Concordance among gene expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen.
Concordance among gene expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen.
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DOI：
10.1093/annonc/mds080
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
Perou CM
中科院分区：
文献类型：
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作者：
Prat A;Parker JS;Fan C;Cheang MCU;Miller LD;Bergh J;Chia SKL;Bernard PS;Nielsen TO;Ellis MJ;Carey LA;Perou CM
ER阳性（ER+）乳腺癌包括所有的内在分子亚型，尽管管腔A和B亚型占主导地位。在这项研究中，我们评估了六个临床相关的基因组特征预测ER+肿瘤患者复发的能力，这些患者仅接受他莫昔芬辅助治疗。将四个微阵列数据集组合，并评估基于研究的PAM 50内在亚型和复发风险（PAM 50-ROR）评分、21基因复发评分（OncotypeDX）、Mammaprint、鹿特丹76基因、内分泌治疗敏感性指数（SET）和雌激素诱导的基因集。采用Kaplan-Meier和对数秩检验估计无远处复发生存期（DRFS），采用考克斯回归分析进行多变量分析。Harrell的C指数也被用来估计性能。在ER+淋巴结阴性肿瘤患者中，所有特征均具有预后性，而在ER+淋巴结阳性疾病中，大多数特征具有预后性。在评估的特征中，一致发现PAM 50-ROR、OncotypeDX、Mammaprint和SET是复发的独立预测因子。所有特征的组合显著提高了性能预测。重要的是，低风险肿瘤（8.5年时DRFS>90%）仅在淋巴结阴性疾病中通过大多数特征识别，并且这些肿瘤大多为管腔A型（78%-100%）。大多数已建立的基因组特征在ER+乳腺癌的结果预测中是成功的，并提供了统计学上独立的信息。从临床角度来看，多个特征组合在一起最准确地预测结果，但一个共同的发现是，每个特征识别了一个淋巴结阴性疾病的管腔A型患者子集，这些患者可能被认为是单独辅助内分泌治疗的合适候选人。
ER-positive (ER+ ) breast cancer includes all of the intrinsic molecular subtypes, although the luminal A and B subtypes predominate. In this study, we evaluated the ability of six clinically relevant genomic signatures to predict relapse in patients with ER+ tumors treated with adjuvant tamoxifen only. Four microarray datasets were combined and research-based versions of PAM50 intrinsic subtyping and risk of relapse (PAM50-ROR) score, 21-gene recurrence score (OncotypeDX), Mammaprint, Rotterdam 76 gene, index of sensitivity to endocrine therapy (SET) and an estrogen-induced gene set were evaluated. Distant relapse-free survival (DRFS) was estimated by Kaplan–Meier and log-rank tests, and multivariable analyses were done using Cox regression analysis. Harrell's C-index was also used to estimate performance. All signatures were prognostic in patients with ER+ node-negative tumors, whereas most were prognostic in ER+ node-positive disease. Among the signatures evaluated, PAM50-ROR, OncotypeDX, Mammaprint and SET were consistently found to be independent predictors of relapse. A combination of all signatures significantly increased the performance prediction. Importantly, low-risk tumors (>90% DRFS at 8.5 years) were identified by the majority of signatures only within node-negative disease, and these tumors were mostly luminal A (78%–100%). Most established genomic signatures were successful in outcome predictions in ER+ breast cancer and provided statistically independent information. From a clinical perspective, multiple signatures combined together most accurately predicted outcome, but a common finding was that each signature identified a subset of luminal A patients with node-negative disease who might be considered suitable candidates for adjuvant endocrine therapy alone.