1. 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 rs
SOX11 rs
Tumor supressors
CSMD1 rs
FOXF1 rs
Tobacco induced NSCLC
ABCB5 rs rs rs
Regulators of transcription
DROSHA rs
HDAC9 rs rs rs
KIAA0947 rs
Regulators of inflammation
PellinoE3 rs rs
TRIM9 rs
Related with cancer
ABHD6 rs
GRIK1
rs465835
RAB40B rs
SCN1A rs
SLC24A2 rs rs
SLC25A26 rs
ZFYVE26 rs
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 for protective SNPs and 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 ( 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 ).
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 for protective SNP and 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, AP , 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 ( 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:
- 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:
- 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:
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