Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning
Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning
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DOI：
10.1016/j.molonc.2015.07.006
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发表时间：
2016-01-01
影响因子：
6.6
通讯作者：
Rogan, Peter K.
中科院分区：
文献类型：
--
作者：
Dorman, Stephanie N.;Baranoua, Katherina;Rogan, Peter K.
越来越多地，乳腺癌辅助化疗药物的有效性与肿瘤基因组图谱的变化有关。我们研究了紫杉醇和吉西他滨(GI50)的生长抑制浓度(GI50)与基因拷贝数、突变和表达之间的相关性，首先是在乳腺癌细胞系中，然后是在患者中。分析了编码这些药物的直接靶点、代谢酶、转运体以及那些先前与紫杉醇(n=31)或吉西他滨(n=18)耐药相关的基因。多因素主成分分析(MFA)表明紫杉醇的表达是紫杉醇敏感性的最强指标，而拷贝数和表达是吉西他滨的信息量。使用支持向量机对这些因素进行组合。15个基因(ABCC10、BCL2、BCL2L1、BIRC5、BMF、FGF2、FN1、MAP4、MAPT、NFKB2、SLCO1B3、TLR6、TMEM243、Twist1和CSAG2)的表达预测紫杉醇敏感性的准确率为82%。3个基因(ABCC10、NT5C、Tyms)的拷贝数谱结合7个基因(ABCB1、ABCC10、CMPK1、DCTD、NME1、RRM1、RRM2B)的表达预测吉西他滨疗效的准确率为85%。然后用这些模型分析两组独立的已知反应的患者的表达和拷贝数研究。其中包括21名同时接受紫杉醇和吉西他滨治疗的患者的肿瘤块，以及319名接受紫杉醇和蒽环类药物治疗的患者。一个新的紫杉醇支持向量机是从11个基因子集衍生而来的，因为原始基因中的4个没有数据可用。这种支持向量机在细胞系和肿瘤块上的准确率相似(70%-71%)。由于存在核酸完整性较差的样本，吉西他滨支持向量机对肿瘤块的预测准确率为62%。然而，紫杉醇支持向量机预测了84%无残留或微小残留病变的患者的敏感性。(C)2015年欧洲生化学会联合会。爱思唯尔出版，版权所有。
Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease. (C) 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.