SSBP2 variants are associated with survival in glioblastoma patients

Academic Article


  • Purpose: Glioblastoma is a devastating, incurable disease with few known prognostic factors. Here, we present the first genome-wide survival and validation study for glioblastoma. Experimental Design: Cox regressions for survival with 314,635 inherited autosomal single-nucleotide polymorphisms (SNP) among 315 San Francisco Adult Glioma Study patients for discovery and three independent validation data sets [87 Mayo Clinic, 232 glioma patients recruited from several medical centers in Southeastern United States (GliomaSE), and 115 The Cancer Genome Atlas patients] were used to identify SNPs associated with overall survival for Caucasian glioblastoma patients treated with the current standard of care, resection, radiation, and temozolomide (total n=749). Tumor expression of the gene that contained the identified prognostic SNP was examined in three separate data sets (total n=619). Genotype imputation was used to estimate hazard ratios (HR) for SNPs that had not been directly genotyped. Results: From the discovery and validation analyses, we identified a variant in single-stranded DNAbinding protein 2 (SSBP2) on 5q14.1 associated with overall survival in combined analyses (HR, 1.64; P = 1.3×10 -6). Expression of SSBP2 in tumors from three independent data sets also was significantly related to patient survival (P=5.3×10 -4). Using genotype imputation, the SSBP2 SNP rs17296479 had the strongest statistically significant genome-wide association with poorer overall patient survival (HR, 1.79; 95% CI, 1.45-2.22; P = 1.0 × 10 -7). Conclusion: The minor allele of SSBP2 SNP rs17296479 and the increased tumor expression of SSBP2 were statistically significantly associated with poorer overall survival among glioblastoma patients. With further confirmation, previously unrecognized inherited variations influencing survival may warrant inclusion in clinical trials to improve randomization. Unaccounted for genetic influence on survival could produce unwanted bias in such studies. ©2012 AACR.
  • Digital Object Identifier (doi)

    Author List

  • Xiao Y; Decker PA; Rice T; McCoy LS; Smirnov I; Patoka JS; Hansen HM; Wiemels JL; Tihan T; Prados MD
  • Start Page

  • 3154
  • End Page

  • 3162
  • Volume

  • 18
  • Issue

  • 11