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

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

Species-dependent posttranscriptional regulation of NOS1 by FMRP in the developing cerebral cortex.

Fragile X syndrome (FXS), the leading monogenic cause of intellectual disability and autism, results from loss of function of the RNA-binding protein FMRP. Here, we show that FMRP regulates translation of neuronal nitric oxide synthase 1 (NOS1) in the developing human neocortex. Whereas NOS1 mRNA is widely expressed, NOS1 protein is transiently coexpressed with FMRP during early synaptogenesis in layer- and region-specific pyramidal neurons. These include midfetal layer 5 subcortically projecting neurons arranged into alternating columns in the prospective Broca's area and orofacial motor cortex. Human NOS1 translation is activated by FMRP via interactions with coding region binding motifs absent from mouse Nos1 mRNA, which is expressed in mouse pyramidal neurons, but not efficiently translated. Correspondingly, neocortical NOS1 protein levels are severely reduced in developing human FXS cases, but not FMRP-deficient mice. Thus, alterations in FMRP posttranscriptional regulation of NOS1 in developing neocortical circuits may contribute to cognitive dysfunction in FXS.

Detection of Pancreatic Ductal Adenocarcinoma-Associated Proteins in Serum.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types, partly because it is frequently identified at an advanced stage, when surgery is no longer feasible. Therefore, early detection using minimally invasive methods such as blood tests may improve outcomes. However, studies to discover molecular signatures for the early detection of PDAC using blood tests have only been marginally successful. In the current study, a quantitative glycoproteomic approach via data-independent acquisition mass spectrometry was utilized to detect glycoproteins in 29 patient-matched PDAC tissues and sera. A total of 892 N-linked glycopeptides originating from 141 glycoproteins had PDAC-associated changes beyond normal variation. We further evaluated the specificity of these serum-detectable glycoproteins by comparing their abundance in 53 independent PDAC patient sera and 65 cancer-free controls. The PDAC tissue-associated glycoproteins we have identified represent an inventory of serum-detectable PDAC-associated glycoproteins as candidate biomarkers that can be potentially used for the detection of PDAC using blood tests.

VCAM-1 complements CA-125 in detecting recurrent ovarian cancer.

BACKGROUND: Close to three-quarters of ovarian cancer cases are frequently diagnosed at an advanced stage, with more than 70% of them failing to respond to primary therapy and relapsing within 5 years. There is an urgent need to identify strategies for early detection of ovarian cancer recurrence, which may lead to earlier intervention and better outcomes. METHODS: A customized magnetic bead-based 8-plex immunoassay was evaluated using a Bio-Plex 200 Suspension Array System. Target protein levels were analyzed in sera from 58 patients diagnosed with advanced ovarian cancer (including 34 primary and 24 recurrent tumors) and 46 healthy controls. The clinical performance of these biomarkers was evaluated individually and in combination for their ability to detect recurrent ovarian cancer. RESULTS: An 8-plex immunoassay was evaluated with high analytical performance suitable for biomarker validation studies. Logistic regression modeling selected a two-marker panel of CA-125 and VCAM-1 that improved the performance of CA-125 alone in detecting recurrent ovarian cancer (AUC: 0.813 versus 0.700). At a fixed specificity of 83%, the two-marker panel significantly improved sensitivity in separating primary from recurrent tumors (70.8% versus 37.5%, P = 0.004), demonstrating that VCAM-1 was significantly complementary to CA-125 in detecting recurrent ovarian cancer. CONCLUSIONS: A two-marker panel of CA-125 and VCAM-1 showed strong diagnostic performance and improvement over the use of CA-125 alone in detecting recurrent ovarian cancer. The experimental results warrant further clinical validation to determine their role in the early detection of recurrent ovarian cancer.

Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.

BACKGROUND: Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts. METHODS: To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model. RESULTS: GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls. CONCLUSIONS: GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.

Impact of Prostate Health Index Results for Prediction of Biopsy Grade Reclassification During Active Surveillance.

PURPOSE: We assessed whether Prostate Health Index results improve prediction of grade reclassification for men on active surveillance. METHODS AND MATERIALS: We identified men in Canary Prostate Active Surveillance Study with Grade Group 1 cancer. Outcome was grade reclassification to Grade Group 2+ cancer. We considered decision rules to maximize specificity with sensitivity set at 95%. We derived rules based on clinical data (R1) vs clinical data+Prostate Health Index (R3). We considered an "or"-logic rule combining clinical score and Prostate Health Index (R4), and a "2-step" rule using clinical data followed by risk stratification based on Prostate Health Index (R2). Rules were applied to a validation set, where values of R2-R4 vs R1 for specificity and sensitivity were evaluated. RESULTS: We included 1,532 biopsies (n = 610 discovery; n = 922 validation) among 1,142 men. Grade reclassification was seen in 27% of biopsies (23% discovery, 29% validation). Among the discovery set, at 95% sensitivity, R2 yielded highest specificity at 27% vs 17% for R1. In the validation set, R3 had best performance vs R1 with Δsensitivity = -4% and Δspecificity = +6%. There was slight improvement for R3 vs R1 for confirmatory biopsy (AUC 0.745 vs R1 0.724, ΔAUC 0.021, 95% CI 0.002-0.041) but not for subsequent biopsies (ΔAUC -0.012, 95% CI -0.031-0.006). R3 did not have better discrimination vs R1 among the biopsy cohort overall (ΔAUC 0.007, 95% CI -0.007-0.020). CONCLUSIONS: Among active surveillance patients, using Prostate Health Index with clinical data modestly improved prediction of grade reclassification on confirmatory biopsy and did not improve prediction on subsequent biopsies.

Neoplastic cell enrichment of tumor tissues using coring and laser microdissection for proteomic and genomic analyses of pancreatic ductal adenocarcinoma.

BACKGROUND: The identification of differentially expressed tumor-associated proteins and genomic alterations driving neoplasia is critical in the development of clinical assays to detect cancers and forms the foundation for understanding cancer biology. One of the challenges in the analysis of pancreatic ductal adenocarcinoma (PDAC) is the low neoplastic cellularity and heterogeneous composition of bulk tumors. To enrich neoplastic cells from bulk tumor tissue, coring, and laser microdissection (LMD) sampling techniques have been employed. In this study, we assessed the protein and KRAS mutation changes associated with samples obtained by these enrichment techniques and evaluated the fraction of neoplastic cells in PDAC for proteomic and genomic analyses. METHODS: Three fresh frozen PDAC tumors and their tumor-matched normal adjacent tissues (NATs) were obtained from three sampling techniques using bulk, coring, and LMD; and analyzed by TMT-based quantitative proteomics. The protein profiles and characterizations of differentially expressed proteins in three sampling groups were determined. These three PDACs and samples of five additional PDACs obtained by the same three sampling techniques were also subjected to genomic analysis to characterize KRAS mutations. RESULTS: The neoplastic cellularity of eight PDACs ranged from less than 10% to over 80% based on morphological review. Distinctive proteomic patterns and abundances of certain tumor-associated proteins were revealed when comparing the tumors and NATs by different sampling techniques. Coring and bulk tissues had comparable proteome profiles, while LMD samples had the most distinct proteome composition compared to bulk tissues. Further genomic analysis of bulk, cored, or LMD samples demonstrated that KRAS mutations were significantly enriched in LMD samples while coring was less effective in enriching for KRAS mutations when bulk tissues contained a relatively low neoplastic cellularity. CONCLUSIONS: In addition to bulk tissues, samples from LMD and coring techniques can be used for proteogenomic studies. The greatest enrichment of neoplastic cellularity is obtained with the LMD technique.

Proteome-wide Tyrosine Phosphorylation Analysis Reveals Dysregulated Signaling Pathways in Ovarian Tumors.

The recent accomplishment of comprehensive proteogenomic analysis of high-grade serous ovarian carcinoma (HGSOC) tissues reveals cancer associated molecular alterations were not limited to variations among DNA, and mRNA/protein expression, but are a result of complex reprogramming of signaling pathways/networks mediated by the protein and post-translational modification (PTM) interactomes. A systematic, multiplexed approach interrogating enzyme-substrate relationships in the context of PTMs is fundamental in understanding the dynamics of these pathways, regulation of cellular processes, and their roles in disease processes. Here, as part of Clinical Proteomic Tumor Analysis Consortium (CPTAC) project, we established a multiplexed PTM assay (tyrosine phosphorylation, and lysine acetylation, ubiquitylation and SUMOylation) method to identify protein probes' PTMs on the human proteome array. Further, we focused on the tyrosine phosphorylation and identified 19 kinases are potentially responsible for the dysregulated signaling pathways observed in HGSOC. Additionally, elevated kinase activity was observed when 14 ovarian cancer cell lines or tumor tissues were subjected to test the autophosphorylation status of PTK2 (pY397) and PTK2B (pY402) as a proxy for kinase activity. Taken together, this report demonstrates that PTM signatures based on lysate reactions on human proteome array is a powerful, unbiased approach to identify dysregulated PTM pathways in tumors.

Challenges and opportunities in the proteomic characterization of clear cell renal cell carcinoma (ccRCC): A critical step towards the personalized care of renal cancers.

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer, comprising approximately 75% of all kidney tumors. Recent the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) studies have significantly advanced the molecular characterization of RCC and facilitated the development of targeted therapies. Such advances have improved the median survival of patients with advanced disease from less than 10 months prior to 2004 to 30 months by 2011. However, approximately 30% of localized ccRCC patients will nevertheless develop recurrence or metastasis after surgical resection of their tumor. Therefore, it is critical to further analyze potential tumor-associated proteins and their profiles during disease progression. Over the past decade, tremendous effort has been focused on the study of molecular pathways, including genomics, transcriptomics, and proteomics in order to identify potential molecular biomarkers, as well as to facilitate early detection, monitor tumor progression and uncover potentially therapeutic targets. In this review, we focus on recent advances in the proteomic analysis of ccRCC, current strategies and challenges, and perspectives in the field. This insight will highlight the discovery of tumor-associated proteins, and their potential clinical impact on personalized precision-based care in ccRCC.

An Integrated Workflow for Global, Glyco-, and Phospho-proteomic Analysis of Tumor Tissues.

Recently, the rapid development and application of mass spectrometry (MS)-based technologies have markedly improved the comprehensive proteomic characterization of global proteome and protein post-translational modifications (PTMs). However, the current conventional approach for global proteomic analysis is often carried out separately from PTM analysis. In our study, we developed an integrated workflow for multiplex analysis of global, glyco-, and phospho-proteomics using breast cancer patient-derived xenograft (PDX) tumor samples. Our approach included the following steps: trypsin-digested tumor samples were enriched for phosphopeptides through immobilized metal ion affinity chromatography (IMAC), followed by enrichment of glycopeptides through mixed anion exchange (MAX) method, and then the flow-through peptides were analyzed for global proteomics. Our workflow demonstrated an increased identification of peptides and associated proteins in global proteome, as compared to those using the peptides without PTM depletion. In addition to global proteome, the workflow identified phosphopeptides and glycopeptides from the PTM enrichment. We also found a subset of glycans with unique distribution profiles in the IMAC flow-through, as compared to those enriched directly using the MAX method. Our integrated workflow provided an effective platform for simultaneous global proteomic and PTM analysis of biospecimens.

Correction to: Proteomic analysis of degradation ubiquitin signaling by ubiquitin occupancy changes responding to 26S proteasome inhibition.

[This corrects the article DOI: 10.1186/s12014-020-9265-x.].

Proteomic analysis of degradation ubiquitin signaling by ubiquitin occupancy changes responding to 26S proteasome inhibition.

BACKGROUND: Ubiquitination is a post-translational modification where ubiquitin is covalently attached to lysine residues on substrate proteins to signal their degradation by the 26S proteasome or initiate other non-degradation functions such as cellular trafficking. The diversity of ubiquitin modifications can be attributed to the variable number of ubiquitin molecules attached to a lysine residue (mono- vs. poly-ubiquitin chains), the type of covalent linkages within poly-ubiquitin chains and the number of lysine residues on a substrate that are occupied by ubiquitin at any given time. The integral role ubiquitination plays in cell homeostasis is reflected by the multitude of diseases associated with impaired ubiquitin modification, rendering it the focus of extensive research initiatives and proteomic discovery studies. However, determining the functional role of distinct ubiquitin modifications directly from proteomic data remains challenging and represents a bottleneck in the process of deciphering how ubiquitination at specific substrate sites impacts cell signaling. METHODS: In this study SILAC coupled with LC-MS/MS is used to identify ubiquitinated proteins in SKOV3 ovarian cancer cells, with the implementation of a computational approach that measures relative ubiquitin occupancy at distinct modification sites upon 26S proteasome inhibition and uses that data to infer functional significance. RESULTS: In addition to identifying and quantifying relative ubiquitin occupancy at distinct post-translational modification sites to distinguish degradation from non-degradation signaling, this research led to the discovery of nine ubiquitination sites in the oncoprotein HER2 that have not been previously reported in ovarian cancer. Subsequently the computational approach applied in this study was utilized to infer the functional role of individual HER2 ubiquitin-modified residues. CONCLUSIONS: In summary, the computational method, previously described for glycosylation analysis, was used in this study for the assessment of ubiquitin stoichiometries and applied directly to proteomic data to distinguish degradation from non-degradation ubiquitin functions.

Cancer biomarker discovery and translation: proteomics and beyond.

INTRODUCTION: Cancer is often diagnosed at late stages when the chance of cure is relatively low and although research initiatives in oncology discover many potential cancer biomarkers, few transition to clinical applications. This review addresses the current landscape of cancer biomarker discovery and translation with a focus on proteomics and beyond. Areas covered: The review examines proteomic and genomic techniques for cancer biomarker detection and outlines advantages and challenges of integrating multiple omics approaches to achieve optimal sensitivity and address tumor heterogeneity. This discussion is based on a systematic literature review and direct participation in translational studies. Expert commentary: Identifying aggressive cancers early on requires improved sensitivity and implementation of biomarkers representative of tumor heterogeneity. During the last decade of genomic and proteomic research, significant advancements have been made in next generation sequencing and mass spectrometry techniques. This in turn has led to a dramatic increase in identification of potential genomic and proteomic cancer biomarkers. However, limited successes have been shown with translation of these discoveries into clinical practice. We believe that the integration of these omics approaches is the most promising molecular tool for comprehensive cancer evaluation, early detection and transition to Precision Medicine in oncology.

A multiplex immunoassay of serum biomarkers for the detection of uveal melanoma.

BACKGROUND: Approximately 50% of uveal melanoma (UM) patients develop metastases preferentially in the liver leading to death within 15 months. Currently, there is no effective treatment for metastatic UM, in part because the tumor burden is typically high when liver metastases are detected through abnormal liver function tests (LFTs) or imaging studies. The use of LFTs results followed by diagnostic tests has high specificity and predictive values but low sensitivity, and better tests are needed for early diagnosis of the primary tumor as well as its metastatic spread. To evaluate serum biomarkers for the early detection of UM, multiplex immunoassays were developed. METHODS: Magnetic bead-based multiplex immunoassays were developed for the selected serum biomarkers using a Bio-Plex 200 system. The dynamic ranges, lower limits of detection and quantification, cross-reactivity, and intra- and inter-assay precision were assessed. All proteins were analyzed in sera of 48 patients diagnosed with UM (14 metastatic, 9 disease-free (DF) ≥ 5 years, 25 unknown) and 36 healthy controls. The performance of the biomarkers was evaluated individually and in combination for their ability to detect UM. RESULTS: A 7-plex immunoassay of OPN, MIA, CEACAM-1, MIC-1, SPON1, POSTN and HSP27 was developed with negligible cross-reactivity, recovery of 84-105%, and intra-assay and inter-assay precision of 2.3-7.5% or 2.8-20.8%, respectively. Logistic regression identified a two-marker panel of HSP27 and OPN that significantly improved the individual biomarker performance in discriminating UM from healthy controls. The improved discrimination of a two-marker panel of MIA and MIC-1 was also observed between metastatic UM and DF, however not statistically significant due to the small sample size. CONCLUSIONS: The multiplex immunoassay provides sufficient analytical performance to evaluate serum biomarkers that complement each other in detection of UM, and warrants further validation with a larger number of patient samples.

Development of a glycoproteomic strategy to detect more aggressive prostate cancer using lectin-immunoassays for serum fucosylated PSA.

BACKGROUND: Prostate-specific antigen (PSA) is commonly used as a serum biomarker for the detection of prostate cancer. However, levels of PSA in serum do not reliably distinguish aggressive prostate cancer from non-aggressive disease. Therefore, there is an urgent need for biomarkers that can differentiate aggressive prostate cancers from non-aggressive phenotypes. Fucosylation is one of the glycosylation-based protein modifications. Previously we demonstrated increased levels of serum fucosylated PSA in patients with aggressive prostate cancer using lectin selection followed by PSA immunoassay. METHODS: We developed two lectin-immunoassays, Lens culinaris agglutinin (LCA) and Aleuria aurantia lectin (AAL) followed by clinical PSA immunoassay and investigated the levels of PSA and its fucosylated glycoforms in serum specimens from prostate cancer patients with different Gleason scores. First, we developed standard curves for lectins enrichment, which were applied to lectin-immunoassay for fucosylated PSA-LCA and PSA-AAL quantification in serum samples. RESULTS: Our results showed that both LCA- and AAL-immunoassays detected elevated fucosylated PSA and were correlated with higher Gleason scores but only AAL-immunoassay detected an increased percentage of fucosylated PSA in patient serum with higher Gleason scores. CONCLUSION: We have developed quantitative lectin-immunoassays for serum fucosylated PSA. Our data demonstrated that fucosylated PSA-AAL, % fucosylated PSA-AAL and fucosylated PSA-LCA levels could be effective biomarkers to differentiate aggressive prostate cancer [especially Gleason 7 (4 + 3) or above] from non-aggressive disease. We believe that application of these lectin-immunoassays to a larger patient population is needed to evaluate the clinical utilities of fucosylated PSA using AAL-PSA and LCA-PSA for aggressive prostate cancer.

Validation of a novel model for the early detection of hepatocellular carcinoma.

BACKGROUND: The biomarkers alpha-fetoprotein (AFP) and protein induced by vitamin K absence/antagonist-II (PIVKA-II) may be useful for detecting early-stage hepatocellular carcinoma (HCC). We evaluated the performance of AFP and PIVKA-II levels, alone and in combination with clinical factors, for the early detection of HCC. METHODS: In a case-control study, serum AFP and PIVKA-II were measured using the ARCHITECT immunoassay analyzer system in a cohort of 119 patients with HCC, 215 patients with non-malignant liver disease, and 34 healthy subjects. Five predictive models for detecting HCC were developed based on age, gender, AFP, and/or PIVKA-II levels; the best model was validated in an independent cohort of 416 patients with HCC and 412 control subjects with cirrhosis. RESULTS: In both cohorts, AFP and PIVKA-II concentrations were higher in patients with HCC compared to healthy controls and patients with non-malignant liver disease. The model that combined AFP and PIVKA-II, age, and gender had the highest AUC of 0.95 (0.95, 95% CI 0.93-0.98), with a sensitivity of 93% and a specificity of 84% in the development cohort, and an AUC of 0.87 (95% CI 0.85-0.90), sensitivity of 74%, and specificity of 85% in the validation cohort. When limiting the validation cohort to only early-stage HCC, the AUC was 0.85 (95% CI 0.81-0.88), sensitivity was 70%, and specificity was 86%. CONCLUSIONS: Compared to each biomarker alone, the combination of AFP and PIVKA-II with age and gender improved the accuracy of detecting HCC and differentiating HCC from non-malignant liver disease.

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction.

Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this "guilt-by-association" (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies.

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues.

Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.

Protein signatures of molecular pathways in non-small cell lung carcinoma (NSCLC): comparison of glycoproteomics and global proteomics.

BACKGROUND: Non-small cell lung carcinoma (NSCLC) remains the leading cause of cancer deaths in the United States. More than half of NSCLC patients have clinical presentations with locally advanced or metastatic disease at the time of diagnosis. The large-scale genomic analysis of NSCLC has demonstrated that molecular alterations are substantially different between adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). However, a comprehensive analysis of proteins and glycoproteins in different subtypes of NSCLC using advanced proteomic approaches has not yet been conducted. METHODS: We applied mass spectrometry (MS) technology featuring proteomics and glycoproteomics to analyze six primary lung SqCCs and eleven ADCs, and we compared the expression level of proteins and glycoproteins in tumors using quantitative proteomics. Glycoproteins were analyzed by enrichment using a chemoenzymatic method, solid-phase extraction of glycopeptides, and quantified by iTRAQ-LC-MS/MS. Protein quantitation was further annotated via Ingenuity Pathway Analysis. RESULTS: Over 6000 global proteins and 480 glycoproteins were quantitatively identified in both SqCC and ADC. ADC proteins (8337) consisted of enzymes (22.11%), kinases (5.11%), transcription factors (6.85%), transporters (6.79%), and peptidases (3.30%). SqCC proteins (6967) had a very similar distribution. The identified glycoproteins, in order of relative abundance, included membrane (42%) and extracellular matrix (>33%) glycoproteins. Oncogene-coded proteins (82) increased 1.5-fold among 1047 oncogenes identified in ADC, while 124 proteins from SqCC were up-regulated in tumor tissues among a total of 827 proteins. We identified 680 and 563 tumor suppressor genes from ADC and SqCC, respectively. CONCLUSION: Our systematic analysis of proteins and glycoproteins demonstrates changes of protein and glycoprotein relative abundance in SqCC (TP53, U2AF1, and RXR) and in ADC (SMARCA4, NOTCH1, PTEN, and MST1). Among them, eleven glycoproteins were upregulated in both ADC and SqCC. Two glycoproteins (ELANE and IGFBP3) were only increased in SqCC, and six glycoproteins (ACAN, LAMC2, THBS1, LTBP1, PSAP and COL1A2) were increased in ADC. Ingenuity Pathway Analysis (IPA) showed that several crucial pathways were activated in SqCC and ADC tumor tissues.

An integrated proteomic and glycoproteomic approach uncovers differences in glycosylation occupancy from benign and malignant epithelial ovarian tumors.

BACKGROUND: Epithelial ovarian carcinomas encompass a heterogeneous group of diseases with a poor 5-year survival rate. Serous carcinoma is the most common type. Most FDA-approved serum tumor markers are glycoproteins. These glycoproteins on cell surface or shed into the bloodstream could serve as therapeutic targets as well as surrogates of tumor. In addition to glycoprotein expressions, the analysis of protein glycosylation occupancy could be important for the understanding of cancer biology as well as the identification of potential glycoprotein changes in cancer. In this study, we used an integrated proteomics and glycoproteomics approach to analyze global glycoprotein abundance and glycosylation occupancy for proteins from high-grade ovarian serous carcinoma (HGSC) and serous cystadenoma, a benign epithelial ovarian tumor, by using LC-MS/MS-based technique. METHODS: Fresh-frozen ovarian HGSC tissues and benign serous cystadenoma cases were quantitatively analyzed using isobaric tags for relative and absolute quantitation for both global and glycoproteomic analyses by two dimensional fractionation followed by LC-MS/MS analysis using a Orbitrap Velos mass spectrometer. RESULTS: Proteins and N-linked glycosite-containing peptides were identified and quantified using the integrated global proteomic and glycoproteomic approach. Among the identified N-linked glycosite-containing peptides, the relative abundances of glycosite-containing peptide and the glycoprotein levels were compared using glycoproteomic and proteomic data. The glycosite-containing peptides with unique changes in glycosylation occupancies rather than the protein expression levels were identified. CONCLUSION: In this study, we presented an integrated proteomics and glycoproteomics approach to identify changes of glycoproteins in protein expression and glycosylation occupancy in HGSC and serous cystadenoma and determined the changes of glycosylation occupancy that are associated with malignant and benign tumor tissues. Specific changes in glycoprotein expression or glycosylation occupancy have the potential to be used in the discrimination between benign and malignant epithelial ovarian tumors and to improve our understanding of ovarian cancer biology.