A Comprehensive Search of Non-Canonical Proteins in Non-Small Cell Lung Cancer and Their Impact on the Immune Response.

There is substantial interest in mining neoantigens for cancer applications. Non-canonical proteins resulting from frameshift mutations have been identified as neoantigens in cancer. We investigated the landscape of non-canonical proteins in non-small cell lung cancer (NSCLC) and their induced immune response in the form of autoantibodies. A database of cryptoproteins was computationally constructed and comprised all alternate open reading frames (altORFs) and ORFs identified in pseudogenes, noncoding RNAs, and untranslated regions of mRNAs that did not align with known canonical proteins. Proteomic profiles of seventeen lung adenocarcinoma (LUAD) cell lines were searched to evaluate the occurrence of cryptoproteins. To assess the immunogenicity, immunoglobulin (Ig)-bound cryptoproteins in plasmas were profiled by mass spectrometry. The specimen set consisted of plasmas from 30 newly diagnosed NSCLC cases, pre-diagnostic plasmas from 51 NSCLC cases, and 102 control plasmas. An analysis of LUAD cell lines identified 420 cryptoproteins. Plasma Ig-bound analyses revealed 90 cryptoproteins uniquely found in cases and 14 cryptoproteins that had a fold-change >2 compared to controls. In pre-diagnostic samples, 17 Ig-bound cryptoproteins yielded an odds ratio ≥2. Eight Ig-bound cryptoproteins were elevated in both pre-diagnostic and newly diagnosed cases compared to controls. Cryptoproteins represent a class of neoantigens that induce an autoantibody response in NSCLC.

8-plex LC-MS/MS Analysis of Permethylated N-Glycans Achieved by Using Stable Isotopic Iodomethane.

Glycosylation is an important post-translational modification of proteins. Many diseases, such as cancer, have proved to be related to aberrant glycosylation. High throughput quantitative methods have gained attention recently in the study of glycomics. With the development of high-resolution mass spectrometry, the sensitivity of detection in glycomics has largely improved; however, most of the commonly used MS-based techniques are focused on relative quantitative analysis, which can hardly provide direct comparative glycomic quantitation results. In this study, we developed a novel multiplex glycomic analysis method on an LC-ESI-MS platform. Reduced glycans were stable isotopic labeled during the permethylation procedure, with the use of iodomethane reagents CH2DI, CHD2I, CD3I, 13CH3I, 13CH2DI, 13CHD2I, 13CD3I, and CH3I. Up to 8-plex glycomic profiling was possible in a single analysis by LC-MS, and a 100 k mass resolution was sufficient to allow a baseline resolution of the mass differences among the 8-plex labeled glycans. The major advantages of this method are that it overcomes quantitative fluctuations caused by nanoESI, it facilitates a level of comparative quantitative glycomic analysis that accurately reflects the quantitative information in samples, and it dramatically shortens analysis time. Quantitation validation was tested on glycans released from bovine fetuin and model glycoprotein mixtures (RNase B, bovine fetuin, and IgG) with good linearity (R2 = 0.9884) and a dynamic range from 0.1 to 10. The 8-plex strategy was successfully applied to a comparative glycomic study of cancer cell lines. The results demonstrate that different distributions of sialylated glycans are related to the metastatic properties of cell lines and provide important clues for a better understanding of breast cancer brain metastasis.

Automated Glycan Sequencing from Tandem Mass Spectra of N-Linked Glycopeptides.

Mass spectrometry has become a routine experimental tool for proteomic biomarker analysis of human blood samples, partly due to the large availability of informatics tools. As one of the most common protein post-translational modifications (PTMs) in mammals, protein glycosylation has been observed to alter in multiple human diseases and thus may potentially be candidate markers of disease progression. While mass spectrometry instrumentation has seen advancements in capabilities, discovering glycosylation-related markers using existing software is currently not straightforward. Complete characterization of protein glycosylation requires the identification of intact glycopeptides in samples, including identification of the modification site as well as the structure of the attached glycans. In this paper, we present GlycoSeq, an open-source software tool that implements a heuristic iterated glycan sequencing algorithm coupled with prior knowledge for automated elucidation of the glycan structure within a glycopeptide from its collision-induced dissociation tandem mass spectrum. GlycoSeq employs rules of glycosidic linkage as defined by glycan synthetic pathways to eliminate improbable glycan structures and build reasonable glycan trees. We tested the tool on two sets of tandem mass spectra of N-linked glycopeptides cell lines acquired from breast cancer patients. After employing enzymatic specificity within the N-linked glycan synthetic pathway, the sequencing results of GlycoSeq were highly consistent with the manually curated glycan structures. Hence, GlycoSeq is ready to be used for the characterization of glycan structures in glycopeptides from MS/MS analysis. GlycoSeq is released as open source software at https://github.com/chpaul/GlycoSeq/ .

Characterization of the Glycosylation Site of Human PSA Prompted by Missense Mutation using LC-MS/MS.

Prostate specific antigen (PSA) is currently used as a diagnostic biomarker for prostate cancer. It is a glycoprotein possessing a single glycosylation site at N69. During our previous study of PSA N69 glycosylation, additional glycopeptides were observed in the PSA sample that were not previously reported and did not match glycopeptides of impure glycoproteins existing in the sample. This extra glycosylation site of PSA is associated with a mutation in KLK3 genes. Among single nucleotide polymorphisms (SNPs) of KLKs families, the rs61752561 in KLK3 genes is an unusual missense mutation resulting in the conversion of D102 to N in PSA amino acid sequence. Accordingly, a new N-linked glycosylation site is created with an N102MS motif. Here we report the first qualitative and quantitative glycoproteomic study of PSA N102 glycosylation site by LC-MS/MS. We successfully applied tandem MS to verify the amino acid sequence possessing N102 glycosylation site and associated glycoforms of PSA samples acquired from different suppliers. Among the three PSA samples, HexNAc2Hex5 was the predominant glycoform at N102, while HexNAc4Hex5Fuc1NeuAc1 or HexNAc4Hex5Fuc1NeuAc2 was the primary glycoforms at N69. D102 is the first amino acid of "kallikrein loop", which is close to a zinc-binding site and catalytic triad. The different glycosylation of N102 relative to N69 might be influenced by the close vicinity of N102 to these functional sites and steric hindrance.

Identification of Glycopeptides with Multiple Hydroxylysine O-Glycosylation Sites by Tandem Mass Spectrometry.

Glycosylation is one of the most common post-translational modifications in proteins, existing in ~50% of mammalian proteins. Several research groups have demonstrated that mass spectrometry is an efficient technique for glycopeptide identification; however, this problem is still challenging because of the enormous diversity of glycan structures and the microheterogeneity of glycans. In addition, a glycopeptide may contain multiple glycosylation sites, making the problem complex. Current software tools often fail to identify glycopeptides with multiple glycosylation sites, and hence we present GlycoMID, a graph-based spectral alignment algorithm that can identify glycopeptides with multiple hydroxylysine O-glycosylation sites by tandem mass spectra. GlycoMID was tested on mass spectrometry data sets of the bovine collagen α-(II) chain protein, and experimental results showed that it identified more glycopeptide-spectrum matches than other existing tools, including many glycopeptides with two glycosylation sites.

Automated annotation and quantitation of glycans by liquid chromatography/electrospray ionization mass spectrometric analysis using the MultiGlycan-ESI computational tool.

RATIONALE: Liquid chromatography/mass spectrometry (LC/MS) is currently considered to be a conventional glycomics analysis strategy due to the high sensitivity and ability to handle complex biological samples. Interpretation of LC/MS data is a major bottleneck in high-throughput glycomics LC/MS-based analysis. The complexity of LC/MS data associated with biological samples prompts the needs to develop computational tools capable of facilitating automated data annotation and quantitation. METHODS: An LC/MS-based automated data annotation and quantitation software, MultiGlycan-ESI, was developed and utilized for glycan quantitation. Data generated by the software from LC/MS analysis of permethylated N-glycans derived from fetuin were initially validated by manual integration to assess the performance of the software. The performance of MultiGlycan-ESI was then assessed for the quantitation of permethylated fetuin N-glycans analyzed at different concentrations or spiked with permethylated N-glycans derived from human blood serum. RESULTS: The relative abundance differences between data generated by the software and those generated by manual integration were less than 5%, indicating the reliability of MultiGlycan-ESI in quantitation of permethylated glycans analyzed by LC/MS. Automated quantitation resulted in a linear relationship for all six N-glycans derived from 50 ng to 400 ng fetuin with correlation coefficients (R(2) ) greater than 0.93. Spiking of permethylated fetuin N-glycans at different concentrations in permethylated N-glycan samples derived from a 0.02 µL of HBS also exhibited linear agreement with R(2) values greater than 0.9. CONCLUSIONS: With a variety of options, including mass accuracy, merged adducts, and filtering criteria, MultiGlycan-ESI allows automated annotation and quantitation of LC/ESI-MS N-glycan data. The software allows the reliable quantitation of glycan LC/MS data. The software is reliable for automated glycan quantitation, thus facilitating rapid and reliable high-throughput glycomics studies.

Glycoproteomics: identifying the glycosylation of prostate specific antigen at normal and high isoelectric points by LC-MS/MS.

Prostate specific antigen (PSA) is currently used as a biomarker to diagnose prostate cancer. PSA testing has been widely used to detect and screen prostate cancer. However, in the diagnostic gray zone, the PSA test does not clearly distinguish between benign prostate hypertrophy and prostate cancer due to their overlap. To develop more specific and sensitive candidate biomarkers for prostate cancer, an in-depth understanding of the biochemical characteristics of PSA (such as glycosylation) is needed. PSA has a single glycosylation site at Asn69, with glycans constituting approximately 8% of the protein by weight. Here, we report the comprehensive identification and quantitation of N-glycans from two PSA isoforms using LC-MS/MS. There were 56 N-glycans associated with PSA, whereas 57 N-glycans were observed in the case of the PSA-high isoelectric point (pI) isoform (PSAH). Three sulfated/phosphorylated glycopeptides were detected, the identification of which was supported by tandem MS data. One of these sulfated/phosphorylated N-glycans, HexNAc5Hex4dHex1s/p1 was identified in both PSA and PSAH at relative intensities of 0.52 and 0.28%, respectively. Quantitatively, the variations were monitored between these two isoforms. Because we were one of the laboratories participating in the 2012 ABRF Glycoprotein Research Group (gPRG) study, those results were compared to that presented in this study. Our qualitative and quantitative results summarized here were comparable to those that were summarized in the interlaboratory study.

Label-free glycopeptide quantification for biomarker discovery in human sera.

Glycan moieties of glycoproteins modulate many biological processes in mammals, such as immune response, inflammation, and cell signaling. Numerous studies show that many human diseases are correlated with quantitative alteration of protein glycosylation. In some cases, these changes can occur for certain types of glycans over specific sites in a glycoprotein rather than on the global abundance of the glycoprotein. Conventional analytical techniques that analyze the abundance of glycans cleaved from glycoproteins cannot reveal these subtle effects. Here we present a novel statistical method to quantify the site-specific glycosylation of glycoproteins in complex samples using label-free mass spectrometric techniques. Abundance variations between sites of a glycoprotein as well as different glycoforms, that is, glycopeptides with different glycans attached to the same site, can be detected using these techniques. We applied our method to an esophageal cancer study based on blood serum samples from cancer patients in an attempt to detect potential biomarkers of site-specific N-linked glycosylation. A few glycoproteins, including vitronectin, showed significantly different site-specific glycosylations within cancer/control samples, indicating that our method is ready to be used for the discovery of glycosylated biomarkers.

Computational framework for identification of intact glycopeptides in complex samples.

Glycosylation is an important protein modification that involves enzymatic attachment of sugars to amino acid residues. Understanding the structure of these sugars and the effects of glycosylation are vital for developing indicators of disease development and progression. Although computational methods based on mass spectrometric data have proven to be effective in monitoring changes in the glycome, developing such methods for the glycoproteome are challenging, largely due to the inherent complexity in simultaneously studying glycan structures with their corresponding glycosylation sites. This paper introduces a computational framework for identifying intact N-linked glycopeptides, i.e. glycopeptides with N-linked glycans attached to their glycosylation sites, in complex proteome samples. Scoring algorithms are presented for tandem mass spectra of glycopeptides resulting from collision-induced dissociation (CID), higher-energy C-trap dissociation (HCD), and electron transfer dissociation (ETD) fragmentation modes. An empirical false-discovery rate estimation method, based on a target-decoy search approach, is derived for assigning confidence. The power of our method is further enhanced when multiple data sets are pooled together to increase identification confidence. Using this framework, 103 highly confident N-linked glycopeptides from 53 sites across 33 glycoproteins were identified in complex human serum proteome samples using conventional proteomic platforms with standard depletion of the 7-most abundant proteins. These results indicate that our method is ready to be used for characterizing site-specific protein glycosylation in complex samples.

Automated annotation and quantification of glycans using liquid chromatography-mass spectrometry.

UNLABELLED: As a common post-translational modification, protein glycosylation plays an important role in many biological processes, and it is known to be associated with human diseases. Mass spectrometry (MS)-based glycomic profiling techniques have been developed to measure the abundances of glycans in complex biological samples and applied to the discovery of putative glycan biomarkers. To automate the annotation of glycomic profiles in the liquid chromatography-MS (LC-MS) data, we present here a user-friendly software tool, MultiGlycan, implemented in C# on Windows systems. We tested MultiGlycan by using several glycomic profiling datasets acquired using LC-MS under different preparations and show that MultiGlycan executes fast and generates robust and reliable results. AVAILABILITY: MultiGlycan can be freely downloaded at http://darwin.informatics.indiana.edu/MultiGlycan/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteogenomic landscape of gastric adenocarcinoma peritoneal metastases.

Advanced gastric adenocarcinoma (GAC) often leads to peritoneal carcinomatosis (PC) and is associated with very poor outcome. Here we report the comprehensive proteogenomic study of ascites derived cells from a prospective GAC cohort (n = 26 patients with peritoneal carcinomatosis, PC). A total of 16,449 proteins were detected from whole cell extracts (TCEs). Unsupervised hierarchical clustering resulted in three distinct groups that reflected extent of enrichment in tumor cells. Integrated analysis revealed enriched biological pathways and notably, some druggable targets (cancer-testis antigens, kinases, and receptors) that could be exploited to develop effective therapies and/or tumor stratifications. Systematic comparison of expression levels of proteins and mRNAs revealed special expression patterns of key therapeutics target notably high mRNA and low protein expression of HAVCR2 (TIM-3), and low mRNA but high protein expression of cancer-testis antigens CTAGE1 and CTNNA2. These results inform strategies to target GAC vulnerabilities.

N-glycan profiling by microchip electrophoresis to differentiate disease states related to esophageal adenocarcinoma.

We report analysis of N-glycans derived from disease-free individuals and patients with Barrett's esophagus, high-grade dysplasia, and esophageal adenocarcinoma by microchip electrophoresis with laser-induced fluorescence detection. Serum samples in 10 µL aliquots are enzymatically treated to cleave the N-glycans that are subsequently reacted with 8-aminopyrene-1,3,6-trisulfonic acid to add charge and a fluorescent label. Separations at 1250 V/cm and over 22 cm yielded efficiencies up to 700,000 plates for the N-glycans and analysis times under 100 s. Principal component analysis (PCA) and analysis of variance (ANOVA) tests of the peak areas and migration times are used to evaluate N-glycan profiles from native and desialylated samples and determine differences among the four sample groups. With microchip electrophoresis, we are able to distinguish the three patient groups from each other and from disease-free individuals.

MaXIC-Q Web: a fully automated web service using statistical and computational methods for protein quantitation based on stable isotope labeling and LC-MS.

Isotope labeling combined with liquid chromatography-mass spectrometry (LC-MS) provides a robust platform for analyzing differential protein expression in proteomics research. We present a web service, called MaXIC-Q Web (http://ms.iis.sinica.edu.tw/MaXIC-Q_Web/), for quantitation analysis of large-scale datasets generated from proteomics experiments using various stable isotope-labeling techniques, e.g. SILAC, ICAT and user-developed labeling methods. It accepts spectral files in the standard mzXML format and search results from SEQUEST, Mascot and ProteinProphet as input. Furthermore, MaXIC-Q Web uses statistical and computational methods to construct two kinds of elution profiles for each ion, namely, PIMS (projected ion mass spectrum) and XIC (extracted ion chromatogram) from MS data. Toward accurate quantitation, a stringent validation procedure is performed on PIMSs to filter out peptide ions interfered with co-eluting peptides or noise. The areas of XICs determine ion abundances, which are used to calculate peptide and protein ratios. Since MaXIC-Q Web adopts stringent validation on spectral data, it achieves high accuracy so that manual validation effort can be substantially reduced. Furthermore, it provides various visualization diagrams and comprehensive quantitation reports so that users can conveniently inspect quantitation results. In summary, MaXIC-Q Web is a user-friendly, interactive, robust, generic web service for quantitation based on ICAT and SILAC labeling techniques.

Protein citrullination as a source of cancer neoantigens.

BACKGROUND: Citrulline post-translational modification of proteins is mediated by protein arginine deiminase (PADI) family members and has been associated with autoimmune diseases. The role of PADI-citrullinome in immune response in cancer has not been evaluated. We hypothesized that PADI-mediated citrullinome is a source of neoantigens in cancer that induces immune response. METHODS: Protein expression of PADI family members was evaluated in 196 cancer cell lines by means of indepth proteomic profiling. Gene expression was assessed using messenger RNA data sets from The Cancer Genome Atlas. Immunohistochemical analysis of PADI2 and peptidyl-citrulline was performed using breast cancer tissue sections. Citrullinated 12-34-mer peptides in the putative Major Histocompatibility Complex-II (MHC-II) binding range were profiled in breast cancer cell lines to investigate the relationship between protein citrullination and antigen presentation. We further evaluated immunoglobulin-bound citrullinome by mass spectrometry using 156 patients with breast cancer and 113 cancer-free controls. RESULTS: Proteomic and gene expression analyses revealed PADI2 to be highly expressed in several cancer types including breast cancer. Immunohistochemical analysis of 422 breast tumor tissues revealed increased expression of PADI2 in ER- tumors (p<0.0001); PADI2 protein expression was positively correlated (p<0.0001) with peptidyl-citrulline staining. PADI2 expression exhibited strong positive correlations with a B cell immune signature and with MHC-II-bound citrullinated peptides. Increased circulating citrullinated antigen-antibody complexes occurred among newly diagnosed breast cancer cases relative to controls (p=0.0012). CONCLUSIONS: An immune response associated with citrullinome is a rich source of neoantigens in breast cancer with a potential for diagnostic and therapeutic applications.

Plasma Based Protein Signatures Associated with Small Cell Lung Cancer.

Small-cell-lung cancer (SCLC) is associated with overexpression of oncogenes including Myc family genes and YAP1 and inactivation of tumor suppressor genes. We performed in-depth proteomic profiling of plasmas collected from 15 individuals with newly diagnosed early stage SCLC and from 15 individuals before the diagnosis of SCLC and compared findings with plasma proteomic profiles of 30 matched controls to determine the occurrence of signatures that reflect disease pathogenesis. A total of 272 proteins were elevated (area under the receiver operating characteristic curve (AUC) ≥ 0.60) among newly diagnosed cases compared to matched controls of which 31 proteins were also elevated (AUC ≥ 0.60) in case plasmas collected within one year prior to diagnosis. Ingenuity Pathway analyses of SCLC-associated proteins revealed enrichment of signatures of oncogenic MYC and YAP1. Intersection of proteins elevated in case plasmas with proteomic profiles of conditioned medium from 17 SCLC cell lines yielded 52 overlapping proteins characterized by YAP1-associated signatures of cytoskeletal re-arrangement and epithelial-to-mesenchymal transition. Among samples collected more than one year prior to diagnosis there was a predominance of inflammatory markers. Our integrated analyses identified novel circulating protein features in early stage SCLC associated with oncogenic drivers.

Association Between Plasma Diacetylspermine and Tumor Spermine Synthase With Outcome in Triple-Negative Breast Cancer.

BACKGROUND: MYC is an oncogenic driver of development and progression in triple-negative breast cancer (TNBC). Ornithine decarboxylase, the rate-limiting enzyme in polyamine metabolism, is a transcriptional target of MYC. We therefore hypothesized that a plasma polyamine signature may be predictive of TNBC development and progression. METHODS: Using liquid chromatography mass spectrometry, polyamine levels were determined in plasma samples from newly diagnosed patients with TNBC (n = 87) and cancer-free controls (n = 115). Findings were validated in plasma samples from an independent prospective cohort of 54 TNBC, 55 estrogen receptor negative (ER-) and progesterone receptor negative (PR-) and HER2 positive (HER2+), and 73 ER+ case patients, and 30 cancer-free control subjects. Gene expression data and clinical data for 921 and 2359 breast cancer tumors were obtained from The Cancer Genome Atlas repository and the Oncomine database, respectively. Relationships between plasma diacetylspermine (DAS) and tumor spermine synthase (SMS) mRNA expression with metastasis-free survival and overall survival were determined using Cox proportional hazard models; Fisher exact tests were used to assess risk of distant metastasis in relation to tumor SMS mRNA expression. RESULTS: An increase in plasma DAS, a catabolic product of spermine mediated through SMS, was observed in the TNBC subtype of breast cancer. Plasma levels of DAS in TNBC associated with increased risk of metastasis (plasma DAS value ≥ 1.16, hazard ratio = 3.06, 95% confidence interval [CI] = 1.15 to 8.13, two-sided P = .03). SMS mRNA expression in TNBC tumor tissue was also found to be predictive of poor overall survival (top 25th percentile hazard ratio = 2.06, 95% CI = 1.04 to 4.08, one-sided P = .04) and increased risk of distant metastasis in TNBC (comparison of lowest SMS quartile [reference] to highest SMS quartile relative risk = 1.90, 95% CI = 0.97 to 4.06, one-sided Fisher exact test P=.03). CONCLUSIONS: Metabolomic profiling identified plasma DAS as a predictive marker for TNBC progression and metastasis.

Proteome Profiling Uncovers an Autoimmune Response Signature That Reflects Ovarian Cancer Pathogenesis.

Harnessing the immune response to tumor antigens in the form of autoantibodies, which occurs early during tumor development, has relevance to the detection of cancer at early stages. We conducted an initial screen of antigens associated with an autoantibody response in serous ovarian cancer using recombinant protein arrays. The top 25 recombinants that exhibited increased reactivity with cases compared to controls revealed TP53 and MYC, which are ovarian cancer driver genes, as major network nodes. A mass spectrometry based independent analysis of circulating immunoglobulin (Ig)-bound proteins in ovarian cancer and of ovarian cancer cell surface MHC-II bound peptides also revealed a TP53-MYC related network of antigens. Our findings support the occurrence of a humoral immune response to antigens linked to ovarian cancer driver genes that may have utility for early detection applications.

The Multi-Q web server for multiplexed protein quantitation.

The Multi-Q web server provides an automated data analysis tool for multiplexed protein quantitation based on the iTRAQ labeling method. The web server is designed as a platform that can accommodate various input data formats from search engines and mass spectrometer manufacturers. Compared to the previous stand-alone version, the new web server version provides many enhanced features and flexible options for quantitation. The workflow of the web server is represented by a quantitation wizard so that the tool is easy to use. It also provides a friendly interface that helps users configure their parameter settings before running the program. The web server generates a standard report for quantitation results. In addition, it allows users to customize their output reports and information of interest can be easily highlighted. The output also provides visualization of mass spectral data so that users can conveniently validate the results. The Multi-Q web server is a fully automated and easy to use quantitation tool that is suitable for large-scale multiplexed protein quantitation. Users can download the Multi-Q Web Server from http://ms.iis.sinica.edu.tw/Multi-Q-Web.

Bioinformatics protocols in glycomics and glycoproteomics.

Glycomics aims to identify the whole set of functional glycans of glycoconjugates (attached to proteins or lipids) in biological samples. Glycoproteomics aims to characterize the complete structure of all glycoproteins in biological samples, including the glycosylation sites of proteins and the various glycan structures attached to each of these sites. Mass spectrometry (MS) and microarray are high-throughput technologies that are commonly used in glycomics and glycoproteomics, which often result in the generation of large experimental datasets. Bioinformatics approaches play an essential role in automated analysis and interpretation of such data. This unit describes and discusses the computational tools currently available for these analyses, and their glycomics and glycoproteomics applications.

Software tools for glycan profiling.

We introduce three software tools, Cartoonist, GlycoWorkbench, and MultiGlycan, for N-glycan profiling of complex biological samples. Detailed instructions for using these tools are provided, and their performances are demonstrated by using real glycan profiling data.