Exploring the proteomic landscape of a gastric cancer biopsy with the shotgun imaging analyzer

Quantitative proteomic analysis of gastric cancer tissue reveals novel proteins in platelet-derived growth factor b signaling pathway

Gastric cancer is one of the most common cancers in Asian countries. Searching for reliable biomarkers involving the development of gastric cancer is important for clinical practice. Quantitative proteomics has become an important method contributed to the discovery of novel diagnostic or therapeutic targets for the management of cancer. Here, we identified differently expressed proteins in gastric cancer and normal gastric tissues by using the high resolution mass spectrometer. Among the total of 2280 identified proteins, 87 were differentially expressed between gastric cancer and normal gastric tissues. Notably, several significant proteins are in the PDGF-B signaling pathway, including peroxiredoxin5 (PRDX5), S100A6, calreticulin (CALR) and cathepsin D (CTSD), which were validated by western blot. Furthermore, upstream regulators including PDGF-B, PDGFR-β, Akt, eIF4E and p70s6K were found significantly increased in the gastric cancer tissues. In addition, silencing of PRDX5 and PDGF-B suppressed the proliferation of gastric cancer cells in vitro. The administration of exogenous PDGF-BB recovered the reduced expression of PDGF-B signaling pathway in PDGF-B knockdown cells. Taken together, our findings suggested that PDGF-B signaling pathway plays an important role in the regulation of gastric cancer proliferation and the inhibition of this pathway may be a potential approach for treatment of gastric cancer.

iTRAQ-Based Shotgun Proteomics Approach for Relative Protein Quantification

Shotgun proteomics has a key role in quantitative estimation of proteins from biological systems under different conditions, which is crucial in the understanding of their functional roles. Isobaric tagging for relative and absolute quantitation (iTRAQ) mass spectrometry is based on pre-labeling of peptides with mass tags which allows the multiplex analysis of up to eight proteomes simultaneously. We describe here a detailed protocol for sample preparation and iTRAQ 4-plex labeling for relative quantification of multiple samples from human and plant tissues. We also present two strategies for peptide fractionation after the iTRAQ labeling protocol.

A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF

Mass spectrometry imaging (MSI) is a powerful tool to visualize the spatial distribution of molecules on a tissue section. The main limitation of MALDI-MSI of proteins is the lack of direct identification. Therefore, this study focuses on a MSI~LC-MS/MS-LF workflow to link the results from MALDI-MSI with potential peak identification and label-free quantitation, using only one tissue section. At first, we studied the impact of matrix deposition and laser ablation on protein extraction from the tissue section. Then, we did a back-correlation of the m/z of the proteins detected by MALDI-MSI to those identified by label-free quantitation. This allowed us to compare the label-free quantitation of proteins obtained in LC-MS/MS with the peak intensities observed in MALDI-MSI. We managed to link identification to nine peaks observed by MALDI-MSI. The results showed that the MSI~LC-MS/MS-LF workflow (i) allowed us to study a representative muscle proteome compared to a classical bottom-up workflow; and (ii) was sparsely impacted by matrix deposition and laser ablation. This workflow, performed as a proof-of-concept, suggests that a single tissue section can be used to perform MALDI-MSI and protein extraction, identification, and relative quantitation.

Recent advances in mass spectrometry-based proteomics of gastric cancer

The last decade has witnessed remarkable technological advances in mass spectrometry-based proteomics. The development of proteomics techniques has enabled the reliable analysis of complex proteomes, leading to the identification and quantification of thousands of proteins in gastric cancer cells, tissues, and sera. This quantitative information has been used to profile the anomalies in gastric cancer and provide insights into the pathogenic mechanism of the disease. In this review, we mainly focus on the advances in mass spectrometry and quantitative proteomics that were achieved in the last five years and how these up-and-coming technologies are employed to track biochemical changes in gastric cancer cells. We conclude by presenting a perspective on quantitative proteomics and its future applications in the clinic and translational gastric cancer research.

A Time-Based and Intratumoral Proteomic Assessment of a Recurrent Glioblastoma Multiforme

Tumors consist of cells in different stages of transformation with molecular and cellular heterogeneity. By far, heterogeneity is the hallmark of glioblastoma multiforme (GBM), the most malignant and aggressive type of glioma. Most proteomic studies aim in comparing tumors from different patients, but here we dive into exploring the intratumoral proteome diversity of a single GBM. For this, we profiled tumor fragments from the profound region of the same patient’s GBM but obtained from two surgeries a year’s time apart. Our analysis also included GBM‘s fragments from different anatomical regions. Our quantitative proteomic strategy employed 4-plex iTRAQ peptide labeling followed by a four-step strong cation chromatographic separation; each fraction was then analyzed by reversed-phase nano-chromatography coupled on-line with an Orbitrap-Velos mass spectrometer. Unsupervised clustering grouped the proteomic profiles into four major distinct groups and showed that most changes were related to the tumor’s anatomical region. Nevertheless, we report differentially abundant proteins from GBM’s fragments of the same region but obtained 1 year apart. We discuss several key proteins (e.g., S100A9) and enriched pathways linked with GBM such as the Ras pathway, RHO GTPases activate PKNs, and those related to apoptosis, to name a few. As far as we know, this is the only report that compares GBM fragments proteomic profiles from the same patient. Ultimately, our results fuel the forefront of scientific discussion on the importance in exploring the richness of subproteomes within a single tissue sample for a better understanding of the disease, as each tumor is unique.

Progress in the development of protein biomarkers of oesophageal and gastric cancers

Upper gastrointestinal cancers originating in the oesophagus and stomach often present late and have a very poor prognosis. Treatment options include surgery for localised disease but, increasingly, neoadjuvant chemotherapy and radiotherapy are being employed to improve outcome. There is often a variable response to neoadjuvant treatment between individual patients and side effects are relatively common. There is an urgent need for novel biomarkers of upper gastrointestinal cancer, not only to improve screening and early diagnosis of the oesophageal and gastric cancers when treatment options are potentially more effective, but also to accurately guide therapy in more advanced disease. The development of predictive biomarkers will also help to more effectively identify those patients that will benefit from targeted therapies. Although many promising results have been derived from these studies there remains a lack of validated clinically applicable biomarkers available for translation into routine clinical use. This review will provide an overview of the recent proteomic research on upper gastrointestinal cancer protein biomarker identification and validation. The challenges faced in the development of validated, clinically acceptable and accurate protein biomarkers will also be discussed, along with possible areas of future progress.

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

Integrated Proteomic and Genomic Analysis of Gastric Cancer Patient Tissues

V-erb-b2 erythroblastic leukemia viral oncogene homologue 2, known as ERBB2, is an important oncogene in the development of certain cancers. It can form a heterodimer with other epidermal growth factor receptor family members and activate kinase-mediated downstream signaling pathways. ERBB2 gene is located on chromosome 17 and is amplified in a subset of cancers, such as breast, gastric, and colon cancer. Of particular interest to the Chromosome-Centric Human Proteome Project (C-HPP) initiative is the amplification mechanism that typically results in overexpression of a set of genes adjacent to ERBB2, which provides evidence of a linkage between gene location and expression. In this report we studied patient samples from ERBB2-positive together with adjacent control nontumor tissues. In addition, non-ERBB2-expressing patient samples were selected as comparison to study the effect of expression of this oncogene. We detected 196 proteins in ERBB2-positive patient tumor samples that had minimal overlap (29 proteins) with the non-ERBB2 tumor samples. Interaction and pathway analysis identified extracellular signal regulated kinase (ERK) cascade and actin polymerization and actinmyosin assembly contraction as pathways of importance in ERBB2+ and ERBB2- gastric cancer samples, respectively. The raw data files are deposited at ProteomeXchange (identifier: PXD002674) as well as GPMDB.

Onco-proteogenomics identifies urinary S100A9 and GRN as potential combinatorial biomarkers for early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the major type of liver cancer, is among the most lethal cancers owing to its aggressive nature and frequently late detection. Therefore, the possibility to identify early diagnostic markers could be of significant benefit. Urine has especially become one of the most attractive body fluids in biomarker discovery as it can be obtained non-invasively in large quantities and is stable as compared with other body fluids. To identify potential protein biomarker for early diagnosis of HCC, we explored protein expression profiles in urine from HCC patients and normal controls (n = 44) by shotgun proteomics using nano-liquid chromatography coupled tandem mass spectrometry (nanoLC–MS/MS) and stable isotope dimethyl labeling. We have systematically mapped 91 proteins with differential expressions (p < 0.05), which included 8 down-regulated microtubule proteins and 83 up-regulated proteins involved in signal and inflammation response. Further integrated proteogenomic approach composed of proteomic, genomic and transcriptomic analysis identified that S100A9 and GRN were co-amplified (p < 0.001) and co-expressed (p < 0.01) in HCC tumors and urine samples. In addition, the amplifications of S100A9 or GRN were found to be associated with poor survival in HCC patients, and their co-amplification was also prognosed worse overall survival than individual ones. Our results suggest that urinary S100A9 and GRN as potential combinatorial biomarkers can be applied to early diagnosis of hepatocellular carcinoma and highlight the utility of onco-proteogenomics for identifying protein markers that can be applied to disease-oriented translational medicine.

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An integrated proteogenomic analysis is applied to identify biomarkers for HCC.

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Genomic amplifications of S100A9 and GRN co-occur in tumors from HCC patients.

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S100A9 and GRN are co-expressed in tumor and urine samples from HCC patients.

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Amplifications of S100A9 and GRN are associated with poor survival of HCC patients.

Onco-proteogenomics: cancer proteomics joins forces with genomics

The complexities of tumor genomes are rapidly being uncovered, but how they are regulated into functional proteomes remains poorly understood. Standard proteomics workflows use databases of known proteins, but these databases do not capture the uniqueness of the cancer transcriptome, with its point mutations, unusual splice variants and gene fusions. Onco-proteogenomics integrates mass spectrometry-generated data with genomic information to identify tumor-specific peptides. Linking tumor-derived DNA, RNA and protein measurements into a central-dogma perspective has the potential to improve our understanding of cancer biology.

Proteome analysis in the assessment of ageing

Based on demographic trends, the societies in many developed countries are facing an increasing number and proportion of people over the age of 65. The raise in elderly populations along with improved health-care will be concomitant with an increased prevalence of ageing-associated chronic conditions like cardiovascular, renal, and respiratory diseases, arthritis, dementia, and diabetes mellitus. This is expected to pose unprecedented challenges both for individuals and societies and their health care systems. An ultimate goal of ageing research is therefore the understanding of physiological ageing and the achievement of 'healthy' ageing by decreasing age-related pathologies. However, on a molecular level, ageing is a complex multi-mechanistic process whose contributing factors may vary individually, partly overlap with pathological alterations, and are often poorly understood. Proteome analysis potentially allows modelling of these multifactorial processes. This review summarises recent proteomic research on age-related changes identified in animal models and human studies. We combined this information with pathway analysis to identify molecular mechanisms associated with ageing. We identified some molecular pathways that are affected in most or even all organs and others that are organ-specific. However, appropriately powered studies are needed to confirm these findings based in in silico evaluation.