Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.
Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.
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DOI：
10.3390/cancers13040885
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发表时间：
2021-02-20
期刊：
影响因子：
5.2
通讯作者：
Huang RS
中科院分区：
文献类型：
--
作者：
Gruener RF;Ling A;Chang YF;Morrison G;Geeleher P;Greene GL;Huang RS
虽然诸如癌症基因组图谱（TCGA）的患者数据集通常包含过多的“组学”数据，但是相应的药物反应信息是有限的并且不适合于新药发现。通过整合体外高通量药物筛选数据和患者肿瘤分子信息，我们创建了一个虚拟药物筛选管道，使药物发现与患者群体的生物标志物同时鉴定。使用三阴性乳腺癌（TNBC）作为我们感兴趣的人群，我们展示了从铅识别到生物标志物发现，再到化合物AZD-1775的体外和体内验证的流程。(1)背景：药物插补方法通常旨在将体外药物反应转化为体内药物疗效预测。虽然通常用于回顾性分析，我们的目的是研究使用药物预测方法产生的新药发现假说。三阴性乳腺癌（TNBC）是一个严峻的临床挑战，需要新的治疗方法。(2)研究方法：我们使用了一种成熟的机器学习方法来建立基于细胞系转录组数据的药物反应模型，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和患者临床特征之间的关系。(3)结果：我们的分析概括了TNBC中的几个可疑漏洞，并确定了一些感兴趣的化合物。AZD-1775是一种Wee 1抑制剂，预计在TNBC中具有优先活性（p < 2.2 × 10−16），其疗效与TP 53突变高度相关（p = 1.2 × 10−46）。我们使用独立的细胞系筛选数据和途径分析验证了这些发现。此外，在TNBC异种移植小鼠模型中，AZD-1775与标准治疗紫杉醇联合给药能够抑制肿瘤生长（p < 0.05）并增加存活率（p < 0.01）。(4)结论：总的来说，这项研究提供了一个框架，将任何癌症转录组数据集转化为药物发现的数据集。使用这个框架，人们可以快速地为感兴趣的癌症人群生成有意义的药物发现假设。
While patient datasets such as The Cancer Genome Atlas (TCGA) often contain a plethora of “-omics” data, the corresponding drug response information are limited and not suited for novel drug discovery. By integrating in vitro high throughput drug screening data and patient tumor molecular information, we created a virtual drug screening pipeline that enables drug discovery with simultaneous biomarker identification for a patient population. Using triple-negative breast cancer (TNBC) as our population of interest, we demonstrated the pipeline from lead identification, to biomarker discovery, to in vitro and in vivo validation of the compound AZD-1775. (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 (p < 2.2 × 10−16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10−46). 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 (p < 0.05) and increase survival (p < 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.
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影响因子：
14.9
作者：
Colaprico A;Silva TC;Olsen C;Garofano L;Cava C;Garolini D;Sabedot TS;Malta TM;Pagnotta SM;Castiglioni I;Ceccarelli M;Bontempi G;Noushmehr H
通讯作者：
Noushmehr H
影响因子：
9.5
作者：
Azuaje F
通讯作者：
Azuaje F
影响因子：
22.7
作者：
Corsello, Steven M.;Nagari, Rohith T.;Golub, Todd R.
通讯作者：
Golub, Todd R.
影响因子：
64.5
作者：
Hoadley KA;Yau C;Wolf DM;Cherniack AD;Tamborero D;Ng S;Leiserson MDM;Niu B;McLellan MD;Uzunangelov V;Zhang J;Kandoth C;Akbani R;Shen H;Omberg L;Chu A;Margolin AA;Van't Veer LJ;Lopez-Bigas N;Laird PW;Raphael BJ;Ding L;Robertson AG;Byers LA;Mills GB;Weinstein JN;Van Waes C;Chen Z;Collisson EA;Cancer Genome Atlas Research Network;Benz CC;Perou CM;Stuart JM
通讯作者：
Stuart JM
影响因子：
12.3
作者：
Geeleher P;Cox NJ;Huang RS
通讯作者：
Huang RS