Peng Wei, Lin Jiangzhen, Dai Wei, Yu Ning, Wang Jianxin
IEEE J Biomed Health Inform. 2024 Nov 6;PP. doi: 10.1109/JBHI.2024.3492806.
Patients with the same type of cancer often respond differently to identical drug treatments due to unique genomic traits. Accurately predicting a patient's response to drug is crucial in guiding treatment decisions, alleviating patient suffering, and improving cancer prognosis. Current computational methods utilize deep learning models trained on extensive drug screening data to predict anti-cancer drug responses based on features of cell lines and drugs. However, the interaction between cell lines and drugs is a complex biological process involving interactions across various levels, from internal cellular and drug structures to the external interactions among different molecules.To address this complexity, we propose a novel Hierarchical graph representation Learning with Multi-Granularity features (HLMG) algorithm for predicting anti-cancer drug responses. The HLMG algorithm combines features at two granularities: the overall gene expression and pathway substructures of cell lines, and the overall molecular fingerprints and substructures of drugs. Subsequently, it constructs a heterogeneous graph including cell lines, drugs, known cell line-drug responses, and the associations between similar cell lines and similar drugs. Through a graph convolutional network model, the HLMG learns the final cell line and drug representations by aggregating features of their multi-level neighbor in the heterogeneous graph. The multi-level neighbors consist of the node self, directly related drugs/cell lines, and indirectly related similar drugs/cell lines. Finally, a linear correlation coefficient decoder is employed to reconstruct the cell line-drug correlation matrix to predict anti-cancer drug responses. Our model was tested on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) databases. Results indicate that HLMG outperforms other state-of-the-art methods in accurately predicting anti-cancer drug responses.
由于独特的基因组特征，患有相同类型癌症的患者对相同的药物治疗往往有不同的反应。准确预测患者对药物的反应对于指导治疗决策、减轻患者痛苦和改善癌症预后至关重要。当前的计算方法利用在大量药物筛选数据上训练的深度学习模型，根据细胞系和药物的特征来预测抗癌药物反应。然而，细胞系和药物之间的相互作用是一个复杂的生物学过程，涉及从内部细胞和药物结构到不同分子之间的外部相互作用等各个层面的相互作用。为了解决这种复杂性，我们提出了一种用于预测抗癌药物反应的新型多粒度特征层次图表示学习（HLMG）算法。HLMG算法结合了两个粒度的特征：细胞系的整体基因表达和通路子结构，以及药物的整体分子指纹和子结构。随后，它构建了一个包含细胞系、药物、已知的细胞系 - 药物反应以及相似细胞系和相似药物之间关联的异构图。通过图卷积网络模型，HLMG通过聚合异构图中其多级邻居的特征来学习最终的细胞系和药物表示。多级邻居由节点自身、直接相关的药物/细胞系以及间接相关的相似药物/细胞系组成。最后，采用线性相关系数解码器来重建细胞系 - 药物相关矩阵以预测抗癌药物反应。我们的模型在癌症药物敏感性基因组学（GDSC）和癌细胞系百科全书（CCLE）数据库上进行了测试。结果表明，HLMG在准确预测抗癌药物反应方面优于其他现有最先进的方法。
