Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy
In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated th...
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/9/9/278 |
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| author | Jin Li Yang Huo Xue Wu Enze Liu Zhi Zeng Zhen Tian Kunjie Fan Daniel Stover Lijun Cheng Lang Li |
| author_facet | Jin Li Yang Huo Xue Wu Enze Liu Zhi Zeng Zhen Tian Kunjie Fan Daniel Stover Lijun Cheng Lang Li |
| author_sort | Jin Li |
| collection | DOAJ |
| description | In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction. |
| first_indexed | 2024-03-10T16:30:37Z |
| format | Article |
| id | doaj.art-5735c7087c664af097e7f6ec83057eac |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-10T16:30:37Z |
| publishDate | 2020-09-01 |
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| spelling | doaj.art-5735c7087c664af097e7f6ec83057eac2023-11-20T12:53:03ZengMDPI AGBiology2079-77372020-09-019927810.3390/biology9090278Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination SynergyJin Li0Yang Huo1Xue Wu2Enze Liu3Zhi Zeng4Zhen Tian5Kunjie Fan6Daniel Stover7Lijun Cheng8Lang Li9Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43202, USASchool of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USADepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43202, USASchool of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USADepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43202, USADepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43202, USADepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43202, USADivision of Medical Oncology, Department of Medicine, The Ohio State University, Columbus, OH 43202, USADepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43202, USADepartment of Biomedical Informatics, The Ohio State University, Columbus, OH 43202, USAIn the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.https://www.mdpi.com/2079-7737/9/9/278drug-combination synergy predictiondrug targetgene essentialitygene expressionKEGG pathway |
| spellingShingle | Jin Li Yang Huo Xue Wu Enze Liu Zhi Zeng Zhen Tian Kunjie Fan Daniel Stover Lijun Cheng Lang Li Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy Biology drug-combination synergy prediction drug target gene essentiality gene expression KEGG pathway |
| title | Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy |
| title_full | Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy |
| title_fullStr | Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy |
| title_full_unstemmed | Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy |
| title_short | Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy |
| title_sort | essentiality and transcriptome enriched pathway scores predict drug combination synergy |
| topic | drug-combination synergy prediction drug target gene essentiality gene expression KEGG pathway |
| url | https://www.mdpi.com/2079-7737/9/9/278 |
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