Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies
While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the siz...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2022.1032875/full |
| _version_ | 1811177686802890752 |
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| author | Shengnan She Hengwei Chen Wei Ji Mengqiu Sun Jiaxi Cheng Mengjie Rui Chunlai Feng |
| author_facet | Shengnan She Hengwei Chen Wei Ji Mengqiu Sun Jiaxi Cheng Mengjie Rui Chunlai Feng |
| author_sort | Shengnan She |
| collection | DOAJ |
| description | While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications. |
| first_indexed | 2024-04-11T06:05:14Z |
| format | Article |
| id | doaj.art-7c4594aba65948bd86991c9c8cf56724 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-11T06:05:14Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-7c4594aba65948bd86991c9c8cf567242022-12-22T04:41:30ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122022-12-011310.3389/fphar.2022.10328751032875Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapiesShengnan SheHengwei ChenWei JiMengqiu SunJiaxi ChengMengjie RuiChunlai FengWhile synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.https://www.frontiersin.org/articles/10.3389/fphar.2022.1032875/fullanti-cancer combination therapyhigh-order drug combinationscancer cell subtype-specific modelsdeep learning frameworkprecision oncology |
| spellingShingle | Shengnan She Hengwei Chen Wei Ji Mengqiu Sun Jiaxi Cheng Mengjie Rui Chunlai Feng Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies Frontiers in Pharmacology anti-cancer combination therapy high-order drug combinations cancer cell subtype-specific models deep learning framework precision oncology |
| title | Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies |
| title_full | Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies |
| title_fullStr | Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies |
| title_full_unstemmed | Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies |
| title_short | Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies |
| title_sort | deep learning based multi drug synergy prediction model for individually tailored anti cancer therapies |
| topic | anti-cancer combination therapy high-order drug combinations cancer cell subtype-specific models deep learning framework precision oncology |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2022.1032875/full |
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