Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma
Abstract Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-023-00292-7 |
| _version_ | 1797752683643273216 |
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| author | Adèle L’Hostis Jean-Louis Palgen Angélique Perrillat-Mercerot Emmanuel Peyronnet Evgueni Jacob James Bosley Michaël Duruisseaux Raphaël Toueg Lucile Lefèvre Riad Kahoul Nicoletta Ceres Claudio Monteiro |
| author_facet | Adèle L’Hostis Jean-Louis Palgen Angélique Perrillat-Mercerot Emmanuel Peyronnet Evgueni Jacob James Bosley Michaël Duruisseaux Raphaël Toueg Lucile Lefèvre Riad Kahoul Nicoletta Ceres Claudio Monteiro |
| author_sort | Adèle L’Hostis |
| collection | DOAJ |
| description | Abstract Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients’ individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms. |
| first_indexed | 2024-03-12T17:06:55Z |
| format | Article |
| id | doaj.art-3e54093f844f49948535eddc19fa9f5f |
| institution | Directory Open Access Journal |
| issn | 2056-7189 |
| language | English |
| last_indexed | 2024-03-12T17:06:55Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj.art-3e54093f844f49948535eddc19fa9f5f2023-08-06T11:18:23ZengNature Portfolionpj Systems Biology and Applications2056-71892023-07-019111310.1038/s41540-023-00292-7Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinomaAdèle L’Hostis0Jean-Louis Palgen1Angélique Perrillat-Mercerot2Emmanuel Peyronnet3Evgueni Jacob4James Bosley5Michaël Duruisseaux6Raphaël Toueg7Lucile Lefèvre8Riad Kahoul9Nicoletta Ceres10Claudio Monteiro11Novadiscovery SANovadiscovery SANovadiscovery SANovadiscovery SANovadiscovery SANovadiscovery SARespiratory Department and Early Phase, Louis Pradel Hospital, Hospices Civils de Lyon Cancer InstituteJanssen-CilagJanssen-CilagNovadiscovery SANovadiscovery SANovadiscovery SAAbstract Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients’ individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms.https://doi.org/10.1038/s41540-023-00292-7 |
| spellingShingle | Adèle L’Hostis Jean-Louis Palgen Angélique Perrillat-Mercerot Emmanuel Peyronnet Evgueni Jacob James Bosley Michaël Duruisseaux Raphaël Toueg Lucile Lefèvre Riad Kahoul Nicoletta Ceres Claudio Monteiro Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma npj Systems Biology and Applications |
| title | Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma |
| title_full | Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma |
| title_fullStr | Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma |
| title_full_unstemmed | Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma |
| title_short | Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma |
| title_sort | knowledge based mechanistic modeling accurately predicts disease progression with gefitinib in egfr mutant lung adenocarcinoma |
| url | https://doi.org/10.1038/s41540-023-00292-7 |
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