Potential responders to anticancer therapy for colorectal cancer by predictor genes

为了建立识别FOLFOX治疗应答者的程序，83例结直肠癌（CRC）患者（包括42例应答者和41例无应答者）被分为训练组（54例）和测试组（29例）。在训练集中使用随机森林（RF）算法识别FOLFOX治疗的预测基因，并将其应用于测试样本。在训练集中，27例应答者中有22例（81.4%）正确分类，27例无应答者中有23例（85.1%）正确分类。为了改进预测模型，我们去除了由RF确定的异常值，该模型可以正确分类训练集中23个应答者中的21个（91.3%）和23个无应答者中的22个（95.6%），在29个独立测试样本中，灵敏度为80.0%，特异性为92.8%，准确率为69.2%。基于CRC患者基因表达数据的RF能够有效地对FOLFOX治疗的应答者进行分层，并且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.