Skip to Main Content

COVID-19 is an emerging, rapidly evolving situation.

What people with cancer should know: https://www.cancer.gov/coronavirus

Get the latest public health information from CDC: https://www.coronavirus.gov

Get the latest research information from NIH: https://www.nih.gov/coronavirus

Principal Investigator
Name
Christine Lambert
Degrees
M.D, Ph.D
Institution
University of Minnesota
Position Title
Pulmonary Critical Care Fellow
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-563
Initial CDAS Request Approval
Jan 30, 2020
Title
Use of Tobacco Biomarkers in Lung Cancer Risk Assessment
Summary
Lung cancer screening with low dose computed tomography has been shown to reduce mortality from lung cancer, the leading cause of cancer deaths in the United States. Screening guidelines are based on age and pack-year history. Pack-year history is difficult to estimate accurately and has limitations as a robust measure of tobacco exposure. For current smokers, a risk model that incorporates tobacco specific biomarkers may provide a more precise representation of smoking history, helping to ensure lung cancer screening is performed in those at greatest risk of lung cancer. Risk models, such as PLCOm2012 which was developed from data collected through the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, use historical measures of tobacco exposure. We propose to test a lung cancer risk model that adds biological markers of tobacco exposure.

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.

Approach
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.
Aims

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.

Collaborators

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