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.
Neuro Oncol. 2017 Jul 1;19(7):908-917. doi: 10.1093/neuonc/now312.
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.
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.
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.
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.
生物标志物可以提高临床试验的效率，但设计和解释基于生物标志物的试验需要了解生物标志物、临床协变量和终点之间的关系。我们在本机构（DF/BWCC）内研究了胶质母细胞瘤（GBM）的基因组亚组之间的这些关系，在癌症基因组图谱（TCGA）中验证了结果，并展示了对临床试验设计和解释的潜在影响。
我们在 DF/BWCC 确定了基因分型患者，并比较了 4 个常见 GBM 基因组生物标志物组的临床关联以及总生存期（OS）、无进展生存期（PFS）和进展后生存期（SPP）。在 TCGA 中验证了显著的相关性。使用各种假设模拟了基于生物标志物的临床试验。
表皮生长因子受体（EGFR）（+）和 p53（-）亚组更可能是异柠檬酸脱氢酶（IDH）野生型。磷酸肌醇-3 激酶（PI3K）（+）患者年龄较大，而 O6- DNA 甲基鸟嘌呤-甲基转移酶（MGMT）-启动子甲基化的患者更多为女性。IDH 突变和 MGMT 甲基化患者的 OS、PFS 和 SPP 均较长，但其他基因组亚组没有独立的预后价值。然而，在 IDH 野生型肿瘤中，PI3K（+）患者的 PFS 更短，并且在 DF/BWCC 中没有长期幸存者是 EGFR（+）（0%比 7%，P =.014）或 p53（-）（0%比 10%，P =.005）。生物标志物重叠的程度影响贝叶斯自适应临床试验的效率，而 PFS 和 OS 分布的变化影响较小。在所有设计中，生物标志物的频率与样本量成正比。
我们确定了 GBM 基因组亚组与临床或分子预后协变量之间的几种关系，并在所有生存期间验证了已知的预后因素。这些结果对于基于生物标志物的试验设计和对生物标志物仅和非随机试验的解释很重要。
