DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples.

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Differentially expressed glycoproteins in pre- and post-digital rectal examination urine samples for detecting aggressive prostate cancer.

Urinary glycoproteins associated with aggressive prostate cancer (AG-PCa) were previously reported using post-digital rectal examination (DRE) urine specimens. To explore the potential of using pre-DRE urine specimens for detecting AG-PCa, we compared glycoproteins between pre- and post-DRE urine specimens, verified the previously identified post-DRE AG-PCa-associated urinary glycoproteins in pre-DRE urine specimens, and explored potential new glycoproteins for AG-PCa detection in pre-DRE urine specimens. Quantitative glycoproteomic data were acquired for 154 pre-DRE urine specimens from 41 patients with no cancer at biopsy, 48 patients with non-AG-PCa (Gleason score = 6), and 65 patients with AG-PCa (Gleason score 7 or above). Compared to glycopeptides from the post-DRE urine data, humoral immunity-related proteins were enriched in pre-DRE urine samples, whereas cell mediated immune response proteins were enriched in post-DRE urine samples. Analyses of AG-PCa-associated glycoproteins from pre-DRE urine revealed that the three urinary glycoproteins, prostate-specific antigen (PSA), prostatic acid phosphatase (ACPP), and CD97 antigen (CD97) that were previously identified in post-DRE urine samples, were also observed as AG-PCa associated glycoproteins in pre-DRE urine. In addition, we identified three new glycoproteins, fibrillin 1 (FBN1), vitronectin (VTN), and hemicentin 2 (HMCN2), to be potentially associated with AG-PCa in pre-DRE urine specimens. In summary, glycoprotein profiles differ between pre- and post-DRE urine specimens. The identified AG-PCa-associated glycoproteins may be further evaluated in large cohort of pre-DRE urine specimens for detecting clinically significant PCa.

Urinary marker panels for aggressive prostate cancer detection.

Majority of patients with indolent prostate cancer (PCa) can be managed with active surveillance. Therefore, finding biomarkers for classifying patients between indolent and aggressive PCa is essential. In this study, we investigated urinary marker panels composed of urinary glycopeptides and/or urinary prostate-specific antigen (PSA) for their clinical utility in distinguishing non-aggressive (Grade Group 1) from aggressive (Grade Group ≥ 2) PCa. Urinary glycopeptides acquired via data-independent acquisition mass spectrometry (DIA-MS) were quantitatively analyzed, where prostatic acid phosphatase (ACPP), clusterin (CLU), alpha-1-acid glycoprotein 1 (ORM1), and CD antigen 97 (CD97) were selected to be evaluated in various combinations with and without urinary PSA. Targeted parallel reaction monitoring (PRM) assays of the glycopeptides from urinary ACPP and CLU were investigated along with urinary PSA for the ability of aggressive PCa detection. The multi-urinary marker panels, combined via logistic regression, were statistically evaluated using bootstrap resampling and validated by an independent cohort. Majority of the multi-urinary marker panels (e.g., a panel consisted of ACPP, CLU, and Urinary PSA) achieved area under the curve (AUC) ranged from 0.70 to 0.85. Thus, multi-marker panels investigated in this study showed clinically meaningful results on aggressive PCa detection to separate Grade Group 1 from Grade Group 2 and above warranting further evaluation in clinical setting in future.

Optimized data-independent acquisition approach for proteomic analysis at single-cell level.

BACKGROUND: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. METHODS: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. RESULTS: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. CONCLUSIONS: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Improving the detection of aggressive prostate cancer using immunohistochemical staining of protein marker panels.

Prostate cancer (PCa) is a heterogeneous group of tumors, including non-aggressive (NAG) and aggressive (AG) cancer, with variable clinical outcomes. Clinically, in order to assess the aggressiveness of a PCa, a core needle biopsy of a tumor is usually obtained to evaluate the Gleason pattern and score of the tumor. However, it may be difficult to assign on a small biopsy sample using histology. Therefore, additional tool is needed to aid in the assessment. We studied the diagnostic utility of 12 protein markers to identify AG tumors using immunohistochemistry (IHC) and tumor tissue microarray (TMA), including 215 cores of PCa and 111 cores of tumor-matched normal adjacent tissue (NAT). Protein markers were evaluated for their potential utility as single or combined panels for identification of AG. Of 12 proteins, PSMA, phospho-EGFR, AR and P16 were over-expressed in AG. Galectin-3, DPP4 and MAN1B1 revealed stronger staining patterns in NAG. The sensitivity and specificity of individual marker varied widely. Based on AUC values of individual marker, we constructed two- and three-marker panels. In two-marker panels, especially in the panel of DPP4 and PSMA, the AUC value reached 0.83 (ranging from 0.76 to 0.83). In three-marker panels, containing both DPP4 and PSMA with either Galectin-3 or phospho-EGFR, the AUC value reached 0.86 (ranging from 0.83 to 0.86). The specificities at 95% sensitivity of three-marker panels were also significantly improved. In addition to Gleason score, our IHC panels provide a practical tool to assess the aggressiveness of PCa.

Comparing Urinary Glycoproteins among Three Urogenital Cancers and Identifying Prostate Cancer-Specific Glycoproteins.

Prostate cancer, bladder cancer, and renal cancers are major urogenital cancers. Of which, prostate cancer is the most commonly diagnosed and second leading cause of cancer death for men in the United States. For urogenital cancers, urine is considered as proximate body fluid to the tumor site for developing non-invasiveness tests. However, the specific molecular signatures from different urogenital cancers are needed to relate changes in urine to various cancer detections. Herein, we utilized a previously published C4-Tip and C18/MAX-Tip workflow for enrichment of glycopeptides from urine samples and evaluated urinary glycopeptides for its cancer specificity. We analyzed 66 urine samples from bladder cancer (n = 27), prostate cancer (n = 4), clear cell renal cell carcinoma (ccRCC, n = 3), and benign plastic hyperplasia (BPH, n = 32) and then compared them with a previous publication that reported glycopeptides associated with aggressive prostate cancer (Gleason score ≥ 8). We further demonstrated the cancer specificity of the glycopeptides associated with aggressive prostate cancer. In this study, a total of 33 glycopeptides were identified to be specifically differentially expressed in prostate cancer compared to other urogenital cancer types as well as BPH urines. By cross-comparison with our previous urinary glycoproteomic dataset for aggressive prostate cancer, we reported a total of four glycopeptides from glycoproteins DSC2, MGAM, PIK3IP1, and CD55, commonly identified to be prostate cancer-specific. Together, these results deepen our understanding of the urinary glycoproteins associated with urogenital cancer types and expand our knowledge of the cancer specificity of urinary glycoproteins among urogenital cancer progression.

Data-Independent Acquisition-Based Mass Spectrometry (DIA-MS) for Quantitative Analysis of Intact N-Linked Glycopeptides.

N-linked protein glycosylation is a key regulator in various biological functions. Previous studies have shown that aberrant glycosylation is associated with many diseases. Therefore, it is essential to elucidate protein modifications of glycosylation by quantitatively profiling intact N-linked glycopeptides. Data-independent acquisition (DIA) mass spectrometry (MS) is a cost-effective, flexible, and high-throughput method for global proteomics. However, substantial challenges are still present in the quantitative analysis of intact glycopeptides with high accuracy at high throughput. In this study, we have established a novel integrated platform for the DIA analysis of intact glycopeptides isolated from complex samples. The established analysis platform utilizes a well-designed DIA-MS method for raw data collection, a spectral library constructed specifically for intact glycopeptide quantification providing accurate results by the inclusion of Y ions for quantification and filtering of quantified intact glycopeptides with low-quality MS2 spectra automatically using a set of criteria. Intact glycopeptides isolated from human serum were used to evaluate the performance of the integrated platform. By utilizing 100 isolation windows for DIA data acquisition, a well-constructed human serum spectral library containing 1123 nonredundant intact glycopeptides with Y ions, and automated data inspection, 620 intact glycopeptides were quantified with high confidence from DIA-MS. In summary, our integrated platform can serve as a reliable quantitative tool for characterizing intact glycopeptides isolated from complex biological samples to assist our understanding of biological functions of N-linked glycosylation.

Proteomic characterization of primary and metastatic prostate cancer reveals reduced proteinase activity in aggressive tumors.

Prostate cancer (PCa) is a heterogeneous group of tumors with variable clinical courses. In order to improve patient outcomes, it is critical to clinically separate aggressive PCa (AG) from non-aggressive PCa (NAG). Although recent genomic studies have identified a spectrum of molecular abnormalities associated with aggressive PCa, it is still challenging to separate AG from NAG. To better understand the functional consequences of PCa progression and the unique features of the AG subtype, we studied the proteomic signatures of primary AG, NAG and metastatic PCa. 39 PCa and 10 benign prostate controls in a discovery cohort and 57 PCa in a validation cohort were analyzed using a data-independent acquisition (DIA) SWATH-MS platform. Proteins with the highest variances (top 500 proteins) were annotated for the pathway enrichment analysis. Functional analysis of differentially expressed proteins in NAG and AG was performed. Data was further validated using a validation cohort; and was also compared with a TCGA mRNA expression dataset and confirmed by immunohistochemistry (IHC) using PCa tissue microarray (TMA). 4,415 proteins were identified in the tumor and benign control tissues, including 158 up-regulated and 116 down-regulated proteins in AG tumors. A functional analysis of tumor-associated proteins revealed reduced expressions of several proteinases, including dipeptidyl peptidase 4 (DPP4), carboxypeptidase E (CPE) and prostate specific antigen (KLK3) in AG and metastatic PCa. A targeted analysis further identified that the reduced expression of DPP4 was associated with the accumulation of DPP4 substrates and the reduced ratio of DPP4 cleaved peptide to intact substrate peptide. Findings were further validated using an independently-collected tumor cohort, correlated with a TCGA mRNA dataset, and confirmed by immunohistochemical stains of PCa tumor microarray (TMA). Our study is the first large-scale proteomics analysis of PCa tissue using a DIA SWATH-MS platform. It provides not only an interrogative proteomic signature of PCa subtypes, but also indicates the critical roles played by certain proteinases during tumor progression. The spectrum map and protein profile generated in the study can be used to investigate potential biological mechanisms involved in PCa and for the development of a clinical assay to distinguish aggressive from indolent PCa.

Development of Parallel Reaction Monitoring Assays for the Detection of Aggressive Prostate Cancer Using Urinary Glycoproteins.

Recently, we have found that two urinary glycoproteins, prostatic acid phosphatase (ACPP) and clusterin (CLU), combined with serum prostate-specific antigen (PSA) can serve as a three-signature panel for detecting aggressive prostate cancer (PCa) based on a quantitative glycoproteomic study. To facilitate the translation of candidates into clinically applicable tests, robust and accurate targeted parallel reaction monitoring (PRM) assays that can be widely adopted in multiple labs were developed in this study. The developed PRM assays for the urinary glycopeptides, FLN*ESYK from ACPP and EDALN*ETR from CLU, demonstrated good repeatability and a sufficient working range covering three to four orders of magnitude, and their performance in differentiating aggressive PCa was assessed by the quantitative analysis of urine specimens collected from 69 nonaggressive (Gleason score = 6) and 73 aggressive (Gleason ≥ 8) PCa patients. When ACPP combined with CLU, the discrimination power was improved from an area under a curve (AUC) of 0.66 to 0.78. By combining ACPP, CLU, and serum PSA to form a three-signature panel, the AUC was further improved to 0.83 (sensitivity: 84.9%, specificity: 66.7%). Since the serum PSA test alone had an AUC of 0.68, our results demonstrated that the new urinary glycopeptide PRM assays can serve as an adjunct to the serum PSA test to achieve better predictive power toward aggressive PCa. In summary, our developed PRM assays for urinary glycopeptides were successfully applied to clinical PCa urine samples with a promising performance in aggressive PCa detection.

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.

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.

Decoding the Effect of Isobaric Substitutions on Identifying Missing Proteins and Variant Peptides in Human Proteome.

To confirm the existence of missing proteins, we need to identify at least two unique peptides with length of 9-40 amino acids of a missing protein in bottom-up mass-spectrometry-based proteomic experiments. However, an identified unique peptide of the missing protein, even identified with high level of confidence, could possibly coincide with a peptide of a commonly observed protein due to isobaric substitutions, mass modifications, alternative splice isoforms, or single amino acid variants (SAAVs). Besides unique peptides of missing proteins, identified variant peptides (SAAV-containing peptides) could also alternatively map to peptides of other proteins due to the aforementioned issues. Therefore, we conducted a thorough comparative analysis on data sets in PeptideAtlas Tiered Human Integrated Search Proteome (THISP, 2017-03 release), including neXtProt (2017-01 release), to systematically investigate the possibility of unique peptides in missing proteins (PE2-4), unique peptides in dubious proteins, and variant peptides affected by isobaric substitutions, causing doubtful identification results. In this study, we considered 11 isobaric substitutions. From our analysis, we found <5% of the unique peptides of missing proteins and >6% of variant peptides became shared with peptides of PE1 proteins after isobaric substitutions.