Diagnostic Accuracy of MALDI Mass Spectrometric Analysis of Unfractionated Serum in Lung Cancer

• Published in: Journal of thoracic oncology 2007-10, Vol.2 (10), p.893-901
• Main Authors: Yildiz, Pinar B., Shyr, Yu, Rahman, Jamshedur S.M., Wardwell, Noel R., Zimmerman, Lisa J., Shakhtour, Bashar, Gray, William H., Chen, Shuo, Li, Ming, Roder, Heinrich, Liebler, Daniel C., Bigbee, William L., Siegfried, Jill M., Weissfeld, Joel L., Gonzalez, Adriana L., Ninan, Mathew, Johnson, David H., Carbone, David P., Caprioli, Richard M., Massion, Pierre P.
• Format: Article
• Language: English
• Subjects: Adenocarcinoma - blood; Adenocarcinoma - pathology; Biomarker; Biomarkers, Tumor - blood; Blood; Blood Proteins - metabolism; Carcinoma, Large Cell - blood; Carcinoma, Large Cell - pathology; Carcinoma, Non-Small-Cell Lung - blood; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Small Cell - blood; Carcinoma, Small Cell - pathology; Case-Control Studies; Chromatography, Liquid; Cohort Studies; Diagnosis; Female; Humans; Lung Neoplasms - blood; Lung Neoplasms - pathology; Male; Mass spectrometry; Middle Aged; Neoplasm Proteins - metabolism; Neoplasm Staging; Neoplasms, Squamous Cell - blood; Neoplasms, Squamous Cell - pathology; Prognosis; Proteomics; Sensitivity and Specificity; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
• ISSN: 1556-0864; 1556-1380
• DOI: 10.1097/JTO.0b013e31814b8be7

There is a critical need for improvements in the noninvasive diagnosis of lung cancer. We hypothesized that matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) analysis of the most abundant peptides in the serum may distinguish lung cancer cases from matched controls. We used MALDI MS to analyze unfractionated serum from a total of 288 cases and matched controls split into training (n = 182) and test sets (n = 106). We used a training–testing paradigm with application of the model profile defined in a training set to a blinded test cohort. Reproducibility and lack of analytical bias was confirmed in quality-control studies. A serum proteomic signature of seven features in the training set reached an overall accuracy of 78%, a sensitivity of 67.4%, and a specificity of 88.9%. In the blinded test set, this signature reached an overall accuracy of 72.6 %, a sensitivity of 58%, and a specificity of 85.7%. The serum signature was associated with the diagnosis of lung cancer independently of gender, smoking status, smoking pack-years, and C-reactive protein levels. From this signature, we identified three discriminatory features as members of a cluster of truncated forms of serum amyloid A. We found a serum proteomic profile that discriminates lung cancer from matched controls. Proteomic analysis of unfractionated serum may have a role in the noninvasive diagnosis of lung cancer and will require methodological refinements and prospective validation to achieve clinical utility.