A subcomponent-guided deep learning method for interpretable cancer drug response prediction.
Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011382 |
| _version_ | 1797692834903490560 |
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| author | Xuan Liu Wen Zhang |
| author_facet | Xuan Liu Wen Zhang |
| author_sort | Xuan Liu |
| collection | DOAJ |
| description | Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level 'subcomponents', such as privileged substructures of the drug or gene signatures of the cancer cell, thus producing predictions that are hard to explain. Herein, we present SubCDR, a subcomponent-guided deep learning method for interpretable CDR prediction, to recognize the most relevant subcomponents driving response outcomes. Technically, SubCDR is built upon a line of deep neural networks that enables a set of functional subcomponents to be extracted from each drug and cell line profile, and breaks the CDR prediction down to identifying pairwise interactions between subcomponents. Such a subcomponent interaction form can offer a traceable path to explicitly indicate which subcomponents contribute more to the response outcome. We verify the superiority of SubCDR over state-of-the-art CDR prediction methods through extensive computational experiments on the GDSC dataset. Crucially, we found many predicted cases that demonstrate the strength of SubCDR in finding the key subcomponents driving responses and exploiting these subcomponents to discover new therapeutic drugs. These results suggest that SubCDR will be highly useful for biomedical researchers, particularly in anti-cancer drug design. |
| first_indexed | 2024-03-12T02:33:19Z |
| format | Article |
| id | doaj.art-e7221077544f48e2919dbdc10a503caa |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-12T02:33:19Z |
| publishDate | 2023-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-e7221077544f48e2919dbdc10a503caa2023-09-05T05:31:10ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-08-01198e101138210.1371/journal.pcbi.1011382A subcomponent-guided deep learning method for interpretable cancer drug response prediction.Xuan LiuWen ZhangAccurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level 'subcomponents', such as privileged substructures of the drug or gene signatures of the cancer cell, thus producing predictions that are hard to explain. Herein, we present SubCDR, a subcomponent-guided deep learning method for interpretable CDR prediction, to recognize the most relevant subcomponents driving response outcomes. Technically, SubCDR is built upon a line of deep neural networks that enables a set of functional subcomponents to be extracted from each drug and cell line profile, and breaks the CDR prediction down to identifying pairwise interactions between subcomponents. Such a subcomponent interaction form can offer a traceable path to explicitly indicate which subcomponents contribute more to the response outcome. We verify the superiority of SubCDR over state-of-the-art CDR prediction methods through extensive computational experiments on the GDSC dataset. Crucially, we found many predicted cases that demonstrate the strength of SubCDR in finding the key subcomponents driving responses and exploiting these subcomponents to discover new therapeutic drugs. These results suggest that SubCDR will be highly useful for biomedical researchers, particularly in anti-cancer drug design.https://doi.org/10.1371/journal.pcbi.1011382 |
| spellingShingle | Xuan Liu Wen Zhang A subcomponent-guided deep learning method for interpretable cancer drug response prediction. PLoS Computational Biology |
| title | A subcomponent-guided deep learning method for interpretable cancer drug response prediction. |
| title_full | A subcomponent-guided deep learning method for interpretable cancer drug response prediction. |
| title_fullStr | A subcomponent-guided deep learning method for interpretable cancer drug response prediction. |
| title_full_unstemmed | A subcomponent-guided deep learning method for interpretable cancer drug response prediction. |
| title_short | A subcomponent-guided deep learning method for interpretable cancer drug response prediction. |
| title_sort | subcomponent guided deep learning method for interpretable cancer drug response prediction |
| url | https://doi.org/10.1371/journal.pcbi.1011382 |
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