Abstract IA21: Risk prediction models for lung cancer
Authors: Spitz Margaret R. , Etzel Carol 
Published: March 2013

Abstract
Background: There are an estimated 45 million current smokers in the US and 49 million former smokers. The challenge is to construct reliable risk prediction models to identify that fraction of smokers most likely to get lung cancer. High-risk individuals could undergo a program of screening surveillance that might not be appropriate for a lower risk population and receive the most intensive smoking cessation interventions. Models with improved discriminatory ability will also have clinical benefits. In the US, 150,000 patients are diagnosed with focal pulmonary lesions annually, with the prevalence of malignancy ranging from 10 to 70%. Current guidelines are to employ best clinical judgment and/or validated models. Finally, risk prediction tools could be incorporated into the design of smaller, more powerful, and “smarter” prevention trials. Cardiovascular and type 2 diabetes risk profiles using epidemiologic data have been effectively used for nearly two decades. In the cancer arena, risk prediction models for breast cancer have the longest history, although models have also been generated for prostate, lung, melanoma, ovary, colorectal, and bladder cancers. The advent of genome-wide association studies to identify low-penetrance common susceptibility alleles now heralds the possibility of incorporating panels of gene variants into existing models and to assess improvement in model performance. However, to date, the expanded models for many cancer sites have shown only modest incremental improvements in discrimination.
Lung Risk Models: We and others have explored various approaches for lung cancer prediction. Such tools hold promise, but their interpretation is complex.
Epidemiologic/clinical: Bach published the original risk prediction model based on data from the Carotene and Retinol Efficacy Trial (CARET) of 14,000 heavy smokers and &gt;4,000 asbestos-exposed men, mostly white (1). The variables included age, gender, asbestos exposure, smoking history, cigarettes per day, duration of smoking and of cessation. The Bach model has been validated in an independent data from the placebo arm of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention study. Our baseline model was based on data derived from an ongoing lung cancer case control study (2). Variables included were environmental tobacco smoke (for never and former smokers only), family cancer history, asbestos and dust exposures, prior respiratory disease, history of hay fever, and smoking history variables. All variables have strong biologically plausible etiologic roles, and are relatively easy to ascertain through patient interview. The validated area under the curve (AUC) 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. If age and smoking status (case-control matching variables) had been included in the models, the models would likely have performed far better.
The Liverpool Lung Project (LLP) variables (3) include smoking duration, prior diagnosis of pneumonia, occupational exposure to asbestos, prior cancer diagnosis and family history of lung cancer. Most recently a carefully constructed model based on data from 70,962 control subjects in the Prostate, Lung, Colorectal, Ovarian cancer screening trial (PLCO) was published (4). This model includes age, SES (education), BMI, family history of lung cancer, COPD, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. A second model also included smoking quit-time. External validation was performed with 44,223 PLCO intervention arm participants. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites.
Extended Models:
Functional Data: We have estimated the improvement in model performance by incorporating two measures of DNA repair capacity that have been shown in case-control analyses to be associated with increased lung cancer risk (5). Addition of the biomarker assays does improve the sensitivity of the models over epidemiologic and clinical data alone. These in vitro lymphocyte culture assays, however, are time-consuming and require some level of technical expertise. Therefore while feasible in a controlled academic setting, they are not applicable for widespread population-based implementation.
Genetic Data: Genetic data are stable, inexpensive to ascertain, accurate and amenable to high-throughput analysis. However, it remains uncertain whether enriched multimarker models give better discrimination. We have added three SNPS that were most significant in GWAS of lung cancer to the baseline lung epidemiologic model. These include a replicated SNP in the 15q25 chromosomal region that encompasses the nicotinic acetylcholine receptor subunit genes, CHRNA3 and CHRNA5, that have a defined role in nicotine dependence, and a hypothesized direct role in downstream signaling pathways that promote carcinogenesis. We also included two SNPs from the 5p15.33 locus (rs2736100 and rs401681) that contains two known genes: the TERT (human telomerase reverse transcriptase) gene and the CLPTM1L. The AUC showed only modest improvement. Such small increases in discriminative accuracy are unlikely to be of diagnostic or predictive utility.
Young (6) developed a risk model using a 20-SNP panel including metabolizing, inflammation, DNA repair, anti-oxidant, apoptosis and addiction genes. The final model included the 20 SNPs, age, history of COPD, family history of lung cancer and gender. When numeric scores were assigned to both the SNP and demographic data, and sequentially combined by a simple algorithm in a risk model, the composite score was found to be linearly related to lung cancer risk with a bimodal distribution.
Spira et al (7) have advanced the notion that upper airway gene expression in smokers may serve as a relatively noninvasive surrogate marker of the physiologic response of the lung to tobacco smoke and could be used in large-scale screening and chemoprevention studies for lung cancer. Gene expression profiles in cytologically normal large airway epithelium were obtained via bronchoscopic brushings and were predictive of cancer status in a combined clinicogenomic model (P &lt; 0.005). There was a significant improvement in performance of the clinicogenomic relative to the clinical model (P &lt; 0.05). Use of the clinicogenomic model may reduce invasive diagnostic procedures for individuals without lung cancer.
The ROC curve may not be sensitive to differences in probabilities between models, and therefore insufficient to assess the impact of adding a new predictor. A substantial gain in performance may not yield a substantial increase in AUC, and only a very large independent association of the new marker with risk will yield a meaningful larger AUC. As an example, the Framingham Risk Score that is widely applied has an AUC of about 0.80. New metrics need to be developed to compare nested models.
In summary, the ability to accurately predict risk of lung cancer among former and current smokers has public health, clinical and financial implications for primary prevention, surveillance programs, screening programs and chemoprevention trials.