Low dose computed tomography (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign (BN) or malignant (MN) involves either repeated scans or invasive procedures that sample the lung tissue. Non-invasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. MN samples were predominantly Stage 1 by pathologic diagnosis and 97% of BN were confirmed by four years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data was then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC) which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed 3 clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, non-invasive method for the diagnosis of pulmonary nodules in non-small cell lung cancer patients.

<p>Ranking of Nanostring Custom panel probes in nPNC classifier</p>

<div>Abstract<p>Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6–20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non–small cell lung cancer.</p>Significance:<p>These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.</p></div>

<p>Demographics for patients used in the microarray study</p>

<p>Performance of the Pulmonary Nodule Classifier</p>