Use of Tobacco Biomarkers in Lung Cancer Risk Assessment
Current lung cancer risk prediction models include individual demographic and smoking history variables. The tobacco-specific biomarker 4-(methyl-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an International Agency for Research on Cancer (IARC) Group 1 carcinogen, and NNAL is a biomarker of NNK uptake. A prospective relationship between NNAL and lung cancer risk has been shown using PLCO biospecimens. Use of total NNAL, the biomarkers r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene (PheT), and cotinine can characterize tobacco carcinogen uptake and metabolism, aspects of smoking exposure that are missing from prior lung cancer risk models.
Masonic Cancer Center collaborators will provide biomarker data from 100 lung cancer cases and 100 controls from PLCO that were previously analyzed. We are requesting the additional variables from PLCO needed to calculate the PLCOm2012 risk score. Logistic regression will be used to model diagnosis of lung cancer within 6 years (the primary outcome) for all observations. Covariates included in the model will be total NNAL, PheT, cotinine and a lung cancer risk score (i.e., the predicted log odds) from the PLCOm2012 model. Due to the expected skewed distributions of the three biomarkers, log-transformations will be used. Two models will be investigated where 1) all covariates are modelled linearly, and 2) risk score is modeled linearly and all three log-transformed biomarkers are modelled nonlinearly using restricted cubic splines. The final model will be selected taking into consideration performance metrics. Cross validation will be used to internally validate the model given use of the same data for model development and validation. Performance metrics including ROC curves, AUC, and sensitivity at a set specificity of 63%, which is the specificity achieved in previous models, will be obtained. These performance metrics will be used to compare against the PLCOm2012 model. Statistical analyses will be performed using R v 3.6.0.
Specific Aim 1: To develop a lung cancer risk prediction model based on the previously validated PLCOm2012 model with the addition of tobacco biomarkers total NNAL, PheT, and cotinine.
Specific Aim 2: To validate tobacco biomarker lung cancer risk model using a nested case-control population within the PLCO source cohort.
Anne Joseph, Wexler Professor of Medicine, Division of General Internal Medicine, University of Minnesota
Stephen Hecht, Wallin Land Grant Professor of Cancer Prevention, Department of Laboratory Medicine and Pathology, University of Minnesota
Sharon Murphy, Professor, Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota
Christine Wendt, Section Chief, Pulmonary, Allergy, Critical Care and Sleep Medicine, Minneapolis VA Health Care System
Ashley Peterson, Assistant Professor, Division of Biostatistics, University of Minnesota
Katelyn Tessier, Biostatistician Masonic Cancer Center, University of Minnesota