Zhang Lin, Chen Xing, Guan Na-Na, Liu Hui, Li Jian-Qiang
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Front Pharmacol. 2018 Sep 12;9:1017. doi: 10.3389/fphar.2018.01017. eCollection 2018.
Individualized therapies ask for the most effective regimen for each patient, while the patients' response may differ from each other. However, it is impossible to clinically evaluate each patient's response due to the large population. Human cell lines have harbored most of the same genetic changes found in patients' tumors, thus are widely used to help understand initial responses of drugs. Based on the more credible assumption that similar cell lines and similar drugs exhibit similar responses, we formulated drug response prediction as a recommender system problem, and then adopted a hybrid interpolation weighted collaborative filtering (HIWCF) method to predict anti-cancer drug responses of cell lines by incorporating cell line similarity and drug similarity shown from gene expression profiles, drug chemical structure as well as drug response similarity. Specifically, we estimated the baseline based on the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. The similarity scores were then shrunk and weighted by the correlation coefficients drawn from the know response between each pair. Before used to find the K most similar neighbors for further prediction, they went through the case amplification strategy to emphasize high similarity and neglect low similarity. In the last step for prediction, cell line-oriented and drug-oriented collaborative filtering models were carried out, and the average of predicted values from both models was used as the final predicted sensitivity. Through 10-fold cross validation, this approach was shown to reach accurate and reproducible outcome for those missing drug sensitivities. We also found that the drug response similarity between cell lines or drugs may play important role in the prediction. Finally, we discussed the biological outcomes based on the newly predicted response values in GDSC dataset.
个体化治疗需要为每个患者找到最有效的治疗方案，然而患者的反应可能各不相同。然而，由于患者数量众多，临床上不可能评估每个患者的反应。人类细胞系具有患者肿瘤中发现的大多数相同基因变化，因此被广泛用于帮助了解药物的初始反应。基于更可靠的假设，即相似的细胞系和相似的药物表现出相似的反应，我们将药物反应预测制定为一个推荐系统问题，然后采用混合插值加权协同过滤（HIWCF）方法，通过整合基因表达谱、药物化学结构以及药物反应相似性所显示的细胞系相似性和药物相似性，来预测细胞系的抗癌药物反应。具体而言，我们根据可用反应估计基线，并缩小每个细胞系对以及每个药物对的相似性得分。然后，相似性得分通过从每对之间已知反应得出的相关系数进行缩小和加权。在用于找到K个最相似邻居以进行进一步预测之前，它们经过案例放大策略以强调高相似性并忽略低相似性。在预测的最后一步，进行面向细胞系和面向药物的协同过滤模型，并将两个模型预测值的平均值用作最终预测的敏感性。通过10折交叉验证，该方法对于那些缺失的药物敏感性显示出准确且可重复的结果。我们还发现细胞系或药物之间的药物反应相似性可能在预测中起重要作用。最后，我们基于GDSC数据集中新预测的反应值讨论了生物学结果。
