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Breast Cancer Res Treat. 2022 Nov;196(2):299-310. doi: 10.1007/s10549-022-06729-7. Epub 2022 Sep 10.
Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2- breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts.
Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41-5.4), and poor (PPG > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient.
Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278-0.346)]/PREDICT [κ = 0.398 (0.35-0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB.
Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.
临床医生使用多基因/生物标志物预后检测和免费在线工具来优化早期 ER+/HER2-乳腺癌的治疗。在这里,我们报告了 CanAssist Breast(CAB)、Nottingham Prognostic Index(NPI)和 PREDICT 的复发风险预测的比较,并比较了这些检测在印度和欧洲队列中的表现差异。
本研究使用了来自欧洲、印度和美国的 1474 名患者的回顾性队列。NPI 风险组分为三个预后组,良好(GPG-NPI 指数≤3.4)、中度(MPG 3.41-5.4)和差(PPG >5.4)。PREDICT 预测化疗获益<2%的患者为低风险,≥2%为高风险。我们通过 Kappa 系数评估了 CAB 和 NPI/PREDICT 风险组之间的一致性。
所有工具生成的风险比例为:CAB 低:高 74:26;NPI 良好:中度:差预后组 38:55:7;PREDICT 低:高 63:37。总体而言,CAB 与 NPI/PREDICT 之间存在中等一致性[κ=0.31(0.278-0.346)]/PREDICT [κ=0.398(0.35-0.446)],CAB 与 NPI/PREDICT 低风险类别之间的一致性为 97%/88%。65%的 NPI-MPG 患者被 CAB 归类为低风险。从 PREDICT 高风险患者中,CAB 将 51%的患者分为低风险,从而避免了这些患者的过度治疗。在 T1N0 患者数量较多的(欧洲)队列中,NPI/PREDICT 比 CAB 更倾向于将更多患者归类为 LR,这表明具有侵袭性生物学特性的 T1N0 患者被在线工具漏掉了,但 CAB 却没有。
数据表明,CAB 可用于早期乳腺癌的整体治疗,特别是在 NPI-MPG 和 PREDICT 高风险患者中,以做出关于化疗使用的准确决策。CAB 在不同地理位置的队列中提供了无偏倚的风险分层,对临床参数的影响最小。