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Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy
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 pathol...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276679/ https://www.ncbi.nlm.nih.gov/pubmed/35821064 http://dx.doi.org/10.1038/s41746-022-00636-3 |
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author | Hutchinson, L. G. Grimm, O. |
author_facet | Hutchinson, L. G. Grimm, O. |
author_sort | Hutchinson, L. G. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9276679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92766792022-07-14 Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy Hutchinson, L. G. Grimm, O. NPJ Digit Med Article 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. Nature Publishing Group UK 2022-07-12 /pmc/articles/PMC9276679/ /pubmed/35821064 http://dx.doi.org/10.1038/s41746-022-00636-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Hutchinson, L. G. Grimm, O. Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy |
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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276679/ https://www.ncbi.nlm.nih.gov/pubmed/35821064 http://dx.doi.org/10.1038/s41746-022-00636-3 |
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