Proteomic analysis of formalin-fixed paraffin-embedded pancreatic tissue using liquid chromatography tandem mass spectrometry (original) (raw)
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Molecular BioSystems, 2013
Hospital tissue repositories host an invaluable supply of diseased samples with matched retrospective clinical information. In this work, a recently optimized method for extracting full-length proteins from formalin-fixed, paraffin-embedded (FFPE) tissues was evaluated on lung neuroendocrine tumor (LNET) samples collected from hospital repositories. LNETs comprise a heterogeneous spectrum of diseases, for which subtype-specific diagnostic markers are lacking. Six archival samples diagnosed as typical carcinoid (TC) or small cell lung carcinoma (SCLC) were subjected to a full-length protein extraction followed by a GeLC-MS/MS analysis, enabling the identification of over 300 distinct proteins per tumor subtype.
Data in Brief, 2016
Here we present a dataset generated using formalin-fixed paraffinembedded archival samples from two rare lung neuroendocrine tumor subtypes (namely, two atypical carcinoids, ACs, and two large-cell neuroendocrine carcinomas, LCNECs). Samples were subjected to a shotgun proteomics pipeline, comprising full-length protein extraction, SDS removal through spin columns, in solution trypsin digestion, long gradient liquid chromatography peptide separation and LTQ-Orbitrap mass spectrometry analysis. A total of 1260 and 2436 proteins were identified in the AC and LCNEC samples, respectively, with FDR o 1%. MS data are
Biomarker Discovery from Pancreatic Cancer Secretome Using a Differential Proteomic Approach
Molecular & Cellular Proteomics, 2005
Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin 1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies. Molecular & Cellular
Biomarker discovery from pancreatic cancer secretome using a differential proteomics approach
Molecular Cellular Proteomics, 2005
Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin 1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies. Molecular & Cellular Proteomics 5:157-171, 2006.
Applying Proteomic-Based Biomarker Tools for the Accurate Diagnosis of Pancreatic Cancer
Journal of Gastrointestinal Surgery, 2008
Background The proteome varies with physiologic and disease states. Few studies have been reported that differentiate the proteome of those with pancreatic cancer. Aim To apply proteomic-based technologies to body fluids. To differentiate pancreatic neoplasia from nonneoplastic pancreatic disease. Methods Samples from 50 patients (15 healthy (H), 24 cancer (Ca), 11 chronic pancreatitis (CP)) were prospectively collected and underwent analysis. A high-throughput method, using high-affinity solid lipophilic extraction resins, enriched low molecular weight proteins for extraction with a high-speed 200-Hz matrix-assisted laser desorption/ionization time-offlight mass spectrometer (MALDI-MS; Bruker Ultraflex III). Samples underwent software processing with FlexAnalysis, Clinprot, MatLab, and Statistica (baseline, align, and normalize spectra). Nonparametric pairwise statistics, multidimensional scaling, hierarchical analysis, and leave-one-out cross validation completed the analysis. Sensitivity (sn) and specificity (sp) of group comparisons were determined. Two top-down-directed protein identification approaches were combined with MALDI-MS and tandem mass spectrometry to fully characterize the most significant protein biomarker. Results Using eight serum features, we differentiated Ca from H (sn 88%, sp 93%), Ca from CP (sn 88%, sp 30%), and Ca from both H and CP combined (sn 88%, sp 66%). In addition, nine features obtained from urine differentiated Ca from both H and CP combined with high efficiency (sn 90%, sp 90%). Interestingly, the plasma samples (considered by the Human Proteome Organization to be the preferred biological fluid) did not show significant differences. Multidimensional scaling indicated that markers from both serum and urine led to a highly effective clinical indicator of each specific disease state. Conclusions The proteomic analysis of noninvasively acquired biological fluids provided a high level of predictability for diagnosing pancreatic cancer. While the proteomic analysis of serum was capable of screening individuals for pancreatic disease (i.e., CP and Ca vs. H), specific urine biomarkers further distinguished malignancy (Ca) from chronic inflammation (CP).
Proteomics. Clinical applications, 2016
Sample processing protocols that enable compatible recovery of differentially expressed transcripts and proteins are necessary for integration of the multiomics data applied in the analysis of tumors. In this pilot study, we compared two different isolation methods for extracting RNA and protein from laryngopharyngeal tumor tissues and the corresponding adjacent normal sections. In Method 1, RNA and protein were isolated from a single tissue section sequentially and in Method 2, the extraction was carried out using two different sections and two independent and parallel protocols for RNA and protein. RNA and protein from both methods were subjected to RNA-seq and iTRAQ-based LC-MS/MS analysis, respectively. Analysis of data revealed that a higher number of differentially expressed transcripts and proteins were concordant in their regulation trends in Method 1 as compared to Method 2. Cross-method comparison of concordant entities revealed that RNA and protein extraction from the sam...
Oncogene, 2007
Successful treatment of multiple cancer types requires early detection and identification of reliable biomarkers present in specific cancer tissues. To test the feasibility of identifying proteins from archival cancer tissues, we have developed a methodology, termed direct tissue proteomics (DTP), which can be used to identify proteins directly from formalin-fixed paraffin-embedded prostate cancer tissue samples. Using minute prostate biopsy sections, we demonstrate the identification of 428 prostate-expressed proteins using the shotgun method. Because the DTP method is not quantitative, we employed the absolute quantification method and demonstrate picogram level quantification of prostate-specific antigen. In depth bioinformatics analysis of these expressed proteins affords the categorization of metabolic pathways that may be important for distinct stages of prostate carcinogenesis. Furthermore, we validate Wnt-3 as an upregulated protein in cancerous prostate cells by immunohistochemistry. We propose that this general strategy provides a roadmap for successful identification of critical molecular targets of multiple cancer types.