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

被引：22

作者：

Tanguturi, Shyam K. ^{[1
,2
]}

Trippa, Lorenzo ^{[4
]}

Ramkissoon, Shakti H.

Pelton, Kristine ^{[2
]}

Knoff, David ^{[2
]}

Sandak, David ^{[5
]}

Lindeman, Neal I. ^{[2
]}

Ligon, Azra H. ^{[2
]}

Beroukhim, Rameen ^{[3
]}

Parmigiani, Giovanni ^{[4
]}

Wen, Patrick Y. ^{[3
]}

Ligon, Keith L. ^{[2
]}

Alexander, Brian M. ^{[1
,6
]}

机构：

[1] Harvard Med Sch, DF BWCC, Dept Radiat Oncol, 420 Brookline Ave, Boston, MA 02215 USA

[2] Harvard Med Sch, DF BWCC, Dept Pathol, Boston, MA USA

[3] Harvard Med Sch, DF BWCC, Ctr Neurooncol, Boston, MA USA

[4] Harvard Med Sch, Dana Farber Canc Inst, Dept Biostat, Boston, MA USA

[5] Accelerate Brain Canc Cure ABC2, Washington, DC USA

[6] Harvard Med Sch, Harvard Program Therapeut Sci, Boston, MA USA

来源：

NEURO-ONCOLOGY | 2017年 / 19卷 / 07期

关键词：

biomarkers; clinical trial design; glioblastoma; PROGRESSION-FREE SURVIVAL; END-POINT; GENE; TEMOZOLOMIDE; BEVACIZUMAB; MUTATIONS; THERAPIES; GLIOMA;

D O I：

10.1093/neuonc/now312

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 [肿瘤学];

摘要：

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 O-6-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.

引用

页码：908 / 917

页数：10

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