Patients of the same cancer may differ in their responses to a specific medical therapy. Identification of predictive molecular features for drug sensitivity holds the key in the era of precision medicine. Human cell lines have harbored most of the same genetic changes found in patients’ tumors and thus are widely used in the research of drug response. In this work, we formulated drug-response prediction as a recommender system problem and then adopted a neighbor-based collaborative filtering with global effect removal (NCFGER) method to estimate anti-cancer drug responses of cell lines by integrating cell-line similarity networks and drug similarity networks based on the fact that similar cell lines and similar drugs exhibit similar responses. Specifically, we removed the global effect in the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. We then used the K most similar neighbors (hybrid of cell-line-oriented and drug-oriented) in the available responses to predict the unknown ones. Through 10-fold cross-validation, this approach was shown to reach accurate and reproducible outcomes of drug sensitivity. We also discussed the biological outcomes based on the newly predicted response values.

患有相同癌症的患者对特定药物疗法的反应可能有所不同。鉴定药物敏感性的预测分子特征是精密医学时代的关键。人类细胞系具有与患者肿瘤中发现的大多数相同的遗传变化，因此被广泛用于药物反应的研究。在这项工作中，我们将药物反应预测公式化为推荐系统问题，然后采用基于邻居的全局效应去除协同过滤（NCFGER）方法，通过整合细胞系相似性网络和基于相似细胞系和相似药物表现出相似反应这一事实的药物相似性网络。具体来说，我们删除了可用响应中的全局效应，并缩小了每个细胞系对以及每个药物对的相似性评分。然后，我们在可用响应中使用K个最相似的邻居（面向细胞系和面向药物的杂种）来预测未知的邻居。通过10倍交叉验证，该方法被证明可以达到准确而可重复的药物敏感性结果。我们还讨论了基于新预测的反应值的生物学结果。