Hess Kenneth R, Anderson Keith, Symmans W Fraser, Valero Vicente, Ibrahim Nuhad, Mejia Jaime A, Booser Daniel, Theriault Richard L, Buzdar Aman U, Dempsey Peter J, Rouzier Roman, Sneige Nour, Ross Jeffrey S, Vidaurre Tatiana, Gómez Henry L, Hortobagyi Gabriel N, Pusztai Lajos
Department of Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77230-1439, USA.
J Clin Oncol. 2006 Sep 10;24(26):4236-44. doi: 10.1200/JCO.2006.05.6861. Epub 2006 Aug 8.
We developed a multigene predictor of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil-doxorubicin-cyclophosphamide (T/FAC) chemotherapy and assessed its predictive accuracy on independent cases.
One hundred thirty-three patients with stage I-III breast cancer were included. Pretreatment gene expression profiling was performed with oligonecleotide microarrays on fine-needle aspiration specimens. We developed predictors of pCR from 82 cases and assessed accuracy on 51 independent cases.
Overall pCR rate was 26% in both cohorts. In the training set, 56 probes were identified as differentially expressed between pCR versus residual disease, at a false discovery rate of 1%. We examined the performance of 780 distinct classifiers (set of genes + prediction algorithm) in full cross-validation. Many predictors performed equally well. A nominally best 30-probe set Diagonal Linear Discriminant Analysis classifier was selected for independent validation. It showed significantly higher sensitivity (92% v 61%) than a clinical predictor including age, grade, and estrogen receptor status. The negative predictive value (96% v 86%) and area under the curve (0.877 v 0.811) were nominally better but not statistically significant. The combination of genomic and clinical information yielded a predictor not significantly different from the genomic predictor alone. In 31 samples, RNA was hybridized in replicate with resulting predictions that were 97% concordant.
A 30-probe set pharmacogenomic predictor predicted pCR to T/FAC chemotherapy with high sensitivity and negative predictive value. This test correctly identified all but one of the patients who achieved pCR (12 of 13 patients) and all but one of those who were predicted to have residual disease had residual cancer (27 of 28 patients).
我们开发了一种多基因预测模型，用于预测术前每周使用紫杉醇及氟尿嘧啶-阿霉素-环磷酰胺（T/FAC）化疗后的病理完全缓解（pCR）情况，并在独立病例中评估其预测准确性。
纳入133例I - III期乳腺癌患者。对细针穿刺标本进行寡核苷酸微阵列预处理基因表达谱分析。我们从82例病例中开发pCR预测模型，并在51例独立病例中评估其准确性。
两个队列的总体pCR率均为26%。在训练集中，以1%的错误发现率鉴定出56个在pCR与残留疾病之间差异表达的探针。我们在完全交叉验证中检查了780种不同分类器（基因集+预测算法）的性能。许多预测模型表现相当。选择了一个名义上最佳的30探针集对角线性判别分析分类器进行独立验证。它显示出比包括年龄、分级和雌激素受体状态的临床预测模型显著更高的敏感性（92%对61%）。阴性预测值（96%对86%）和曲线下面积（0.877对0.811）名义上更好但无统计学意义。基因组信息与临床信息的组合产生的预测模型与单独的基因组预测模型无显著差异。在31个样本中，RNA进行了重复杂交，所得预测结果的一致性为97%。
一个30探针集的药物基因组预测模型对T/FAC化疗的pCR具有高敏感性和阴性预测值。该检测正确识别出除1例之外的所有达到pCR的患者（13例患者中的12例），并且除1例之外，所有被预测有残留疾病的患者都有残留癌（28例患者中的27例）。
