title: A blood-based proteomic classifier for the molecular characterization of pulmonary nodules.
abstract: 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.
authors.affiliation: Integrated Diagnostics, Seattle, WA 98109, USA.
authors.forename: Xiao-jun
authors.initials: XJ
authors.lastname: Li
authors.forename: Clive
authors.initials: C
authors.lastname: Hayward
authors.forename: Pui-Yee
authors.initials: PY
authors.lastname: Fong
authors.forename: Michel
authors.initials: M
authors.lastname: Dominguez
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Hunsucker
authors.forename: Lik Wee
authors.initials: LW
authors.lastname: Lee
authors.forename: Matthew
authors.initials: M
authors.lastname: McLean
authors.forename: Scott
authors.initials: S
authors.lastname: Law
authors.forename: Heather
authors.initials: H
authors.lastname: Butler
authors.forename: Michael
authors.initials: M
authors.lastname: Schirm
authors.forename: Olivier
authors.initials: O
authors.lastname: Gingras
authors.forename: Julie
authors.initials: J
authors.lastname: Lamontagne
authors.forename: Rene
authors.initials: R
authors.lastname: Allard
authors.forename: Daniel
authors.initials: D
authors.lastname: Chelsky
authors.forename: Nathan D
authors.initials: ND
authors.lastname: Price
authors.forename: Stephen
authors.initials: S
authors.lastname: Lam
authors.forename: Pierre P
authors.initials: PP
authors.lastname: Massion
authors.forename: Harvey
authors.initials: H
authors.lastname: Pass
authors.forename: William N
authors.initials: WN
authors.lastname: Rom
authors.forename: Anil
authors.initials: A
authors.lastname: Vachani
authors.forename: Kenneth C
authors.initials: KC
authors.lastname: Fang
authors.forename: Leroy
authors.initials: L
authors.lastname: Hood
authors.forename: Paul
authors.initials: P
authors.lastname: Kearney
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000097:blood; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000097:blood / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009363:Neoplasm Proteins / Q000097:blood; D040901:Proteomics; D015203:Reproducibility of Results; D003074:Solitary Pulmonary Nodule / Q000097:blood* / Q000378:metabolism*
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins