Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action.
Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action.
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DOI：
10.1038/s41467-020-19563-6
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发表时间：
2020-11-17
影响因子：
16.6
通讯作者：
Huang RS
中科院分区：
文献类型：
--
作者：
Ling A;Huang RS
最近出现的证据表明，许多临床癌症药物组合可能从独立药物作用（IDA）中获得疗效，其中患者仅从药物组合中最有效的单一药物中获益。在这里，我们提出了IDACombo，一种基于IDA的方法，使用来自高通量癌细胞系筛选的单药治疗数据来预测药物组合的疗效。我们表明，IDACombo预测与体外测量的药物组合疗效（将预测疗效与>5000种组合的测量疗效进行比较时，Pearson相关性= 0.93）和系统选择的一组临床试验（预测26项一线治疗试验患者结局统计学显著改善的准确性> 84%）密切一致。最后，我们展示了如何使用IDACombo系统地优先考虑特定癌症环境中的组合开发，为快速将现有的单药治疗细胞系数据转化为有临床意义的药物组合疗效预测提供了一个框架。可以预测药物组合功效的计算模型通常基于药物协同作用。在这里，作者开发了一种不同的方法，使用来自高通量癌细胞系筛选的单药治疗数据来计算预测药物组合的疗效。
Evidence has recently emerged that many clinical cancer drug combinations may derive their efficacy from independent drug action (IDA), where patients only receive benefit from the single most effective drug in a drug combination. Here we present IDACombo, an IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens. We show that IDACombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson’s correlation = 0.93 when comparing predicted efficacies to measured efficacies for >5000 combinations) and in a systematically selected set of clinical trials (accuracy > 84% for predicting statistically significant improvements in patient outcomes for 26 first line therapy trials). Finally, we demonstrate how IDACombo can be used to systematically prioritize combinations for development in specific cancer settings, providing a framework for quickly translating existing monotherapy cell line data into clinically meaningful predictions of drug combination efficacy. Computational models that can predict drug combination efficacy are often based on drug synergy. Here, the authors develop a different approach to computationally predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens.
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影响因子：
64.5
作者：
Basu A;Bodycombe NE;Cheah JH;Price EV;Liu K;Schaefer GI;Ebright RY;Stewart ML;Ito D;Wang S;Bracha AL;Liefeld T;Wawer M;Gilbert JC;Wilson AJ;Stransky N;Kryukov GV;Dancik V;Barretina J;Garraway LA;Hon CS;Munoz B;Bittker JA;Stockwell BR;Khabele D;Stern AM;Clemons PA;Shamji AF;Schreiber SL
通讯作者：
Schreiber SL
影响因子：
2.6
作者：
Foucquier, Julie;Guedj, Mickael
通讯作者：
Guedj, Mickael
DOI：
10.5114/wo.2014.43975
发表时间：
2015
期刊：
Contemporary oncology (Poznan, Poland)
影响因子：
--
作者：
Bukowska B;Gajek A;Marczak A
通讯作者：
Marczak A
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
6.2
作者：
CANTAROVICH, F;BIZOLLON, C;TRAEGER, J
通讯作者：
TRAEGER, J