PO.RSP01.01 · 监管科学与政策

用于新辅助乳腺癌试验中生存期替代终点预测的一种新型统计框架

A novel statistical framework for surrogate endpoint prediction of survival in neoadjuvant breast cancer trials

编号 1401 展板 10 时间 4/20 09:00–12:00 区域 Section 2 主讲 Keli Santos-Parker, MD;MS;PhD

分会场 Regulatory Science and Policy

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作者与单位 Authors & Affiliations

Keli S. Santos-Parker¹, Jessica R. Santos-Parker¹, W. Fraser Symmans², Laura J. Esserman¹, Christina Yau¹, Angie DeMichele³, Laura van't Veer¹, Doug Yee⁴, Fabien Reyal⁵, Helena Earl⁶, Jean Abraham⁶, David Cameron⁷, Peter Hall⁷, Judy Boughey⁸, Matthew Goetz⁸, Gabe Sonke⁹, Miguel Martín¹⁰, Sara López-Tarruella¹⁰, Priyanka Sharma¹¹, Rachel Freiberg¹, Jane Perlmutter¹², Aditya Bardia¹³, Martin Eklund¹⁴, Rachel Freiberg¹, Lajos Pusztai¹⁵

¹Surgery, UCSF - University of California San Francisco, San Francisco, CA,²Pathology, MD Anderson Cancer Center, Houston, TX,³Medical Oncology, University of Pennsylvania, Philadelphia, PA, USA, Philadelphia, PA,⁴Medical Oncology, University of Minnesota, Minneapolis, MN,⁵Department of Surgery, Institut Curie, Paris, France,⁶Department of Oncology, University of Cambridge, Cambridge, United Kingdom,⁷Department of Oncology, Western General Hospital, Edinburgh, United Kingdom,⁸Surgery, The Mayo Clinic, Rochester, MN,⁹Medical Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands,¹⁰Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,¹¹Kansas University Medical Center, Kansas City, KS,¹²The Gemini Group, Ann Arbor, MI,¹³University of California, Los Angeles, Los Angeles, CA,¹⁴Karolinska Institutet, Stockholm, Sweden,¹⁵Yale University, New Haven, CT

摘要 Abstract

中文摘要

引言病理完全缓解（pCR）是一个强有力的预后标志物，但在乳腺癌中，比较治疗组与对照组的生存结局与治疗组-对照组之间 pCR 率的差异并不能可靠地一致。一种新型的贝叶斯分层框架在试验内部对治疗进行建模，使我们能够以更高的准确性从 pCR 预测治疗对远处无复发生存期（DRFS）的影响。方法我们分析了 12 项新辅助乳腺癌试验（6,000 例患者，涵盖所有 HR/HER2 亚型；包括 I-SPY2）。将 Burzykowski、Molenberghs 和 Buyse（2005）的框架扩展为一种新型的基于治疗臂的分层结构，在控制亚型（HR/HER2）、N 和 T 分期、分级以及日历年的情况下，提供 pCR 和 DRFS 治疗效应的分布。三项留出（held-out）试验（877 例患者；26 种方案；中位随访 >4 年）验证了从 pCR 预测 DRFS 治疗获益的准确性。所有数据均用于估计治疗效应相关性和替代终点阈值效应（STE）。对残余癌负荷指数（二分类 RCB01、连续型 RCB）重复进行分析。结果 DRFS 获益的预测概率与实际 DRFS 随访结果高度吻合（平均绝对误差 0.06；Pearson r = 0.9）。在所有试验中，pCR 显示出中等程度的替代性（ρ = 0.82，R² = 0.67）。pCR 比值增加 60% 可实现 ≥95% 的 DRFS 获益概率（STE = OR 为 1.60）。RCB 在所有指标上均优于 pCR，在 26 种验证方案中检测 DRFS 获益的敏感性为 92%、特异性为 93%（表 1）。结论这一新型贝叶斯荟萃分析框架揭示，pCR、RCB01 和连续型 RCB 能够在异质性试验中可靠地预测生存获益，其中 RCB 在外部验证中表现最佳。通过在治疗臂层面准确预测生存期，这为在现代新辅助试验设计中使用稳健的早期生物标志物做出加速批准决策提供了统计学基础。表 1 预测 vs. 实际 DRFS 获益：26 种留出方案总体数据替代性指标敏感性特异性平均绝对误差 Pearson r rho 中位数 Pr[rho>0.5] R² 中位数 PR[R²>0.5] STE pCR 0.83 0.71 0.06 0.90 0.82（0.87） 0.67（0.67） OR 1.60 RCB01 0.91 0.80 0.06 0.94 0.90（0.95） 0.80（0.83） OR 1.37 RCB 0.92 0.93 0.05 0.95 0.89（0.96） 0.80（0.82） -9.3% Pr[DRFS 获益]>0.5 用于计算敏感性和特异性的决策阈值。STE 替代终点阈值效应：达到 ≥95% DRFS 获益后验概率所需的改善幅度。

查看英文原文 English abstract

Introduction Pathological complete response (pCR) is a strong prognostic marker, but survival outcomes comparing treatment to control do not reliably align with treatment-control differences in pCR rates in breast cancer. A novel Bayesian hierarchical framework models treatments within trials, allowing us to predict treatment effects on distant recurrence-free survival (DRFS) from pCR with greater accuracy. Methods We analyzed 12 neoadjuvant breast cancer trials (6,000 patients, all HR/HER2 subtypes; including I-SPY2). The framework of Burzykowski, Molenberghs & Buyse (2005) is extended to a novel arm-based hierarchical structure providing a distribution of pCR and DRFS treatment effects controlling for subtype (HR/HER2), N and T stage, grade, and calendar year. Three held-out trials (877 patients; 26 regimens; med follow-up >4 years) validate predictions of DRFS treatment benefit from pCR. All data was used to estimate treatment-effect correlation and the surrogate threshold effect (STE). Analyses were repeated for Residual Cancer Burden Index (binary RCB01, continuous RCB). Results Predicted probability of DRFS benefit closely matched actual DRFS follow-up (mean absolute error 0.06; Pearson r = 0.9). Across all trials, pCR showed moderate surrogacy (ρ = 0.82, R 2 = 0.67). A 60% increase in pCR odds achieves ≥95% probability of DRFS benefit (STE = OR of 1.60). RCB outperforms pCR across all metrics, with 92% sensitivity and 93% specificity for detecting DRFS benefit in 26 validation regimens (Table 1). Conclusion This novel Bayesian meta-analytic framework reveals that pCR, RCB01 and continuous RCB reliably predict survival benefit across heterogeneous trials, with RCB performing the best in an external validation. This provides a statistical foundation for accelerated approval decisions using robust early biomarkers in modern neoadjuvant trial designs by accurately predicting survival at the treatment arm level. Table 1 Predicted vs. Actual DRFS Benefit: 26 Held Out Regimens Overall Data Surrogacy Measures Sensitivity Specificity Mean Absolute Error Pearson r rho Median Pr[rho>0.5] R 2 Median PR[R 2 >0.5] STE pCR 0.83 0.71 0.06 0.90 0.82 (0.87) 0.67 (0.67) OR 1.60 RCB01 0.91 0.80 0.06 0.94 0.90 (0.95) 0.80 (0.83) OR 1.37 RCB 0.92 0.93 0.05 0.95 0.89(0.96) 0.80 (0.82) -9.3% Pr[DRFS benefit] > 0.5 Decision threshold used for Sensitivity and Specificity. STE Surrogate Threshold Effect: improvement needed for ≥95% posterior probability of DRFS benefit

利益披露 Disclosure

K. S. Santos-Parker, None. J. R. Santos-Parker, Johnson and Johnson Other, Academic-Industry Education Research Fellow. W. Symmans, IONIS Pharmaceuticals and Delphi Diagnostics Stock. Method for calculating residual cancer burden Patent. L. J. Esserman, Blue Cross and Blue Shield Independent Contractor, Travel. Quantum Leap Healthcare Collaborative g., Board of Directors, non-salaried role). Moderna ). C. Yau, None. A. DeMichele, Pfizer Independent Contractor, ). Genentech ). Novartis ). Neogenomics ). L. van't Veer, None.. D. Yee, None.. F. Reyal, None.. H. Earl, None.. J. Abraham, None.. D. Cameron, None.. P. Hall, None.. J. Boughey, None.. M. Goetz, None.. G. Sonke, None.. M. Martín, None.. S. López-Tarruella, None.. P. Sharma, None.. R. Freiberg, None.. J. Perlmutter, None. A. Bardia, Pfizer Independent Contractor, ). Novartis Independent Contractor, ). Genentech Independent Contractor, ). Merck Independent Contractor, ). Menarini Independent Contractor, ). Gilead Independent Contractor, ). Alyssum Independent Contractor. Vyome Independent Contractor. Sanofi Independent Contractor. Daiichi Pharma/Astra Zeneca Independent Contractor, ). BMS Independent Contractor. Eli Lilly Independent Contractor, ). OnKure ). OnKure ). M. Eklund, None.. R. Freiberg, None. L. Pusztai, Pfizer Independent Contractor. Astra Zeneca Independent Contractor, ). Merck Independent Contractor, ). Bristol-Myers Squibb Independent Contractor. Stemline-Menarini Independent Contractor. BeOne Independent Contractor. Personalis Independent Contractor. Natera Independent Contractor. Agendia Independent Contractor. Exact Sciences Independent Contractor, ). Radionetics Independent Contractor, ). Pfizer ). Menarini-Stemline ). Merck Independent Contractor, ). Ataraxis Stock Option.

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