大规模表征癌细胞系临床相关蛋白的药物反应，这一成果由美国德克萨斯大学MD安德森癌症中心Han Liang和俄勒冈健康与科学大学Gordon B. Mills课题组合作取得。相关论文在线发表在2020年11月5日的《癌细胞》杂志上。

在本研究中，使用反相蛋白阵列对约170种药物化合物进行反应，研究人员在> 12,000个癌细胞系样品中生成并编制了约210种临床相关蛋白的扰动表达谱。研究表明对扰动蛋白质反应信号的整合提供了对耐药机制的理解、提高了对药物敏感性的预测并有助于确定有效的药物组合。

研究人员建立了“蛋白质-药物”连接性系统图，并研发了社区可用的用户数据库。该研究为探究癌细胞的行为以及治疗反应的依赖性提供了丰富的资源，从而实现了广泛的生物医学应用。

据介绍，扰动生物学是用于对定量细胞行为进行建模并了解详细疾病机制的有效方法。然而，癌细胞系不适于大规模蛋白质扰动生物学反应库，这导致了关键的知识缺口。

附：英文原文

Title: Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines

Author: Wei Zhao, Jun Li, Mei-Ju M. Chen, Yikai Luo, Zhenlin Ju, Nicole K. Nesser, Katie Johnson-Camacho, Christopher T. Boniface, Yancey Lawrence, Nupur T. Pande, Michael A. Davies, Meenhard Herlyn, Taru Muranen, Ioannis K. Zervantonakis, Erika von Euw, Andre Schultz, Shwetha V. Kumar, Anil Korkut, Paul T. Spellman, Rehan Akbani, Dennis J. Slamon, Joe W. Gray, Joan S. Brugge, Yiling Lu, Gordon B. Mills, Han Liang

Issue&Volume: 2020-11-05

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

DOI: 10.1016/j.ccell.2020.10.008

Source: https://www.cell.com/cancer-cell/fulltext/S1535-6108(20)30539-0