缩小差距：拉丁美洲激素依赖性乳腺癌队列中用于个性化医疗的预后和预测生物标志物验证
Closing the gap: prognostic and predictive biomarker validation for personalized care in a Latin American hormone-dependent breast cancer cohort.
作者信息
Alves da Quinta Daniela, Rocha Darío, Retamales Javier, Giunta Diego, Artagaveytia Nora, Velazquez Carlos, Daneri-Navarro Adrian, Müller Bettina, Abdelhay Eliana, Bravo Alicia I, Castro Mónica, Rosales Cristina, Alcoba Elsa, Acosta Haab Gabriela, Carrizo Fernando, Sorin Irene, Di Sibio Alejandro, Marques-Silveira Márcia, Binato Renata, Caserta Benedicta, Greif Gonzalo, Del Toro-Arreola Alicia, Quintero-Ramos Antonio, Gómez Jorge, Podhajcer Osvaldo L, Fernández Elmer A, Llera Andrea S
机构信息
Laboratorio de Terapia Molecular y Celular, Fundación Instituto Leloir-CONICET, Ciudad de Buenos Aires, Argentina.
Universidad Argentina de la Empresa (UADE), Instituto de Tecnología (INTEC), Buenos Aires, Argentina.
出版信息
Oncologist. 2024 Dec 6;29(12):e1701-e1713. doi: 10.1093/oncolo/oyae191.
BACKGROUND
Several guidelines recommend the use of different classifiers to determine the risk of recurrence (ROR) and treatment decisions in patients with HR+HER2- breast cancer. However, data are still lacking for their usefulness in Latin American (LA) patients. Our aim was to evaluate the comparative prognostic and predictive performance of different ROR classifiers in a real-world LA cohort.
METHODS
The Molecular Profile of Breast Cancer Study (MPBCS) is an LA case-cohort study with 5-year follow-up. Stages I and II, clinically node-negative HR+HER2- patients (n = 340) who received adjuvant hormone therapy and/or chemotherapy, were analyzed. Time-dependent receiver-operator characteristic-area under the curve, univariate and multivariate Cox proportional hazards regression (CPHR) models were used to compare the prognostic performance of several risk biomarkers. Multivariate CPHR with interaction models tested the predictive ability of selected risk classifiers.
RESULTS
Within this cohort, transcriptomic-based classifiers such as the recurrence score (RS), EndoPredict (EP risk and EPClin), and PAM50-risk of recurrence scores (ROR-S and ROR-PC) presented better prognostic performances for node-negative patients (univariate C-index 0.61-0.68, adjusted C-index 0.77-0.80, adjusted hazard ratios [HR] between high and low risk: 4.06-9.97) than the traditional classifiers Ki67 and Nottingham Prognostic Index (univariate C-index 0.53-0.59, adjusted C-index 0.72-0.75, and adjusted HR 1.85-2.54). RS (and to some extent, EndoPredict) also showed predictive capacity for chemotherapy benefit in node-negative patients (interaction P = .0200 and .0510, respectively).
CONCLUSION
In summary, we could prove the clinical validity of most transcriptomic-based risk classifiers and their superiority over clinical and immunohistochemical-based methods in the heterogenous, real-world node-negative HR+HER2- MPBCS cohort.
背景
多项指南推荐使用不同的分类器来确定激素受体阳性（HR+）、人表皮生长因子受体2阴性（HER2-）乳腺癌患者的复发风险（ROR）并做出治疗决策。然而，关于这些分类器在拉丁美洲（LA）患者中的有效性的数据仍然缺乏。我们的目的是评估不同ROR分类器在一个真实世界的LA队列中的比较预后和预测性能。
方法
乳腺癌分子谱研究（MPBCS）是一项LA病例队列研究，随访5年。分析了接受辅助激素治疗和/或化疗的I期和II期、临床淋巴结阴性的HR+HER2-患者（n = 340）。使用时间依赖性受试者工作特征曲线下面积、单变量和多变量Cox比例风险回归（CPHR）模型来比较几种风险生物标志物的预后性能。带有交互模型的多变量CPHR测试了所选风险分类器的预测能力。
结果
在这个队列中，基于转录组学的分类器，如复发评分（RS）、EndoPredict（EP风险和EPClin）以及PAM50复发风险评分（ROR-S和ROR-PC），对于淋巴结阴性患者表现出比传统分类器Ki67和诺丁汉预后指数更好的预后性能（单变量C指数0.61 - 0.68，调整后C指数0.77 - 0.80，高风险与低风险之间的调整风险比[HR]：4.06 - 9.97）（单变量C指数0.53 - 0.59，调整后C指数0.72 - 0.75，调整后HR 1.85 - 2.54）。RS（在一定程度上还有EndoPredict）在淋巴结阴性患者中也显示出对化疗获益的预测能力（交互P值分别为0.0200和0.0510）。
结论
总之，我们能够证明大多数基于转录组学的风险分类器在异质性的、真实世界的淋巴结阴性HR+HER2- MPBCS队列中的临床有效性及其相对于基于临床和免疫组化方法的优越性。
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