Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

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.

Self-Assembled STrap for Global Proteomics and Salivary Biomarker Discovery.

Clinical biomarkers identified by shotgun proteomics require proteins in body fluids or tissues to be enzymatically digested before being separated and sequenced by liquid chromatography-tandem mass spectrometry. How well peptide signals can be resolved and detected is largely dependent on the quality of sample preparation. Conventional approaches such as in-gel, in-solution, and filter-based digestion, despite their extensive implementation by the community, become less appealing due to their unsatisfying protein/peptide recovery rate, lengthy sample processing, and/or lowcost-effectiveness. Suspension trapping has recently been demonstrated as an ultrafast approach for proteomic analysis. Here, for the first time, we extend its application to human salivary proteome analyses. In particular, we present a simple self-assembled glass fiber filter device which can be packed with minimal difficulty, is extremely cost-effective, and maintains the same performance as commercial filters. As a proof-of-principle, we analyzed the whole saliva from 8 healthy individuals as well as a cohort of 10 subjects of oral squamous cell carcinoma (OSCC) patients and non-OSCC subjects. Label-free quantification revealed surprisingly low interindividual variability and several known markers. Our study provides the first evidence of an easy-to-use and low-cost device for clinical proteomics as well as for general proteomic sample preparation.

S-Trap, an Ultrafast Sample-Preparation Approach for Shotgun Proteomics.

The success of shotgun proteomic analysis depends largely on how samples are prepared. Current approaches (such as those that are gel-, solution-, or filter-based), although being extensively employed in the field, are time-consuming and less effective with respect to the repetitive sample processing, recovery, and overall yield. As an alternative, the suspension trapping (S-Trap) filter has been commercially available very recently in the format of a single or 96-well filter plate. In contrast to the conventional filter-aided sample preparation (FASP) approach, which utilizes a molecular weight cut-off (MWCO) membrane as the filter and requires hours of processing before digestion-ready proteins can be obtained, the S-Trap employs a three-dimensional porous material as filter media and traps particulate protein suspensions with the subsequent depletion of interfering substances and in-filter digestion. Due to the large (submicron) pore size, each centrifugation cycle of the S-Trap filter only takes 1 min, which significantly reduces the total processing time from approximately 3 h by FASP to less than 15 min, suggesting an ultrafast sample-preparation approach for shotgun proteomics. Here, we comprehensively evaluate the performance of the individual S-Trap filter and 96-well filter plate in the context of global protein identification and quantitation using whole-cell lysate and clinically relevant sputum samples.

Characterization of core fucosylation via sequential enzymatic treatments of intact glycopeptides and mass spectrometry analysis.

Core fucosylation of N-linked glycoproteins has been linked to the functions of glycoproteins in physiological and pathological processes. However, quantitative characterization of core fucosylation remains challenging due to the complexity and heterogeneity of N-linked glycosylation. Here we report a mass spectrometry-based method that employs sequential treatment of intact glycopeptides with enzymes (STAGE) to analyze site-specific core fucosylation of glycoproteins. The STAGE method utilizes Endo F3 followed by PNGase F treatment to generate mass signatures for glycosites that are formerly modified by core fucosylated N-linked glycans. We benchmark the STAGE method and use it to characterize site specific core fucosylation of glycoproteins from human hepatocellular carcinoma and pancreatic ductal adenocarcinoma, resulting in the identification of 1130 and 782 core fucosylated glycosites, respectively. These results indicate that our STAGE method enables quantitative characterization of core fucosylation events from complex protein mixtures, which may benefit our understanding of core fucosylation functions in various diseases.

Lab-on-a-Filter Techniques for Economical, Effective, and Flexible Proteome Analysis.

Effective and reliable protease digestion of biological samples is critical to the success in bottom-up proteomics analysis. Various filter-based approaches using different types of membranes have been developed in the past several years and largely implemented in sample preparations for modern proteomics. However, these approaches rely heavily on commercial filter products, which are not only costly but also limited in membrane options. Here, we present a plug-and-play device for filter assembly and protease digestion. The device can accommodate a variety of membrane types, can be packed in-house with minimal difficulty, and is extremely cost-effective and reliable. Our protocol offers a versatile platform for general proteome analyses and clinical mass spectrometry.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma.

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

Predictive Signatures of 19 Antibiotic-Induced Escherichia coli Proteomes.

Identifying the mode of action (MOA) of antibacterial compounds is the fundamental basis for the development of new antibiotics, and the challenge increases with the emerging secondary and indirect effect from antibiotic stress. Although various omics-based system biology approaches are currently available, enhanced throughput, accuracy, and comprehensiveness are still desirable to better define antibiotic MOA. Using label-free quantitative proteomics, we present here a comprehensive reference map of proteomic signatures of Escherichia coli under challenge of 19 individual antibiotics. Applying several machine learning techniques, we derived a panel of 14 proteins that can be used to classify the antibiotics into different MOAs with nearly 100% accuracy. These proteins tend to mediate diverse bacterial cellular and metabolic processes. Transcriptomic level profiling correlates well with protein expression changes in discriminating different antibiotics. The reported expression signatures will aid future studies in identifying MOA of unknown compounds and facilitate the discovery of novel antibiotics.

Proteomic signatures of 16 major types of human cancer reveal universal and cancer-type-specific proteins for the identification of potential therapeutic targets.

BACKGROUND: Proteomic characterization of cancers is essential for a comprehensive understanding of key molecular aberrations. However, proteomic profiling of a large cohort of cancer tissues is often limited by the conventional approaches. METHODS: We present a proteomic landscape of 16 major types of human cancer, based on the analysis of 126 treatment-naïve primary tumor tissues, 94 tumor-matched normal adjacent tissues, and 12 normal tissues, using mass spectrometry-based data-independent acquisition approach. RESULTS: In our study, a total of 8527 proteins were mapped to brain, head and neck, breast, lung (both small cell and non-small cell lung cancers), esophagus, stomach, pancreas, liver, colon, kidney, bladder, prostate, uterus and ovary cancers, including 2458 tissue-enriched proteins. Our DIA-based proteomic approach has characterized major human cancers and identified universally expressed proteins as well as tissue-type-specific and cancer-type-specific proteins. In addition, 1139 therapeutic targetable proteins and 21 cancer/testis (CT) antigens were observed. CONCLUSIONS: Our discoveries not only advance our understanding of human cancers, but also have implications for the design of future large-scale cancer proteomic studies to assist the development of diagnostic and/or therapeutic targets in multiple cancers.

Urinary glycoproteins associated with aggressive prostate cancer.

Background: There is an urgent need for the detection of aggressive prostate cancer. Glycoproteins play essential roles in cancer development, while urine is a noninvasive and easily obtainable biological fluid that contains secretory glycoproteins from the urogenital system. Therefore, here we aimed to identify urinary glycoproteins that are capable of differentiating aggressive from non-aggressive prostate cancer. Methods: Quantitative mass spectrometry data of glycopeptides from a discovery cohort comprised of 74 aggressive (Gleason score ≥8) and 68 non-aggressive (Gleason score = 6) prostate cancer urine specimens were acquired via a data independent acquisition approach. The glycopeptides showing distinct expression profiles in aggressive relative to non-aggressive prostate cancer were further evaluated for their performance in distinguishing the two groups either individually or in combination with others using repeated 5-fold cross validation with logistic regression to build predictive models. Predictive models showing good performance from the discovery cohort were further evaluated using a validation cohort. Results: Among the 20 candidate glycoproteins, urinary ACPP outperformed the other candidates. Urinary ACPP can also serve as an adjunct to serum PSA to further improve the discrimination power for aggressive prostate cancer (AUC= 0.82, 95% confidence interval 0.75 to 0.89). A three-signature panel including urinary ACPP, urinary CLU, and serum PSA displayed the ability to distinguish aggressive prostate cancer from non-aggressive prostate cancer with an AUC of 0.86 (95% confidence interval 0.8 to 0.92). Another three-signature panel containing urinary ACPP, urinary LOX, and serum PSA also demonstrated its ability in recognizing aggressive prostate cancer (AUC=0.82, 95% confidence interval 0.75 to 0.9). Moreover, consistent performance was observed from each panel when evaluated using a validation cohort. Conclusion: We have identified glycopeptides of urinary glycoproteins associated with aggressive prostate cancer using a quantitative mass spectrometry-based glycoproteomic approach and demonstrated their potential to serve as noninvasive urinary glycoprotein biomarkers worthy of further validation by a multi-center study.