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Christopher Ian Amos
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MA10 - Emerging Technologies for Lung Cancer Detection (ID 129)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Moderators:Fabrizio Bianchi, Marcelo Sanchez
- Coordinates: 9/08/2019, 15:15 - 16:45, Dublin (1997)
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MA10.07 - Integrative Analysis of Epistasis Involving Oncogenesis-Related Genes in Lung Cancer Risk Development (Now Available) (ID 2502)
15:15 - 16:45 | Author(s): Christopher Ian Amos
- Abstract
- Presentation
Background
Our previous study identified significant genetic interactions within oncogenesis-related genes in lung cancer risk development. More genetic interactions may exist between oncogenesis-related genes and outside regions in the genome. A functional annotation and pathway analysis of the identified epistasis-related genes will advance our understanding about the complicated biological mechanisms underlying lung tumorigenesis.
Method
The genotypes from two independent lung cancer GWAS studies including a total of 23,351 lung cancer patients and 19,657 health controls with European ancestry were collected for the analysis. Pairwise epistasis was conducted between 27,722 SNPs, from 2,027 oncogenesis-related genes, and 317,624 SNPs from the rest of the genome. A two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis were applied in the analysis, Additional genotyping and gene expression data from 409 independent individuals with Caucasian ancestry were used to evaluate the effect of identified epistasis on gene expression levels. The epistasis-involved genes, were submitted to DAVID, Reactome, and GeneMANIA for gene functional annotation and pathway analysis.
Result
Significant genetic interactions were identified between SNPs in gene pairs ATR-GALNT18 (Interaction OR=0.76, p value=7.98x10-13) and MET-DPF3 (Interaction OR=0.76, p value=1.62x10-12) in lung adenocarcinoma; and PICALM-PDZRN4 (Interaction OR=1.47, p value=1.67x10-12) in lung squamous carcinoma. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis revealed the significant association in expression levels between joint genotypes at rs637304:rs285581 and the PICALM gene expression (p=0.009). A total of 12 unique genes, from six significant interactions, including those from within oncogenesis-related genes and between oncogenesis-related genes and outside variants, were submitted to functional annotation and pathway analysis. Three of them (ATR, MET and FHIT) are shown to be related with lung cancer, and six of them (RAD51B, FHIT, CALNT18, RGL1, SYNE1 and TSPAN8) are involved in tobacco-use disorders. The top 10 pathways include TP53 regulates transcription of DNA repair genes (FDR=1.67x10-2), homologous DNA pairing and strand exchange (FDR=2.57x10-2), and Meiotic synapsis (3.08x10-2), etc. GeneMANIA predicted one gene network harboring all the 12 candidate genes, supporting the epistasis at 3 genes pairs and indirect interactions at 3 gene pairs.
Conclusion
We identified novel genes involved in lung cancer risk development by interacting with other genetic variants. The study provides evidence that epistasis explains part of the missing heritability in lung cancer; and complex gene network and pathways contribute to lung carcinogenesis.
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MS18 - Role of Biomarkers in Lung Cancer Screening (ID 81)
- Event: WCLC 2019
- Type: Mini Symposium
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Moderators:Luis M Montuenga, Luis M Seijo
- Coordinates: 9/10/2019, 14:30 - 16:00, Vancouver (2003)
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MS18.03 - Amolecular Diagnostics, Incorporating GWAS and Risk Models: Future Approaches to the Identification of High-Risk Individuals (Now Available) (ID 3546)
14:30 - 16:00 | Author(s): Christopher Ian Amos
- Abstract
- Presentation
Abstract
Role of Biomarkers in Lung Cancer Screening
As a part of ongoing research to understand the etiology and early detection of lung cancer, the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) consortium has been genotyping large numbers of lung cancer cases and controls and analyzing biomarkers from case cohort members prior to their diagnosis with lung cancer and matched controls. We are also assembling knowledge about predictors of lung cancer risk to identify biomarkers that will be applied along with radiomic features to select individuals at highest risk for lung cancer to enroll in screening studies and to assist in resolution of cancer risk among those found to have small nodules. To date we have analyzed genetic data from 29,266 patients and 56,450 controls of European descent(1) and curated genotyping information from 20 studies conducted in European descent, 14 from Asian descent and 1 study in African-American Populations(2). Ongoing imputation is allowing us to integrate most of these data for a further analysis that brings together world populations for genetic discovery. Results of these studies have identified 12 strongly replicated loci and an additional 38 loci that are highly significant in some studies but less well replicated. Among the variants that we identified, a variant in BRCA2 (1) is remarkable for conferring over 2 fold increased risk for lung cancer development independent of smoking behavior and thereby indicating a small subset of high risk individuals based on genotype. Further studies to identify rare variants that confer a high risk of lung cancer have identified mutations in ATM and KIAA0930 with odds ratios well over 2. The ATM variant is associated with loss of heterozygosity in tumors but does not cause Ataxia Telangiectasia in homozygotes. We have used genetic information to develop polygenic risk scores and a model that included 221 variants yielded the most improvement in accuracy. Results comparing models to identify individuals at high risk for lung cancer development based on risk scores compared with models based on demographic, clinical and smoking information show a modest increase in prediction accuracy, but identify selected individuals who are at high risk and for whom lung screening would be particularly indicated.
The genetic information we have developed and curated can also be used with additional approaches to identify predictors of lung cancer risk using shared heritability and Mendelian randomization analyses. Shared heritability analysis identifies strong genetic correlations with all measures of smoking behavior and also with primary biliary cirrhosis and schizophrenia. Mendelian randomization, which removes concerns about change in BMI during cancer development, shows that increased BMI is associated with squamous and small cell lung cancer and not associated with adenocarcinoma(3). Mendelian randomization studies found association of increased lung cancer risk with longer germline telomere length and increased risk associated with higher levels of vitamin B12(4). Further Mendelian randomization studies are underway to evaluate other biochemical factors that may associate with increased lung cancer risk.
Cohort studies to identify biomarker signatures of risk have identified a reliable panel(5) comprising CEA125, CEA, CYFRA 21-1 and pro-SFTB that along with smoking behavior provide an area under the receiver operator curve of 83%, indicating that a strategy that seeks to identify high risk individuals using data from questionnaires about smoking along with biomarker analysis could substantially improve the yield of low dose spiral CT screening. Further studies of panels of biomarkers including microRNA and circulating cell-free DNA are underway to evaluate the utility of adding additional biomarkers to further identify higher risk individuals.
1. McKay JD, et al. (2017) Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nature genetics 49(7):1126-1132.
2. Bosse Y & Amos CI (2018) A Decade of GWAS Results in Lung Cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 27(4):363-379.
3. Carreras-Torres R, et al. (2017) Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study. PloS one 12(6):e0177875.
4. Fanidi A, et al. (2018) Is high vitamin B12 status a cause of lung cancer? International journal of cancer. Journal international du cancer.
5. Integrative Analysis of Lung Cancer E, et al. (2018) Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. JAMA oncology 4(10):e182078.
Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.
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P1.11 - Screening and Early Detection (ID 177)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.11-05 - Genetic Architecture of Lung Cancer Using Machine-Learning Approaches in Genome-Wide Association Studies (ID 547)
09:45 - 18:00 | Author(s): Christopher Ian Amos
- Abstract
Background
Genome-wide association studies (GWAS) consisting of up to millions of single nucleotide polymorphisms (SNPs) have studied genetic influences to complex diseases and have identified thousands of associations. Few GWAS have explored interactions among SNPs that influence disease risks.
Method
Machine learning applications can define how SNPs jointly influence disease risks through interactions. Tree-based machine-learning applications; classification and regression trees (CART) and random forest (RF) methods are popular and convenient tools for understanding interactions influencing disease development. Here we apply these methods to elucidate the higher-order interactions that influence lung cancer risk. We applied tree-based approaches using 18,444 cases and 14,027 controls from lung cancer OncoArray GWAS data. We first selected the SNPs very significantly associated (p<0.00001) with lung cancer risk. RF, which consists of systematically fitting classification trees, was run 1,000 times to identify the most influential SNPs. Subsequently we applied CART to summarize and visualize interactions that predict risk.
Result
The final parsimonious tree included effects from genetic variants in CHRNA5, CLPTM1L, ZNRD1ASP, HCG9, TERT, CHRNB4, and DNAJC5 for overall lung. The final tree for adenocarcinoma lung showed the combination of genetic effects in or near ATM, CLPTLM1L, TERT, FSTL5, and DCTN4. The final tree for squamous cell carcinoma included CHRNA5, MRPL21, HLA, CASP8, and TAP2.
Conclusion
Our results confirmed associations with CHRAN5, TERT, and HLA observed in previous study (McKay et al., 2017). Machine learning methods in genomics provide some benefits over logistic regression model with respect to identifying subgroups at higher risk of lung cancer development on the basis of genetic characteristics.
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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): Christopher Ian Amos
- Abstract
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
Conclusion
We 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.
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: 1
- Now Available
- Moderators:John Kirkpatrick Field, James L Mulshine
- Coordinates: 9/07/2019, 07:00 - 12:00, Colorado Springs (1994)
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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 | Presenting Author(s): Christopher Ian Amos
- Abstract
- Presentation
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.
Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.
