• Xiao-jun Li, Clive Hayward, Pui-Yee Fong, Michel Dominguez, Stephen W Hunsucker, Lik Wee Lee, Matthew McLean, Scott Law, Heather Butler, Michael Schirm, Olivier Gingras, Julie Lamontagne, Rene Allard, Daniel Chelsky, Nathan D Price, Stephen Lam, Pierre P Massion, Harvey Pass, William N Rom, Anil Vachani, Kenneth C Fang, Leroy Hood, and Paul Kearney.
• Integrated Diagnostics, Seattle, WA 98109, USA.
• Sci Transl Med. 2013 Oct 16;5(207):207ra142.

AbstractEach 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.

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