Author of

• 35916

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  P53 - Tumor Biology and Systems Biology - Basic and Translational Science - Misc. Topics (ID 213)

  • Event: WCLC 2020
  • Type: Posters
  • Track: Tumor Biology and Systems Biology - Basic and Translational Science
  • Presentations: 1
  • Moderators:
  • Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall

  • 35920

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    P53.03 - Genome-Wide Gene-Smoking Interaction Study Identifies Novel Rare Variants in Non-Small Cell Lung Cancer in Population with European Descent (ID 3225)

    00:00 - 00:00  |  Presenting Author(s): yafang Li
    • Abstract
    • Slides

    Introduction
    

    Tobacco smoking is the well-established risk factor for lung cancer. While interactions between genes and smoking are known to play important roles in lung cancer risk, genome-wide interaction analysis still remains a challenge due to lack of power. A large-scale genome-wide gene-by-smoking interaction analysis can help identify novel susceptibility loci that contribute to lung cancer risk in interaction with smoking, which are potentially missed by main-effect association studies.

    Methods
    

    We conducted a genome-wide gene-smoking interaction analysis by leveraging the imputed genotype data from INTEGRAL (Integrative Analysis of Lung Cancer Etiology and Risk) consortium using ~ 44,000 individuals with European descent to compare ever versus never smokers. For the discovery study (n=29,905), we applied a two-stage analysis; we first conducted a case-only gene-smoking interaction analysis using 16,847 lung cancer cases only. As a second stage analysis in the discovery data, we followed up the SNPs with P < 1x10-6 based on the case-only interaction test and applied a robust empirical Bayes (EB) interaction test. In the replication study (n=14,532), we examined the set of candidate SNPs (with P< 1x10-6) by applying both case-only and EB tests. Analyses were performed in overall lung cancer cases as well as among subgroups defined by histologic subtypes such as lung adenocarcinoma (ADE), squamous cell lung cancer (SQC), and small cell lung cancer.

    Results
    

    We identified 6 independent regions (TMEM119, KCNK13, FLJ41200 and 3 unknown regions at chr1p12, chr7q22.3 and chr12q12) with significant interactions with smoking behavior in lung cancer (meta-analysis < 5x10-8 and effects with same direction in both discovery and replication study). Multiple SNP-smoking interactions were identified in genes KCNK13 (EB test: rs6575107, p-value=2.32x10-8, ADE) and FLI41200 (case-only test: rs787669, p-value=9.75x10-9, SQC). All the significant variants have MAF (minor allele frequency) < 0.05. Further smoking-status stratified analysis displayed that all the variants have opposite risk effects in never-smoking group vs. the ever-smoking group. For example, rs6575107 has an OR (odds ratio) of 1.50 (p=4.29x10-4) in ever-smokers and OR of 0.89 (p=7.56x10-2) in never-smokers from ADE cohort; rs787669 has an OR of 14 (p=4.83x10-7) in ever-smokers and no significant effect in never-smokers from SQC cohort.

    Conclusion
    

    We conducted one of the largest gene-smoking interaction analyses in lung cancer in European population. Novel rare variants were identified in lung cancer whose effects were modulated by smoking behavior, suggesting the existence of variants in lung cancer that may be missed from previous association studies due to insufficient gene-smoking analyses. The results provide evidence gene-smoking interaction is an important mechanism in lung carcinogenesis and our findings advance the understanding about the genetic architecture in lung cancer.

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