Affiliations: Department of Medicine, Baylor College of Medicine,
Houston, TX, USA | Medical Statistics Section, Department of Medicine,
The University of Alabama at Birmingham, Birmingham, AL, USA | Department of Pathology, The University of Alabama at
Birmingham, Birmingham, AL, USA | Department of Nutrition Sciences, The University of
Alabama at Birmingham, Birmingham, AL, USA
Note: [] Corresponding author. Department of Medicine, Baylor College of
Medicine, Room 335A, One Baylor Plaza, MS: BCM 600, Houston, TX, 77030, USA.
Tel.: +1 713 798 1630; Fax: +1 713 798 1642; E-mail:
[email protected]
Abstract: The role of biomarkers in disease prognosis continues to be an
important investigation in many cancer studies. In order for these biomarkers
to have practical application in clinical decision making regarding patient
treatment and follow-up, it is common to dichotomize patients into those with
low vs. high expression levels. In this study, receiver operating
characteristic (ROC) curves, area under the curve (AUC) of the ROC,
sensitivity, specificity, as well as likelihood ratios were calculated to
determine levels of growth factor biomarkers that best differentiate lung
cancer cases versus control subjects. Selected cut-off points for
p185^{erbB-2} and EGFR membrane appear to have good
discriminating power to differentiate control tissues versus uninvolved tissues
from patients with lung cancer (AUC = 89% and 90%, respectively); while AUC
increased to at least 90% for selected cut-off points for
p185^{erbB-2} membrane, EGFR membrane, and FASE when
comparing between control versus carcinoma tissues from lung cancer cases.
Using data from control subjects compared to patients with lung cancer, we
presented a simple and intuitive approach to determine dichotomized levels of
biomarkers and validated the value of these biomarkers as surrogate endpoints
for cancer outcome.
Journal: Disease Markers, vol. 19, no. 6, pp. 273-278, 2003,2004
