Westmead Breast Cancer Institute, Westmead, NSW, 2145, Australia.
Crown Princess Mary Cancer Centre, Western Sydney Local Health District, Westmead, NSW, 2145, Australia.
Breast Cancer Res Treat. 2022 Feb;191(3):501-511. doi: 10.1007/s10549-021-06456-5. Epub 2021 Dec 1.
Genomic tests improve accuracy of risk prediction for early breast cancers but these are expensive. This study evaluated the clinical utility of EndoPredict®, in terms of impact on adjuvant therapy recommendations and identification of parameters to guide selective application.
Patients with ER-positive, HER2-negative, and early-stage invasive breast cancer were tested with EndoPredict®. Two cohorts were recruited: one consecutively and another at clinical team discretion. Systemic treatment recommendations were recorded before and after EndoPredict® results were revealed to the multidisciplinary team.
233 patients were recruited across five sites: 123 consecutive and 110 at clinical team discretion. In the consecutive cohort 50.6% (62/123) cases were classified high risk of recurrence by EndoPredict®, compared with 62.7% (69/110) in the selective cohort. A change in treatment recommendation was significantly more likely (p < 0.0001) in the selective cohort (43/110, 39.1%) compared to the consecutive group (11/123, 8.9%). The strongest driver of selective recruitment was intermediate grade histology, whilst logistic regression modelling demonstrated that nodal status (p < 0.001), proliferative rate (p = 0.001), and progesterone receptor positivity (p < 0.001) were the strongest discriminators of risk.
Whilst molecular risk can be predicted by traditional variables in a high proportion of cases, EndoPredict® had a greater impact on treatment decisions in those cases selected for testing at team discretion. This is indicative of the robust ability of the clinical team to identify cases most likely to benefit from testing, underscoring the value of genomic tests in the oncologists' tool kit.
基因组测试提高了早期乳腺癌风险预测的准确性,但这些测试费用昂贵。本研究评估了 EndoPredict®在辅助治疗建议方面的临床实用性,并确定了指导选择性应用的参数。
对 ER 阳性、HER2 阴性和早期浸润性乳腺癌患者进行 EndoPredict®检测。招募了两个队列:一个连续招募,另一个由临床团队自行决定。在将 EndoPredict®结果告知多学科团队之前和之后,记录了系统治疗建议。
在五个地点共招募了 233 名患者:123 名连续招募,110 名由临床团队自行决定。在连续队列中,50.6%(62/123)的病例被 EndoPredict®归类为高复发风险,而选择性队列中这一比例为 62.7%(69/110)。选择性队列(43/110,39.1%)治疗建议的改变明显更有可能(p<0.0001),而连续组(11/123,8.9%)则没有。选择性招募的最强驱动因素是中间等级的组织学,而逻辑回归模型表明,淋巴结状态(p<0.001)、增殖率(p=0.001)和孕激素受体阳性(p<0.001)是风险的最强判别因素。
虽然传统变量可以预测分子风险,但在临床团队自行决定进行测试的病例中,EndoPredict®对治疗决策的影响更大。这表明临床团队有能力识别最有可能从测试中受益的病例,突显了基因组测试在肿瘤学家工具包中的价值。