patients who had recurrence (Figure 4D). Further, we stratified patients by low (below median) and high (above median) 2320 m/z abundance and found high 2320 m/z patients had poorer survival by Kaplan-Meier analysis (Figure 4E).
Figure 4. Disease recurrence in all prostate tumor patients can be predicted by N-glycans.
(A) Multivariate analysis of tumor N-glycans found in patients with and without disease recurrence by PLS-DA displaying 95% confidence intervals. (B) VIP analysis showing top 15 most discriminant N-glycans revealed by PLS-DA. (C) Multivariate ROC analysis of top 5 discriminant glycans revealed by PLS-DA between disease-free and patients with recurrence displaying 95% confidence interval. (D) Relative abundance of N-glycan 2320 m/z for disease-free and patients with recurrence. Values represent individual patient samples ± S.E.M, analyzed by Student’s t-test with Welch’s correction. *p<0.05. (E) Kaplan-Meier analysis of progression free survival for all patients based on high (above median) and low (below median) abundance of 2320 m/z. Structure key: blue square-N-acetylglucosamine, green circle-mannose, yellow circle-galactose, and red triangle-fucose.
N-glycan profiles differ between low grade group tumors from Black and White patients
It is well known that health disparities exist within prostate cancer patient cohorts, specifically in Black men and men from rural Appalachia (5–8). Thus, we expanded our analyses to these distinct patient populations to identify potential N-glycan signatures that could be utilized for biomarker applications. While increasing evidence suggests that molecular and genetic alterations contribute to the racial disparity between Black and White prostate cancer patients (40, 41), robust and specific molecular features that correlate with accelerated disease progression in Black men remains largely unknown. Our TMAs included a modest cohort of Black patients with grade group 1 and 2 prostate tumors treated at the Markey Cancer Center, thus we expanded our analysis to examine the N-glycan profile of low-grade group tumors from White and Black prostate cancer patients. Unsupervised clustering heatmap analysis demonstrated grade group 2 tumors between Black and White patients cluster together, while grade group 1 tumors are distinct between the two patient cohorts (Figure 5A). This was further confirmed by PLS-DA analysis (Figure 5B,C). Specifically, we observed increased abundance of several N-glycans in grade group 1 tumors from Black patients including branched (1891 m/z), bisecting (1866 and 2174 m/z), and biantenarry complex (1663 m/z) (Figure 5D–G). Due to low sample size of Black patients from our cohort with grade group 1 tumors (n=4), we aimed to validate these findings in larger tumor tissues with additional spatial information. We identified one Black and one White patient diagnosed with grade group 1 tumors, treated by radical prostatectomy at the Markey Cancer Center, and analyzed the relative abundance of the four N-glycans found to be significant in our TMA cohort (Supplemental Figure 3A,B). We confirmed an increased in all but one of the N-glycans (1891 m/z) in the Black patient tumor compared to White (Supplemental Figure 3C–G). However, the abundance of all four N-glycans that were higher in the Black patient tumor were further elevated in adjacent benign tissue (Supplementary Figure 4). Together, these data suggest the difference between grade group 1 prostate tumors from Black and White patients could be due to adjacent and intervening benign tissue.
Figure 5. Low grade prostate cancer tumors between Black and White patients exhibit significantly different N-glycan profiles.
(A) Unsupervised clustering heatmap analysis of N-glycans stratified by race and grade group. (B-C) Multivariate analysis of N-glycans between White and Black patients for (B) grade group 1 and (C) grade group 2 tumors by PLS-DA displaying 95% confidence intervals. (D-G) Relative abundance of selected N-glycans in grade group 1 tumor tissue from White and Black patients (left), representative structure (right), and representative TMA cores for White (above) and Black (below): (D) 1891 m/z, (E) 1866 m/z, (F) 2174 m/z, and (G) 1663 m/z. Values represent individual patient samples ± S.E.M, analyzed by Student’s t-test with Welch’s correction. *p<0.05. Structure key: blue square-N-acetylglucosamine, green circle-mannose, yellow circle-galactose, and red triangle-fucose. Intensity gradient from blue (least abundant) to yellow (most abundant), and size scale bar are located beneath the image. Scale bar – 2 mm.
Given the difference in N-glycan profiles between Black and White patients with low-grade group tumors, we hypothesized that changes in N-linked glycosylation may be a potential biomarker for disease progression in Black patients. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue from Black patients with disease recurrence moderately separates from Black patients who did not have disease progression by PLS-DA (Supplemental Figure 5A). The top 15 most discriminant N-glycans revealed by VIP analysis after PLS-DA are listed in Supplemental Figure 5B. Multivariate ROC analysis of the top 5 VIP scored glycans yielded an accuracy of 0.805 (95% CI: 0.314–1) (Supplemental Figure 5C). Further, we identified one sialylated N-glycan (2341 m/z), that was significantly less abundant in patients who had recurrence (Supplemental Figure 5D), although this did not correspond to a difference in disease progression by Kaplan-Meier analysis (Supplemental Figure 5E). Together, we have identified potential differences in N-glycosylation between low grade group tumors from Black and White patients, and unique N-glycans that associate with disease recurrence in Black patients that warrant further study.
N-linked glycan as potential biomarker and predicts overall survival in patients form rural Appalachia
Several epidemiological studies have revealed prostate cancer patients from rural Appalachia have poorer overall survival despite having lower incidence compared to patients from non-Appalachian counties (6, 8). Yet, the molecular mechanisms underlying this health disparity are largely unknown. Unsupervised clustering (Supplemental Figure 6A) and PLS-DA analysis (Supplemental Figure 6B–E) revealed no distinct differences between grade group-matched Appalachian patients compared to non-Appalachian patients. However, given the health disparity in prostate cancer patients from rural Appalachia, we hypothesized specific changes in N-glycans within this population may contribute to poorer clinical outcomes. We compared the N-glycan profile of prostate tumors between Appalachian patients who did or did not have disease recurrence. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue from Appalachian patients with disease recurrence is distinct from patients who did not have disease progression by PLS-DA (Figure 6A). The top 15 most discriminant N-glycans revealed by VIP analysis after PLS-DA are listed in Figure 6B. Multivariate ROC analysis of the top 5 VIP scored glycans yielded an accuracy of 0.849 (95% CI: 0.709–0.985) (Figure 6C), indicating we have identified an N-glycan panel with the potential to predict disease recurrence in Appalachian patients. Moreover, we identified a bisecting N-glycan (1850 m/z) that was significantly more abundant in Appalachian patients who developed recurrence and correlates to significantly poorer overall survival by Kaplan-Meier analysis (Figure 6D,E). Our data suggest N-glycan profiling could be a effective tool to predict prostate cancer recurrence and survival in disparate populations, and highlights the clinical potential of MALDI-MSI to study the health disparity in this distinct patient population.
Figure 6. N-glycans can predict disease recurrence in Appalachian patients.
(A) Multivariate analysis of tumor N-glycans found in Appalachian patients with and without disease recurrence by PLS-DA displaying 95% confidence interval. (B) VIP analysis showing top 15 most discriminant N-glycans revealed by PLS-DA. (C) Multivariate ROC analysis of top 5 discriminant glycans revealed by PLS-DA between disease-free and Appalachian patients with recurrence displaying 95% confidence interval. (D) Relative abundance of N-glycan 1850 m/z for disease-free and patients with recurrence. Values represent individual patient samples ± S.E.M, analyzed by Student’s t-test with Welch’s correction. ****p<0.0001. (E) Kaplan-Meier analysis of overall survival for all Appalachian patients based on high (above median) and low (below median) abundance of 1850 m/z. Structure key: blue square-N-acetylglucosamine, green circle-mannose, yellow circle-galactose, and red triangle-fucose.
Discussion
An increasing body of evidence suggests that aberrant N-glycosylation plays a key role in several aspects of tumorigenesis, such as tumor cell invasion and metastasis, cell-matrix interactions, tumor angiogenesis, and cell signaling and communication (16). With the advent of new high-throughput mass spectrometry based technologies, such as MALDI-MSI, N-linked glycomic profiling of patient tumor tissues has demonstrated remarkable potential for early diagnosis, risk prediction, and treatment outcome for several cancers (42). Moreover, MALDI-MSI analysis of N-glycans provides insight into the function of N-linked glycosylation in tumor metabolism and cancer progression (43, 44). The use of TMAs is advantageous for high-throughput investigation during a single experiment using widely available FFPE patient samples, often including clinical follow-up data. In the present study, we utilized prostate cancer TMAs including benign and tumor tissue resected from over 100 patients with 10 years of clinical follow-up metadata to perform N-glycan profiling by MALDI-MSI analysis. Given patients with low-grade prostate tumors have a 99% 5-year survival rate, having long-term patient follow-up data is an essential for survival analysis. Specifically, with longer clinical follow-up intervals, we were able to correlate glycans with disease recurrence and overall survival. We identified unique intratumoral glycan signatures that correlate with tumor grade group.
We observed significant dysregulations in multiple species of N-glycans between benign prostate tissue and prostate tumor tissue. Specifically, prostate tumors exhibit accumulation of high-mannose glycans, a common feature of human cancers that correlates with more aggressive cancer phenotypes, that increases with tumor grade group (16, 45). Accumulation of high-mannose N-glycans in prostate tumors suggests a lack of N-glycan trimming reactions and a decrease in mannosidase activity, or aberrant mannose metabolism (30–32). Additionally, we found prostate tumors accumulate tri- and tetra-antennary complex glycans containing a core fucose moiety suggesting prostate tumors have enhanced GlcNAc metabolism. N-glycan β1,6-branching, which gives rise to these structures, has been implicated in several tumorigenic processes including neoplastic transformation, cell proliferation, and abnormal cell morphology (18, 33). Our findings suggest that increased N-glycan β1,6-branching and the accumulation of high-mannose glycans may contribute to prostate cancer progression. Further, we also identified several species of N-glycans that were elevated in benign tissue compared to prostate cancer tumors, including core fucosylated, bisecting, and sialylated glycans. Strikingly, biantennary complex glycans with a core fucose moiety are lower in prostate tumor tissue and decrease with tumor grade group, while tri-and tetra-antennary core fucosylated glycans are increased, suggesting that branching, rather than core fucosylation, may contribute to prostate cancer progression. Together, high-mannose and branched N-glycans show promise in distinguishing tumor versus benign prostate tissue. Future analyses should be extended to a wider spread of clinical behaviors (response to therapy, co-morbidities, driver mutations, etc.) to define the full prognostic value of these specific glycans in prostate cancer.
Health disparities among different prostate cancer patient populations have been well documented, with men from rural Appalachia and Black men having higher mortality rates (5–7, 46, 47). Yet, the molecular mechanisms driving poorer patient outcomes in these distinct populations are largely unknown. Our patient cohort is unique in that it includes samples from both disproportionally affected populations. We observed statistically significant increases in several N-glycans in grade group 1 tumors from Black patients compared to White patients. However, larger tissue imaging analysis suggest the differences observed between the two groups extend in the adjacent and intervening benign tissue as well. Due to our limited access to additional low-grade samples from Black patients, this interesting phenotype warrants future investigation with an additional patient cohort. Moreover, we found a unique N-glycan panel that could identify Appalachian patients who had disease progression, of which, patients with higher abundance of 1850 m/z had significantly poorer overall survival.
In summary, our data suggest that aberrant N-linked glycosylation s a molecular feature of prostate cancer, and highlights the application of MALDI-MSI N-glycan profiling as a promising prognostic tool. Moreover, our study reveals interesting differences between prostate tumors from both Black and White and Appalachian patient populations. Overall, these results warrant further investigation to define glycan metabolism and the regulatory mechanisms that contribute to aberrant protein glycosylation in prostate cancer, with an emphasis on defining the unique features of prostate tumors from Black and Appalachian patients, with respect to patient prognosis. Future studies should be expanded to include glycoproteomic analysis to define the specific proteins that are differentially glycosylated. Such studies can provide insight into the molecular drivers of prostate cancer progression and health disparities, which can be used to discover new biomarkers and novel personalized therapies.
Limitations of Study
This study employs mass spectrometry imaging to identify tumor-specific and patient demographic alterations in N-glycosylation in prostate cancer. While MALDI-MSI is a powerful tool for high-throughput N-glycan profiling of a large number of patient samples, we are still limited by the patient cohort selected for this study. For our targeted demographic analysis, sample size was small for several groups; thus, future studies should include more patients to confirm our findings. Moreover, for the majority of the study, we analyzed prostate tumors from small tissue cores rather than larger tissue sections containing both tumor and nontumor stroma regions. As many tumor cores are not purley tumor tissue, this could contribute to increased variance in our results. Future analyses should be expanded to define N-glycosylation in different tumor regions defined by microenvironmental pressure in larger prostate tumor tissue sections.
Supplementary Material
Implications:
MALDI-MSI identifies N-glycan perturbations in prostate tumors compared to benign tissue. This method can be utilized to predict prostate cancer recurrence and study prostate cancer disparities.
Acknowledgements
This study was supported by NIH grant R01 AG066653 (RSC), NINDS R21NS121966 (RSC), St Baldrick’s Career Development Award (RSC), Rally Foundation Independent Investigator Grant (RSC), V-Scholar Grant (RSC), and NIH Training Grant T32CA165990 (LRC). This research was also supported by funding from the University of Kentucky Markey Cancer Center and the NIH-funded Biospecimen Procurement & Translational Pathology Shared Resource Facility, as well as the Cancer Research Informatics Shared Resource Facility, of the University of Kentucky Markey Cancer Center P30CA177558.
Footnotes
The authors have declared that no conflict of interest exists.
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