M&D Data Science Center, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Aichi, Japan.
PLoS One. 2022 May 18;17(5):e0261630. doi: 10.1371/journal.pone.0261630. eCollection 2022.
In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.
近年来，由于精准医学的兴起，药物敏感性预测受到了极大的关注。已经开发了几种用于药物敏感性预测和相关标志物识别的计算方法。然而，大多数先前的研究都忽略了遗传相互作用，尽管复杂疾病（例如癌症）涉及许多基因在分子网络中错综复杂地连接，而不是单个基因的异常。为了有效地预测药物敏感性并理解其机制，我们提出了一种基于样本特异性基因调控网络的可解释药物敏感性预测的新策略，命名为 Xprediction。我们的策略首先估计样本特异性基因调控网络，使我们能够识别细胞系不同临床特征背后的分子相互作用。然后，我们基于估计的样本特异性基因调控网络预测药物敏感性。预测模型基于机器学习方法，即随机森林、核支持向量机和深度神经网络。虽然机器学习模型为预测和分类提供了显著的结果，但我们无法理解模型如何做出决策。换句话说，这些方法存在黑箱问题，因此我们无法识别涉及药物敏感性相关机制的关键分子相互作用。为了解决这个问题，我们提出了一种方法，用于描述每个分子相互作用对药物敏感性预测结果的重要性。该方法使我们能够识别关键的基因-基因相互作用，并根据所识别的标记物来解释预测结果。为了评估我们的策略，我们基于药物敏感性特异性基因调控网络将 Xprediction 应用于 EGFR-TKIs 预测，并确定了用于 EGFR-TKIs 预测的重要分子相互作用。与基于基因表达水平的预测相比，我们的策略有效地进行了药物敏感性预测。我们还通过文献验证了所识别标记物中的大多数与 EGFR-TKIs 相关的机制。我们期望我们的策略成为预测任务和揭示与药理学特征相关的复杂机制（例如获得性耐药或癌细胞敏感性）的有用工具。
