Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy
Abstract In oncology clinical trials, on-treatment biopsy samples are taken to confirm the mode of action of new molecules, among other reasons. Yet, the time point of sample collection is typically scheduled according to 'Expert Best Guess'. We have developed an approach integrating digit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-022-00636-3 |
| _version_ | 1797642273153875968 |
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| author | L. G. Hutchinson O. Grimm |
| author_facet | L. G. Hutchinson O. Grimm |
| author_sort | L. G. Hutchinson |
| collection | DOAJ |
| description | Abstract In oncology clinical trials, on-treatment biopsy samples are taken to confirm the mode of action of new molecules, among other reasons. Yet, the time point of sample collection is typically scheduled according to 'Expert Best Guess'. We have developed an approach integrating digital pathology and mathematical modelling to provide clinical teams with quantitative information to support this decision. Using digitised biopsies from an ongoing clinical trial as the input to an agent-based mathematical model, we have quantitatively optimised and validated the model demonstrating that it accurately recapitulates observed biopsy samples. Furthermore, the validated model can be used to predict the dynamics of simulated biopsies, with applications from protocol design for phase 1–2 studies to the conception of combination therapies, to personalised healthcare. |
| first_indexed | 2024-03-11T13:57:45Z |
| format | Article |
| id | doaj.art-0130411287c445da90a8746920814fd1 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-11T13:57:45Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-0130411287c445da90a8746920814fd12023-11-02T05:36:06ZengNature Portfolionpj Digital Medicine2398-63522022-07-015111310.1038/s41746-022-00636-3Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapyL. G. Hutchinson0O. Grimm1Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche LtdRoche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche LtdAbstract In oncology clinical trials, on-treatment biopsy samples are taken to confirm the mode of action of new molecules, among other reasons. Yet, the time point of sample collection is typically scheduled according to 'Expert Best Guess'. We have developed an approach integrating digital pathology and mathematical modelling to provide clinical teams with quantitative information to support this decision. Using digitised biopsies from an ongoing clinical trial as the input to an agent-based mathematical model, we have quantitatively optimised and validated the model demonstrating that it accurately recapitulates observed biopsy samples. Furthermore, the validated model can be used to predict the dynamics of simulated biopsies, with applications from protocol design for phase 1–2 studies to the conception of combination therapies, to personalised healthcare.https://doi.org/10.1038/s41746-022-00636-3 |
| spellingShingle | L. G. Hutchinson O. Grimm Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy npj Digital Medicine |
| title | Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy |
| title_full | Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy |
| title_fullStr | Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy |
| title_full_unstemmed | Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy |
| title_short | Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy |
| title_sort | integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy |
| url | https://doi.org/10.1038/s41746-022-00636-3 |
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