Daniela Alves da Quinta, Darío Rocha, Javier Retamales, Diego Giunta, Nora Artagaveytia, Carlos Velazquez, Adrian Daneri-Navarro, Bettina Müller, Eliana Abdelhay, Alicia I Bravo, Mónica Castro, Cristina Rosales, Elsa Alcoba, Gabriela Acosta Haab, Fernando Carrizo, Irene Sorin, Alejandro Di Sibio, Márcia Marques-Silveira, Renata Binato, Benedicta Caserta, Gonzalo Greif, Alicia Del Toro-Arreola, Antonio Quintero-Ramos, Jorge Gómez, Osvaldo L Podhajcer, Elmer A Fernández, Andrea S Llera
{"title":"缩小差距：拉丁美洲激素依赖性乳腺癌队列中个性化治疗的预后和预测生物标志物验证。","authors":"Daniela Alves da Quinta, Darío Rocha, Javier Retamales, Diego Giunta, Nora Artagaveytia, Carlos Velazquez, Adrian Daneri-Navarro, Bettina Müller, Eliana Abdelhay, Alicia I Bravo, Mónica Castro, Cristina Rosales, Elsa Alcoba, Gabriela Acosta Haab, Fernando Carrizo, Irene Sorin, Alejandro Di Sibio, Márcia Marques-Silveira, Renata Binato, Benedicta Caserta, Gonzalo Greif, Alicia Del Toro-Arreola, Antonio Quintero-Ramos, Jorge Gómez, Osvaldo L Podhajcer, Elmer A Fernández, Andrea S Llera","doi":"10.1093/oncolo/oyae191","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":54686,"journal":{"name":"Oncologist","volume":" ","pages":"e1701-e1713"},"PeriodicalIF":4.8000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11630759/pdf/","citationCount":"0","resultStr":"{\"title\":\"Closing the gap: prognostic and predictive biomarker validation for personalized care in a Latin American hormone-dependent breast cancer cohort.\",\"authors\":\"Daniela Alves da Quinta, Darío Rocha, Javier Retamales, Diego Giunta, Nora Artagaveytia, Carlos Velazquez, Adrian Daneri-Navarro, Bettina Müller, Eliana Abdelhay, Alicia I Bravo, Mónica Castro, Cristina Rosales, Elsa Alcoba, Gabriela Acosta Haab, Fernando Carrizo, Irene Sorin, Alejandro Di Sibio, Márcia Marques-Silveira, Renata Binato, Benedicta Caserta, Gonzalo Greif, Alicia Del Toro-Arreola, Antonio Quintero-Ramos, Jorge Gómez, Osvaldo L Podhajcer, Elmer A Fernández, Andrea S Llera\",\"doi\":\"10.1093/oncolo/oyae191\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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). 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引用次数: 0
摘要
背景：一些指南建议使用不同的分类器来确定乳腺癌患者的复发风险（ROR）和治疗决策。然而，在拉丁美洲（LA）患者中使用这些分类器的数据仍然缺乏。我们的目的是在一个真实世界的拉丁美洲队列中评估不同ROR分类器的预后和预测性能比较：乳腺癌分子特征研究（MPBCS）是一项为期 5 年的 LA 病例队列研究。研究分析了接受激素辅助治疗和/或化疗的I期和II期临床结节阴性HR+HER2-患者（n = 340）。采用时间依赖性接收器-操作者特征-曲线下面积、单变量和多变量考克斯比例危险回归（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 检验了选定风险分类指标的预测能力：结果：在该队列中，基于转录组的分类器，如复发评分（RS）、EndoPredict（EP risk和EPClin）和PAM50-复发风险评分（ROR-S和ROR-PC），对结节阴性患者具有更好的预后效果（单变量C指数为0.61-0.68，调整后 C 指数 0.77-0.80，调整后高风险和低风险之间的危险比 [HR]：4.06-9.97）优于传统分类指标 Ki67 和诺丁汉预后指数（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（在一定程度上也包括EndoPredict）还显示了对结节阴性患者化疗获益的预测能力（交互作用P=0.0200和0.0510）：总之，我们可以证明大多数基于转录组的风险分类方法的临床有效性，而且在异质性、真实世界的结节阴性HR+HER2- MPBCS队列中，它们优于基于临床和免疫组化的方法。
Closing the gap: prognostic and predictive biomarker validation for personalized care in a Latin American hormone-dependent breast cancer cohort.
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.
期刊介绍：
The Oncologist® is dedicated to translating the latest research developments into the best multidimensional care for cancer patients. Thus, The Oncologist is committed to helping physicians excel in this ever-expanding environment through the publication of timely reviews, original studies, and commentaries on important developments. We believe that the practice of oncology requires both an understanding of a range of disciplines encompassing basic science related to cancer, translational research, and clinical practice, but also the socioeconomic and psychosocial factors that determine access to care and quality of life and function following cancer treatment.
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