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A mathematical model for the quantification of a patient’s sensitivity to checkpoint inhibitors and long-term tumour burden
- Source :
- Nat Biomed Eng
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time course of tumour responses to immune checkpoint inhibitors. The model takes into account intrinsic tumour growth rates, the rates of immune activation and of tumour-immune cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug-cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug-cancer sensitivities in individual patients.
- Subjects :
- 0301 basic medicine
Drug
Oncology
medicine.medical_specialty
Databases, Factual
animal diseases
media_common.quotation_subject
medicine.medical_treatment
Cell
Biomedical Engineering
Medicine (miscellaneous)
chemical and pharmacologic phenomena
Bioengineering
Article
03 medical and health sciences
Antineoplastic Agents, Immunological
0302 clinical medicine
Immune system
Neoplasms
Internal medicine
medicine
Humans
Immune Checkpoint Inhibitors
media_common
Models, Statistical
business.industry
Cancer
Immunotherapy
biochemical phenomena, metabolism, and nutrition
Models, Theoretical
medicine.disease
Immune checkpoint
Tumor Burden
Computer Science Applications
Blockade
Clinical trial
Treatment Outcome
030104 developmental biology
medicine.anatomical_structure
ROC Curve
Area Under Curve
Linear Models
bacteria
business
030217 neurology & neurosurgery
Biotechnology
Subjects
Details
- ISSN :
- 2157846X
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Nature Biomedical Engineering
- Accession number :
- edsair.doi.dedup.....84ec391bd3fcf9822673ed2667394e0e
- Full Text :
- https://doi.org/10.1038/s41551-020-00662-0