Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects
Abstract Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast c...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-39122-z |
| _version_ | 1797806626330116096 |
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| author | Sean M. Gross Farnaz Mohammadi Crystal Sanchez-Aguila Paulina J. Zhan Tiera A. Liby Mark A. Dane Aaron S. Meyer Laura M. Heiser |
| author_facet | Sean M. Gross Farnaz Mohammadi Crystal Sanchez-Aguila Paulina J. Zhan Tiera A. Liby Mark A. Dane Aaron S. Meyer Laura M. Heiser |
| author_sort | Sean M. Gross |
| collection | DOAJ |
| description | Abstract Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies. |
| first_indexed | 2024-03-13T06:10:08Z |
| format | Article |
| id | doaj.art-1ec0ec0a7d134ce08b4c270bf0f38682 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T06:10:08Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-1ec0ec0a7d134ce08b4c270bf0f386822023-06-11T11:18:34ZengNature PortfolioNature Communications2041-17232023-06-0114111210.1038/s41467-023-39122-zAnalysis and modeling of cancer drug responses using cell cycle phase-specific rate effectsSean M. Gross0Farnaz Mohammadi1Crystal Sanchez-Aguila2Paulina J. Zhan3Tiera A. Liby4Mark A. Dane5Aaron S. Meyer6Laura M. Heiser7Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science UniversityDepartment of Bioengineering, University of California, Los Angeles; Jonsson Comprehensive Cancer Center, University of California at Los AngelesDepartment of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science UniversityDepartment of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science UniversityDepartment of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science UniversityDepartment of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science UniversityDepartment of Bioengineering, University of California, Los Angeles; Jonsson Comprehensive Cancer Center, University of California at Los AngelesDepartment of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science UniversityAbstract Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.https://doi.org/10.1038/s41467-023-39122-z |
| spellingShingle | Sean M. Gross Farnaz Mohammadi Crystal Sanchez-Aguila Paulina J. Zhan Tiera A. Liby Mark A. Dane Aaron S. Meyer Laura M. Heiser Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects Nature Communications |
| title | Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects |
| title_full | Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects |
| title_fullStr | Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects |
| title_full_unstemmed | Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects |
| title_short | Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects |
| title_sort | analysis and modeling of cancer drug responses using cell cycle phase specific rate effects |
| url | https://doi.org/10.1038/s41467-023-39122-z |
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