Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition
Abstract Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-023-00405-9 |
| _version_ | 1797414626327003136 |
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| author | Hanwen Wang Theinmozhi Arulraj Holly Kimko Aleksander S. Popel |
| author_facet | Hanwen Wang Theinmozhi Arulraj Holly Kimko Aleksander S. Popel |
| author_sort | Hanwen Wang |
| collection | DOAJ |
| description | Abstract Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models. |
| first_indexed | 2024-03-09T05:36:08Z |
| format | Article |
| id | doaj.art-eb7a0620f4954e65a3aadd7ab5c5b2e4 |
| institution | Directory Open Access Journal |
| issn | 2397-768X |
| language | English |
| last_indexed | 2024-03-09T05:36:08Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj.art-eb7a0620f4954e65a3aadd7ab5c5b2e42023-12-03T12:28:46ZengNature Portfolionpj Precision Oncology2397-768X2023-06-017111410.1038/s41698-023-00405-9Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibitionHanwen Wang0Theinmozhi Arulraj1Holly Kimko2Aleksander S. Popel3Department of Biomedical Engineering, Johns Hopkins University School of MedicineDepartment of Biomedical Engineering, Johns Hopkins University School of MedicineClinical Pharmacology & Quantitative Pharmacology, AstraZenecaDepartment of Biomedical Engineering, Johns Hopkins University School of MedicineAbstract Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.https://doi.org/10.1038/s41698-023-00405-9 |
| spellingShingle | Hanwen Wang Theinmozhi Arulraj Holly Kimko Aleksander S. Popel Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition npj Precision Oncology |
| title | Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition |
| title_full | Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition |
| title_fullStr | Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition |
| title_full_unstemmed | Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition |
| title_short | Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition |
| title_sort | generating immunogenomic data guided virtual patients using a qsp model to predict response of advanced nsclc to pd l1 inhibition |
| url | https://doi.org/10.1038/s41698-023-00405-9 |
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