An Expanded Risk Prediction Model for Lung Cancer

Abstract

Risk prediction models are useful in clinical decision making. We have published an internally validated prediction tool for lung cancer based on easily obtainable epidemiologic and clinical data. Because the precision of the model was modest, we now estimate the improvement obtained by adding two markers of DNA repair capacity.Assay data (host-cell reactivation and mutagen sensitivity) were available for 725 White lung cancer cases and 615 controls, all former or current smokers, a subset of cases and controls from the previous analysis. Multivariable models were constructed from the original variables with addition of the biomarkers separately and together. Pairwise comparisons of the area under the receiver operating characteristic curves (AUC) and 3-fold cross-validations were done.For former smokers, the AUC and 95% confidence intervals were 0.67 (0.63-0.71) for the baseline model and 0.70 (0.66-0.74) for the expanded model. For current smokers, the comparable AUC values were 0.68 (0.64-0.72) and 0.73 (0.69-0.77). For both groups, the expanded models were statistically significantly better than the baseline models (P = 0.006 and P = 0.0048, respectively), although the increases in the concordance statistics were modest. We also recomputed 1-year absolute risks of lung cancer as described previously for two different risk profiles and showed that individuals who exhibited poor repair capacity or heightened mutagen sensitivity had increased absolute risks of lung cancer.Addition of biomarker assays improved the sensitivity of the expanded models.We recently constructed and internally validated clinical tools for lung cancer risk prediction for current, former, and never smokers based on epidemiologic and clinical data derived from 1,851 non-Hispanic White lung cancer patients and 2,001 matched controls (1). Variables included were environmental tobacco smoke (for never smokers only), family cancer history, dust exposure, prior respiratory disease and hay fever, and smoking history variables (pack-years for current smokers and age stopped smoking for former smokers). All have strong biologically plausible etiologic roles and are relatively easy to ascertain through patient interview. The validated concordance statistics for the former and current smoker models were modest (0.63 and 0.58, respectively), although consistent with those from other risk prediction models. The purpose of this new analysis was to improve the precision of the models by incorporating select markers of host DNA repair capacity into the prediction tool. Deficiency in DNA damage recognition and repair leads to destabilization of the genome and increased risk of mutagenesis. Previously, we have shown that suboptimal DNA repair capacity (defined as below the median percent repair in the control population, as measured by an in vitro lymphocyte culture-based host-cell reactivation assay) is associated with up to 2-fold statistically significant increased lung cancer risks (2, 3). Likewise, the in vitro mutagen sensitivity assay quant...

LLPi: Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence

“…To date, most risk prediction models for lung cancer were developed in case-control studies (6)(7)(8)(9). Case-control studies are proficient in studying dynamic populations where follow-up is difficult, are usually less expensive, and are less time consuming, but may be plagued by biases and cannot study the incidence of a disease (10)(11)(12)(13).…”

Incorporation of a Genetic Factor into an Epidemiologic Model for Prediction of Individual Risk of Lung Cancer: The Liverpool Lung Project

“…This expansion of risk factors in predictive risk models beyond the traditional epidemiologic data has been elegantly pursued in cardiovascular diseases, wherein biological assay data and some genetic variants had been added into risk score models for prediction of absolute risk of cardiovascular disease (15,16). Similar efforts in cancer studies include addition of mammographic density data to the Gail model for breast cancer (17), inclusion of assay data for DNA repair capacity and mutagen sensitivity into the Spitz lung cancer risk models for former and current smokers (18), and recent combination of a panel of low-risk SNPs with demographic data to form a simple algorithm for lung cancer risk prediction (7).…”

From systems biology to P4 medicine: applications in respiratory medicine

“…liquid biopsy) tumour marker has been properly validated, but recently a panel of six tumour markers showed a very high specificity and sensitivity in patients referred to a tertiary hospital because of the clinical suspicion of lung cancer [104,105]. Given that inherited genetic variants play a significant role in lung cancer development [106], but contribute little to risk estimates of classical predictive statistical models [107][108][109], it is hoped that systems biology approaches will allow the comparison multilevel high-throughput omics data between tumour and normal tissue, and facilitate the identification of early diagnostic lung cancer biomarkers. WGCNA has already been used successfully in lung cancer research.…”