PURPOSE: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples. EXPERIMENTAL DESIGN: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA). RESULTS: Several proteins, with masses ranging from 2778 to 20800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls. CONCLUSIONS: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.
PURPOSE: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples. EXPERIMENTAL DESIGN: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA). RESULTS: Several proteins, with masses ranging from 2778 to 20800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls. CONCLUSIONS: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.
Authors: Christine L Gatlin-Bunai; Lisa H Cazares; William E Cooke; Oliver J Semmes; Dariya I Malyarenko Journal: J Proteome Res Date: 2007-10-05 Impact factor: 4.466
Authors: Pinar B Yildiz; Yu Shyr; Jamshedur S M Rahman; Noel R Wardwell; Lisa J Zimmerman; Bashar Shakhtour; William H Gray; Shuo Chen; Ming Li; Heinrich Roder; Daniel C Liebler; William L Bigbee; Jill M Siegfried; Joel L Weissfeld; Adriana L Gonzalez; Mathew Ninan; David H Johnson; David P Carbone; Richard M Caprioli; Pierre P Massion Journal: J Thorac Oncol Date: 2007-10 Impact factor: 15.609