Department of Anesthesiology, Sun Yat-Sen University Cancer Center, The State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Breast. 2024 Aug;76:103740. doi: 10.1016/j.breast.2024.103740. Epub 2024 May 6.
To explore whether specific clinicopathological covariates are predictive for a benefit from capecitabine maintenance in early-stage triple-negative breast cancer (TNBC) in the SYSUCC-001 phase III clinical trial.
Candidate covariates included age, menstrual status, type of surgery, postoperative chemotherapy regimen, Ki-67 percentage, histologic grade, primary tumor size, lymphovascular invasion, node status, and capecitabine medication. Their nonlinear effects were modeled by restricted cubic spline. The primary endpoint was disease-free survival (DFS). A survival prediction model was constructed using Cox proportional hazards regression analysis.
All 434 participants (306 in development cohort and 128 in validation cohort) were analyzed. The estimated 5-year DFS in development and validation cohorts were 77.8 % (95 % CI, 72.9%-82.7 %) and 78.2 % (95 % CI, 70.9%-85.5 %), respectively. Age and node status had significant nonlinear effects on DFS. The prediction model constructed using four covariates (node status, lymphovascular invasion, capecitabine maintenance, and age) demonstrated satisfactory calibration and fair discrimination ability, with C-index of 0.722 (95 % CI, 0.662-0.781) and 0.764 (95 % CI, 0.668-0.859) in development and validation cohorts, respectively. Moreover, patient classification was conducted according to their risk scores calculated using our model, in which, notable survival benefits were reported in low-risk subpopulations. An easy-to-use online calculator for predicting benefit of capecitabine maintenance was also designed.
The evidence-based prediction model can be readily assessed at baseline, which might help decision making in clinical practice and optimize patient stratification, especially for those with low-risk, capecitabine maintenance might be a potential strategy in the early-disease setting.
在 SYSUCC-001 三期临床试验中，探索特定的临床病理协变量是否对早期三阴性乳腺癌（TNBC）患者接受卡培他滨维持治疗的获益有预测作用。
候选协变量包括年龄、月经状态、手术类型、术后化疗方案、Ki-67 百分比、组织学分级、原发肿瘤大小、脉管侵犯、淋巴结状态和卡培他滨用药。通过限制立方样条对它们的非线性效应进行建模。主要终点为无病生存期（DFS）。使用 Cox 比例风险回归分析构建生存预测模型。
所有 434 名参与者（发展队列 306 名，验证队列 128 名）均进行了分析。发展和验证队列中估计的 5 年 DFS 分别为 77.8%（95%CI，72.9%-82.7%）和 78.2%（95%CI，70.9%-85.5%）。年龄和淋巴结状态对 DFS 有显著的非线性影响。使用 4 个协变量（淋巴结状态、脉管侵犯、卡培他滨维持和年龄）构建的预测模型具有良好的校准度和公平的区分能力，发展和验证队列中的 C 指数分别为 0.722（95%CI，0.662-0.781）和 0.764（95%CI，0.668-0.859）。此外，根据我们的模型计算出的风险评分对患者进行分类，结果显示低危亚组的生存获益显著。还设计了一个易于使用的卡培他滨维持治疗获益预测在线计算器。
基于证据的预测模型可在基线时进行简便评估，有助于指导临床实践中的决策，并优化患者分层，特别是对于低危患者，卡培他滨维持治疗可能是早期疾病治疗的一种潜在策略。
