Data Science, Pharma Research and Development, Roche Innovation Center Munich, Munich, Germany.
Early Clinical Development Oncology, Pharma Research and Development, Roche Innovation Center Munich, Munich, Germany.
Ann Oncol. 2020 Nov;31(11):1561-1568. doi: 10.1016/j.annonc.2020.07.013. Epub 2020 Jul 31.
By understanding prognostic biomarkers, we gain insights into disease biology and may improve design, conduct, and data analysis of clinical trials and real-world data. In this context, we used the Flatiron Health Electronic Health Record-derived deidentified database that provides treatment outcome and biomarker data from >280 oncology centers in the USA, organized into 17 cohorts defined by cancer type.
In 122 694 patients, we analyzed demographic, clinical, routine hematology, and blood chemistry parameters within a Cox proportional hazard framework to derive a multivariable prognostic risk model for overall survival (OS), the 'Real wOrld PROgnostic score (ROPRO)'. We validated ROPRO in two independent phase I and III clinical studies.
A total of 27 variables contributed independently and homogeneously across cancer indications to OS. In the largest cohort (advanced non-small-cell lung cancer), for example, patients with elevated ROPRO scores (upper 10%) had a 7.91-fold (95% confidence interval 7.45-8.39) increased death hazard compared with patients with low scores (lower 10%). Median survival was 23.9 months (23.3-24.5) in the lowest ROPRO quartile Q1, 14.8 months (14.4-15.2) in Q2, 9.4 months (9.1-9.7) in Q3, and 4.7 months (4.6-4.8) in Q4. The ROPRO model performance indicators [C-index = 0.747 (standard error 0.001), 3-month area under the curve (AUC) = 0.822 (0.819-0.825)] strongly outperformed those of the Royal Marsden Hospital Score [C-index = 0.54 (standard error 0.0005), 3-month AUC = 0.579 (0.577-0.581)]. We confirmed the high prognostic relevance of ROPRO in clinical Phase 1 and III trials.
The ROPRO provides improved prognostic power for OS. In oncology clinical development, it has great potential for applications in patient stratification, patient enrichment strategies, data interpretation, and early decision-making in clinical studies.
通过了解预后生物标志物，我们深入了解疾病生物学，并可能改进临床试验和真实世界数据的设计、实施和数据分析。在这种情况下，我们使用了 Flatiron Health 电子健康记录衍生的去识别数据库，该数据库提供了来自美国 280 多个肿瘤中心的治疗结果和生物标志物数据，这些数据被组织成 17 个按癌症类型定义的队列。
在 122694 名患者中，我们在 Cox 比例风险框架内分析了人口统计学、临床、常规血液学和血液化学参数，以得出总生存期（OS）的多变量预后风险模型，即“真实世界预后评分（ROPRO）”。我们在两项独立的 I 期和 III 期临床研究中验证了 ROPRO。
共有 27 个变量独立且均匀地分布在各种癌症适应症中，对 OS 有贡献。例如，在最大的队列（晚期非小细胞肺癌）中，与低评分（低 10%）的患者相比，评分较高（高 10%）的患者的死亡风险增加了 7.91 倍（95%置信区间 7.45-8.39）。最低 ROPRO 四分位数 Q1 的中位生存期为 23.9 个月（23.3-24.5），Q2 为 14.8 个月（14.4-15.2），Q3 为 9.4 个月（9.1-9.7），Q4 为 4.7 个月（4.6-4.8）。ROPRO 模型的性能指标[C 指数=0.747（标准误差 0.001），3 个月曲线下面积（AUC）=0.822（0.819-0.825）]明显优于皇家马斯登医院评分[C 指数=0.54（标准误差 0.0005），3 个月 AUC=0.579（0.577-0.581）]。我们证实了 ROPRO 在临床 I 期和 III 期试验中的高预后相关性。
ROPRO 为 OS 提供了更好的预后能力。在肿瘤临床开发中，它在患者分层、患者富集策略、数据解释和临床试验中的早期决策方面具有很大的应用潜力。
