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F. Duan
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OA 06 - Global Tobacco Control and Epidemiology I (ID 662)
- Event: WCLC 2017
- Type: Oral
- Track: Epidemiology/Primary Prevention/Tobacco Control and Cessation
- Presentations: 1
- Moderators:G. Kovács, Yun Fan
- Coordinates: 10/16/2017, 15:45 - 17:30, Room 511 + 512
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OA 06.06 - Chr15q25 Locus Confers Risk of Lung Cancer, COPD and Smoking: Triple Whammy Effect in the ACRIN NLST Sub-Study (N=9,270) (ID 8611)
15:45 - 17:30 | Author(s): F. Duan
- Abstract
- Presentation
Background:
Several large genetic studies have consistently linked the cholinergic nicotinic receptor polymorphism (CHRNA3/5, rs 16969968) to lung cancer and smoking addiction. However, we have shown that this genetic variant was also independently associated with susceptibility to chronic obstructive pulmonary disease (COPD). To date this observation, independently linking CHRNA3/5 polymorphism to both COPD and lung cancer, has not been tested prospectively in a cohort study. Using 10,054 subjects from the ACRIN-NLST cohort, a sub-study of the NLST that recruited high risk smokers and followed them for an average of 6.4 years, we examined the association between the CHRNA3/5 variant in the development of lung cancer relative to its role in COPD and lifelong smoking exposure.
Method:
We compared the frequency of the high risk AA genotype in our cohort of 10,054 high risk smokers according to their smoking status, lung function and COPD status. We also compared the lung cancer incidence rate according to the CHRNA3/5 genotype. Lastly we used stepwise logistic regression and mediation analysis to examine the role of the AA genotype of the CHRNA3/5 variant in smoking exposure, COPD and lung cancer.
Result:
Relative to healthy smoking controls, the AA genotype was associated with the presence of COPD (OR=1.3, P<0.001) and the development of lung cancer overall (OR=1.5, P<0.01), lung cancer with pre-existing COPD (OR=1.5, P=0.04), lung cancer with no COPD (OR=1.5, P=0.04). Compared to the GG and AG genotypes, the AA genotype was associated with (a) lower FEV1%pred, lower FEV1/FVC and a 19% increase in risk of COPD; and (b) greater cigs/day, pack years exposure and a 47% increased risk of lung cancer. No differences were found for age, gender, smoking duration, years since quitting and current smoking status. In stepwise logistic regression, we found that age, BMI, AA genotype, smoking exposure and lung function were independently associated with getting lung cancer. In the mediation analyses we found that the AA genotype was independently associated with smoking (OR=1.42,P<0.05), COPD (OR=1.25,P<0.05) and getting lung cancer (OR=1.37, P<0.05).
Conclusion:
This is the first prospective study to confirm that while the CHRNA3/5 variant is associated with lung cancer, more importantly this variant is also independently associated with airflow limitation (COPD). It is also the first cohort study to show that while this genetic variant is also associated with smoking exposure (triple whammy effect), this is independent of its effects on predisposition to both COPD and lung cancer.
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OA 15 - Diagnostic Radiology, Staging and Screening for Lung Cancer II (ID 684)
- Event: WCLC 2017
- Type: Oral
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:Y. Satoh, Jin Mo Goo
- Coordinates: 10/18/2017, 14:30 - 16:15, Room 303 + 304
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OA 15.03 - Gene-Based Risk Stratification of NLST-ACRIN Screening Participants Identifies The "Sweet Spot" of Screening (N=10,054) (ID 8625)
14:30 - 16:15 | Author(s): F. Duan
- Abstract
- Presentation
Background:
Screening of high risk smokers with computed tomography (CT) aims to identify early stage lung cancers in screening participants amenable to curative surgery. The National Lung Screening Trial (NLST) demonstrated a 20% reduction in lung cancer specific mortality in the CT arm compared to chest x-ray (control) arm. European screening trial results to date have failed to show any evidence of a reduction in lung cancer mortality. Reduction in lung cancer mortality comes from the combined effects of successful surgical removal of life-threatening early stage lung cancers and post-operative survival. In screening participants of the NLST, who are older chronic smokers, there exists a balance between mortality from lung cancer and mortality from non-lung cancer related causes.
Method:
This study aimed to validate a gene-based risk tool for dying of lung cancer and examine the outcomes from screening according to tertiles of risk. It also aimed to establish the utility of adding SNP-based data to risk prediction and efficacy in identifying which screening participants get the best outcomes from screening. Using prospective data from the NLST-ACRIN cohort (N=10,054), we examined the utility of combining risk genotypes with clinical risk variables in our risk model for dying of lung cancer. We then stratified screening participants into risk tertiles according to our risk model and compared the outcomes from CT versus CXR screening
Result:
The addition of risk genotypes (combined genetic risk score) to our clinical risk model for dying of lung cancer was significantly improved (AUC increased from 0.61 to 0.66, P=0.014). We show that screening participants in the middle risk tertile achieves a lung cancer specific mortality reduction of 55% and all-cause mortality reduction of 21%. In this group the number of lung cancers averted is maximised (12/1000 person screened) and number needed to screen to avert one lung cancer reduced to 84. We show that this is achieved through minimising pre-existing co-morbid disease and by maximising screen detected lung cancers amenable to CT detection and successful surgical intervention. We believe genetic data provides useful information on lung cancer biology.
Conclusion:
The “Sweet spot” of CT screening comes from identifying high risk smokers optimised according to co-existing premorbid disease (especially COPD), early stage lung cancers amenable to surgical cure and least likely to die of other complications of smoking. Gene-based risk testing appears superior to just clinical risk models alone in prioritising high risk smokers for screening.
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P2.13 - Radiology/Staging/Screening (ID 714)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 10/17/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P2.13-014 - Computed Tomography-Based Radiomic Classifier Distinguishes Malignant from Benign Pulmonary Nodules in the National Lung Screening Trial (ID 10244)
09:30 - 16:00 | Author(s): F. Duan
- Abstract
Background:
In the National Lung Screening Trial (NLST), indeterminate pulmonary nodules were detected in 40% of high-risk individuals screened by low dose high-resolution computed tomography (HRCT). However 96% of these nodules were benign indicating that overdiagnosis represents a major challenge for the clinical implantation of CT based lung cancer screening. While current clinical-radiological risk prediction models are very valuable, optimization of the clinical management of larger (≥ 7 mm) screen-detected nodules to avoid unnecessary diagnostic interventions including futile thoracotomies better strategies are needed. Herein we demonstrate the potential value of a novel radiomics based approach for the classification of screen-detected indeterminate nodules.
Method:
Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature, using 726 nodules (all ≥ 7 mm) were developed from the NLST dataset (benign, n=318 and malignant, n=408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. To increase the stability of the modeling, LASSO was run 1,000 times and the variables that were selected in at least 50% of the runs were included into the final multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model.
Result:
Eight radiologic features were selected by LASSO multivariate modeling out of 57 quantitative radiological variables considered for inclusion. These 8 features include variables capturing vertical location (centroid_Z), volume estimate (Min Enclosing Brick), flatness, texture analysis (SILA_Tex), surface complexity (Max_SI and Avg_SI), and estimates of surface curvature (Avg_PosMeanCurv and Min_MeanCurv), all with P<0.01. The optimism-corrected AUC is 0.939.
Conclusion:
Our novel radiomic HRCT-based approach to non-invasive screen-detected nodule characterization appears extremely promising. Independent external validation is needed.
