Designing combination therapies with modeling chaperoned machine learning.
Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a com...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007158 |
| _version_ | 1797984443904819200 |
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| author | Yin Zhang Julie M Huynh Guan-Sheng Liu Richard Ballweg Kayenat S Aryeh Andrew L Paek Tongli Zhang |
| author_facet | Yin Zhang Julie M Huynh Guan-Sheng Liu Richard Ballweg Kayenat S Aryeh Andrew L Paek Tongli Zhang |
| author_sort | Yin Zhang |
| collection | DOAJ |
| description | Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy. |
| first_indexed | 2024-04-11T07:02:55Z |
| format | Article |
| id | doaj.art-f501b1749c324b698edad8e4e09907c9 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T07:02:55Z |
| publishDate | 2019-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f501b1749c324b698edad8e4e09907c92022-12-22T04:38:36ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-09-01159e100715810.1371/journal.pcbi.1007158Designing combination therapies with modeling chaperoned machine learning.Yin ZhangJulie M HuynhGuan-Sheng LiuRichard BallwegKayenat S AryehAndrew L PaekTongli ZhangChemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.https://doi.org/10.1371/journal.pcbi.1007158 |
| spellingShingle | Yin Zhang Julie M Huynh Guan-Sheng Liu Richard Ballweg Kayenat S Aryeh Andrew L Paek Tongli Zhang Designing combination therapies with modeling chaperoned machine learning. PLoS Computational Biology |
| title | Designing combination therapies with modeling chaperoned machine learning. |
| title_full | Designing combination therapies with modeling chaperoned machine learning. |
| title_fullStr | Designing combination therapies with modeling chaperoned machine learning. |
| title_full_unstemmed | Designing combination therapies with modeling chaperoned machine learning. |
| title_short | Designing combination therapies with modeling chaperoned machine learning. |
| title_sort | designing combination therapies with modeling chaperoned machine learning |
| url | https://doi.org/10.1371/journal.pcbi.1007158 |
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