Gene expression-based biomarkers designating glioblastomas resistant to multiple treatment strategies

Abstract

Abstract Despite advances in molecular characterization of glioblastoma multiforme (GBM), only a handful of predictive biomarkers exist with limited clinical relevance. We aimed to identify differentially expressed genes in tumor samples collected at surgery associated with response to subsequent treatment, including temozolomide (TMZ) and nitrosoureas. Gene expression was collected from multiple independent datasets. Patients were categorized as responders/nonresponders based on their survival status at 16 months postsurgery. For each gene, the expression was compared between responders and nonresponders with a Mann–Whitney U-test and receiver operating characteristic. The package ‘roc’ was used to calculate the area under the curve (AUC). The integrated database comprises 454 GBM patients from 3 independent datasets and 10 103 genes. The highest proportion of responders (68%) were among patients treated with TMZ combined with nitrosoureas, where FCGR2B upregulation provided the strongest predictive value (AUC = 0.72, P < 0.001). Elevated expression of CSTA and MRPS17 was associated with a lack of response to multiple treatment strategies. DLL3 upregulation was present in subsequent responders to any treatment combination containing TMZ. Three genes (PLSCR1, MX1 and MDM2) upregulated both in the younger cohort and in patients expressing low MGMT delineate a subset of patients with worse prognosis within a population generally associated with a favorable outcome. The identified transcriptomic changes provide biomarkers of responsiveness, offer avenues for preclinical studies and may enhance future GBM patient stratifications. The described methodology provides a reliable pipeline for the initial testing of potential biomarker candidates for future validation studies.