Gruener Robert F, Ling Alexander, Chang Ya-Fang, Morrison Gladys, Geeleher Paul, Greene Geoffrey L, Huang R Stephanie
Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA.
Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA.
Cancers (Basel). 2021 Feb 20;13(4):885. doi: 10.3390/cancers13040885.
(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC ( < 2.2 × 10) and its efficacy was highly associated with mutations ( = 1.2 × 10). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth ( < 0.05) and increase survival ( < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.
(1) 背景：药物估算方法通常旨在将体外药物反应转化为体内药物疗效预测。虽然常用于回顾性分析，但我们的目的是研究药物预测方法在生成新的药物发现假设方面的应用。三阴性乳腺癌（TNBC）是一种急需新疗法的严峻临床挑战。(2) 方法：我们使用一种既定的机器学习方法，基于细胞系转录组数据构建药物反应模型，然后将其应用于患者肿瘤数据，以获得1000多名乳腺癌患者中数百种药物的预测敏感性评分。然后，我们研究了预测的药物反应与患者临床特征之间的关系。(3) 结果：我们的分析重现了TNBC中几个可疑的脆弱性，并确定了一些感兴趣的化合物。Wee1抑制剂AZD - 1775预计在TNBC中具有优先活性（<2.2×10），其疗效与 突变高度相关（=1.2×10）。我们使用独立的细胞系筛选数据和通路分析验证了这些发现。此外，在TNBC的异种移植小鼠模型中，将AZD - 1775与标准护理药物紫杉醇联合使用能够抑制肿瘤生长（<0.05）并延长生存期（<0.01）。(4) 结论：总体而言，本研究提供了一个框架，可将任何癌症转录组数据集转化为药物发现数据集。使用这个框架，可以快速为感兴趣的癌症群体生成有意义的药物发现假设。
