Serum Protein Profiles to Identify Head and Neck Cancer

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 e...

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Bibliographic Details
Published in:Clinical cancer research 2004-03, Vol.10 (5), p.1625-1632
Main Authors: WADSWORTH, J. Trad, SOMERS, Kenneth D, CAZARES, Lisa H, MALIK, Gunjan, ADAM, Bao-Ling, STACK, Brendan C, WRIGHT, George L, SEMMES, O. John
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Antineoplastic agents
Biological and medical sciences
Biomarkers, Tumor - blood
Blood Proteins - analysis
Carcinoma, Squamous Cell - blood
Carcinoma, Squamous Cell - diagnosis
Female
Head and Neck Neoplasms - blood
Head and Neck Neoplasms - diagnosis
Humans
Male
Medical sciences
Middle Aged
Pharmacology. Drug treatments
Protein Array Analysis - methods
Reference Values
Tumors
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Summary: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 2,778 to 20,800 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.
ISSN:1078-0432
1557-3265
DOI:10.1158/1078-0432.CCR-0297-3