A blood-based proteomic classifier for the molecular characterization of pulmonary nodules
| Autor: | Olivier Gingras, Anil Vachani, William N. Rom, Rene Allard, Pui Yee Fong, Leroy Hood, Paul Kearney, Scott M. Law, Heather Butler, Pierre P. Massion, Julie Lamontagne, Nathan D. Price, Stephen W. Hunsucker, Xiao-Jun Li, Daniel Chelsky, Michael Schirm, Michel Dominguez, Kenneth C. Fang, Harvey I. Pass, Stephen Lam, Lik Wee Lee, Clive Hayward, Matthew McLean |
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| Rok vydání: | 2013 |
| Předmět: |
Male
Proteomics Pathology medicine.medical_specialty Lung Neoplasms Article Text mining Biopsy medicine Biomarkers Tumor Humans Lung cancer Solitary pulmonary nodule Lung medicine.diagnostic_test business.industry Reproducibility of Results Solitary Pulmonary Nodule General Medicine Middle Aged medicine.disease Pathway analysis Neoplasm Proteins medicine.anatomical_structure Logistic Models Multivariate Analysis Female business Classifier (UML) Algorithms |
| Zdroj: | Science translational medicine. 5(207) |
| ISSN: | 1946-6242 |
| Popis: | Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis. |
| Databáze: | OpenAIRE |
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