Kelly Cooke

Quantitative proteomics analysis reveals that proteins differentially expressed in chronic pancreatitis are also frequently involved in pancreatic cancer.

The effective treatment of pancreatic cancer relies on the diagnosis of the disease at an early stage, a difficult challenge. One major obstacle in the development of diagnostic biomarkers of early pancreatic cancer has been the dual expression of potential biomarkers in both chronic pancreatitis and cancer. To better understand the limitations of potential protein biomarkers, we used ICAT technology and tandem mass spectrometry-based proteomics to systematically study protein expression in chronic pancreatitis. Among the 116 differentially expressed proteins identified in chronic pancreatitis, most biological processes were responses to wounding and inflammation, a finding consistent with the underlining inflammation and tissue repair associated with chronic pancreatitis. Furthermore 40% of the differentially expressed proteins identified in chronic pancreatitis have been implicated previously in pancreatic cancer, suggesting some commonality in protein expression between these two diseases. Biological network analysis further identified c-MYC as a common prominent regulatory protein in pancreatic cancer and chronic pancreatitis. Lastly five proteins were selected for validation by Western blot and immunohistochemistry. Annexin A2 and insulin-like growth factor-binding protein 2 were overexpressed in cancer but not in chronic pancreatitis, making them promising biomarker candidates for pancreatic cancer. In addition, our study validated that cathepsin D, integrin β1, and plasminogen were overexpressed in both pancreatic cancer and chronic pancreatitis. The positive involvement of these proteins in chronic pancreatitis and pancreatic cancer will potentially lower the specificity of these proteins as biomarker candidates for pancreatic cancer. Altogether our study provides some insights into the molecular events in chronic pancreatitis that may lead to diverse strategies for diagnosis and treatment of these diseases.

Comparison of Pancreas Juice Proteins from Cancer Versus Pancreatitis Using Quantitative Proteomic Analysis

Objectives: Pancreatitis is an inflammatory condition of the pancreas. However, it often shares many molecular features with pancreatic cancer. Biomarkers present in pancreatic cancer frequently occur in the setting of pancreatitis. The efforts to develop diagnostic biomarkers for pancreatic cancer have thus been complicated by the false-positive involvement of pancreatitis. Methods: In an attempt to develop protein biomarkers for pancreatic cancer, we previously use quantitative proteomics to identify and quantify the proteins from pancreatic cancer juice. Pancreatic juice is a rich source of proteins that are shed by the pancreatic ductal cells. In this study, we used a similar approach to identify and quantify proteins from pancreatitis juice. Results: In total, 72 proteins were identified and quantified in the comparison of pancreatic juice from pancreatitis patients versus pooled normal control juice. Nineteen of the juice proteins were overexpressed, and 8 were underexpressed in pancreatitis juice by at least 2-fold compared with normal pancreatic juice. Of these 27 differentially expressed proteins in pancreatitis, 9 proteins were also differentially expressed in the pancreatic juice from pancreatic cancer patient. Conclusions: Identification of these differentially expressed proteins from pancreatitis juice provides useful information for future study of specific pancreatitis-associated proteins and to eliminate potential false-positive biomarkers for pancreatic cancer.

UniPep - a database for human N-linked glycosites: a resource for biomarker discovery

There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep http://www.unipep.org - a database of human N-linked glycosites - as a resource for biomarker discovery.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics

BackgroundQuantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.ResultsWe have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.ConclusionThe Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

Assessing Bias in Experiment Design for Large Scale Mass Spectrometry-based Quantitative Proteomics

Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently much emphasis has been placed upon producing highly reliable data for quantitative profiling for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation, and storage protocols and LC-MS settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of systematic quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols, and instrument choices. Moreover we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.

Quantitative proteomics investigation of pancreatic intraepithelial neoplasia

Patients with pancreatic cancer are usually diagnosed at late stages, when the disease is incurable. Pancreatic intraepithelial neoplasia (PanIN) 3 is believed to be the immediate precursor lesion of pancreatic adenocarcinoma, and would be an ideal stage to diagnose patients, when intervention and cure are possible and patients are curable. In this study, we used quantitative proteomics to identify dysregulated proteins in PanIN 3 lesions. Altogether, over 200 dysregulated proteins were identified in the PanIN 3 tissues, with a minimum of a 1.75‐fold change compared with the proteins in normal pancreas. These dysregulated PanIN 3 proteins play roles in cell motility, the inflammatory response, the blood clotting cascade, the cell cycle and its regulation, and protein degradation. Further network analysis of the proteins identified c‐MYC as an important regulatory protein in PanIN 3 lesions. Finally, three of the overexpressed proteins, laminin beta‐1, galectin‐1, and actinin‐4 were validated by immunohistochemistry analysis. All three of these proteins were overexpressed in the stroma or ductal epithelial cells of advanced PanIN lesions as well as in pancreatic cancer tissue. Our findings suggest that these three proteins may be useful as biomarkers for advanced PanIN and pancreatic cancer if further validated. The dysregulated proteins identified in this study may assist in the selection of candidates for future development of biomarkers for detecting early and curable pancreatic neoplasia.

Proteins That Underlie Neoplastic Progression of Ulcerative Colitis

Patients with ulcerative colitis (UC) have an increased risk for developing colorectal cancer. Because UC tumorigenesis is associated with genomic field defects that can extend throughout the entire colon, including the non‐dysplastic mucosa, we hypothesized that the same field defects will include abnormally expressed proteins. Here, we applied proteomics to study the protein expression of UC neoplastic progression. The protein profiles of colonic epithelium were compared with (i) UC patients without dysplasia (non‐progressors), (ii) non‐dysplastic colonic tissue from UC patient with high‐grade dysplasia or cancer (progressors), (iii) high‐grade dysplastic tissue from UC progressors, and (iv) normal colon. We identified differential protein expression associated with UC neoplastic progression. Proteins relating to mitochondria, oxidative activity, and calcium‐binding proteins were some of the interesting classes of these proteins. Network analysis discovered that Sp1 and c‐myc proteins may play roles in UC early and late stages of neoplastic progression, respectively. Two over‐expressed proteins in the non‐dysplastic tissue of UC progressors, carbamoyl‐phosphate synthase 1 and S100P, were further confirmed by immunohistochemistry analysis. Our study provides insight into the molecular events associated with UC neoplastic progression, which could be exploited for the development of protein biomarkers in fields of non‐dysplastic mucosa that identify a patients risk for UC dysplasia.