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Proteomics of endometrial cancer diagnosis, treatment, and prognosis

Mittal P, Klingler-Hoffmann M, Arentz G, Zhang C, Kaur G, Oehler MK, et al.

Proteomics Clin Appl, 2016 Mar;10(3):217-29.
PMID: 26541900 DOI: 10.1002/prca.201500055

This review discusses the current status of proteomics technology in endometrial cancer diagnosis, treatment and prognosis. The first part of this review focuses on recently identified biomarkers for endometrial cancer, their importance in clinical use as well as the proteomic methods used in their discovery. The second part highlights some of the emerging mass spectrometry based proteomic technologies that promise to contribute to a better understanding of endometrial cancer by comparing the abundance of hundreds or thousands of proteins simultaneously.
Fulltext N-glycan MALDI Imaging Mass Spectrometry on Formalin-Fixed Paraffin-Embedded Tissue Enables the Delineation of Ovarian Cancer Tissues

Everest-Dass AV, Briggs MT, Kaur G, Oehler MK, Hoffmann P, Packer NH

Mol Cell Proteomics, 2016 09;15(9):3003-16.
PMID: 27412689 DOI: 10.1074/mcp.M116.059816

Ovarian cancer is a fatal gynaecological malignancy in adult women with a five-year overall survival rate of only 30%. Glycomic and glycoproteomic profiling studies have reported extensive protein glycosylation pattern alterations in ovarian cancer. Therefore, spatio-temporal investigation of these glycosylation changes may unearth tissue-specific changes that occur in the development and progression of ovarian cancer. A novel method for investigating tissue-specific N-linked glycans is using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) on formalin-fixed paraffin-embedded (FFPE) tissue sections that can spatially profile N-glycan compositions released from proteins in tissue-specific regions. In this study, tissue regions of interest (e.g. tumor, stroma, adipose tissue and necrotic areas) were isolated from FFPE tissue sections of advanced serous ovarian cancers (n = 3). PGC-LC-ESI-MS/MS and MALDI-MSI were used as complementary techniques to firstly generate structural information on the tissue-specific glycans in order to then obtain high resolution images of the glycan structure distribution in ovarian cancer tissue. The N-linked glycan repertoires carried by the proteins in these tissue regions were structurally characterized for the first time in FFPE ovarian cancer tissue regions, using enzymatic peptide-N-glycosidase F (PNGase F) release of N-glycans. The released glycans were analyzed by porous graphitized carbon liquid chromatography (PGC-LC) and collision induced electrospray negative mode MS fragmentation analysis. The N-glycan profiles identified by this analysis were then used to determine the location and distribution of each N-glycan on FFPE ovarian cancer sections that were treated with PNGase F using high resolution MALDI-MSI. A tissue-specific distribution of N-glycan structures identified particular regions of the ovarian cancer sections. For example, high mannose glycans were predominantly expressed in the tumor tissue region whereas complex/hybrid N-glycans were significantly abundant in the intervening stroma. Therefore, tumor and non-tumor tissue regions were clearly demarcated solely on their N-glycan structure distributions.
Fulltext In-House Packed Porous Graphitic Carbon Columns for Liquid Chromatography-Mass Spectrometry Analysis of N-Glycans

Young C, Condina MR, Briggs MT, Moh ESX, Kaur G, Oehler MK, et al.

Front Chem, 2021;9:653959.
PMID: 34178940 DOI: 10.3389/fchem.2021.653959

Protein glycosylation is a common post-translational modification that modulates biological processes such as the immune response and protein trafficking. Altered glycosylation profiles are associated with cancer and inflammatory diseases, as well as impacting the efficacy of therapeutic monoclonal antibodies. Consisting of oligosaccharides attached to asparagine residues, enzymatically released N-linked glycans are analytically challenging due to the diversity of isomeric structures that exist. A commonly used technique for quantitative N-glycan analysis is liquid chromatography-mass spectrometry (LC-MS), which performs glycan separation and characterization. Although many reversed and normal stationary phases have been utilized for the separation of N-glycans, porous graphitic carbon (PGC) chromatography has become desirable because of its higher resolving capability, but is difficult to implement in a robust and reproducible manner. Herein, we demonstrate the analytical properties of a 15 cm fused silica capillary (75 µm i.d., 360 µm o.d.) packed in-house with Hypercarb PGC (3 µm) coupled to an Agilent 6550 Q-TOF mass spectrometer for N-glycan analysis in positive ion mode. In repeatability and intermediate precision measurements conducted on released N-glycans from a glycoprotein standard mixture, the majority of N-glycans reported low coefficients of variation with respect to retention times (≤4.2%) and peak areas (≤14.4%). N-glycans released from complex samples were also examined by PGC LC-MS. A total of 120 N-glycan structural and compositional isomers were obtained from formalin-fixed paraffin-embedded ovarian cancer tissue sections. Finally, a comparison between early- and late-stage formalin-fixed paraffin-embedded ovarian cancer tissues revealed qualitative changes in the α2,3- and α2,6-sialic acid linkage of a fucosylated bi-antennary complex N-glycan. Although the α2,3-linkage was predominant in late-stage ovarian cancer, the alternate α2,6-linkage was more prevalent in early-stage ovarian cancer. This study establishes the utility of in-house packed PGC columns for the robust and reproducible LC-MS analysis of N-glycans.
Altered N-linked glycosylation in endometrial cancer

Mittal P, Briggs M, Klingler-Hoffmann M, Kaur G, Packer NH, Oehler MK, et al.

Anal Bioanal Chem, 2021 Apr;413(10):2721-2733.
PMID: 33222001 DOI: 10.1007/s00216-020-03039-z

It is well established that cell surface glycans play a vital role in biological processes and their altered form can lead to carcinogenesis. Mass spectrometry-based techniques have become prominent for analysing N-linked glycans, for example using matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). Additionally, MALDI MS can be used to spatially map N-linked glycans directly from cancer tissue using a technique termed MALDI MS imaging (MALDI MSI). This powerful technique combines mass spectrometry and histology to visualise the spatial distribution of N-linked glycans on a single tissue section. Here, we performed N-glycan MALDI MSI on six endometrial cancer (EC) formalin-fixed paraffin-embedded (FFPE) tissue sections and tissue microarrays (TMA) consisting of eight EC patients with lymph node metastasis (LNM) and twenty without LNM. By doing so, several putative N-linked glycan compositions were detected that could significantly distinguish normal from cancerous endometrium. Furthermore, a complex core-fucosylated N-linked glycan was detected that could discriminate a primary tumour with and without LNM. Structural identification of these putative N-linked glycans was performed using porous graphitized carbon liquid chromatography tandem mass spectrometry (PGC-LC-MS/MS). Overall, we observed higher abundance of oligomannose glycans in tumour compared to normal regions with AUC ranging from 0.85-0.99, and lower abundance of complex N-linked glycans with AUC ranges from 0.03-0.28. A comparison of N-linked glycans between primary tumours with and without LNM indicated a reduced abundance of a complex core-fucosylated N-glycan (Hex)2(HexNAc)2(Deoxyhexose)1+(Man)3(GlcNAc)2, in primary tumour with associated lymph node metastasis. In summary, N-linked glycan MALDI MSI can be used to differentiate cancerous endometrium from normal, and endometrial cancer with LNM from endometrial cancer without.
Novel IEF Peptide Fractionation Method Reveals a Detailed Profile of N-Terminal Acetylation in Chemotherapy-Responsive and -Resistant Ovarian Cancer Cells

Weiland F, Arentz G, Klingler-Hoffmann M, McCarthy P, Lokman NA, Kaur G, et al.

J Proteome Res, 2016 11 04;15(11):4073-4081.
PMID: 27569743

Although acetylation is regarded as a common protein modification, a detailed proteome-wide profile of this post-translational modification may reveal important biological insight regarding differential acetylation of individual proteins. Here we optimized a novel peptide IEF fractionation method for use prior to LC-MS/MS analysis to obtain a more in depth coverage of N-terminally acetylated proteins from complex samples. Application of the method to the analysis of the serous ovarian cancer cell line OVCAR-5 identified 344 N-terminally acetylated proteins, 12 of which are previously unreported. The protein peptidyl-prolyl cis-trans isomerase A (PPIA) was detected in both the N-terminally acetylated and unmodified forms and was further analyzed by data-independent acquisition in carboplatin-responsive parental OVCAR-5 cells and carboplatin-resistant OVCAR-5 cells. This revealed a higher ratio of unacetylated to acetylated N-terminal PPIA in the parental compared with the carboplatin-resistant OVCAR-5 cells and a 4.1-fold increase in PPIA abundance overall in the parental cells relative to carboplatin-resistant OVCAR-5 cells (P = 0.015). In summary, the novel IEF peptide fractionation method presented here is robust, reproducible, and can be applied to the profiling of N-terminally acetylated proteins. All mass spectrometry data is available as a ProteomeXchange repository (PXD003547).
N-Glycan matrix-assisted laser desorption/ionization mass spectrometry imaging protocol for formalin-fixed paraffin-embedded tissues

Briggs MT, Ho YY, Kaur G, Oehler MK, Everest-Dass AV, Packer NH, et al.

Rapid Commun Mass Spectrom, 2017 May 30;31(10):825-841.
PMID: 28271569 DOI: 10.1002/rcm.7845

RATIONALE: Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) of the proteome of a tissue has been an established technique for the past decade. In the last few years, MALDI-MSI of the N-glycome has emerged as a novel MALDI-MSI technique. To assess the accuracy and clinical significance of the N-linked glycan spatial distribution, we have developed a method that utilises MALDI-MSI followed by liquid chromatography coupled to tandem mass spectrometry (LC/MS/MS) in order to assign glycan structures to the differentiating MALDI-MSI glycan masses released from the tissue glycoproteins.
METHODS AND RESULTS: Our workflow presents a comprehensive list of instructions on how to (i) apply MALDI-MSI to spatially map the N-glycome across formalin-fixed paraffin-embedded (FFPE) clinical samples, (ii) structurally characterise N-glycans extracted from consecutive FFPE tissue sections by LC/MS/MS, and (iii) match relevant N-glycan masses from MALDI-MSI with confirmed N-glycan structures determined by LC/MS/MS.
CONCLUSIONS: Our protocol provides groups that are new to this technique with instructions how to establish N-glycan MALDI-MSI in their laboratory. Furthermore, the method assigns N-glycan structural detail to the masses obtained in the MALDI-MS image. Copyright © 2017 John Wiley & Sons, Ltd.
MALDI Mass Spectrometry Imaging of Early- and Late-Stage Serous Ovarian Cancer Tissue Reveals Stage-Specific N-Glycans

Briggs MT, Condina MR, Ho YY, Everest-Dass AV, Mittal P, Kaur G, et al.

Proteomics, 2019 11;19(21-22):e1800482.
PMID: 31364262 DOI: 10.1002/pmic.201800482

Epithelial ovarian cancer is one of the most fatal gynecological malignancies in adult women. As studies on protein N-glycosylation have extensively reported aberrant patterns in the ovarian cancer tumor microenvironment, obtaining spatial information will uncover tumor-specific N-glycan alterations in ovarian cancer development and progression. matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is employed to investigate N-glycan distribution on formalin-fixed paraffin-embedded ovarian cancer tissue sections from early- and late-stage patients. Tumor-specific N-glycans are identified and structurally characterized by porous graphitized carbon-liquid chromatography-electrospray ionization-tandem mass spectrometry (PGC-LC-ESI-MS/MS), and then assigned to high-resolution images obtained from MALDI-MSI. Spatial distribution of 14 N-glycans is obtained by MALDI-MSI and 42 N-glycans (including structural and compositional isomers) identified and structurally characterized by LC-MS. The spatial distribution of oligomannose, complex neutral, bisecting, and sialylated N-glycan families are localized to the tumor regions of late-stage ovarian cancer patients relative to early-stage patients. Potential N-glycan diagnostic markers that emerge include the oligomannose structure, (Hex)6 + (Man)3 (GlcNAc)2 , and the complex neutral structure, (Hex)2 (HexNAc)2 (Deoxyhexose)1 + (Man)3 (GlcNAc)2 . The distribution of these markers is evaluated using a tissue microarray of early- and late-stage patients.
Translating N-Glycan Analytical Applications into Clinical Strategies for Ovarian Cancer

Briggs MT, Condina MR, Klingler-Hoffmann M, Arentz G, Everest-Dass AV, Kaur G, et al.

Proteomics Clin Appl, 2019 05;13(3):e1800099.
PMID: 30367710 DOI: 10.1002/prca.201800099

Protein glycosylation, particularly N-linked glycosylation, is a complex posttranslational modification (PTM), which plays an important role in protein folding and conformation, regulating protein stability and activity, cell-cell interaction, and cell signaling pathways. This review focuses on analytical techniques, primarily MS-based techniques, to qualitatively and quantitatively assess N-glycosylation while successfully characterizing compositional, structural, and linkage features with high specificity and sensitivity. The analytical techniques explored in this review include LC-ESI-MS/MS and MALDI time-of-flight MS (MALDI-TOF-MS), which have been used to analyze clinical samples, such as serum, plasma, ascites, and tissue. Targeting the aberrant N-glycosylation patterns observed in MALDI-MS imaging (MSI) offers a platform to visualize N-glycans in tissue-specific regions. The studies on the intra-patient (i.e., a comparison of tissue-specific regions from the same patient) and inter-patient (i.e., a comparison of tissue-specific regions between different patients) variation of early- and late-stage ovarian cancer (OC) patients identify specific N-glycan differences that improve understanding of the tumor microenvironment and potentially improve therapeutic strategies for the clinic.
Lymph node metastasis of primary endometrial cancers: Associated proteins revealed by MALDI imaging

Mittal P, Klingler-Hoffmann M, Arentz G, Winderbaum L, Lokman NA, Zhang C, et al.

Proteomics, 2016 06;16(11-12):1793-801.
PMID: 27061135 DOI: 10.1002/pmic.201500455

Metastasis is a crucial step of malignant progression and is the primary cause of death from endometrial cancer. However, clinicians presently face the challenge that conventional surgical-pathological variables, such as tumour size, depth of myometrial invasion, histological grade, lymphovascular space invasion or radiological imaging are unable to predict with accuracy if the primary tumour has metastasized. In the current retrospective study, we have used primary tumour samples of endometrial cancer patients diagnosed with (n = 16) and without (n = 27) lymph node metastasis to identify potential discriminators. Using peptide matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI), we have identified m/z values which can classify 88% of all tumours correctly. The top discriminative m/z values were identified using a combination of in situ sequencing and LC-MS/MS from digested tumour samples. Two of the proteins identified, plectin and α-Actin-2, were used for validation studies using LC-MS/MS data independent analysis (DIA) and immunohistochemistry. In summary, MALDI-MSI has the potential to identify discriminators of metastasis using primary tumour samples.
Annexin A2 and alpha actinin 4 expression correlates with metastatic potential of primary endometrial cancer

Mittal P, Klingler-Hoffmann M, Arentz G, Winderbaum L, Kaur G, Anderson L, et al.

Biochim Biophys Acta Proteins Proteom, 2017 Jul;1865(7):846-857.
PMID: 27784647 DOI: 10.1016/j.bbapap.2016.10.010

The prediction of lymph node metastasis using clinic-pathological data and molecular information from endometrial cancers lacks accuracy and is therefore currently not routinely used in patient management. Consequently, although only a small percentage of patients with endometrial cancers suffer from metastasis, the majority undergo radical surgery including removal of pelvic lymph nodes. Upon analysis of publically available data and published research, we compiled a list of 60 proteins having the potential to display differential abundance between primary endometrial cancers with versus those without lymph node metastasis. Using data dependent acquisition LC-ESI-MS/MS we were able to detect 23 of these proteins in endometrial cancers, and using data independent LC-ESI-MS/MS the differential abundance of five of those proteins was observed. The localization of the differentially expressed proteins, was visualized using peptide MALDI MSI in whole tissue sections as well as tissue microarrays of 43 patients. The proteins identified were further validated by immunohistochemistry. Our data indicate that annexin A2 protein level is upregulated, whereas annexin A1 and α actinin 4 expression are downregulated in tumours with lymph node metastasis compared to those without lymphatic spread. Moreover, our analysis confirmed the potential of these markers, to be included in a statistical model for prediction of lymph node metastasis. The predictive model using highly ranked m/z values identified by MALDI MSI showed significantly higher predictive accuracy than the model using immunohistochemistry data. In summary, using publicly available data and complementary proteomics approaches, we were able to improve the prediction model for lymph node metastasis in EC.
Fulltext Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging

Mittal P, Condina MR, Klingler-Hoffmann M, Kaur G, Oehler MK, Sieber OM, et al.

Cancers (Basel), 2021 Oct 27;13(21).
PMID: 34771551 DOI: 10.3390/cancers13215388

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.

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