A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.
One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010200 |
| _version_ | 1797848151320690688 |
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| author | Delora Baptista Pedro G Ferreira Miguel Rocha |
| author_facet | Delora Baptista Pedro G Ferreira Miguel Rocha |
| author_sort | Delora Baptista |
| collection | DOAJ |
| description | One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy. |
| first_indexed | 2024-04-09T18:22:50Z |
| format | Article |
| id | doaj.art-0dbbf7bea0af4d148ac26cef8bbb0f2e |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-09T18:22:50Z |
| publishDate | 2023-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-0dbbf7bea0af4d148ac26cef8bbb0f2e2023-04-12T05:31:03ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-03-01193e101020010.1371/journal.pcbi.1010200A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.Delora BaptistaPedro G FerreiraMiguel RochaOne of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.https://doi.org/10.1371/journal.pcbi.1010200 |
| spellingShingle | Delora Baptista Pedro G Ferreira Miguel Rocha A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. PLoS Computational Biology |
| title | A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. |
| title_full | A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. |
| title_fullStr | A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. |
| title_full_unstemmed | A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. |
| title_short | A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. |
| title_sort | systematic evaluation of deep learning methods for the prediction of drug synergy in cancer |
| url | https://doi.org/10.1371/journal.pcbi.1010200 |
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