Age‐specific genome‐wide association study in glioblastoma identifies increased proportion of 'lower grade glioma'‐like features associated with younger age. Issue 10 (19th September 2018)

• Record Type: Journal Article
• Authors: 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.; Eckel‐Passow, 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.; Barnholtz‐Sloan, Jill. S.;
• Is Part Of: International journal of cancer. Volume 143:Issue 10(2018)
• Page Start: 2359
• Page End: 2366
• Publication Date: 2018-09-19
• Subjects: glioma -- brain tumors -- age; Cancer -- Periodicals; Cancer -- Prevention -- Periodicals; 616.994
• DOI: 10.1002/ijc.31759
• Languages: English
• ISSNs: 0020-7136

Abstract

Abstract : 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, OR54–63 = 1.28, 95%CI54–63 = 1.18–1.39; p 64+ = 2.14x10 −11, OR64+ = 1.32, 95%CI64+ = 1.21–1.43] and rs11979158 [ p 54–63 = 6.13x10 −8, OR54–63 = 1.35, 95%CI54–63 = 1.21–1.50; p 64+ = 2.18x10 −10, OR64+ = 1.42, 95%CI64+ = 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, OR18–53 = 1.76, 95%CI18–53 = 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.' Abstract : What's new? The authors performed the first genome‐wide association analysis examining age‐at‐diagnosis and germline risk variants in glioblastoma. They report a higher frequency of germline variants associated with lower grade gliomas (LGG) in the younger cohort (age 18–53), as well as high frequency of LGG‐like somatic variants in The Cancer Genome Atlas Glioblastoma cohort. This could indicate that glioblastoma at a younger age might more often evolve from an LGG (secondary glioblastoma) than at an older age and should be taken into consideration when molecular classification is not available.