Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Potential functions and mechanisms of lysine crotonylation modification (Kcr) in tumorigenesis and lymphatic metastasis of papillary thyroid cancer (PTC)

OBJECTIVES

To examine the putative functions and mechanisms of lysine crotonylation (Kcr) during the development and progression of papillary thyroid cancer (PTC).

METHODS

Samples of thyroid cancer tissues were collected and subjected to liquid chromatography-tandem mass spectrometry. Crotonylated differentially expressed proteins (DEPs) and differentially expressed Kcr sites (DEKSs) were analyzed by Motif, dynamic expression model analysis (Mfuzz), subcellular localization, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, Go Ontology (GO) annotation, and protein-protein interaction analysis (PPI). Validation was performed by immunohistochemistry (IHC).

RESULTS

A total of 262 crotonylated DEPs and 702 DEKSs were quantitated. First, for the tumor/normal comparison, a dynamic expression model analysis (Mfuzz) of the DEKSs revealed that clusters 1, 3, and 4 increased with the progression of thyroid cancer; however, cluster 6 showed a dramatic increase during the transition from N0-tumor to N1-tumor. Furthermore, based on GO annotation, KEGG, and PPI, the crotonylated DEPs were primarily enriched in the PI3K-Akt signaling pathway, Cell cycle, and Hippo signaling pathway. Of note, crosstalk between the proteome and Kcr proteome suggested a differential changing trend, which was enriched in Thyroid hormone synthesis, Pyruvate metabolism, TCA cycle, Cell cycle, and Apoptosis pathways. Similarly, for the LNM comparison group, the DEKSs and related DEPs were primarily enriched in Hydrogen peroxide catabolic process and Tight junction pathway. Finally, according to The Cancer Genome Atlas Program (TCGA) database, the differential expression of Kcr DEPs were associated with the prognosis of thyroid cancer, indicating the prognostic significance of these proteins. Moreover, based on the clinical validation of 47 additional samples, Kcr was highly expressed in thyroid tumor tissues compared with normal tissue (t = 9.792, P < 0.001). In addition, a positive correlation was observed between Kcr and N-cadherin (r = 0.5710, P = 0.0015). Moreover, N-cadherin expression was higher in the relatively high Kcr expression group (χ2 = 18.966, P < 0.001).

CONCLUSIONS

Higher Kcr expression was correlated with thyroid tumorigenesis and lymphatic metastasis, which may regulate thyroid cancer progression by Pyruvate metabolism, TCA cycle, Cell cycle, and other pathways.

Remodelling of the glycome of B-cell precursor acute lymphoblastic leukemia cells developing drug-tolerance

Reduced responsiveness of precursor B-acute lymphoblastic leukemia (BCP-ALL) to chemotherapy can be first detected in the form of minimal residual disease leukemia cells that persist after 28 days of initial treatment. The ability of these cells to resist chemotherapy is partly due to the microenvironment of the bone marrow, which promotes leukemia cell growth and provides protection, particularly under these conditions of stress. It is unknown if and how the glycocalyx of such cells is remodelled during the development of tolerance to drug treatment, even though glycosylation is the most abundant cell surface post-translational modification present on the plasma membrane. To investigate this, we performed omics analysis of BCP-ALL cells that survived a 30-day vincristine chemotherapy treatment while in co-culture with bone marrow stromal cells. Proteomics showed decreased levels of some metabolic enzymes. Overall glycocalyx changes included a shift from Core-2 to less complex Core-1 O-glycans, and reduced overall sialylation, with a shift from α2-6 to α2-3 linked Neu5Ac. Interestingly, there was a clear increase in bisecting complex N-glycans with a concomitant increased mRNA expression of MGAT3 , the only enzyme known to form bisecting N-glycans. These small but reproducible quantitative differences suggest that individual glycoproteins become differentially glycosylated. Glycoproteomics confirmed glycosite-specific modulation of cell surface and lysosomal proteins in drug-tolerant BCP-ALL cells, including HLA-DRA, CD38, LAMP1 and PPT1. We conclude that drug-tolerant persister leukemia cells that grow under continuous chemotherapy stress have characteristic glycotraits that correlate with and perhaps contribute to their ability to survive and could be tested as neoantigens in drug-resistant leukemia.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

Characterization of an expanded set of assays for immunomodulatory proteins using targeted mass spectrometry

Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Advancements in protein glycosylation biomarkers for ovarian cancer through mass spectrometry-based approaches

INTRODUCTION

Ovarian cancer, characterized by metastasis and reduced 5-year survival rates, stands as a substantial factor in the mortality of gynecological malignancies worldwide. The challenge of delayed diagnosis originates from vague early symptoms and the absence of efficient screening and diagnostic biomarkers for early cancer detection. Recent studies have explored the intricate interplay between ovarian cancer and protein glycosylation, unveiling the potential significance of glycosylation-oriented biomarkers.

AREAS COVERED

This review examines the progress in glycosylation biomarker research, with particular emphasis on advances driven by mass spectrometry-based technologies. We document milestones achieved, discuss encountered limitations, and also highlight potential areas for future research and development of protein glycosylation biomarkers for ovarian cancer.

EXPERT OPINION

The association of glycosylation in ovarian cancer is well known, but current research lacks desired sensitivity and specificity for early detection. Notably, investigations into protein-specific and site-specific glycoproteomics have the potential to significantly enhance our understanding of ovarian cancer and facilitate the identification of glycosylation-based biomarkers. Furthermore, the integration of advanced mass spectrometry techniques with AI-driven analysis and glycome databases holds the promise for revolutionizing biomarker discovery for ovarian cancer, ultimately transforming diagnosis and improving patient outcomes.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, but limited knowledge about the regulation and function of most phosphosites restricts our ability to extract meaningful biological insights from phosphoproteomics data. To address this, we combine machine learning and phosphoproteomic data from 1,195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network mapping the co-regulation of 26,280 phosphosites. Integrating network features from CoPheeMap into a machine learning model, CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA reveals 24,015 associations between 9,399 phosphosites and 104 serine/threonine kinases, including many unannotated phosphosites and under-studied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. By applying CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and cancer-associated phosphosites, we demonstrate the effectiveness of these tools in systematically illuminating phosphosites of interest, revealing dysregulated signaling processes in human cancer, and identifying under-studied kinases as putative therapeutic targets.

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

RNA m5C modification upregulates E2F1 expression in a manner dependent on YBX1 phase separation and promotes tumor progression in ovarian cancer

5-Methylcytosine (m5C) is a common RNA modification that modulates gene expression at the posttranscriptional level, but the crosstalk between m5C RNA modification and biomolecule condensation, as well as transcription factor-mediated transcriptional regulation, in ovarian cancer, is poorly understood. In this study, we revealed that the RNA methyltransferase NSUN2 facilitates mRNA m5C modification and forms a positive feedback regulatory loop with the transcription factor E2F1 in ovarian cancer. Specifically, NSUN2 promotes m5C modification of E2F1 mRNA and increases its stability, and E2F1 binds to the NSUN2 promoter, subsequently reciprocally activating NSUN2 transcription. The RNA binding protein YBX1 functions as the m5C reader and is involved in NSUN2-mediated E2F1 regulation. m5C modification promotes YBX1 phase separation, which upregulates E2F1 expression. In ovarian cancer, NSUN2 and YBX1 are amplified and upregulated, and higher expression of NSUN2 and YBX1 predicts a worse prognosis for ovarian cancer patients. Moreover, E2F1 transcriptionally regulates the expression of the oncogenes MYBL2 and RAD54L, driving ovarian cancer progression. Thus, our study delineates a NSUN2-E2F1-NSUN2 loop regulated by m5C modification in a manner dependent on YBX1 phase separation, and this previously unidentified pathway could be a promising target for ovarian cancer treatment.

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

N-Glycosylation as a Modulator of Protein Conformation and Assembly in Disease

Glycosylation, a prevalent post-translational modification, plays a pivotal role in regulating intricate cellular processes by covalently attaching glycans to macromolecules. Dysregulated glycosylation is linked to a spectrum of diseases, encompassing cancer, neurodegenerative disorders, congenital disorders, infections, and inflammation. This review delves into the intricate interplay between glycosylation and protein conformation, with a specific focus on the profound impact of N-glycans on the selection of distinct protein conformations characterized by distinct interactomes-namely, protein assemblies-under normal and pathological conditions across various diseases. We begin by examining the spike protein of the SARS virus, illustrating how N-glycans regulate the infectivity of pathogenic agents. Subsequently, we utilize the prion protein and the chaperone glucose-regulated protein 94 as examples, exploring instances where N-glycosylation transforms physiological protein structures into disease-associated forms. Unraveling these connections provides valuable insights into potential therapeutic avenues and a deeper comprehension of the molecular intricacies that underlie disease conditions. This exploration of glycosylation's influence on protein conformation effectively bridges the gap between the glycome and disease, offering a comprehensive perspective on the therapeutic implications of targeting conformational mutants and their pathologic assemblies in various diseases. The goal is to unravel the nuances of these post-translational modifications, shedding light on how they contribute to the intricate interplay between protein conformation, assembly, and disease.

Integrative proteomics and n-glycoproteomics reveal the synergistic anti-tumor effects of aspirin- and gemcitabine-based chemotherapy on pancreatic cancer cells

OBJECTIVE AND DESIGN

Pancreatic cancer is a highly malignant tumor that is well known for its poor prognosis. Based on glycosylation, we performed integrated quantitative N-glycoproteomics to investigate the synergistic anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells and explore the potential molecular mechanisms of chemotherapy in pancreatic cancer.

METHODS AND RESULTS

Two pancreatic cancer cell lines (PANC-1 and BxPC-3) were treated with gemcitabine, aspirin, and a combination (gemcitabine + aspirin). We found that the addition of aspirin enhanced the inhibitory effect of gemcitabine on the activity of PANC-1 and BxPC-3 cells. Quantitative N-glycoproteome, proteome, phosphorylation, and transcriptome data were obtained from integrated multi-omics analysis to evaluate the anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells. Mfuzz analysis of intact N-glycopeptide profiles revealed two consistent trends associated with the addition of aspirin, which showed a strong relationship between N-glycosylation and the synergistic effect of aspirin. Further analysis demonstrated that the dynamic regulation of sialylation and high-mannose glycoforms on ECM-related proteins (LAMP1, LAMP2, ITGA3, etc.) was a significant factor for the ability of aspirin to promote the anti-tumor activity of gemcitabine and the drug resistance of pancreatic cancer cells.

CONCLUSIONS

In-depth analysis of N-glycosylation-related processes and pathways in pancreatic cancer cells can provide new insight for future studies regarding pancreatic cancer therapeutic targets and drug resistance mechanisms.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

N-glycan profiling of tissue samples to aid breast cancer subtyping

Breast cancer is a highly heterogeneous disease. Its intrinsic subtype classification for diagnosis and choice of therapy traditionally relies on the presence of characteristic receptors. Unfortunately, this classification is often not sufficient for precise prediction of disease prognosis and treatment efficacy. The N-glycan profiles of 145 tumors and 10 healthy breast tissues were determined using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry. The tumor samples were classified into Mucinous, Lobular, No-Special-Type, Human Epidermal Growth Factor 2 + , and Triple-Negative Breast Cancer subtypes. Statistical analysis was conducted using the reproducibility-optimized test statistic software package in R, and the Wilcoxon rank sum test with continuity correction. In total, 92 N-glycans were detected and quantified, with 59 consistently observed in over half of the samples. Significant variations in N-glycan signals were found among subtypes. Mucinous tumor samples exhibited the most distinct changes, with 28 significantly altered N-glycan signals. Increased levels of tri- and tetra-antennary N-glycans were notably present in this subtype. Triple-Negative Breast Cancer showed more N-glycans with additional mannose units, a factor associated with cancer progression. Individual N-glycans differentiated Human Epidermal Growth Factor 2 + , No-Special-Type, and Lobular cancers, whereas lower fucosylation and branching levels were found in N-glycans significantly increased in Luminal subtypes (Lobular and No-Special-Type tumors). Clinically normal breast tissues featured a higher abundance of signals corresponding to N-glycans with bisecting moiety. This research confirms that histologically distinct breast cancer subtypes have a quantitatively unique set of N-glycans linked to clinical parameters like tumor size, proliferative rate, lymphovascular invasion, and metastases to lymph nodes. The presented results provide novel information that N-glycan profiling could accurately classify human breast cancer samples, offer stratification of patients, and ongoing disease monitoring.

VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis

Disrupted protein phosphorylation due to genetic variation is a widespread phenomenon that triggers oncogenic transformation of healthy cells. However, few relevant phosphorylation disruption events have been verified due to limited biological experimental methods. Because of the lack of reliable benchmark datasets, current bioinformatics methods primarily use sequence-based traits to study variant impact on phosphorylation (VIP). Here, we increased the number of experimentally supported VIP events from less than 30 to 740 by manually curating and reanalyzing multi-omics data from 916 patients provided by the Clinical Proteomic Tumor Analysis Consortium. To predict VIP events in cancer cells, we developed VIPpred, a machine learning method characterized by multidimensional features that exhibits robust performance across different cancer types. Our method provided a pan-cancer landscape of VIP events, which are enriched in cancer-related pathways and cancer driver genes. We found that variant-induced increases in phosphorylation events tend to inhibit the protein degradation of oncogenes and promote tumor suppressor protein degradation. Our work provides new insights into phosphorylation-related cancer biology as well as novel avenues for precision therapy.

Proteogenomics Reveal the Overexpression of HLA-I in Cancer

An accurate quantification of HLA class I gene expression is important in understanding the interplay with the tumor microenvironment of antitumor cytotoxic T cell activities. Because HLA-I sequences are highly variable, standard RNAseq and mass spectrometry-based quantification workflows using common genome and protein sequence references do not provide HLA-I allele specific quantifications. Here, we used personalized HLA-I nucleotide and protein reference sequences based on the subjects' HLA-I genotypes and surveyed tumor and adjacent normal samples from patients across nine cancer types. Mass spectrometry using data dependent acquisition data was validated to be sufficient to estimate HLA-A protein expression at the allele level. We found that HLA-I proteins were present in significantly higher levels in tumors compared to adjacent normal tissues from 41 to 63% of head and neck squamous cell carcinoma, uterine corpus endometrial carcinoma, and clear cell renal cell carcinoma patients, and this was driven by increased levels of HLA-I gene transcripts. Most immune cell types are universally enriched in HLA-I high tumors, while endothelial and neuronal cells showed divergent relationships with HLA-I. Pathway analysis revealed that tumor senescence and autophagy activity influence the level of HLA-I proteins in glioblastoma. Genes correlated to HLA-I protein expression are mostly the ones directly involved in HLA-I function in immune response and cell death, while glycosylation genes are exclusively co-expressed with HLA-I at the protein level.

Cyst fluid glycoproteins accurately distinguishing malignancies of pancreatic cystic neoplasm

Pancreatic cystic neoplasms (PCNs) are recognized as precursor lesions of pancreatic cancer, with a marked increase in prevalence. Early detection of malignant PCNs is crucial for improving prognosis; however, current diagnostic methods are insufficient for accurately identifying malignant PCNs. Here, we utilized mass spectrometry (MS)-based glycosite- and glycoform-specific glycoproteomics, combined with proteomics, to explore potential cyst fluid diagnostic biomarkers for PCN. The glycoproteomic and proteomic landscape of pancreatic cyst fluid samples from PCN patients was comprehensively investigated, and its characteristics during the malignant transformation of PCN were analyzed. Under the criteria of screening specific cyst fluid biomarkers for the diagnosis of PCN, a group of cyst fluid glycoprotein biomarkers was identified. Through parallel reaction monitoring (PRM)-based targeted glycoproteomic analysis, we validated these chosen glycoprotein biomarkers in a second cohort, ultimately confirming N-glycosylated PHKB (Asn-935, H5N2F0S0; Asn-935, H4N4F0S0; Asn-935, H5N4F0S0), CEACAM5 (Asn-197, H5N4F0S0) and ATP6V0A4 (Asn-367, H6N4F0S0) as promising diagnostic biomarkers for distinguishing malignant PCNs. These glycoprotein biomarkers exhibited robust performance, with an area under the curve ranging from 0.771 to 0.948. In conclusion, we successfully established and conducted MS-based glycoproteomic analysis to identify novel cyst fluid glycoprotein biomarkers for PCN. These findings hold significant clinical implications, providing valuable insights for PCN decision-making, and potentially offering therapeutic targets for PCN treatment.

A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited. Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts. Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram. The high glycosylation risk score group exhibited higher tumor immune cell infiltration, enrichment scores in immune therapy-related pathways, and a tendency towards a basal subtype. Conversely, the low-risk score group had minimal immune cell infiltration and tended to have a luminal subtype. These findings were consistent in our real-world Xiangya cohort. Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.

A proteogenomics data-driven knowledge base of human cancer

By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.

Proteogenomic insights suggest druggable pathways in endometrial carcinoma

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

Proteogenomic data and resources for pan-cancer analysis

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

In-Depth Mass Spectrometry Analysis Reveals the Plasma Proteomic and N-Glycoproteomic Impact of an Amish-Enriched Cardioprotective Variant in B4GALT1

B4GALT1 encodes β-1,4-galactosyltransferase 1, an enzyme that plays a major role in glycan synthesis in the Golgi apparatus by catalyzing the addition of terminal galactose. Studies increasingly suggest that B4GALT1 may be involved in the regulation of lipid metabolism pathways. Recently, we discovered a single-site missense variant Asn352Ser (N352S) in the functional domain of B4GALT1 in an Amish population, which decreases the level of LDL-cholesterol (LDL-c) as well as the protein levels of ApoB, fibrinogen, and IgG in the blood. To systematically evaluate the effects of this missense variant on protein glycosylation, expression, and secretion, we developed a nano-LC-MS/MS-based platform combined with TMT-labeling for in-depth quantitative proteomic and glycoproteomic analyses in the plasma of individuals homozygous for the B4GALT1 missense variant N352S versus non-carriers (n = 5 per genotype). A total of 488 secreted proteins in the plasma were identified and quantified, 34 of which showed significant fold changes in protein levels between N352S homozygotes and non-carriers. We determined N-glycosylation profiles from 370 glycosylation sites in 151 glycoproteins and identified ten proteins most significantly associated with decreased galactosylation and sialyation in B4GALT1 N352S homozygotes. These results further support that B4GALT1 N352S alters the glycosylation profiles of a variety of critical target proteins, thus governing the functions of these proteins in multiple pathways, such as those involved in lipid metabolism, coagulation, and the immune response.

Discovery and validation of serum glycoprotein biomarkers for high grade serous ovarian cancer

PURPOSE

This study aimed to identify serum glycoprotein biomarkers for early detection of high-grade serous ovarian cancer (HGSOC), the most common and aggressive histotype of ovarian cancer.

EXPERIMENTAL DESIGN

The glycoproteomics pipeline lectin magnetic bead array (LeMBA)-mass spectrometry (MS) was used in age-matched case-control serum samples. Clinical samples collected at diagnosis were divided into discovery (n = 30) and validation (n = 98) sets. We also analysed a set of preclinical sera (n = 30) collected prior to HGSOC diagnosis in the UK Collaborative Trial of Ovarian Cancer Screening.

RESULTS

A 7-lectin LeMBA-MS/MS discovery screen shortlisted 59 candidate proteins and three lectins. Validation analysis using 3-lectin LeMBA-multiple reaction monitoring (MRM) confirmed elevated A1AT, AACT, CO9, HPT and ITIH3 and reduced A2MG, ALS, IBP3 and PON1 glycoforms in HGSOC. The best performing multimarker signature had 87.7% area under the receiver operating curve, 90.7% specificity and 70.4% sensitivity for distinguishing HGSOC from benign and healthy groups. In the preclinical set, CO9, ITIH3 and A2MG glycoforms were altered in samples collected 11.1 ± 5.1 months prior to HGSOC diagnosis, suggesting potential for early detection.

CONCLUSIONS AND CLINICAL RELEVANCE

Our findings provide evidence of candidate early HGSOC serum glycoprotein biomarkers, laying the foundation for further study in larger cohorts.

Proteomic analysis reveals microvesicles containing NAMPT as mediators of radioresistance in glioma

Tumor-initiating cells contained within the aggressive brain tumor glioma (glioma stem cells, GSCs) promote radioresistance and disease recurrence. However, mechanisms of resistance are not well understood. Herein, we show that the proteome-level regulation occurring upon radiation treatment of several patient-derived GSC lines predicts their resistance status, whereas glioma transcriptional subtypes do not. We identify a mechanism of radioresistance mediated by the transfer of the metabolic enzyme NAMPT to radiosensitive cells through microvesicles (NAMPT-high MVs) shed by resistant GSCs. NAMPT-high MVs rescue the proliferation of radiosensitive GSCs and fibroblasts upon irradiation, and upon treatment with a radiomimetic drug or low serum, and increase intracellular NAD(H) levels. Finally, we show that the presence of NAMPT within the MVs and its enzymatic activity in recipient cells are necessary to mediate these effects. Collectively, we demonstrate that the proteome of GSCs provides unique information as it predicts the ability of glioma to resist radiation treatment. Furthermore, we establish NAMPT transfer via MVs as a mechanism for rescuing the proliferation of radiosensitive cells upon irradiation.

A multiplexed assay for quantifying immunomodulatory proteins supports correlative studies in immunotherapy clinical trials

Introduction

Immunotherapy is an effective treatment for a subset of cancer patients, and expanding the benefits of immunotherapy to all cancer patients will require predictive biomarkers of response and immune-related adverse events (irAEs). To support correlative studies in immunotherapy clinical trials, we are developing highly validated assays for quantifying immunomodulatory proteins in human biospecimens.

Methods

Here, we developed a panel of novel monoclonal antibodies and incorporated them into a novel, multiplexed, immuno-multiple reaction monitoring mass spectrometry (MRM-MS)-based proteomic assay targeting 49 proteotypic peptides representing 43 immunomodulatory proteins.

Results and discussion

The multiplex assay was validated in human tissue and plasma matrices, where the linearity of quantification was >3 orders of magnitude with median interday CVs of 8.7% (tissue) and 10.1% (plasma). Proof-of-principle demonstration of the assay was conducted in plasma samples collected in clinical trials from lymphoma patients receiving an immune checkpoint inhibitor. We provide the assays and novel monoclonal antibodies as a publicly available resource for the biomedical community.

Integrative Proteomics and N-Glycoproteomics Analyses of Rheumatoid Arthritis Synovium Reveal Immune-Associated Glycopeptides

Rheumatoid arthritis (RA) is a typical autoimmune disease characterized by synovial inflammation, synovial tissue hyperplasia, and destruction of bone and cartilage. Protein glycosylation plays key roles in the pathogenesis of RA but in-depth glycoproteomics analysis of synovial tissues is still lacking. Here, by using a strategy to quantify intact N-glycopeptides, we identified 1260 intact N-glycopeptides from 481 N-glycosites on 334 glycoproteins in RA synovium. Bioinformatics analysis revealed that the hyper-glycosylated proteins in RA were closely linked to immune responses. By using DNASTAR software, we identified 20 N-glycopeptides whose prototype peptides were highly immunogenic. We next calculated the enrichment scores of nine types of immune cells using specific gene sets from public single-cell transcriptomics data of RA and revealed that the N-glycosylation levels at some sites, such as IGSF10_N2147, MOXD2P_N404, and PTCH2_N812, were significantly correlated with the enrichment scores of certain immune cell types. Furthermore, we showed that aberrant N-glycosylation in the RA synovium was related to increased expression of glycosylation enzymes. Collectively, this work presents, for the first time, the N-glycoproteome of RA synovium and describes immune-associated glycosylation, providing novel insights into RA pathogenesis.

Integrated glycoproteomic characterization of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC), a common form of RCC, is responsible for the high mortality rate of kidney cancer. Dysregulations of glycoproteins have been shown to associate with ccRCC. However, the molecular mechanism has not been well characterized. Here, a comprehensive glycoproteomic analysis is conducted using 103 tumors and 80 paired normal adjacent tissues. Altered glycosylation enzymes and corresponding protein glycosylation are observed, while two of the major ccRCC mutations, BAP1 and PBRM1, show distinct glycosylation profiles. Additionally, inter-tumor heterogeneity and cross-correlation between glycosylation and phosphorylation are observed. The relation of glycoproteomic features to genomic, transcriptomic, proteomic, and phosphoproteomic changes shows the role of glycosylation in ccRCC development with potential for therapeutic interventions. This study reports a large-scale tandem mass tag (TMT)-based quantitative glycoproteomic analysis of ccRCC that can serve as a valuable resource for the community.

Altered Glycosylation in Progression and Management of Bladder Cancer

Bladder cancer (BC) is the 10th most common malignancy worldwide, with an estimated 573,000 new cases and 213,000 deaths in 2020. Available therapeutic approaches are still unable to reduce the incidence of BC metastasis and the high mortality rates of BC patients. Therefore, there is a need to deepen our understanding of the molecular mechanisms underlying BC progression to develop new diagnostic and therapeutic tools. One such mechanism is protein glycosylation. Numerous studies reported changes in glycan biosynthesis during neoplastic transformation, resulting in the appearance of the so-called tumor-associated carbohydrate antigens (TACAs) on the cell surface. TACAs affect a wide range of key biological processes, including tumor cell survival and proliferation, invasion and metastasis, induction of chronic inflammation, angiogenesis, immune evasion, and insensitivity to apoptosis. The purpose of this review is to summarize the current information on how altered glycosylation of bladder cancer cells promotes disease progression and to present the potential use of glycans for diagnostic and therapeutic purposes.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

High-throughput site-specific N-glycoproteomics reveals glyco-signatures for liver disease diagnosis

The glycoproteome has emerged as a prominent target for screening biomarkers, as altered glycosylation is a hallmark of cancer cells. In this work, we incorporated tandem mass tag labeling into quantitative glycoproteomics by developing a chemical labeling-assisted complementary dissociation method for the multiplexed analysis of intact N-glycopeptides. Benefiting from the complementary nature of two different mass spectrometry dissociation methods for identification and multiplex labeling for quantification of intact N-glycopeptides, we conducted the most comprehensive site-specific and subclass-specific N-glycosylation profiling of human serum immunoglobulin G (IgG) to date. By analysing the serum of 90 human patients with varying severities of liver diseases, as well as healthy controls, we identified that the combination of IgG1-H3N5F1 and IgG4-H4N3 can be used for distinguishing between different stages of liver diseases. Finally, we used targeted parallel reaction monitoring to successfully validate the expression changes of glycosylation in liver diseases in a different sample cohort that included 45 serum samples.

Mass Spectrometry-Based Glycoproteomic Workflows for Cancer Biomarker Discovery

Glycosylation has a clear role in cancer initiation and progression, with numerous studies identifying distinct glycan features or specific glycoproteoforms associated with cancer. Common findings include that aggressive cancers tend to have higher expression levels of enzymes that regulate glycosylation as well as glycoproteins with greater levels of complexity, increased branching, and enhanced chain length1. Research in cancer glycoproteomics over the last 50-plus years has mainly focused on technology development used to observe global changes in glycosylation. Efforts have also been made to connect glycans to their protein carriers as well as to delineate the role of these modifications in intracellular signaling and subsequent cell function. This review discusses currently available techniques utilizing mass spectrometry-based technologies used to study glycosylation and highlights areas for future advancement.

Proteomics of High-Grade Serous Ovarian Cancer Models Identifies Cancer-Associated Fibroblast Markers Associated with Clinical Outcomes

The tumor microenvironment has recently emerged as a critical component of high-grade serous ovarian cancer (HGSC) disease progression. Specifically, cancer-associated fibroblasts (CAFs) have been recognized as key players in various pro-oncogenic processes. Here, we use mass-spectrometry (MS) to characterize the proteomes of HGSC patient-derived CAFs and compare them to those of the epithelial component of HGSC to gain a deeper understanding into their tumor-promoting phenotype. We integrate our data with primary tissue data to define a proteomic signature of HGSC CAFs and uncover multiple novel CAF proteins that are prognostic in an independent HGSC patient cohort. Our data represent the first MS-based global proteomic characterization of CAFs in HGSC and further highlights the clinical significance of HGSC CAFs.

PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery

Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.

Glycoproteomics Landscape of Asymptomatic and Symptomatic Human Alzheimer's Disease Brain

Molecular changes in the brain of individuals afflicted with Alzheimer's disease (AD) are an intense area of study. Little is known about the role of protein abundance and posttranslational modifications in AD progression and treatment, in particular large-scale intact N-linked glycoproteomics analysis. To elucidate the N-glycoproteome landscape, we developed an approach based on multi-lectin affinity enrichment, hydrophilic interaction chromatography, and LC-MS-based glycoproteomics. We analyzed brain tissue from 10 persons with no cognitive impairment or AD, 10 with asymptomatic AD, and 10 with symptomatic AD, detecting over 300 glycoproteins and 1900 glycoforms across the samples. The majority of glycoproteins have N-glycans that are high-mannosidic or complex chains that are fucosylated and bisected. The Man5 N-glycan was found to occur most frequently at >20% of the total glycoforms. Unlike the glycoproteomes of other tissues, sialylation is a minor feature of the brain N-glycoproteome, occurring at <9% among the glycoforms. We observed AD-associated differences in the number of antennae, frequency of fucosylation, bisection, and other monosaccharides at individual glycosylation sites among samples from our three groups. Further analysis revealed glycosylation differences in subcellular compartments across disease stage, including glycoproteins in the lysosome frequently modified with paucimannosidic glycans. These results illustrate the N-glycoproteomics landscape across the spectrum of AD clinical and pathologic severity and will facilitate a deeper understanding of progression and treatment development.

Aberrant N-glycolylneuraminic acid in breast MCF-7 cancer cells and cancer stem cells

N-Glycolylneuraminic acid (Neu5Gc) is not normally detected in humans because humans lack the hydroxylase enzyme that converts cytidine-5′-monophosphate-N-acetylneuraminic acid (CMP-Neu5Ac) to CMP-Neu5Gc; thus, any Neu5Gc appearing in the human body is aberrant. Neu5Gc has been observed in human cancer cells and tissues. Moreover, antibodies against Neu5Gc have been detected in healthy humans, which are obstacles to clinical xenotransplantation and stem cell therapies. Thus, the study of Neu5Gc in humans has important pathological and clinical relevance. Here, we report the N-glycoproteomics characterization of aberrant Neu5Gc in breast MCF-7 cancer cells and cancer stem cells (CSCs) at the molecular level of intact N-glycopeptides, including comprehensive information (peptide backbones, N-glycosites, N-glycan monosaccharide compositions, and linkage structures) based on a target-decoy theoretical database search strategy and a spectrum-level false discovery rate (FDR) control ≤1%. The existence of Neu5Gc on N-glycan moieties was further confirmed according to its characteristic oxonium fragment ions in the MS/MS spectra of either m/z 308.09816 (Neu5Gc) or 290.08759 (Neu5Gc-H2O). The results are an important addition to previously reported Neu5Ac data and can be further validated with targeted MS methods such as multiple and parallel reaction monitoring and biochemical methods such as immunoassays. This MS-based N-glycoproteomics method can be extended to the discovery and characterization of putative aberrant Neu5Gc in other biological and clinical systems.

Proteome and Glycoproteome Analyses Reveal the Protein N-Linked Glycosylation Specificity of STT3A and STT3B

STT3A and STT3B are the main catalytic subunits of the oligosaccharyltransferase complex (OST-A and OST-B in mammalian cells), which primarily mediate cotranslational and post-translocational N-linked glycosylation, respectively. To determine the specificity of STT3A and STT3B, we performed proteomic and glycoproteomic analyses in the gene knock-out (KO) and wild-type HEK293 cells. In total, 3961 proteins, 4265 unique N-linked intact glycopeptides and 629 glycosites representing 349 glycoproteins were identified from all these cells. Deletion of the STT3A gene had a greater impact on the protein expression than deletion of STT3B, especially on glycoproteins. In addition, total mannosylated N-glycans were reduced and fucosylated N-glycans were increased in STT3A-KO cells, which were caused by the differential expression of glycan-related enzymes. Interestingly, hyperglycosylated proteins were identified in KO cells, and the hyperglycosylation of ENPL was caused by the endoplasmic reticulum (ER) stress due to the STT3A deletion. Furthermore, the increased expression of the ATF6 and PERK indicated that the unfolded protein response also happened in STT3A-KO cells. Overall, the specificity of STT3A and STT3B revealed that defects in the OST subunit not only broadly affect N-linked glycosylation of the protein but also affect protein expression.

Boost-DiLeu: Enhanced Isobaric N,N-Dimethyl Leucine Tagging Strategy for a Comprehensive Quantitative Glycoproteomic Analysis

Intact glycopeptide analysis has been of great interest because it can elucidate glycosylation site information and glycan structural composition at the same time. However, mass spectrometry (MS)-based glycoproteomic analysis is hindered by the low abundance and poor ionization efficiency of glycopeptides. Relatively large amounts of starting materials are needed for the enrichment, which makes the identification and quantification of intact glycopeptides from samples with limited quantity more challenging. To overcome these limitations, we developed an improved isobaric labeling strategy with an additional boosting channel to enhance N,N-dimethyl leucine (DiLeu) tagging-based quantitative glycoproteomic analysis, termed as Boost-DiLeu. With the integration of a one-tube sample processing workflow and high-pH fractionation, 3514 quantifiable N-glycopeptides were identified from 30 μg HeLa cell tryptic digests with reliable quantification performance. Furthermore, this strategy was applied to human cerebrospinal fluid (CSF) samples to differentiate N-glycosylation profiles between Alzheimer's disease (AD) patients and non-AD donors. The results revealed processes and pathways affected by dysregulated N-glycosylation in AD, including platelet degranulation, cell adhesion, and extracellular matrix, which highlighted the involvement of N-glycosylation aberrations in AD pathogenesis. Moreover, weighted gene coexpression network analysis (WGCNA) showed nine modules of glycopeptides, two of which were associated with the AD phenotype. Our results demonstrated the feasibility of using this strategy for in-depth glycoproteomic analysis of size-limited clinical samples. Taken together, we developed and optimized a strategy for the enhanced comprehensive quantitative intact glycopeptide analysis with DiLeu labeling, showing significant promise for identifying novel therapeutic targets or biomarkers in biological systems with a limited sample quantity.

Clinical applications of mass spectrometry-based proteomics in cancer: where are we?

Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic. This article is protected by copyright. All rights reserved.

Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual's leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.

Identification of Novel Drugs Targeting Cell Cycle Regulators for the Treatment of High-Grade Serous Ovarian Cancer via Integrated Bioinformatics Analysis

High-grade serous ovarian carcinoma (HGSC), the most common and aggressive histological type of ovarian cancer, remains the leading cause of cancer-related deaths among females. It is important to develop novel drugs to improve the therapeutic outcomes of HGSC patients, thereby reducing their mortality. Symmetry is one of the most important properties of the biological network, which determines the stability of a biological system. As aberrant gene expression is a critical symmetry-breaking event that perturbs the stability of biological networks and triggers tumor progression, we aim in this study to discover new candidate drugs and predict their targets for HGSC therapy based on differentially expressed genes involved in HGSC pathogenesis. Firstly, 98 up-regulated genes and 108 down-regulated genes were identified from three independent transcriptome datasets. Then, the small-molecule compounds PHA-793887, pidorubicine and lestaurtinib, which target cell-cycle-related processes, were identified as novel candidate drugs for HGSC treatment by adopting the connectivity map (CMap)-based drug repositioning approach. Furthermore, through a topological analysis of the protein–protein interaction network, cell cycle regulators CDK1, TOP2A and AURKA were identified as bottleneck nodes, and their expression patterns were validated at the mRNA and protein expression levels. Moreover, the results of molecular docking analysis showed that PHA-793887, pidorubicine and lestaurtinib had a strong binding affinity for CDK1, TOP2A and AURKA, respectively. Therefore, our study repositioned PHA-793887, pidorubicine and lestaurtinib, which can inhibit cell cycle regulators, as novel agents for HGSC treatment, thereby helping to optimize the therapeutic strategy for HGSC.

Progress of proteomics-driven precision medicine: From a glycosylation view

Currently, cancer is one of the leading causes of death worldwide, partially owing to the lack of early diagnosis methods and effective therapies. With the rapid development of various omics, the precision medicine strategy becomes a promising way to increase the survival rates by considering individual differences. Glycosylation is one of the most essential protein post-translational modifications and plays important roles in a variety of biological processes. Therefore, it is highly possible to acquire understanding of the molecular mechanisms as well as discover novel potential markers for diagnosis and prognosis based on glycoproteomics research. This review summarizes the recent glycoproteomics studies about N-glycosylation of several cancer types, mainly in the past 5 years. We also highlight corresponding mass spectrometry-based analytical methods to give a brief overview on the main techniques applied in glycoproteomics.

Model Cell Lines and Tissues of Different HGSOC Subtypes Differ in Local Estrogen Biosynthesis

Simple Summary

Ovarian cancer (OC) comprises a heterogeneous group of hormone-dependent diseases with very high mortality. Estrogens have been shown to promote the progression of OC; however, their exact role in OC subtypes remains unknown. Here, we investigated the local estrogen biosynthesis in OC. We performed targeted transcriptomics and estrogen metabolism analyses in high-grade serous OC (HGSOC) cell lines that differed in chemoresistance status and compared these data with publicly available transcriptome and proteome data for HGSOC tissues. In HGSOC cells, estrogen metabolism decreased with increasing chemoresistance. In highly chemoresistant cells and platinum-resistant HGSOC tissues, HSD17B14 expression was increased. Proteome data showed differential levels of HSD17B10, SULT1E1, CYP1B1, and NQO1 between the four HGSOC subtypes. Our results confirm that estrogen biosynthesis differs between different HGSOC cell models and possibly between different HGSOC subtypes. Such differentially expressed enzymes have potential as targets in the search of new treatment options.

Abstract

Ovarian cancer (OC) is highly lethal and heterogeneous. Several hormones are involved in OC etiology including estrogens; however, their role in OC is not completely understood. Here, we performed targeted transcriptomics and estrogen metabolism analyses in high-grade serous OC (HGSOC), OVSAHO, Kuramochi, COV632, and immortalized normal ovarian epithelial HIO-80 cells. We compared these data with public transcriptome and proteome data for the HGSOC tissues. In all model systems, high steroid sulfatase expression and weak/undetected aromatase (CYP19A1) expression indicated the formation of estrogens from the precursor estrone-sulfate (E1-S). In OC cells, the metabolism of E1-S to estradiol was the highest in OVSAHO, followed by Kuramochi and COV362 cells, and decreased with increasing chemoresistance. In addition, higher HSD17B14 and CYP1A2 expressions were observed in highly chemoresistant COV362 cells and platinum-resistant tissues compared to those in HIO-80 cells and platinum-sensitive tissues. The HGSOC cell models differed in HSD17B10, CYP1B1, and NQO1 expression. Proteomic data also showed different levels of HSD17B10, CYP1B1, NQO1, and SULT1E1 between the four HGSOC subtypes. These results suggest that different HGSOC subtypes form different levels of estrogens and their metabolites and that the estrogen-biosynthesis-associated targets should be further studied for the development of personalized treatment.

Proteogenomic characterization of 2002 human cancers reveals pan-cancer molecular subtypes and associated pathways

Mass-spectrometry-based proteomic data on human tumors-combined with corresponding multi-omics data-present opportunities for systematic and pan-cancer proteogenomic analyses. Here, we assemble a compendium dataset of proteomics data of 2002 primary tumors from 14 cancer types and 17 studies. Protein expression of genes broadly correlates with corresponding mRNA levels or copy number alterations (CNAs) across tumors, but with notable exceptions. Based on unsupervised clustering, tumors separate into 11 distinct proteome-based subtypes spanning multiple tissue-based cancer types. Two subtypes are enriched for brain tumors, one subtype associating with MYC, Wnt, and Hippo pathways and high CNA burden, and another subtype associating with metabolic pathways and low CNA burden. Somatic alteration of genes in a pathway associates with higher pathway activity as inferred by proteome or transcriptome data. A substantial fraction of cancers shows high MYC pathway activity without MYC copy gain but with mutations in genes with noncanonical roles in MYC. Our proteogenomics survey reveals the interplay between genome and proteome across tumor lineages.