Prediction of metastatic relapse in node-positive breast cancer:: establishment of a clinicogenomic model after FEC100 adjuvant regimen
Prediction of metastatic relapse in node-positive breast cancer:: establishment of a clinicogenomic model after FEC100 adjuvant regimen
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DOI：
10.1007/s10549-007-9673-x
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发表时间：
2008-06-01
影响因子：
3.8
通讯作者：
Jezequel, Pascal
中科院分区：
文献类型：
--
作者：
Campone, Mario;Campion, Loic;Jezequel, Pascal
乳腺癌是一种非常异质性的疾病，疾病亚型和治疗反应的标志物仍然定义不清。因此，我们对淋巴结阳性乳腺癌进行了一项回顾性研究，以确定与无转移生存相关的基因表达的分子特征。患者主要纳入两项多中心前瞻性辅助临床试验（PACS 01和PEGASE 01-FNCLCC合作组）的FEC 100（5-氟尿嘧啶500 mg/m2、表阿霉素100 mg/m2和环磷酰胺500 mg/m2）组。来自含有8，032个cDNA独特序列（代表5，776个不同基因）的尼龙微阵列的数据已用于开发治疗结果的预测模型。我们获得了其中150例患者的基因表达谱，并使用基于考克斯回归结合主成分分析的严格单变量选择技术来鉴定辅助FEC 100方案后转移性复发的基因组特征。在14个选定的基因中，大多数在乳腺癌、致癌或化疗耐药性中具有明确的作用。先前已在其他基因组研究中描述了六种基因（UBE 2C、CENPF、C16 orf 61 [DC 13]、STMN 1、CCT 5和BCL 2A 1）。此外，我们显示了将转录组学数据与临床数据结合到用于患者亚型的临床基因组学模型中的兴趣。所描述的模型将预测准确性增加到由完善的诺丁汉预后指数或我们的基因组特征单独提供的预测准确性。
Breast cancer is a very heterogeneous disease, and markers for disease subtypes and therapy response remain poorly defined. For that reason, we employed a retrospective study in node-positive breast cancer to identify molecular signatures of gene expression correlating with metastatic free survival. Patients were primarily included in FEC100 (5-fluorouracil 500 mg/m(2), epirubicin 100 mg/m(2) and cyclophosphamide 500 mg/m(2)) arms of two multicentric prospective adjuvant clinical trials (PACS01 and PEGASE01-FNCLCC cooperative group). Data from nylon microarrays containing 8,032 cDNA unique sequences, representing 5,776 distinct genes, have been used to develop a predictive model for treatment outcome. We obtained the gene expression profiles for 150 of these patients, and used stringent univariate selection techniques based on Cox regression combined with principal component analysis to identify a genomic signature of metastatic relapse after adjuvant FEC100 regimen. Most of the 14 selected genes have a clear role in breast cancer, carcinogenesis or chemotherapy resistance. Six genes have been previously described in other genomic studies (UBE2C, CENPF, C16orf61 [DC13], STMN1, CCT5 and BCL2A1). Furthermore, we showed the interest of combining transcriptomic data with clinical data into a clinicogenomic model for patients subtyping. The described model adds predictive accuracy to that provided by the well-established Nottingham prognostic index or by our genomic signature alone.