Multi-platform molecular profiling of a large cohort of glioblastomas reveals potential therapeutic strategies

Joanne Xiu, David Piccioni, Tiffany Juarez, Sandeep C. Pingle, Jethro Hu, Jeremy Rudnick, Karen Fink, David B. Spetzler, Todd Maney, Anatole Ghazalpour, Ryan Bender, Zoran Gatalica, Sandeep Reddy, Nader Sanai, Ahmed Idbaih, Michael Glantz, Santosh Kesari

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Glioblastomas (GBM) are the most aggressive and prevalent form of gliomas with abysmal prognosis and limited treatment options. We analyzed clinically relevant molecular aberrations suggestive of response to therapies in 1035 GBM tumors. Our analysis revealed mutations in 39 genes of 48 tested. IHC revealed expression of PD-L1 in 19% and PD-1 in 46%. MGMT-methylation was seen in 43%, EGFRvIII in 19% and 1p19q co-deletion in 2%. TP53 mutation was associated with concurrent mutations, while IDH1 mutation was associated with MGMT-methylation and TP53 mutation and was mutually exclusive of EGFRvIII mutation. Distinct biomarker profiles were seen in GBM compared with WHO grade III astrocytoma, suggesting different biology and potentially different treatment approaches. Analysis of 17 metachronous paired tumors showed frequent biomarker changes, including MGMTmethylation and EGFR aberrations, indicating the need for a re-biopsy for tumor profiling to direct subsequent therapy. MGMT-methylation, PR and TOPO1 appeared as significant prognostic markers in sub-cohorts of GBM defined by age. The current study represents the largest biomarker study on clinical GBM tumors using multiple technologies to detect gene mutation, amplification, protein expression and promoter methylation. These data will inform planning for future personalized biomarker-based clinical trials and identifying effective treatments based on tumor biomarkers.

Original languageEnglish (US)
Pages (from-to)21556-21569
Number of pages14
JournalOncotarget
Volume7
Issue number16
DOIs
StatePublished - Apr 19 2016

All Science Journal Classification (ASJC) codes

  • Oncology

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