Predicting survival in prospective clinical trials using weakly-supervised QSP
Abstract Quantitative systems pharmacology (QSP) models of cancer immunity provide mechanistic insights into cellular dynamics and drug effects that are difficult to study clinically. However, their inability to predict patient survival mechanistically limits their utility in anti-cancer drug develo...
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| Format: | Article |
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Nature Portfolio
2025-04-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00898-6 |
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| author | Matthew West Kenta Yoshida Jiajie Yu Vincent Lemaire |
| author_facet | Matthew West Kenta Yoshida Jiajie Yu Vincent Lemaire |
| author_sort | Matthew West |
| collection | DOAJ |
| description | Abstract Quantitative systems pharmacology (QSP) models of cancer immunity provide mechanistic insights into cellular dynamics and drug effects that are difficult to study clinically. However, their inability to predict patient survival mechanistically limits their utility in anti-cancer drug development. To overcome this, we link virtual patients from a QSP model to real clinical trial patients. Using data from atezolizumab trials in non-small cell lung cancer, we show that tumor-based linkage effectively captures survival outcomes. By treating linked survival and censoring as weak supervision labels, we trained survival models using only QSP model covariates, without clinical covariates. Our approach also predicts survival for treatments not included in training data. Specifically, we accurately estimated survival hazard ratios (HR) for chemotherapy monotherapy and atezolizumab plus chemotherapy combination. The predicted HR of 0.70 (95% prediction interval [PI] 0.55–0.86) closely matches the observed HR of 0.79 (95% PI 0.64–0.98) from the IMpower130 trial. |
| format | Article |
| id | doaj-art-d6cd18e2502d4afb9866f24cd1757e38 |
| institution | OA Journals |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-d6cd18e2502d4afb9866f24cd1757e382025-08-20T02:24:26ZengNature Portfolionpj Precision Oncology2397-768X2025-04-01911810.1038/s41698-025-00898-6Predicting survival in prospective clinical trials using weakly-supervised QSPMatthew West0Kenta Yoshida1Jiajie Yu2Vincent Lemaire3Department of Biostatistics, Harvard T.H. Chan School of Public HealthModeling and Simulation, Clinical Pharmacology, Genentech Inc.Clinical Pharmacology, Genentech Inc.Modeling and Simulation, Clinical Pharmacology, Genentech Inc.Abstract Quantitative systems pharmacology (QSP) models of cancer immunity provide mechanistic insights into cellular dynamics and drug effects that are difficult to study clinically. However, their inability to predict patient survival mechanistically limits their utility in anti-cancer drug development. To overcome this, we link virtual patients from a QSP model to real clinical trial patients. Using data from atezolizumab trials in non-small cell lung cancer, we show that tumor-based linkage effectively captures survival outcomes. By treating linked survival and censoring as weak supervision labels, we trained survival models using only QSP model covariates, without clinical covariates. Our approach also predicts survival for treatments not included in training data. Specifically, we accurately estimated survival hazard ratios (HR) for chemotherapy monotherapy and atezolizumab plus chemotherapy combination. The predicted HR of 0.70 (95% prediction interval [PI] 0.55–0.86) closely matches the observed HR of 0.79 (95% PI 0.64–0.98) from the IMpower130 trial.https://doi.org/10.1038/s41698-025-00898-6 |
| spellingShingle | Matthew West Kenta Yoshida Jiajie Yu Vincent Lemaire Predicting survival in prospective clinical trials using weakly-supervised QSP npj Precision Oncology |
| title | Predicting survival in prospective clinical trials using weakly-supervised QSP |
| title_full | Predicting survival in prospective clinical trials using weakly-supervised QSP |
| title_fullStr | Predicting survival in prospective clinical trials using weakly-supervised QSP |
| title_full_unstemmed | Predicting survival in prospective clinical trials using weakly-supervised QSP |
| title_short | Predicting survival in prospective clinical trials using weakly-supervised QSP |
| title_sort | predicting survival in prospective clinical trials using weakly supervised qsp |
| url | https://doi.org/10.1038/s41698-025-00898-6 |
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