Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA.
Clin Cancer Res. 2010 Jan 15;16(2):711-8. doi: 10.1158/1078-0432.CCR-09-2247. Epub 2010 Jan 12.
Several different multivariate prediction models using routine clinical variables or multigene signatures have been proposed to predict pathologic complete response to combination chemotherapy in breast cancer. Our goal was to compare the performance of four conceptually different predictors in an independent cohort of patients.
Gene expression profiling was done on fine-needle aspirations of 100 stage I to III breast cancers before preoperative paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide combination chemotherapy. Pathologic response was correlated with prediction results from a clinical nomogram, a human cancer-derived genomic predictor (DLDA30), a cell line-based genomic predictor [in vitro coexpression extrapolation (COXEN)], and an optimized cell line-derived (in vivo COXEN) predictor. None of the 100 test cases were used in the development of these predictors.
The in vitro COXEN using a combination of four individual drug sensitivity predictions derived from cell lines was not predictive [area under the receiver operator characteristic curve (AUC), 0.5; 95% confidence interval, (95% CI), 0.41-0.59]. The clinical nomogram (AUC, 0.73; 95% CI, 0.65-0.80) and the DLDA30 (AUC, 0.73; 95% CI, 0.66-0.80) genomic predictor had similar performances. The in vivo COXEN that used informative genes from cell lines but was trained on a separate human data set also showed significant predictive value (AUC, 0.67; 95% CI, 0.60-0.74). These three different prediction scores correlated with each other and were significant in univariate but not in multivariate analysis.
Three conceptually different predictors performed similarly in this validation study and tended to identify the same patients as responders. A genomic predictor that relied solely on a composite of individual drug sensitivity predictions from cell lines did not show any predictive value.
已经提出了几种使用常规临床变量或多基因特征的多元预测模型，以预测乳腺癌联合化疗的病理完全缓解。我们的目标是在独立的患者队列中比较四种概念上不同的预测器的性能。
在术前紫杉醇、5-氟尿嘧啶、多柔比星和环磷酰胺联合化疗前，对 100 例 I 期至 III 期乳腺癌的细针穿刺抽吸物进行基因表达谱分析。病理反应与临床列线图、人类癌症衍生基因组预测器 (DLDA30)、基于细胞系的基因组预测器 [体外共表达外推 (COXEN)] 和优化的细胞系衍生 (体内 COXEN) 预测器的预测结果相关。这些预测器的开发中均未使用这 100 个测试病例。
使用来自细胞系的四种个体药物敏感性预测组合的体外 COXEN 没有预测能力[接收者操作特征曲线下的面积 (AUC)，0.5；95%置信区间 (95%CI)，0.41-0.59]。临床列线图 (AUC，0.73；95%CI，0.65-0.80) 和 DLDA30 基因组预测器具有相似的性能。使用来自细胞系的信息基因但在独立的人类数据集上进行训练的体内 COXEN 也显示出显著的预测价值 (AUC，0.67；95%CI，0.60-0.74)。这三种不同的预测评分相互关联，在单变量分析中具有显著意义，但在多变量分析中没有意义。
在这项验证研究中，三种概念上不同的预测器表现相似，倾向于识别相同的应答者。仅依赖于细胞系个体药物敏感性预测综合的基因组预测器没有显示任何预测价值。
