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Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants

BACKGROUND: Epidemiological and pedigree studies suggest that lung cancer results from the combined effects of age, smoking, impaired lung function and genetic factors. In a case control association study of healthy smokers and lung cancer cases, we identified genetic markers associated with either...

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Autores principales: Young, Robert P., Hopkins, Raewyn J., Hay, Bryan A., Epton, Michael J., Mills, Graham D., Black, Peter N., Gardner, Heather D., Sullivan, Richard, Gamble, Gregory D.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2668761/
https://www.ncbi.nlm.nih.gov/pubmed/19390575
http://dx.doi.org/10.1371/journal.pone.0005302
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author Young, Robert P.
Hopkins, Raewyn J.
Hay, Bryan A.
Epton, Michael J.
Mills, Graham D.
Black, Peter N.
Gardner, Heather D.
Sullivan, Richard
Gamble, Gregory D.
author_facet Young, Robert P.
Hopkins, Raewyn J.
Hay, Bryan A.
Epton, Michael J.
Mills, Graham D.
Black, Peter N.
Gardner, Heather D.
Sullivan, Richard
Gamble, Gregory D.
author_sort Young, Robert P.
collection PubMed
description BACKGROUND: Epidemiological and pedigree studies suggest that lung cancer results from the combined effects of age, smoking, impaired lung function and genetic factors. In a case control association study of healthy smokers and lung cancer cases, we identified genetic markers associated with either susceptibility or protection to lung cancer. METHODOLOGY/PRINCIPAL FINDINGS: We screened 157 candidate single nucleotide polymorphisms (SNP) in a discovery cohort of 439 subjects (200 controls and 239 lung cancer cases) and identified 30 SNPs associated with either the healthy smokers (protective) or lung cancer (susceptibility) phenotype. After genotyping this 30 SNP panel in a validation cohort of 491 subjects (248 controls and 207 lung cancers) and, using the same protective and susceptibility genotypes from our discovery cohort, a 20 SNP panel was selected based on replication of SNP associations in the validation cohort. Following multivariate logistic regression analyses, including the selected SNPs from runs 1 and 2, we found age and family history of lung cancer to be significantly and independently associated with lung cancer. Numeric scores were assigned to both the SNP and demographic data, and combined to form a simple algorithm of risk. CONCLUSIONS/SIGNIFICANCE: Significant differences in the distribution of the lung cancer susceptibility score was found between normal controls and lung cancer cases, which remained after accounting for differences in lung function. Validation in other case-control and prospective cohorts are underway to further define the potential clinical utility of this model.
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spelling pubmed-26687612009-04-23 Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants Young, Robert P. Hopkins, Raewyn J. Hay, Bryan A. Epton, Michael J. Mills, Graham D. Black, Peter N. Gardner, Heather D. Sullivan, Richard Gamble, Gregory D. PLoS One Research Article BACKGROUND: Epidemiological and pedigree studies suggest that lung cancer results from the combined effects of age, smoking, impaired lung function and genetic factors. In a case control association study of healthy smokers and lung cancer cases, we identified genetic markers associated with either susceptibility or protection to lung cancer. METHODOLOGY/PRINCIPAL FINDINGS: We screened 157 candidate single nucleotide polymorphisms (SNP) in a discovery cohort of 439 subjects (200 controls and 239 lung cancer cases) and identified 30 SNPs associated with either the healthy smokers (protective) or lung cancer (susceptibility) phenotype. After genotyping this 30 SNP panel in a validation cohort of 491 subjects (248 controls and 207 lung cancers) and, using the same protective and susceptibility genotypes from our discovery cohort, a 20 SNP panel was selected based on replication of SNP associations in the validation cohort. Following multivariate logistic regression analyses, including the selected SNPs from runs 1 and 2, we found age and family history of lung cancer to be significantly and independently associated with lung cancer. Numeric scores were assigned to both the SNP and demographic data, and combined to form a simple algorithm of risk. CONCLUSIONS/SIGNIFICANCE: Significant differences in the distribution of the lung cancer susceptibility score was found between normal controls and lung cancer cases, which remained after accounting for differences in lung function. Validation in other case-control and prospective cohorts are underway to further define the potential clinical utility of this model. Public Library of Science 2009-04-23 /pmc/articles/PMC2668761/ /pubmed/19390575 http://dx.doi.org/10.1371/journal.pone.0005302 Text en Young et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Young, Robert P.
Hopkins, Raewyn J.
Hay, Bryan A.
Epton, Michael J.
Mills, Graham D.
Black, Peter N.
Gardner, Heather D.
Sullivan, Richard
Gamble, Gregory D.
Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants
title Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants
title_full Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants
title_fullStr Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants
title_full_unstemmed Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants
title_short Lung Cancer Susceptibility Model Based on Age, Family History and Genetic Variants
title_sort lung cancer susceptibility model based on age, family history and genetic variants
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2668761/
https://www.ncbi.nlm.nih.gov/pubmed/19390575
http://dx.doi.org/10.1371/journal.pone.0005302
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