Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
Breast Cancer Res. 2023 Feb 8;25(1):17. doi: 10.1186/s13058-023-01612-9.
Breast cancer is one of the three most common cancers worldwide and is the most common malignancy in women. Treatment approaches for breast cancer are diverse and varied. Clinicians must balance risks and benefits when deciding treatments, and models have been developed to support this decision-making. Genomic risk scores (GRSs) may offer greater clinical value than standard clinicopathological models, but there is limited evidence as to whether these models perform better than the current clinical standard of care.
PREDICT and GRSs were adapted using data from the original papers. Univariable Cox proportional hazards models were produced with breast cancer-specific survival (BCSS) as the outcome. Independent predictors of BCSS were used to build multivariable models with PREDICT. Signatures which provided independent prognostic information in multivariable models were incorporated into the PREDICT algorithm and assessed for calibration, discrimination and reclassification.
EndoPredict, MammaPrint and Prosigna demonstrated prognostic power independent of PREDICT in multivariable models for ER-positive patients; no score predicted BCSS in ER-negative patients. Incorporating these models into PREDICT had only a modest impact upon calibration (with absolute improvements of 0.2-0.8%), discrimination (with no statistically significant c-index improvements) and reclassification (with 4-10% of patients being reclassified).
Addition of GRSs to PREDICT had limited impact on model fit or treatment received. This analysis does not support widespread adoption of current GRSs based on our implementations of commercial products.
乳腺癌是全球最常见的三种癌症之一，也是女性最常见的恶性肿瘤。乳腺癌的治疗方法多种多样。临床医生在决定治疗方法时必须权衡风险和收益，为此已经开发了模型来支持这一决策。基因组风险评分（GRS）可能比标准临床病理模型具有更大的临床价值，但关于这些模型是否比当前的临床标准护理表现更好的证据有限。
使用原始论文中的数据对 PREDICT 和 GRS 进行了改编。使用乳腺癌特异性生存（BCSS）作为结果生成单变量 Cox 比例风险模型。使用多变量模型中的独立预测因子构建 PREDICT 模型。在多变量模型中提供独立预后信息的特征被纳入 PREDICT 算法，并评估其校准、区分和重新分类。
EndoPredict、MammaPrint 和 Prosigna 在 ER 阳性患者的多变量模型中独立于 PREDICT 具有预后能力；没有评分预测 ER 阴性患者的 BCSS。将这些模型纳入 PREDICT 对校准（绝对改善 0.2-0.8%）、区分（无统计学意义的 c 指数改善）和重新分类（4-10%的患者重新分类）的影响仅略有改善。
将 GRS 添加到 PREDICT 对模型拟合或治疗效果的影响有限。根据我们对商业产品的实施，这项分析不支持广泛采用当前的 GRS。
