Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of “protein-drug” connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.

微扰生物学是一种强大的方法来模拟定量细胞行为和了解详细的疾病机制。然而，癌细胞系对扰动的大规模蛋白质反应资源不可用，导致关键的知识差距。在这里，我们使用反相蛋白质阵列生成并编译了超过 12,000 个癌细胞系样本中〜210 种临床相关蛋白质的扰动表达谱，以响应〜170 种药物化合物。我们表明，整合扰动的蛋白质反应信号提供了对耐药性的机械洞察，增加了对药物敏感性的预测能力，并有助于确定有效的药物组合。我们构建了“蛋白质-药物”连通性的系统地图，并开发了一个用户友好的数据门户供社区使用。