Due to cancer's complex nature and variable response to therapy, precision
oncology informed by omics sequence analysis has become the current standard of
care. However, the amount of data produced for each patients makes it difficult
to quickly identify the best treatment regimen. Moreover, limited data
availability has hindered computational methods' abilities to learn patterns
associated with effective drug-cell line pairs. In this work, we propose the
use of contrastive learning to improve learned drug and cell line
representations by preserving relationship structures associated with drug
mechanism of action and cell line cancer types. In addition to achieving
enhanced performance relative to a state-of-the-art method, we find that
classifiers using our learned representations exhibit a more balances reliance
on drug- and cell line-derived features when making predictions. This
facilitates more personalized drug prioritizations that are informed by signals
related to drug resistance.

癌症的复杂性和对治疗的不同反应使得基于组学序列分析的精准肿瘤学成为当前的标准。然而，每个患者产生的大量数据使得快速确定最佳治疗方案变得困难。为了改善对有效药物 - 细胞系对的学习能力，我们提出使用对比学习来改进药物和细胞系的表示，从而保留与药物作用机制和细胞系癌症类型相关的关系结构。除了相对于现有方法实现了更好的性能外，我们发现使用我们学习到的表示的分类器在进行预测时对药物和细胞系特征的依赖更加平衡。这有助于基于与药物耐药性相关的信号提供更个性化的药物优先级选择。