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Not an EDRN Protocol

A prediction model for lung cancer diagnosis that integrates genomic and clinical features.

1009
Beane, JenniferBoston University School of Medicine

Lung cancer is the leading cause of cancer death due, in part, to lack of early diagnostic tools. Bronchoscopy represents a relatively noninvasive initial diagnostic test in smokers with suspect disease, but it has low sensitivity. We have reported a gene expression profile in cytologically normal large airway epithelium obtained via bronchoscopic brushings, which is a sensitive and specific biomarker for lung cancer. Here, we evaluate the independence of the biomarker from other clinical risk factors and determine the performance of a clinicogenomic model that combines clinical factors and gene expression. Training (n = 76) and test (n = 62) sets consisted of smokers undergoing bronchoscopy for suspicion of lung cancer at five medical centers. Logistic regression models describing the likelihood of having lung cancer using the biomarker, clinical factors, and these data combined were tested using the independent set of patients with nondiagnostic bronchoscopies. The model predictions were also compared with physicians' clinical assessment. The gene expression biomarker is associated with cancer status in the combined clinicogenomic model (P < 0.005). There is a significant difference in performance of the clinicogenomic relative to the clinical model (P < 0.05). In the test set, the clinicogenomic model increases sensitivity and negative predictive value to 100% and results in higher specificity (91%) and positive predictive value (81%) compared with other models. The clinicogenomic model has high accuracy where physician assessment is most uncertain. The airway gene expression biomarker provides information about the likelihood of lung cancer not captured by clinical factors, and the clinicogenomic model has the highest prediction accuracy. These findings suggest that use of the clinicogenomic model may expedite more invasive testing and definitive therapy for smokers with lung cancer and reduce invasive diagnostic procedures for individuals without lung cancer.

There are currently no biomarkers annotated for this protocol.

No datasets are currently associated with this protocol.


Announcement 09/14/2014

Thank you to everyone who helped make the 9th EDRN Scientific Workshop a success. We look forward to seeing everyone at the 28th EDRN Steering Committee Meeting from March 31-April 2, 2015, in Atlanta, GA.

Announcement 06/05/2014


Funding Opportunity Available

Both RFAs for Molecular and Cellular Characterization of Screen-Detected Lesions have been published.

RFA-CA-14-010.html

and

RFA-CA-14-011.html