Abstract 2254: Risk models for lung cancer in never-smoking women in Shanghai with implications for screening
Authors: Blechter Batel , Choudhury Parichoy Pal , Shu Xiao-ou , Zheng Wei , Cai Qiuyin , Yang Gong , Wong Jason Y.Y. , Ji Bu-Tian , Hu Wei , Rositch Anne , Chatterjee Nilanjan , Rothman Nathaniel , Lan Qing 
Published: June 2022

Background: The majority of female lung cancer cases in Asia are never-smokers with distinct risk factor profiles. Given the high burden of disease in this population, there is an increasing need to improve the understanding of lung cancer. Current risk models for lung cancer focus on active smokers and individuals of European ancestry. Therefore, we developed statistical models by integrating genetic and environmental risk factors to estimate absolute and population attribute risk of lung cancer among never-smoking women in Asia.

Methods: We built absolute risk models for lung cancer among never-smoking women using data from 71,300 women (760 incident cases) in the Shanghai Women’s Health Study (SWHS), a population-based prospective cohort study. Relative risks were estimated using a multivariable Cox regression model with questionnaire-based risk factors. To account for missing genetic data for some subjects, we simulated genotypes for 10 common single nucleotide polymorphisms (SNP) using information on minor allele frequencies (MAF) and odds ratio estimates from previous genome-wide association studies (GWAS), conditional on family history of lung cancer. We used the iCARE tool to build two models for predicting lifetime (40 years) and 6-year absolute risk of lung cancer using age-specific lung cancer incidence rates, age-specific competing mortality rates, and risk factor distribution with: 1) questionnaire-based risk factors only and 2) questionnaire and genetic data. We then used the full absolute risk model to estimate the population attributable risk (PAR) due to modifiable risk factors, namely coal use and exposure to environmental tobacco smoke (ETS).

Results: The questionnaire-based only model included family history of lung cancer, coal use, exposure to ETS, and body mass index (BMI). The full model also included data on 10 lung cancer related SNPs from our previous GWAS and had a wider spread in distribution of absolute lifetime risk (median=2.41%; range=0.43-12.36) compared to the questionnaire-based only model (median=2.72%; range=1.93-4.87). We used the full model to estimate the PAR and found that 1.74% and 6.33% of lung cancer cases could be prevented if never-smoking women in Shanghai did not use coal and were not exposed to ETS, respectively. Furthermore, we found that the full model estimated that 2.5% of the study population had a 6-year absolute risk of lung cancer higher than 1.51%, which is the suggested risk threshold for screening by existing risk models.

Conclusion: We built risk models for never-smoking Asian women and estimated the contribution of coal use and ETS to the burden of lung cancer in Shanghai. This initial work shows promise for expanding and validating risk models in this population with potential translational implications, such as providing insight to identifying high risk individuals that may be eligible for lung cancer screening and primary prevention efforts.

Citation Format: Batel Blechter, Parichoy Pal Choudhury, Xiao-ou Shu, Wei Zheng, Qiuyin Cai, Gong Yang, Jason Y.Y. Wong, Bu-Tian Ji, Wei Hu, Anne Rositch, Nilanjan Chatterjee, Nathaniel Rothman, Qing Lan. Risk models for lung cancer in never-smoking women in Shanghai with implications for screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2254.