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A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT
Journal article   Open access   Peer reviewed

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT

Andrew V Kossenkov, Rehman Qureshi, Noor B Dawany, Jayamanna Wickramasinghe, Qin Liu, R Sonali Majumdar, Celia Chang, Sandy Widura, Trisha Kumar, Wen-Hwai Horng, …
Cancer research (Chicago, Ill.), v 79(1)
01 Jan 2019
DOI: https://doi.org/10.1158/0008-5472.CAN-18-2032
PMID: 30487137
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1158/0008-5472.can-18-2032View
Accepted (AM)Open Access (License Unspecified),  Open
url
https://doi.org/10.1158/0008-5472.CAN-18-2032View
Published, Version of Record (VoR) Open

Abstract

Aged Algorithms Biomarkers, Tumor - blood Biomarkers, Tumor - genetics Carcinoma, Non-Small-Cell Lung - blood Carcinoma, Non-Small-Cell Lung - diagnosis Carcinoma, Non-Small-Cell Lung - diagnostic imaging Carcinoma, Non-Small-Cell Lung - genetics Diagnosis, Differential Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Lung Neoplasms - blood Lung Neoplasms - diagnosis Lung Neoplasms - diagnostic imaging Lung Neoplasms - genetics Male Middle Aged Multiple Pulmonary Nodules - blood Multiple Pulmonary Nodules - diagnosis Multiple Pulmonary Nodules - diagnostic imaging Multiple Pulmonary Nodules - genetics Prospective Studies Tomography, X-Ray Computed - methods
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. SIGNIFICANCE: These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.

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29 citations in Web of Science
31 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Oncology
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