Identification,prioritization, and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody‐based assays on tissue specimens

Prostate cancer is highly heterogeneous in nature; while the majority of cases are clinically insignificant, some cases are lethal. Currently, there are no reliable screening methods for aggressive prostate cancer. Since most established serum and urine biomarkers are glycoproteins secreted or leaked from the diseased tissue, the current study seeks to identify glycoprotein markers specific to aggressive prostate cancer using tissue specimens. With LC‐MS/MS glycoproteomic analysis, we identified 350 glycopeptides with 17 being altered in aggressive prostate cancer. ELISA assays were developed/purchased to evaluate four candidates, that is, cartilage oligomeric matrix protein (COMP), periostin, membrane primary amine oxidase (VAP‐1), and cathepsin L, in independent tissue sets. In agreement with the proteomic analysis, we found that COMP and periostin expressions were significantly increased in aggressive prostate tumors while VAP‐1 expression was significantly decreased in aggressive tumor. In addition, the expression of these proteins in prostate metastases also follows the same pattern observed in the proteomic analysis. This study provides a workflow for biomarker discovery, prioritization, and evaluation of aggressive prostate cancer markers using tissue specimens. Our data suggest that increase in COMP and periostin and decrease in VAP‐1 expression in the prostate may be associated with aggressive prostate cancer.     

Antibody‐based proteomics and biomarker research—Current status and limitations

Antibody‐based proteomics play a very important role in biomarker discovery and validation, facilitating the high‐throughput evaluation of candidate markers. Most proteomics‐driven discovery is nowadays based on the use of MS. MS has many advantages, including its suitability for hypothesis‐free biomarker discovery, since information on protein content of a sample is not required prior to analysis. However, MS presents one main caveat which is the limited sensitivity in complex samples, especially for body fluids, where protein expression covers a huge dynamic range. Antibody‐based technologies remain the main solution to address this challenge since they reach higher sensitivity. In this article, we review the benefits and limitations of antibody‐based proteomics in preclinical and clinical biomarker research for discovery and validation in body fluids and tissue. The combination of antibodies and MS, utilizing the best of both worlds, opens new avenues in biomarker research.     

Periostin, discovered by nano-flow liquid chromatography and mass spectrometry, is a novel marker of diabetic retinopathy

Diabetes can lead to serious microvascular complications including proliferative diabetic retinopathy (PDR), the leading cause of blindness in adults. Recent studies using gene array technology have attempted to apply a hypothesis-generating approach to elucidate the pathogenesis of PDR, but these studies rely on mRNA differences, which may or may not be related to significant biological processes. To better understand the basic mechanisms of PDR and to identify potential new biomarkers, we performed shotgun liquid chromatography (LC)/tandem mass spectrometry (MS/MS) analysis on pooled protein extracts from neovascular membranes obtained from PDR specimens and compared the results with those from non-vascular epiretinal membrane (ERM) specimens. We detected 226 distinct proteins in neovascular membranes and 154 in ERM. Among these proteins, 102 were specific to neovascular membranes and 30 were specific to ERM. We identified a candidate marker, periostin, as well as several known PDR markers such as pigment epithelium-derived factor (PEDF). We then performed RT-PCR using these markers. The expression of periostin was significantly up-regulated in proliferative membrane specimens. Periostin induces cell attachment and spreading and plays a role in cell adhesion. Proteomic analysis by LC/MS/MS, which permits accurate quantitative comparison, was useful in identifying new candidates such as periostin potentially involved in the pathogenesis of PDR.     

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.     

LC‐MS/MS‐based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label‐free proteomic analysis by LC‐MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC‐associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 ( http://proteomecentral.proteomexchange.org/dataset/PXD001171 ).     

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.     

Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry

Shotgun proteome analysis platforms based on multidimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) provide a powerful means to discover biomarker candidates in tissue specimens. Analysis platforms must balance sensitivity for peptide detection, reproducibility of detected peptide inventories and analytical throughput for protein amounts commonly present in tissue biospecimens (< 100 microg), such that platform stability is sufficient to detect modest changes in complex proteomes. We compared shotgun proteomics platforms by analyzing tryptic digests of whole cell and tissue proteomes using strong cation exchange (SCX) and isoelectric focusing (IEF) separations of peptides prior to LC-MS/MS analysis on a LTQ-Orbitrap hybrid instrument. IEF separations provided superior reproducibility and resolution for peptide fractionation from samples corresponding to both large (100 microg) and small (10 microg) protein inputs. SCX generated more peptide and protein identifications than did IEF with small (10 microg) samples, whereas the two platforms yielded similar numbers of identifications with large (100 microg) samples. In nine replicate analyses of tryptic peptides from 50 microg colon adenocarcinoma protein, overlap in protein detection by the two platforms was 77% of all proteins detected by both methods combined. IEF more quickly approached maximal detection, with 90% of IEF-detectable medium abundance proteins (those detected with a total of 3-4 peptides) detected within three replicate analyses. In contrast, the SCX platform required six replicates to detect 90% of SCX-detectable medium abundance proteins. High reproducibility and efficient resolution of IEF peptide separations make the IEF platform superior to the SCX platform for biomarker discovery via shotgun proteomic analyses of tissue specimens.     

Streamlined Development of Targeted Mass Spectrometry‐Based Method Combining Scout‐MRM and a Web‐Based Tool Indexed with Scout Peptides

MS‐based targeted proteomics is a relevant technology for sensitive and robust relative or absolute quantification of proteins biomarker candidates in complex human biofluids or tissue extracts. Performing a multiplex assay imposes time scheduling of peptide monitoring only around their expected retention time that needs to be defined with synthetic peptide. Time‐scheduled monitoring is clearly a constraint that precludes from straightforward assay transfer between biological matrices or distinct experimental setup. Any unexpected retention time (RT) shift challenges assay robustness and its implementation for large‐scale analysis. Recently, Scout‐multiple reaction monitoring that fully releases multiplexed targeted acquisition from RT scheduling by successively monitoring complex transition groups triggered with sentinel molecules called Scout has been introduced. It is herein documented how Peptide Selector database and tool streamlines the building of a multiplexed method thanks to RT indexation relative to Scout peptides. This case study deals with surrogate peptides of biomarker candidates related to drug‐induced liver and vascular injury, running such on‐line built method (eight Scouts triggering the monitoring of a total of 692 transitions) enables 100% recovery of a panel of 93 spiked‐in heavy labeled standards, despite significant RT shifts between serum, plasma, or urine. This result illustrates the simplicity of automatically building and deploying robust proteomics targeted assay.     

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC–MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC–MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC–MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.     

Multiplexed LC‐MS/MS analysis of horse plasma proteins to study doping in sport

The development of protein biomarkers for the indirect detection of doping in horse is a potential solution to doping threats such as gene and protein doping. A method for biomarker candidate discovery in horse plasma is presented using targeted analysis of proteotypic peptides from horse proteins. These peptides were first identified in a novel list of the abundant proteins in horse plasma. To monitor these peptides, an LC‐MS/MS method using multiple reaction monitoring was developed to study the quantity of 49 proteins in horse plasma in a single run. The method was optimised and validated, and then applied to a population of race‐horses to study protein variance within a population. The method was finally applied to longitudinal time courses of horse plasma collected after administration of an anabolic steroid to demonstrate utility for hypothesis‐driven discovery of doping biomarker candidates.     

Intact protein profiling in breast cancer biomarker discovery: Protein identification issue and the solutions based on 3D protein separation,bottom‐up and top‐down mass spectrometry

Proteomics profiling of intact proteins based on MALDI‐TOF MS and derived platforms has been used in cancer biomarker discovery studies. This approach suffers from a number of limitations such as low resolution, low sensitivity, and that no knowledge is available on the identity of the respective proteins in the discovery mode. Nevertheless, it remains the most high‐throughput, untargeted mode of clinical proteomics studies to date. Here we compare key protein separation and MS techniques available for protein biomarker identification in this type of studies and define reasons of uncertainty in protein peak identity. As a result of critical data analysis, we consider 3D protein separation and identification workflows as optimal procedures. Subsequently, we present a new protocol based on 3D LC‐MS/MS with top‐down at high resolution that enabled the identification of HNRNP A2/B1 intact peptide as correlating with the estrogen receptor expression in breast cancer tissues. Additional development of this general concept toward next generation, top‐down based protein profiling at high resolution is discussed.     

Overexpression of Periostin in Stroma Positively Associated with Aggressive Prostate Cancer

Background

Periostin is an important extracellular matrix protein involved in cell development and adhesion. Previously, we identified periostin to be up-regulated in aggressive prostate cancer (CaP) using quantitative glycoproteomics and mass spectrometry. The expression of periostin was further evaluated in primary radical prostatectomy (RP) prostate tumors and adjacent non-tumorous prostate tissues using immunohistochemistry (IHC). Our IHC results revealed a low background periostin levels in the adjacent non-tumorous prostate tissues, but overexpressed periostin levels in the peritumoral stroma of primary CaP tumors.

Methods

In this study, periostin expression in CaP was further examined on multiple tissue microarrays (TMAs), which were conducted in four laboratories. To achieve consistent staining, all TMAs were stained with same protocol and scored by same image computation tool to determine the total periostin staining intensities. The TMAs were further scored by pathologists to characterize the stromal staining and epithelial staining.

Results

The periostin staining was observed mainly in peritumoral stromal cells and in some cases in tumor epithelial cells though the stronger staining was found in peritumoral stromal cells. Both periostin stromal staining and epithelial staining can differentiate BPH from CaP including low grade CaP (Gleason score ≤6), with significant p-value of 2.2e-16 and 0.001, respectively. Periostin epithelial staining differentiated PIN from low grade CaP (Gleason score ≤6) (p=0.001), while periostin stromal staining differentiated low grade Cap (Gleason score ≤6) from high grade Cap (Gleason score ≤6) (p=1.7e-05). In addition, a positive correlation between total periostin staining and Gleason score was observed (r=0.87, p=0.002).

Conclusions

The results showed that periostin staining was positively correlated with increasing Gleason score and the aggressiveness of prostate disease.     

Potential sampling error in fine needle aspiration biopsy of dedifferentiated chondrosarcoma: a report of 4 cases

BACKGROUND: Dedifferentiated chondrosarcoma is a rare, poorly understood and often fatal sarcoma that usually manifests as a high grade, non-cartilage-producing sarcoma juxtaposed against a low grade chondrosarcoma. A correct diagnosis requires recognition of both components. In the absence of complete resection, rendering a specific diagnosis on small biopsy specimens, such as fine needle aspiration biopsy (FNAB), may be extraordinarily difficult. CASES: We retrospectively reviewed 4 cytology samples (3 primary, 1 metastatic) from 3 patients with dedifferentiated chondrosarcoma, initially analyzed by FNAB, emphasizing the potential for sampling error. Two women, aged 78 and 57 years, both of whom had prior histories of carcinoma, presented with lesions involving the right and left femur, respectively. One 27-year-old man with multiple osteochondromatosis developed a dedifferentiated chondrosarcoma of the left pelvis. Two primary cytologic specimens consisted of moderately cellular smears containing a spindled to polygonal, nonspecific, pleomorphic sarcoma unaccompanied by definite matrix material; 1 of these had a concomitant core needle biopsy (CNB), also demonstrating pleomorphic sarcoma. The third primary cytologic specimen revealed low grade chondrosarcoma, but a concomitant CNB showed only a high grade, non-matrix-producing sarcoma. The last patient developed a metastasis to the opposite femur; FNAB revealed a high grade spindle cell sarcoma. In none of the FNAB or CNB specimens were both low and high grade components of dedifferentiated chondrosarcoma recognized. However, the diagnosis was strongly suspected based on the clinical and radiographic findings. CONCLUSION: Due to sampling error, the diagnosis of dedifferentiated chondrosarcoma may be difficult to establish by cytologic examination alone. Clinical and radiographic correlation is essential for an accurate diagnosis.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)‐based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous “triangular strategies” aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a “rectangular” plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision‐making, and we discuss some first steps in this direction.     

A targeted proteomic strategy for the measurement of oral cancer candidate biomarkers in human saliva

Head and neck cancers, including oral squamous cell carcinoma (OSCC), are the sixth most common malignancy in the world and are characterized by poor prognosis and a low survival rate. Saliva is oral fluid with intimate contact with OSCC. Besides non‐invasive, simple, and rapid to collect, saliva is a potential source of biomarkers. In this study, we build an SRM assay that targets fourteen OSCC candidate biomarker proteins, which were evaluated in a set of clinically‐derived saliva samples. Using Skyline software package, we demonstrated a statistically significant higher abundance of the C1R, LCN2, SLPI, FAM49B, TAGLN2, CFB, C3, C4B, LRG1, SERPINA1 candidate biomarkers in the saliva of OSCC patients. Furthermore, our study also demonstrated that CFB, C3, C4B, SERPINA1 and LRG1 are associated with the risk of developing OSCC. Overall, this study successfully used targeted proteomics to measure in saliva a panel of biomarker candidates for OSCC.     

Cancer Stem Cell-Related Gene Periostin: A Novel Prognostic Marker for Breast Cancer

We investigated the expression status of periostin in breast cancer stem cells and its clinical implications in order to lay a foundation for managing breast cancer. CD44+/CD24−/line- tumor cells (CSC) from clinical specimens were sorted using flow cytometry. Periostin expression status was detected in CSC cells and 1,086 breast cancer specimens by Western blot and immunohistochemistry staining, with the CSC ratio determined by immunofluorescence double staining. The relationship between the periostin protein and clinico-pathological parameters and prognosis was subsequently determined. As a result, CSC cells are more likely to generate new tumors in mice and cell microspheres that are deficient in NOD/SCID compared to the control group. Periostin protein was expressed higher in CSC cells compared to the control cells and was found to be related to CSC chemotherapy resistance. Moreover, periostin expression was found to be related to the CSC ratio in 1,086 breast cancer specimens (P = 0.001). In total, 334 (30.76%) of the 1,086 breast cases showed high periostin expression. After universal and Spearman regression correlation analysis, periostin was observed to be related to histological grade, CSC ratio, lymph node metastasis, tumor size, and triple-negative breast cancer (all P<0.05). Furthermore, periostin was shown to attain a significantly more distant bone metastasis and worse disease-specific survival than those with none or low-expressed periostin protein (P = 0.001). In the Cox regression test, periostin protein was detected as an independent prognostic factor (P = 0.001). In conclusion, periostin was found to be related to the CSC and an independent prognostic factor for breast cancer. It is also perhaps a potential target to breast cancer.