Abstract：
Background and objectives: Breast cancer is the most prevalent type of cancer among women. The effectiveness of anticancer pharmacological therapy may get adversely affected by tumor heterogeneity that includes genetic and transcriptomic features. This leads to clinical variability in patient response to therapeutic drugs. Anticancer drug design and cancer understanding require precise identification of cancer drug responses. The performance of drug response prediction models can be improved by integrating multi-omics data and drug structure data. Methods: In this paper, we propose an Autoencoder (AE) and Graph Convolutional Network (AGCN) for drug response prediction, which integrates multi-omics data and drug structure data. Specifically, we first converted the high dimensional representation of each omic data to a lower dimensional representation using an AE for each omic data set. Subsequently, these individual features are combined with drug structure data obtained using a Graph Convolutional Network and given to a Convolutional Neural Network to calculate IC[Formula: see text] values for every combination of cell lines and drugs. Then a threshold IC[Formula: see text] value is obtained for each drug by performing K-means clustering of their known IC[Formula: see text] values. Finally, with the help of this threshold value, cell lines are classified as either sensitive or resistant to each drug. Results: Experimental results indicate that AGCN has an accuracy of 0.82 and performs better than many existing methods. In addition to that, we have done external validation of AGCN using data taken from The Cancer Genome Atlas (TCGA) clinical database, and we got an accuracy of 0.91. Conclusion: According to the results obtained, concatenating multi-omics data with drug structure data using AGCN for drug response prediction tasks greatly improves the accuracy of the prediction task.
摘要：
背景和目的：乳腺癌是女性中最常见的癌症类型。抗癌药物治疗的有效性可能会受到肿瘤异质性的不利影响，包括遗传和转录组特征。这导致患者对治疗药物的反应的临床变异性。抗癌药物的设计和对癌症的理解需要精确识别癌症药物的反应。通过整合多组学数据和药物结构数据可以提高药物反应预测模型的性能。方法:本文,我们提出了一种自动编码器(AE)和图卷积网络(AGCN)用于药物反应预测，它整合了多组学数据和药物结构数据。具体来说,我们首先使用每个omic数据集的AE将每个omic数据的高维表示转换为低维表示。随后，将这些个体特征与使用图卷积网络获得的药物结构数据相结合,并将其提供给卷积神经网络,以计算细胞系和药物的每种组合的IC[公式:见正文]值。然后,通过对每种药物的已知IC[公式:见文本]值进行K均值聚类来获得每种药物的阈值IC[公式:见文本]值。最后,在这个阈值的帮助下，细胞系被分类为对每种药物敏感或抗性。结果：实验结果表明，AGCN的准确率为0.82，并且比许多现有方法更好。除此之外,我们已经使用来自癌症基因组图谱（TCGA）临床数据库的数据对AGCN进行了外部验证，我们得到了0.91的准确度.结论:根据所得结果,使用AGCN进行药物反应预测任务，将多组学数据与药物结构数据连接起来，大大提高了预测任务的准确性。
