Ring Brian Z, Seitz Robert S, Beck Rod, Shasteen William J, Tarr Shannon M, Cheang Maggie C U, Yoder Brian J, Budd G Thomas, Nielsen Torsten O, Hicks David G, Estopinal Noel C, Ross Douglas T
Applied Genomics Inc, 863 Mitten Rd #103, Burlingame, CA, USA.
J Clin Oncol. 2006 Jul 1;24(19):3039-47. doi: 10.1200/JCO.2006.05.6564.
Patients with breast cancer experience progression and respond to treatment in diverse ways, but prognostic and predictive tools for the oncologist are limited. We have used gene expression data to guide the production of hundreds of novel antibody reagents to discover novel diagnostic tools for stratifying carcinoma patients.
One hundred forty novel and 23 commercial antisera, selected on their ability to differentially stain tumor samples, were used to stain paraffin blocks from a retrospective breast cancer cohort. Cox proportional hazards and regression tree analysis identified minimal panels of reagents able to predict risk of recurrence. We tested the prognostic association of these prospectively defined algorithms in two independent cohorts.
In both validation cohorts, the Kaplan-Meier estimates of recurrence confirmed that both the Cox model using five reagents (p53, NDRG1, CEACAM5, SLC7A5, and HTF9C) and the regression tree model using six reagents (p53, PR, Ki67, NAT1, SLC7A5, and HTF9C) distinguished estrogen receptor (ER)-positive patients with poor outcomes. The Cox model was superior and distinguished patients with poor outcomes from patients with good or moderate outcomes with a hazard ratio of 2.21 (P = .0008) in validation cohort 1 and 1.88 (P = .004) in cohort 2. In multivariable analysis, the calculated risk of recurrence was independent of stage, grade, and lymph node status. A model proposed for ER-negative patients failed validation in the independent cohorts.
A panel of five antibodies can significantly improve on traditional prognosticators in predicting outcome for ER-positive breast cancer patients.
乳腺癌患者病情进展各异，对治疗的反应也不尽相同，但肿瘤学家可用的预后和预测工具有限。我们利用基因表达数据指导生产了数百种新型抗体试剂，以发现用于对癌症患者进行分层的新型诊断工具。
根据对肿瘤样本进行差异染色的能力，从140种新型抗血清和23种商业抗血清中选出部分抗血清，用于对一个回顾性乳腺癌队列的石蜡块进行染色。Cox比例风险模型和回归树分析确定了能够预测复发风险的最小试剂组合。我们在两个独立队列中测试了这些前瞻性定义算法的预后相关性。
在两个验证队列中，Kaplan-Meier复发估计值均证实，使用5种试剂（p53、NDRG1、CEACAM5、SLC7A5和HTF9C）的Cox模型以及使用6种试剂（p53、PR、Ki67、NAT1、SLC7A5和HTF9C）的回归树模型均能区分出预后较差的雌激素受体（ER）阳性患者。Cox模型表现更优，在验证队列1中，其将预后较差的患者与预后良好或中等的患者区分开来，风险比为2.21（P = .0008）；在队列2中，风险比为1.88（P = .004）。在多变量分析中，计算出的复发风险与分期、分级和淋巴结状态无关。为ER阴性患者提出的模型在独立队列中未通过验证。
一组5种抗体在预测ER阳性乳腺癌患者的预后方面，能显著优于传统预后指标。
