Development of a gene expression–based prognostic signature for IDH wild-type glioblastoma

Author:

Johnson Radia M1,Phillips Heidi S1,Bais Carlos2,Brennan Cameron W3,Cloughesy Timothy F4,Daemen Anneleen5,Herrlinger Ulrich6,Jenkins Robert B7,Lai Albert4,Mancao Christoph8,Weller Michael9ORCID,Wick Wolfgang10,Bourgon Richard1ORCID,Garcia Josep11

Affiliation:

1. Department of Bioinformatics and Computational Biology, Genentech Inc, South San Francisco, California, USA

2. Oncology Biomarker Development, Genentech Inc., South San Francisco, California, USA

3. Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA

4. Department of Neurology, University of California Los Angeles (UCLA), Los Angeles, California, USA

5. Department of Translational Medicine, ORIC Pharmaceuticals Inc, South San Francisco, California, USA

6. Division of Clinical Neurooncology, Department of Neurology, University Hospital Bonn, Bonn, Germany

7. Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

8. Oncology Biomarker Development, Genentech Inc., Basel, Switzerland

9. Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland

10. Department of Neurology, Ruprecht-Karls University Heidelberg and German Cancer Research Center, Heidelberg, Germany

11. Global Clinical Development, F. Hoffmann-La Roche Ltd, Basel, Switzerland

Abstract

Abstract Background We aimed to develop a gene expression–based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma using clinical trial datasets representative of glioblastoma clinical trial populations. Methods Samples were collected from newly diagnosed patients with IDH wild-type glioblastoma in the ARTE, TAMIGA, EORTC 26101 (referred to as “ATE”), AVAglio, and GLARIUS trials, or treated at UCLA. Transcriptional profiling was achieved with the NanoString gene expression platform. To identify genes prognostic for overall survival (OS), we built an elastic net penalized Cox proportional hazards regression model using the discovery ATE dataset. For validation in independent datasets (AVAglio, GLARIUS, UCLA), we combined elastic net–selected genes into a robust z-score signature (ATE score) to overcome gene expression platform differences between discovery and validation cohorts. Results NanoString data were available from 512 patients in the ATE dataset. Elastic net identified a prognostic signature of 9 genes (CHEK1, GPR17, IGF2BP3, MGMT, MTHFD1L, PTRH2, SOX11, S100A9, and TFRC). Translating weighted elastic net scores to the ATE score conserved the prognostic value of the genes. The ATE score was prognostic for OS in the ATE dataset (P < 0.0001), as expected, and in the validation cohorts (AVAglio, P < 0.0001; GLARIUS, P = 0.02; UCLA, P = 0.004). The ATE score remained prognostic following adjustment for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and corticosteroid use at baseline. A positive correlation between ATE score and proneural/proliferative subtypes was observed in patients with MGMT non-methylated promoter status. Conclusions The ATE score showed prognostic value and may enable clinical trial stratification for IDH wild-type glioblastoma.

Funder

Roche/Genentech

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Neurology (clinical),Oncology

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