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BMC Med Genomics. 2015 May 6;8:18. doi: 10.1186/s12920-015-0091-3.

Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy.

Author information

1
Allegro Diagnostics, Corp, Maynard, MA, USA. Duncan.whitney@veracyte.com.
2
Present affiliation: Veracyte, Inc, South San Francisco, CA, USA. Duncan.whitney@veracyte.com.
3
Elashoff Consulting, LLC, Redwood City, CA, USA. Elashoff@gmail.com.
4
Allegro Diagnostics, Corp, Maynard, MA, USA. Kate@veracyte.com.
5
Present affiliation: Veracyte, Inc, South San Francisco, CA, USA. Kate@veracyte.com.
6
Boston University School of Medicine, Boston, MA, USA. agower@bu.edu.
7
University of Pennsylvania School of Medicine, Philadelphia, PA, USA. avachani@mail.med.upenn.edu.
8
University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. jsferguson@medicine.wisc.edu.
9
Medical University of South Carolina, Charleston, SC, USA. silvestri@musc.edu.
10
Boston University School of Medicine, Boston, MA, USA. jbrody@bu.edu.
11
Boston University School of Medicine, Boston, MA, USA. mlenburg@bu.edu.
12
Boston University School of Medicine, Boston, MA, USA. aspira@bu.edu.

Abstract

BACKGROUND:

The gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort.

METHODS:

We collected bronchial epithelial cells (BECs) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study.

RESULTS:

We found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy.

CONCLUSION:

We developed a gene expression classifier measured in bronchial airway epithelial cells that is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.

PMID:
25944280
PMCID:
PMC4434538
DOI:
10.1186/s12920-015-0091-3
[Indexed for MEDLINE]
Free PMC Article

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