122 IMAGING THE RENAL CELL CARCINOMA PROTEOME

Abstract

You have accessJournal of UrologyKidney Cancer: Basic Research1 Apr 2011122 IMAGING THE RENAL CELL CARCINOMA PROTEOME Todd M. Morgan, Erin H. Seeley, Oluwole Fadare, Richard M. Caprioli, David L. Hachey, and Peter E. Clark Todd M. MorganTodd M. Morgan Nashville, TN More articles by this author , Erin H. SeeleyErin H. Seeley Nashville, TN More articles by this author , Oluwole FadareOluwole Fadare Nashville, TN More articles by this author , Richard M. CaprioliRichard M. Caprioli Nashville, TN More articles by this author , David L. HacheyDavid L. Hachey Nashville, TN More articles by this author , and Peter E. ClarkPeter E. Clark Nashville, TN More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.188AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES No biomarkers for renal cell carcinoma (RCC) are in routine use. However, rapid developments in proteomics offer substantial promise for identifying novel diagnostic and prognostic biomarkers. One barrier to identification of tissue biomarkers is the heterogeneity of protein expression within tumor and normal renal parenchyma. Imaging mass spectrometry (IMS) can provide spectra for every 0.05mm2 area of tissue and therefore reveal the spatial distribution of peptides within a section of tissue. We sought to determine whether this approach could be used to identify and map protein signatures differentially expressed between RCC and normal renal tissue. METHODS We constructed a tissue microarray with two cores each of matched tumor and normal tissue from nephrectomy specimens of 35 patients with clear cell RCC. After mounting sections on a conductive glass slide, removal of paraffin, and antigen retrieval, trypsin digestion was performed directly on the tissue. Samples were analyzed utilizing an AutoFlex Speed matrix assisted laser desorption ionization (MALDI) time-of-flight mass spectrometer (MS). Data analysis was performed with ClinProTools 2.2 and FlexImaging 2.1 software. RESULTS An average of ∼500 peptides was identified in each mass spectrum. Comparison of peptide expression in RCC vs. normal tissue revealed a number of individual peptides that discriminated between malignant and disease-free renal tissue with a high degree of accuracy. For example, receiver operator characteristic (ROC) curve analysis revealed four individual peptides able to identify malignant tissue with >90% accuracy (area under the curve [AUC] 0.91–0.94). Seven peptides demonstrated a classification accuracy of 95% for each 0.05mm2 spot. In toto, 138 of 140 cores (98.6%) were accurately classified (Figure). CONCLUSIONS IMS was able to identify and map specific peptides that accurately distinguished malignant from normal renal tissue, demonstrating its potential as a novel, high-throughput approach to RCC biomarker discovery. Additional work to determine the identity of these differentially expressed proteins may lend insight into the pathogenesis of RCC. Furthermore, given the multiple pathways involved in tumors such as RCC, multiple peptide signatures may offer advantages as both diagnostic and prognostic biomarkers compared to traditional protein biomarkers. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e51 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.MetricsAuthor Information Todd M. Morgan Nashville, TN More articles by this author Erin H. Seeley Nashville, TN More articles by this author Oluwole Fadare Nashville, TN More articles by this author Richard M. Caprioli Nashville, TN More articles by this author David L. Hachey Nashville, TN More articles by this author Peter E. Clark Nashville, TN More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...