基于基因组学特征分布对齐和药物结构信息的癌症药物敏感性预测方法

Prediction of cancer drug sensitivity based on genomic feature distribution alignment and drug structure information

摘要:

近年来精准医学在癌症治疗中得到了广泛的应用，其重点在于如何准确地预测不同的患者对药物治疗的反应。本研究设计了一种基于基因组学特征分布对齐和药物结构信息的癌症药物敏感性预测方法，该方法首先对齐来自细胞系的基因组学特征与来自患者的基因组学特征的分布并去除基因表达数据中的噪声，之后融合药物结构信息，使用多任务学习的方式进行患者药物敏感性预测。结果表明，在癌症相关药物敏感性基因组学数据集(genomics of drug sensitivity in cancer, GDSC)上，此方法的预测结果中均方误差降至0.905 2，相关系数提升至0.875 4，准确率提升至0.836 0，显著优于最近发表的方法，在癌症基因组图谱数据集(the cancer genome atlas, TCGA)上，此方法预测药物敏感性的平均召回率提升至0.571 4，F1-分数提升至0.658 0，表现出优秀的泛化性能。这展现了本方法未来用于辅助选择临床治疗方案的潜力。

Abstract:

In recent years, precision medicine has demonstrated wide applications in cancer therapy, and the focus of precision medicine lies in accurately predicting the responses of different patients to drug treatment. We propose a model for predicting cancer drug sensitivity based on genomic feature distribution alignment and drug structure information. This model initially aligns the genomic features from cell lines with those from patients and removes noise from gene expression data. Subsequently, it integrates drug structure features and employs multi-task learning to predict the drug sensitivity of patients. The experimental results on the genomics of drug sensitivity in cancer (GDSC) dataset indicates that this method achieved a reduced mean square error of 0.905 2, an increased correlation coefficient of 0.875 4, and an enhanced accuracy rate of 0.836 0 which significantly outperformed the recently published methods. On the cancer genome atlas (TCGA) dataset, this method demonstrates an improved average recall rate of 0.571 4 and an increased F1-score of 0.658 0 in predicting drug sensitivity, exhibiting excellent generalization performance. The result demonstrates the potential of this method to assist in the selection of clinical treatment plans in the future.