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Neuro Oncol. 2017 Jul 1;19(7):908-917. doi: 10.1093/neuonc/now312.

Leveraging molecular datasets for biomarker-based clinical trial design in glioblastoma.

Author information

1
Department of Radiation Oncology, Department of Pathology, Center for Neuro-Oncology, Dana-Farber/Brigham & Women's Cancer Center (DF/BWCC), Harvard Medical School, Boston, Massachusetts; Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts; Accelerate Brain Cancer Cure (ABC2), Washington, DC; Harvard Program in Therapeutic Science, Harvard Medical School, Boston, Massachusetts.

Abstract

Background:

Biomarkers can improve clinical trial efficiency, but designing and interpreting biomarker-driven trials require knowledge of relationships among biomarkers, clinical covariates, and endpoints. We investigated these relationships across genomic subgroups of glioblastoma (GBM) within our institution (DF/BWCC), validated results in The Cancer Genome Atlas (TCGA), and demonstrated potential impacts on clinical trial design and interpretation.

Methods:

We identified genotyped patients at DF/BWCC, and clinical associations across 4 common GBM genomic biomarker groups were compared along with overall survival (OS), progression-free survival (PFS), and survival post-progression (SPP). Significant associations were validated in TCGA. Biomarker-based clinical trials were simulated using various assumptions.

Results:

Epidermal growth factor receptor (EGFR)(+) and p53(-) subgroups were more likely isocitrate dehydrogenase (IDH) wild-type. Phosphatidylinositol-3 kinase (PI3K)(+) patients were older, and patients with O6-DNA methylguanine-methyltransferase (MGMT)-promoter methylation were more often female. OS, PFS, and SPP were all longer for IDH mutant and MGMT methylated patients, but there was no independent prognostic value for other genomic subgroups. PI3K(+) patients had shorter PFS among IDH wild-type tumors, however, and no DF/BWCC long-term survivors were either EGFR(+) (0% vs 7%, P = .014) or p53(-) (0% vs 10%, P = .005). The degree of biomarker overlap impacted the efficiency of Bayesian-adaptive clinical trials, while PFS and OS distribution variation had less impact. Biomarker frequency was proportionally associated with sample size in all designs.

Conclusions:

We identified several associations between GBM genomic subgroups and clinical or molecular prognostic covariates and validated known prognostic factors in all survival periods. These results are important for biomarker-based trial design and interpretation of biomarker-only and nonrandomized trials.

KEYWORDS:

biomarkers; clinical trial design; glioblastoma

PMID:
28339723
PMCID:
PMC5570228
DOI:
10.1093/neuonc/now312
[Indexed for MEDLINE]
Free PMC Article

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