Abstract LB045: Transcriptome-wide association study identifies susceptibility loci and genes for lung cancer risk
Authors: Zhao Tianying , Shi Jiajun , Yang Yaohua , Ping Jie , Shu Xiao Ou , Zheng Wei , Long Jirong , Cai Qiuyin 
Published: April 2023

Objective: Genome-wide association studies (GWAS) have identified more than 50 loci for lung cancer risk. However, susceptibility genes and the underlying mechanisms for these risk loci remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify susceptibility genes for lung cancer.

Methods: Transcriptome data from normal lung tissue and whole-genome sequencing data from 444 participants of European ancestry in the Genotype-Tissue Expression (GTEx, version 8) were used to build lung-tissue models to predict gene expression levels. Lung-tissue prediction models were successfully established for 10,802 genes with a model performance r &gt; 0.1 and P &lt; 0.05. We also built joint-tissue models using the joint-tissue imputation (JTI) framework, which leverages transcriptome data from lung tissue and 48 other tissue types from the 444 participants in GTEx. Joint-tissue models were successfully established for 12,629 genes with a model performance r &gt; 0.1 and P &lt; 0.05. These prediction models were applied to the GWAS data comprised of 29,266 lung cancer cases and 56,450 controls of European ancestry, in order to evaluate genetically predicted gene expression levels in association with lung cancer risk.

Results: We found 44 genes whose genetically predicted expression levels were significantly associated with overall lung cancer risk at the Bonferroni correction significance threshold. Among the 44 genes, four were located at least 500kb away from any of the leading variants identified previously in GWAS. Of these four genes, only the CCHCR1 gene has been reported in previous TWAS, and the other three genes, LY6G5B, PRSS16, and C19orf54, have never been reported in previous GWAS or TWAS. For each of these three novel genes, consistent associations were observed in both lung-tissue and joint-tissue models. For the 40 genes located in previously identified GWAS loci, after adjusting for GWAS-identified variants, the associations for the majority of them became non-significant. However, the associations did not change materially for UCKL1 or PRPF6. These results suggest that the associations for these two genes were independent from previous GWSA-identified signals. We also identified 10 genes located at least 500kb away from GWAS-identified loci that were associated with lung cancer histological subtypes, e.g., adenocarcinoma (DCBLD1 and AQP3), squamous cell carcinoma (ZSCAN26, BLOC1S2, ABCF1, and ZSCAN9), and small cell lung cancer (BTN2A2, TMA16, RP11-218F10.3, and FRS3). An additional 7 genes located in GWAS loci were associated with risk of lung cancer histological subtypes but not with overall lung cancer risk, including 4 genes for adenocarcinoma (TP63, STN1, FAM227B, and TPRG1) and 3 genes for squamous cell carcinoma (DDAH2, OR2H2, and NELFE).

Conclusion: Our TWAS identified lung cancer susceptibility genes, providing new insight into the genetics of lung cancer etiology.

Citation Format: Tianying Zhao, Jiajun Shi, Yaohua Yang, Jie Ping, Xiao Ou Shu, Wei Zheng, Jirong Long, Qiuyin Cai. Transcriptome-wide association study identifies susceptibility loci and genes for lung cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB045.