Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling.

BACKGROUND: There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery. METHODS: We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues. RESULTS: We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues. CONCLUSIONS: Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy.

The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.

Overexpression of Exportin-5 Overrides the Inhibitory Effect of miRNAs Regulation Control and Stabilize Proteins via Posttranslation Modifications in Prostate Cancer.

Although XPO5 has been characterized to have tumor-suppressor features in the miRNA biogenesis pathway, the impact of altered expression of XPO5 in cancers is unexplored. Here we report a novel "oncogenic" role of XPO5 in advanced prostate cancer. Using prostate cancer models, we found that excess levels of XPO5 override the inhibitory effect of the canoncial miRNA-mRNA regulation, resulting in a global increase in proteins expression. Importantly, we found that decreased expression of XPO5 could promote an increase in proteasome degradation, whereas overexpression of XPO5 leads to altered protein posttranslational modification via hyperglycosylation, resulting in cellular protein stability. We evaluated the therapeutic advantage of targeting XPO5 in prostate cancer and found that knocking down XPO5 in prostate cancer cells suppressed cellular proliferation and tumor development without significantly impacting normal fibroblast cells survival. To our knowledge, this is the first report describing the oncogenic role of XPO5 in overriding the miRNAs regulation control. Furthermore, we believe that these findings will provide an explanation as to why, in some cancers that express higher abundance of mature miRNAs, fail to suppress their potential protein targets.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

Comprehensive analysis of protein glycosylation by solid-phase extraction of N-linked glycans and glycosite-containing peptides.

Comprehensive characterization of protein glycosylation is critical for understanding the structure and function of glycoproteins. However, due to the complexity and heterogeneity of glycoprotein conformations, current glycoprotein analyses focus mainly on either the de-glycosylated glycosylation site (glycosite)-containing peptides or the released glycans. Here, we describe a chemoenzymatic method called solid phase extraction of N-linked glycans and glycosite-containing peptides (NGAG) for the comprehensive characterization of glycoproteins that is able to determine glycan heterogeneity for individual glycosites in addition to providing information about the total N-linked glycan, glycosite-containing peptide and glycoprotein content of complex samples. The NGAG method can also be applied to quantitatively detect glycoprotein alterations in total and site-specific glycan occupancies.

MRMPlus: an open source quality control and assessment tool for SRM/MRM assay development.

BACKGROUND: Selected and multiple reaction monitoring involves monitoring a multiplexed assay of proteotypic peptides and associated transitions in mass spectrometry runs. To describe peptide and associated transitions as stable, quantifiable, and reproducible representatives of proteins of interest, experimental and analytical validation is required. However, inadequate and disparate analytical tools and validation methods predispose assay performance measures to errors and inconsistencies. RESULTS: Implemented as a freely available, open-source tool in the platform independent Java programing language, MRMPlus computes analytical measures as recommended recently by the Clinical Proteomics Tumor Analysis Consortium Assay Development Working Group for "Tier 2" assays - that is, non-clinical assays sufficient enough to measure changes due to both biological and experimental perturbations. Computed measures include; limit of detection, lower limit of quantification, linearity, carry-over, partial validation of specificity, and upper limit of quantification. CONCLUSIONS: MRMPlus streamlines assay development analytical workflow and therefore minimizes error predisposition. MRMPlus may also be used for performance estimation for targeted assays not described by the Assay Development Working Group. MRMPlus' source codes and compiled binaries can be freely downloaded from https://bitbucket.org/paiyetan/mrmplusgui and https://bitbucket.org/paiyetan/mrmplusgui/downloads respectively.

Multiplexed Targeted Mass Spectrometry-Based Assays for the Quantification of N-Linked Glycosite-Containing Peptides in Serum.

Protein glycosylation is one of the most common protein modifications, and the quantitative analysis of glycoproteins has the potential to reveal biological functions and their association with disease. However, the high throughput accurate quantification of glycoproteins is technically challenging due to the scarcity of robust assays to detect and quantify glycoproteins. Here we describe the development of multiplexed targeted MS assays to quantify N-linked glycosite-containing peptides in serum using parallel reaction monitoring (PRM). Each assay was characterized by its performance metrics and criteria established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (NCI CPTAC) to facilitate the widespread adoption of the assays in studies designed to confidently detect changes in the relative abundance of these analytes. An in-house developed software program, MRMPlus, was used to compute assay performance parameters including specificity, precision, and repeatability. We show that 43 selected N-linked glycosite-containing peptides identified in prostate cancer tissue studies carried out in our group were detected in the sera of prostate cancer patients within the quantitative range of the developed PRM assays. A total of 41 of these formerly N-linked glycosite-containing peptides (corresponding to 37 proteins) were reproducibly quantified based on their relative peak area ratios in human serum during PRM assay development, with 4 proteins showing differential significance in serum from nonaggressive (NAG) vs aggressive (AG) prostate cancer patient serum (n = 50, NAG vs AG). The data demonstrate that the assays can be used for the high throughput and reproducible quantification of a panel of formerly N-linked glycosite-containing peptides. The developed assays can also be used for the quantification of formerly N-linked glycosite-containing peptides in human serum irrespective of disease state.

Glycoproteomic analysis identifies human glycoproteins secreted from HIV latently infected T cells and reveals their presence in HIV+ plasma.

Glycoproteins secreted into plasma from T cells infected with human immunodeficiency virus (HIV) latent infection may provide insight into understanding the host response to HIV infection in vivo. Glycoproteomics, which evaluates the level of the glycoproteome, remains a novel approach to study this host response to HIV. In order to identify human glycoproteins secreted from T cells with latent HIV infection, the medium from cultured HIV replication-competent T cells was compared with the medium from cultured parental A3.01 cells via solid phase extraction of glycopeptides (SPEG) and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Using these methods, 59 human glycoproteins were identified as having significantly different abundance levels between the media from these two cell lines. The relevance of these 59 proteins to HIV infection in vivo was assessed in plasma from HIV+ and HIV- subjects. Comparison between T cell and plasma revealed that six glycoproteins (galectin-3-binding protein, L-selectin, neogenin, adenosine deaminase CECR1, ICOS ligand and phospholipid transfer protein) were significantly elevated in the HIV+ T cells and plasma studies. These findings suggest that the response of T cells harboring latent HIV infection contributed, in part, to the glycoprotein changes in HIV+ plasma. These proteins, once validated, could provide insight into host-HIV interaction.

Analysis of N-glycoproteins using genomic N-glycosite prediction.

Protein glycosylation has long been recognized as one of the most common post-translational modifications. Most membrane proteins and extracellular proteins are N-linked glycosylated, and they account for the majority of current clinical diagnostic markers or therapeutic targets. Quantitative proteomic analysis of detectable N-linked glycoproteins from cells or tissues using mass spectrometry has the potential to provide biological basis for disease development and identify disease associated glycoproteins. However, the information of low abundance but important peptides is lost due to the lack of MS/MS fragmentation or low quality of MS/MS spectra for low abundance peptides. Here, we show the feasibility of formerly N-glycopeptide identification and quantification at MS1 level using genomic N-glycosite prediction (GenoGlyco) coupled with stable isotopic labeling and accurate mass matching. The GenoGlyco Analyzer software uses accurate precursor masses of detected N-deglycopeptide peaks to match them to N-linked deglycopeptides that are predicted from genes expressed in the cells. This method results in more robust glycopeptide identification compared to MS/MS-based identification. Our results showed that over three times the quantity of N-deglycopeptide assignments from the same mass spectrometry data could be produced in ovarian cancer cell lines compared to a MS/MS fragmentation method. Furthermore, the method was also applied to N-deglycopeptide analysis of ovarian tumors using the identified deglycopeptides from the two ovarian cell lines as heavy standards. We show that the described method has a great potential in the analysis of detectable N-glycoproteins from cells and tissues.

Analysis of the genomic response of human prostate cancer cells to histone deacetylase inhibitors.

Histone deacetylases (HDACs) have emerged as important targets for cancer treatment. HDAC-inhibitors (HDACis) are well tolerated in patients and have been approved for the treatment of patients with cutaneous T-cell lymphoma (CTCL). To improve the clinical benefit of HDACis in solid tumors, combination strategies with HDACis could be employed. In this study, we applied Analysis of Functional Annotation (AFA) to provide a comprehensive list of genes and pathways affected upon HDACi-treatment in prostate cancer cells. This approach provides an unbiased and objective approach to high throughput data mining. By performing AFA on gene expression data from prostate cancer cell lines DU-145 (an HDACi-sensitive cell line) and PC3 (a relatively HDACi-resistant cell line) treated with HDACis valproic acid or vorinostat, we identified biological processes that are affected by HDACis and are therefore potential treatment targets for combination therapy. Our analysis revealed that HDAC-inhibition resulted among others in upregulation of major histocompatibility complex (MHC) genes and deregulation of the mitotic spindle checkpoint by downregulation of genes involved in mitosis. These findings were confirmed by AFA on publicly available data sets from HDACi-treated prostate cancer cells. In total, we analyzed 375 microarrays with HDACi treated and non-treated (control) prostate cancer cells. All results from this extensive analysis are provided as an online research source (available at the journal's website and at http://luigimarchionni.org/HDACIs.html). By publishing this data, we aim to enhance our understanding of the cellular changes after HDAC-inhibition, and to identify novel potential combination strategies with HDACis for the treatment of prostate cancer patients.

Glycoproteomic analysis of bronchoalveolar lavage (BAL) fluid identifies tumor-associated glycoproteins from lung adenocarcinoma.

Cytological examination of cells from bronchoalveolar lavage (BAL) is commonly used for the diagnosis of lung cancer. Proteins released from lung cancer cells into BAL may serve as biomarkers for cancer detection. In this study, N-glycoproteins in eight cases of BAL fluid, as well as eight lung adenocarcinoma tissues and eight tumor-matched normal lung tissues, were analyzed using the solid-phase extraction of N-glycoprotein (SPEG), iTRAQ labeling, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Of 80 glycoproteins found in BAL specimens, 32 were identified in both cancer BAL and cancer tissues, with levels of 25 glycoproteins showing at least a 2-fold difference between cancer and benign BAL. Among them, eight glycoproteins showed greater than 2-fold elevations in cancer BAL, including Neutrophil elastase (NE), Integrin alpha-M, Cullin-4B, Napsin A, lysosome-associated membrane protein 2 (LAMP2), Cathepsin D, BPI fold-containing family B member 2, and Neutrophil gelatinase-associated lipocalin. The levels of Napsin A in cancer BAL were further verified in independently collected 39 BAL specimens using an ELISA assay. Our study demonstrates that potential protein biomarkers in BAL fluid can be detected and quantified.

Mass spectrometric analysis of sialylated glycans with use of solid-phase labeling of sialic acids.

The analysis of sialylated glycans is critical for understanding the role of sialic acid in normal biological processes as well as in disease. However, the labile nature of sialic acid typically renders routine analysis of this monosaccharide by mass spectrometric methods difficult. To overcome this difficulty we pursued derivatization methodologies, extending established acetohydrazide approaches to aniline-based methods, and finally to optimized p-toluidine derivatization. This new quantitative glycoform profiling method with use of MALDI-TOF in positive ion mode was validated by first comparing N-glycans isolated from fetuin and serum and was then exploited to analyze the effects of increased metabolic flux through the sialic acid pathway in SW1990 pancreatic cancer cells by using a colabeling strategy with light and heavy toluidine. The latter results established that metabolic flux, in a complementary manner to the more well-known impact of sialyltransferase expression, can critically modulate the sialylation of specific glycans while leaving others virtually unchanged.