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Age‐specific genome‐wide association study in glioblastoma identifies increased proportion of ‘lower grade glioma’‐like features associated with younger age
Author(s) -
Ostrom Quinn T.,
Kinnersley Ben,
Armstrong Georgina,
Rice Terri,
Chen Yanwen,
Wiencke John K.,
McCoy Lucie S.,
Hansen Helen M.,
Amos Christopher I.,
Bernstein Jonine L.,
Claus Elizabeth B.,
EckelPassow Jeanette E.,
Il'yasova Dora,
Johansen Christoffer,
Lachance Daniel H.,
Lai Rose K.,
Merrell Ryan T.,
Olson Sara H.,
Sadetzki Siegal,
Schildkraut Joellen M.,
Shete Sanjay,
Rubin Joshua B.,
Andersson Ulrika,
Rajaraman Preetha,
Chanock Stephen J.,
Linet Martha S.,
Wang Zhaoming,
Yeager Meredith,
Houlston Richard S.,
Jenkins Robert B.,
Wrensch Margaret R.,
Melin Beatrice,
Bondy Melissa L.,
BarnholtzSloan Jill. S.
Publication year - 2018
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.31759
Subject(s) - medicine , genome wide association study , odds ratio , glioma , confidence interval , single nucleotide polymorphism , incidence (geometry) , oncology , glioblastoma , logistic regression , gastroenterology , genotype , biology , genetics , gene , cancer research , physics , optics
Glioblastoma (GBM) is the most common malignant brain tumor in the United States. Incidence of GBM increases with age, and younger age‐at‐diagnosis is significantly associated with improved prognosis. While the relationship between candidate GBM risk SNPs and age‐at‐diagnosis has been explored, genome‐wide association studies (GWAS) have not previously been stratified by age. Potential age‐specific genetic effects were assessed in autosomal SNPs for GBM patients using data from four previous GWAS. Using age distribution tertiles (18–53, 54–64, 65+) datasets were analyzed using age‐stratified logistic regression to generate p values, odds ratios (OR), and 95% confidence intervals (95%CI), and then combined using meta‐analysis. There were 4,512 total GBM cases, and 10,582 controls used for analysis. Significant associations were detected at two previously identified SNPs in 7p11.2 (rs723527 [ p 54–63 = 1.50x10 −9 , OR 54–63 = 1.28, 95%CI 54–63 = 1.18–1.39; p 64+ = 2.14x10 −11 , OR 64+ = 1.32, 95%CI 64+ = 1.21–1.43] and rs11979158 [ p 54–63 = 6.13x10 −8 , OR 54–63 = 1.35, 95%CI 54–63 = 1.21–1.50; p 64+ = 2.18x10 −10 , OR 64+ = 1.42, 95%CI 64+ = 1.27–1.58]) but only in persons >54. There was also a significant association at the previously identified lower grade glioma (LGG) risk locus at 8q24.21 (rs55705857) in persons ages 18–53 ( p 18–53 = 9.30 × 10 −11 , OR 18–53 = 1.76, 95%CI 18–53 = 1.49–2.10). Within The Cancer Genome Atlas (TCGA) there was higher prevalence of ‘LGG’‐like tumor characteristics in GBM samples in those 18–53, with IDH1/2 mutation frequency of 15%, as compared to 2.1% [54–63] and 0.8% [64+] ( p = 0.0005). Age‐specific differences in cancer susceptibility can provide important clues to etiology. The association of a SNP known to confer risk for IDH1/2 mutant glioma and higher prevalence of IDH1/2 mutation within younger individuals 18–53 suggests that more younger individuals may present initially with ‘secondary glioblastoma.’