Potential responders to anticancer therapy for colorectal cancer by predictor genes

为建立确定FOLFOX治疗有效者的程序，83例结直肠癌(CRC)患者被分为训练组(54例)和测试组(29例)，其中42例为应答组，41例为无反应组。在训练集中使用随机森林(RF)算法识别FOLFOX治疗的预测基因，并用于测试样本。在训练集中，27名应答者中的22名(81.4%)和27名无应答者中的23名(85.1%)被正确分类。为了改进预测模型，我们去除了RF确定的离群点，该模型在29个独立测试样本中正确分类了23个应答者中的21个(91.3%)和23个无应答者中的22个(95.6%)，敏感度和特异度分别为80.0%和92.8%，准确率为69.2%。结直肠癌患者的RF基因表达数据能够有效地对FOLFOX治疗的应答者进行分层，在抗癌治疗中使用药物基因组学是计划个性化治疗的第一步。

To establish the procedures to identify responders to FOLFOX therapy, 83 colorectal cancer (CRC) patients including 42 responders and 41 non-responders were divided into training (54 patients) and test (29 patients) sets. Using Random Forests (RF) algorithm in the training set, predictor genes for FOLFOX therapy were identified, which were applied to test samplesIn the training set, 22 of 27 responders (81.4%) and 23 of 27 non-responders (85.1%) were correctly classified. To improve the prediction model, we removed the outliers determined by RF, and the model could correctly classify 21 of 23 responders (91.3%) and 22 of 23 non-responders (95.6%) in the training set, and 80.0% sensitivity and 92.8% specificity, with an accuracy of 69.2% in 29 independent test samples. RF on gene-expression data for CRC patients was effectively able to stratify responders to FOLFOX therapy, and use of pharmacogenomics in anticancer therapy is the first step in planning personalised therapy.