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Rayjean J. Hung On Behalf Of The International Lung Cancer Consortium



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    MS12 - Genome Screenings (ID 75)

    • Event: WCLC 2019
    • Type: Mini Symposium
    • Track: Biology
    • Presentations: 1
    • Now Available
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      MS12.04 - The International Lung Cancer Consortium (ILCCO), an International Study to Identify Risk Factors for Lung Cancer Development (Now Available) (ID 3509)

      11:30 - 13:00  |  Presenting Author(s): Rayjean J. Hung On Behalf Of The International Lung Cancer Consortium

      • Abstract
      • Presentation
      • Slides

      Abstract

      Background: The International Lung Cancer Consortium (ILCCO) was established in 2004 to maximize research efficiency for lung cancer and to share comparable epidemiological and clinical data, and biological samples across studies. Since its establishment, over 70 studies have participated in the ILCCO and shared comparable clinico-epidemiological data and a subset with biological samples and genomic data. The data harmonization was conducted at the Sinai Health System in Toronto, and genomic data is managed at the Dartmouth College of Medicine/Baylor College of Medicine. In total, the ILCCO Data Repository now has epidemiological data for over 1.2 million study participants, including 100,000 lung cancer patients, and genomic data on approximately 50,000 study participants. The large-scale epidemiological and genomic data allow us to extensively study and characterize the etiological factors, including lifestyle risk factors, medical history and genomic architectures for lung cancer development.

      Methods: Data submitted from all studies are systematically checked for missing values, outliers, inadmissible values, aberrant distributions and internal inconsistencies before harmonization. Common variable definitions were developed. For lifestyle risk factors and medical history, we conducted meta-analysis based on study-specific estimates, when applicable. If heterogeneities were present, random effects models were employed to account for the heterogeneity across studies. For subgroup of interests or when sample size is limited, pooled-analyses based on individual-level data were applied. When applicable, the non-linearity relationship was assessed. For genetic susceptibility of lung cancer, we investigated the genetic loci associated with lung cancer risk using log-additive model adjusted for population ancestry and account for multiple comparisons. To assess the causality of specific exposures and lung cancer risk, we applied Mendelian Randomization and mediation analytical approaches. To estimate 5-year lung cancer absolute risk, we incorporated risk factors, medical history and genetic factors based on age-specific lung cancer incidence and the competing risk.

      Results: Based on 17 ILCCO studies (24,000 cases and 81,000 controls), we observed a robust association between lung cancer risk and emphysema and pneumonia, even among never smokers, and after long latency period. Based on 24 ILCCO studies, we quantified the association between family history of lung cancer and its risk by their smoking status and affected relative types. Based on 6 studies in UK, Canada, UK and New Zealand, we assessed the association between cannabis smoking and lung cancer risk by intensity, duration and cumulative exposures and by histological subtypes. We have recently completed a largest lung cancer genetic analysis based over 29,000 lung cancer cases and 56,000 controls. We identified 10 novel lung cancer susceptibility loci, in addition to the known regions, such as TERT/CLPTM1L, CHRNA5, MHC region, RAD52, CHEK2 and found specific associations mediated through mRNA expression. We helped to quantify the effect of specific genetic variant in nicotinic receptor gene on smoking cessation and age of onset. Using genetic instruments and Mendelian Randomization approach, we confirmed the association between lung cancer risk and long telomere length. Most recently, we investigated the association between impaired lung function and lung cancer risk based on UK Biobank and ILCCO OncoArray data, and we found that impaired lung function was associated with lung cancer risk in never smokers and particularly for adenocarcinoma, most likely through immune-mediated pathways. When combining all factors into an integrative risk model, we found that individuals with highly polygenic risk scores reached lung cancer screening threshold at younger age than those with average genetic risk background.

      Conclusions and Future Perspectives: ILCCO provides a powerful research platform for research on lung cancer. The collaborative projects based on ILCCO have contributed to the understanding of lung cancer etiology beyond tobacco smoking. As future perspectives, ILCCO has obtained clinical prognosis data for over 50,000 lung cancer patients and will also be able to investigate factors associated with lung cancer prognosis in depth. Finally, ILCCO has built close collaborations with several lung cancer low-dose computed tomography screening programs to jointly investigate the optimal strategy for risk stratification and early detection for lung cancer.

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    P2.03 - Biology (ID 162)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Biology
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.03-18 - Pathogenic Germline Rare Variants and Risk of Lung Cancer (Now Available) (ID 2699)

      10:15 - 18:15  |  Author(s): Rayjean J. Hung On Behalf Of The International Lung Cancer Consortium

      • Abstract
      • Slides

      Background

      Recent studies suggest that rare variants, with minor allele frequencies (MAFs) of less than 0.01, exhibit stronger effect sizes than common variants, might play a crucial role in the etiology of complex traits and could account for missing heritability unexplained by common variants.

      Method

      Germline DNA from 1059 lung cancer cases and 899 controls from the Transdisciplinary Research in Cancer of the Lung and International Lung Cancer Consortium study were sequenced, utilizing the Agilent SureSelect XT Custom ELID and Whole Exome v5 capture. To unveil the inherited rare causal variants, allelic association analysis of single variant and gene-based collapsing tests of multiple variants were performed, including variants per gene association test, the Kernel-based adaptive cluster test, and SNP-set Kernel association test. Odds ratio (OR), 95% confidence intervals (CIs), and false discovery rate (FDR) adjusted P values were calculated.

      Result

      table 1.pngWe identified 32 highly deleterious rare heterozygotes, including 14 rare and 18 novel variants -- absent from prior databases of genetic variation (Table 1). The top candidate substitutions including NEBstop gain p.Q7971* (nine cases versus zero control carriers, P = 0.0056), OGG1 upstream Chr 3:9816129(11 cases versus one control carriers, P = 0.0087),CDKN2B transcription end site (16 cases versus three controls carriers, P = 0.0081), ATP6V0A2 regulatory Chr 12:124242486 (eight cases versus zero control carriers, P = 0.0089), KCNN4 transcription factor binding site (15 cases versus two controls carriers, P = 0.0044), and TEX28P1 regulatory rs1445670979 (11 cases versus one control carriers, P = 0.0087). We also identified candidates in known genes which have been previously implicated in lung cancer risk, i.e., HLA, TP53, POT1, PTEN, ERC, GPC, RGS17, and LAMC1. Among the candidate genes with multiple rare deleterious SNVs, the top five genes with strong association (FDR adjusted P < 0.01 in burden tests) are NBPF20 (OR5.69, 95% CI 2.4-13.5), ERC1 (OR 4.49, 95% CI 2.19-9.23), LOC440434 (OR 1.85, 95% CI 1.32-2.59), GPC5 (OR 1.55, 95% CI 1.21-1.99), and NOTCH2NL(OR 5.46, 95% CI 1.61-18.5). The KEGG pathway analysis shown the 1st and 4th significant pathways are from small cell and non-small cell lung cancer, respectively.

      Conclusion

      Our analyses led to identification of 32 pathogenic germline rare variants associated with lung cancer susceptibility. However, replication in additional populations is necessary to confirm potential genetic differences in lung cancer risk.

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    S01 - IASLC CT Screening Symposium: Forefront Advances in Lung Cancer Screening (Ticketed Session) (ID 96)

    • Event: WCLC 2019
    • Type: Symposium
    • Track: Screening and Early Detection
    • Presentations: 2
    • Now Available
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      S01.06 - Session II: U19 – Implications for the Future Integration of Biomarkers in the Selection of High Risk Individuals for Lung Cancer Screening (ID 3632)

      07:00 - 12:00  |  Author(s): Rayjean J. Hung On Behalf Of The International Lung Cancer Consortium

      • Abstract

      Abstract not provided

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      S01.07 - The U19 Plans for Integration of Biomarkers Into Future Lung Cancer Screening (Now Available) (ID 3633)

      07:00 - 12:00  |  Author(s): Rayjean J. Hung On Behalf Of The International Lung Cancer Consortium

      • Abstract
      • Presentation
      • Slides

      Abstract

      The goal of the U19 Integrative analysis of Lung Cancer Etiology and Risk (INTEGRAL) consortium is to develop biomarkers that characterize individual risk for development and progression from lung cancer. We are using a comprehensive strategy, depicted below in Figure 1, for this analysis and we are drawing on world-wide resources and expertise.

      There are three projects focusing on i) genetics of smoking behavior and lung cancer risk, ii) biomarker discovery and validation for identifying individuals at highest risk for developing lung cancer and iii) evaluation of these biomarkers in screening cohorts along with radiographic analaysis to evaluate risk for lung cancer development and nodule behavior. There are also administrative and biostatistics cores.

      We will discuss strategies and novel findings from these projects. For Project 1, to assist in genetic analysis, we have reimputed all the available data from lung cancer cases and controls using the haplotype reference consortium to bring together a data lake comprising data from over 100,000 individuals. The consortium provides data to its members and to collaborators who would like to evaluate hypotheses related to lung cancer by providing access for analyses and we currently are supporting 107 projects evaluating lung cancer risk. Additionally, consortium members from the University of Laval have performed transcriptomic analysis of normal lung tissue from over 500 participants undergoing surgery for lung cancer treatment. We are also studying the role that genetic factors have in influencing smoking behavior by collaborating with other large consortia and by studying multiethnic variation using Hawaiian multiethnic populations.

      Analyses of the genetic data and further extension to the UK Biobank have identified novel genetic loci that contribute to risk. Interaction analysis of the CHRNA3/A5/B4 cluster with all other genomic regions identifies interactions with the 15q25.1 nicotinic receptors that influence lung cancer risk. Results identified genes in the neuroactive ligand receptor interaction pathway as playing a key role in increasing lung cancer risk. A cross-ethnicity analysis identified genetic factors in the major histocompatibility complex (MHC) that affect risk for lung cancer. We imputed sequence variation for 26,044 cases and 20,836 controls in classical HLA genes, fine-mapped MHC associations for lung cancer risk with major histologies and compared results among ethnicities. Independent and novel associations within HLA genes were identified in Europeans primarily affecting risk for squamous cell histology including amino acids in the HLA-B*0801 peptide binding groove and an independent HLA-DQB1*06 loci group. In Asians, associations are driven by two independent HLA allele sets affecting adenocarcinoma risk primarily that both increase risk in HLA-DQB1*0401 and HLA-DRB1*0701; the latter was better represented by the amino acid Ala-104. These results implicate several HLA-tumor peptide interactions as the major MHC factor modulating lung cancer susceptibility. A rare variant analysis yielded a mutation of the ATM gene that is rare in all populations except individuals of Jewish descent that primarily increase risk for adenocarcinoma and has highest risk in nonsmoking women. Analyses of smoking and genetic data have identified gene-smoking interactions that contribute to lung cancer risk, and particularly several genes that protect at-risk smokers from lung cancer development. Mendelian randomization and mediation analyses are underway to evaluate novel biomarkers that can be further studied in project 2. This effort found a surprising result that elevated levels of vitamin B12 increase risk for lung cancer development.

      Project 2 has been bringing together an approach to analyzing biomarkers using data from existing cohort consortia, which have collected samples prior to the clinical presentation of lung cancers. Results of an initial study showed that analysis of 4 circulating proteins (CEA125, CEA, CYFRA 21-1 and pro-SFTB) yielded an area under the receiver operator curve accuracy of 83%. This level of accuracy is sufficient to consider the panel for recruitment of individuals for screening studies, but we anticipate that adding additional biomarkers will further improve the accuracy of risk prediction. Biomarkers that are being further considered include additional protein markers along with micoRNA species, the inclusion of polygenic risk scores and additional serum-derived biomarkers like vitamins B-6 and B-12 that have been shown in mendelian randomization studies to help in identifying high risk subjects.

      Project 3 is focused on the establishment and validation of the models in the LDCT screening programs. In collaboration with National Lung Screening Trial, Canadian LDCT screening programs, NELSON and United Kingdom Lung Study (UKLS), we have begun the data harmonization across LDCT studies, including clinic-epidemiological data as well as nodule characteristics. We have established a pipeline of feature extractions for the radiomics analysis and compared the inter-reader variability. The intraclass correlation coefficients are >0.75 for the majority of the radiomics features extracted. We will conduct cross-study validation for the model building to ensure the maximum generalizability of the model. We will start the work on biomarkers and assess their added values in these models.


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