TY - JOUR
T1 - A blood-based proteomic classifier for the molecular characterization of pulmonary nodules
AU - Li, Xiao Jun
AU - Hayward, Clive
AU - Fong, Pui Yee
AU - Dominguez, Michel
AU - Hunsucker, Stephen W.
AU - Lee, Lik Wee
AU - McLean, Matthew
AU - Law, Scott
AU - Butler, Heather
AU - Schirm, Michael
AU - Gingras, Olivier
AU - Lamontagne, Julie
AU - Allard, Rene
AU - Chelsky, Daniel
AU - Price, Nathan D.
AU - Lam, Stephen
AU - Massion, Pierre P.
AU - Pass, Harvey
AU - Rom, William N.
AU - Vachani, Anil
AU - Fang, Kenneth C.
AU - Hood, Leroy
AU - Kearney, Paul
PY - 2013/10/16
Y1 - 2013/10/16
N2 - 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 assayswere applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancermatched 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.
AB - 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 assayswere applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancermatched 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.
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U2 - 10.1126/scitranslmed.3007013
DO - 10.1126/scitranslmed.3007013
M3 - Article
C2 - 24132637
AN - SCOPUS:84886447263
SN - 1946-6234
VL - 5
JO - Science translational medicine
JF - Science translational medicine
IS - 207
M1 - 207ra142
ER -