Regulators of transcription Regulators of inflammation Identification through genome-wide association study (GWAS) of single nucleotide polymorphisms (SNPs) associated with extreme phenotypes of tobacco-induced non-small cell lung cancer (NSCLC) risk.r J.L. Perez-Gracia,1 M.J. Pajares,2 M.P. Andueza,1 G. Pita,3 J. de Torres Tajes,4 C. Casanova,5 J. Zulueta,4 A. Gurpide, 1 J.M. López-Picazo, 1 R. Baz Davila,5 R. Alonso,3 N. Alvarez,3 R. Pio Oses,2 I. Melero,2 M.F. Sanmamed,1 A. Agudo,6 C. Gonzalez,6 J. Benitez, 3 L.M. Montuenga, 2 A. Gonzalez-Neira.3 1 Department of Oncology. Clinica Universidad de Navarra, Pamplona, Spain; 2 Center for Applied Medical Research, Pamplona, Spain; 3 Spanish National Cancer Research Centre (CNIO), Madrid, Spain; 4 Neumology Department, Clinica Universidad de Navarra, Pamplona, Spain; 5 Pulmonary Department and Research Department. Hospital Universitario La Candelaria, Santa Cruz de Tenerife, Spain; 6 Unit of Nutrition, Environment and Cancer, Catalan Institute of Oncology-ICO, IDIBELL, Barcelona, Spain Poster Title METHODS ABSTRACT 1576P Table 1: functions and positions of selected genes related with candidate SNP from the identification series. Function Gene Position Oncogenes MSX2 rs17064225 SOX11 rs6727285 Tumor supressors CSMD1 rs13253703 FOXF1 rs8055638 Tobacco induced NSCLC ABCB5 rs10281505 rs78057207 rs2390348 Regulators of transcription DROSHA rs17405280 HDAC9 rs13232564 rs2240419 rs10246722 KIAA0947 rs2913366 Regulators of inflammation PellinoE3 rs1189107 rs1300661 TRIM9 rs4375571 Related with cancer ABHD6 rs7640259 GRIK1 rs465835 RAB40B rs2306911 SCN1A rs1020852 SLC24A2 rs4977304 rs4391516 SLC25A26 rs28544143 ZFYVE26 rs2235967 Cases were selected from an identification cohort (n=3631). All individuals selected were caucasian heavy smokers belonging to two cohorts; cancer cohort: formed by heavy smokers that developed NSCLC at an early age (< 55 years cancer free cohort: formed by heavy smokers that did not present NSCLC at an advanced age (>75 years). Genomic DNA of the selected individuals was analyzed using the array Illumina HumanOmni 2.5 Quad, This array includes over 2 million powerful markers selected from the 1000 Genomes Project, targeting genetic variation down to 1% minor allele frequency. Aim We analyzed the genome of individuals presenting extreme phenotypes of sensitivity and resistance to develop tobacco induced NSCLC to identify SNPs associated with these phenotypes. For this purpose, we used one of the most powerful GWAS platforms available. We hypothesized that SNPs may modulate individual susceptibility to carcinogens and that selection of extreme phenotypes would enrich the frequencies of alleles that contribute to the trait, thus increasing the power to identify them.   Methods From an identification cohort (n=3631) we selected caucasian heavy smokers that either developed NSCLC at an early age (cancer-cohort) or that did not present NSCLC at an advanced age (cancer-free cohort). We analyzed their genomic DNA using the array Illumina HumanOmni 2.5 Quad that includes over 2 million powerful markers selected from the 1000 Genomes Project, targeting genetic variation down to 1% minor allele frequency. Statistical significance of SNPs was calculated using logistic regression and Fisher´s test. Results 96 patients (48 per cohort) were selected. Mean age for the cancer and cancer-free cohorts was 49 years (range 38-55) and 76 years (72-84). Mean tobacco consumption was 41 pack-years (range 18-99) and 68 pack-years (40-120). GWAS identified 8 SNPs differentially expressed by logistic regression and 54 SNP by Fisher´s test (p<10-5). Odds-ratio ranged between 0.08-0.29 for protective SNPs and 3.4-11.2 for SNPs that increased NSCLC risk. Candidate SNPs were located within or in adjacent regions of genes that have been previously related with cancer and that constitute potentially relevant targets (table 1): Conclusions We identified candidate SNPs associated with the risk of developing tobacco-induced NSCLC in individuals with extreme phenotypes. Several identified SNPs were located within or near genes that constitute potentially relevant targets for modulation of cancer risk. Validation of the most significant SNPs in an independent set of individuals with similar phenotypes, selected from the EPIC-Spain project (www.epic-spain.com, n=40,000) is ongoing. RESULTS CONCLUSIONS Patient characteristics: Cancer cohort (n = 48): mean age 49 years (range 38-55); and mean tobacco consumption 41 pack-years (range 18-99). Cancer-free cohort: (n = 48): mean age 76 years (range 72-84); and mean tobacco cosumption 68 pack-years (range 40-120). Identified SNPs: GWAS identified 8 SNPs differentially expressed by logistic regression and 54 SNP by Fisher´s test (p<10-5). Odds-ratio ranged between 0.08-0.29 for protective SNP and 3.4-11.2 for SNP that increased NSCLC risk. Candidate SNPs were located within or in adjacent regions of genes related to (table 1): a) oncogenic and tumor-suppressor pathways: CSMD1, FOXF1, MSX2, SOX11; b) tobacco induced NSCLC: ABCB5; c) regulation of transcription: DROSHA, HDAC9, KIAA0947; d) regulators of inflammation through the NF-kappa pathway: pellino E3, TRIM9; e) previously linked to carcinogenesis and cancer prognosis: ABHD6, GRIK1, RAB40B, SCN1A, SLC24A2, SLC25A26, ZFYVE26; and f) not previously linked to cancer: ACER3, AP000946.2, ATP10A, ATP10D, CNTN5, CYYR1, LINC00572, PDE10A, RP11-115D19.1, RP11-202D1.3, RP11-521E5.1, SYTL5, ZPBP. - We identified candidate SNPs potentially associated with the risk of developing tobacco induced NSCLC in individuals with extreme phenotypes. Validation of the most significant SNPs in an independent set of individuals with similar phenotypes selected from the EPIC-Spain project (www.epic-spain.com, n=40,000) is ongoing. - If confirmed, our findings will be relevant: to identify individuals with high-risk of developing NSCLC, allowing to establish prevention and early diagnosis strategies; and to characterize molecular mechanisms of carcinogenesis and resistance to develop cancer REFERENCES OBJECTIVES To identify SNPs associated with individuals presenting extreme phenotypes of tobacco induced NSCLC risk. To validate the methodology of extreme phenotype selection applied to GWAS. Perez-Gracia JL, Ruiz-Ilundain MG. Cancer protective mutations: looking for the needle in the haystack. Clin Transl Oncol 2001; 3: 169-171. - Perez-Gracia JL, Gloria Ruiz-Ilundain M, Garcia-Ribas I, Maria Carrasco E . The role of extreme phenotype selection studies in the identification of clinically relevant genotypes in cancer research. Cancer 2002; 95: 1605-1610. - Perez-Gracia JL, Gurpide A, Ruiz-Ilundain MG, et al. Selection of extreme phenotypes: the role of clinical observation in translational research. Clin Transl Oncol 2010; 12: 174-180. Printed by