Tobacco biomarkers disappoint in prediction of lung cancer risk

Previous studies have shown that tobacco biomarkers were associated with lung cancer. Nonetheless, a case-control study showed that the integration of 3 relevant tobacco biomarkers in the validated lung cancer prediction model PLCOm2012 did not improve the performance of this validated model.

The lung cancer risk prediction model PLCOm2012 is a validated logistic regression lung cancer risk prediction model based on data collected from the control arm of the Prostate, Lung, Colorectal and Ovarian Cancer Screening (PLCO) trial. This model uses historical and demographic data to identify individuals who should receive cancer screening. Previous studies have shown that biomarkers of tobacco exposure were associated with lung cancer, but these biomarkers were never used in the prediction of lung cancer risk. Therefore, Dr Christine Lambert (University of Minnesota, USA) and colleagues created a new lung cancer risk prediction model that incorporated 3 tobacco biomarkers into the PLCOm2012 model: serum cotinine levels, NNAL, and PheT [1]. Cotinine is a metabolite of nicotine. Both NNAL and PheT are metabolites of polycyclic aromatic hydrocarbons (PAH), which are believed to be among the principal causative agents for lung cancer in smokers [2]. The aim of this case-control study was to assess whether including these biomarkers could improve the prediction of 6-year lung cancer risk.

Lung cancer cases (n=72) diagnosed within 6 years of PLCOm2012 randomisation –to match the prediction time frame of this model– were compared with 115 cancer-free controls drawn from current smokers in the screening arm of the PLCO. Smoking-pack years and levels of all 3 tobacco biomarkers were higher in lung cancer cases compared with controls. “In the logistic regression model, the PLCOm2012 was a significant predictor of lung cancer risk,” Dr Lambert said. In contrast, all 3 biomarkers did not predict lung cancer risk after accounting for the PLCOm2012 risk score. “The addition of biomarkers did not improve the performance of the prediction model for lung cancer risk compared with the PLCOm2012 model alone,” concluded Dr Lambert. Lack of study population diversity and heavy smoking amongst cases and controls may have limited the tobacco biomarker effect and predictive ability.

1. Lambert C, et al. Use of tobacco biomarkers in lung cancer risk assessment. Session TP136: Thematic Poster session: Lung cancer, thoracic oncology. ATS 2021 International conference, 14-19 May 2021.
2. Yuan JM, et al. Cancer Res 2011:71:6749-57.

Posted on 18 May, 20214 August, 2021