Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFN$α$ therapy against Myeloproliferative Neoplasms
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866929499718811648 |
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| author | Hermange, Gurvan Vainchenker, William Plo, Isabelle Cournède, Paul-Henry |
| author_facet | Hermange, Gurvan Vainchenker, William Plo, Isabelle Cournède, Paul-Henry |
| contents | Myeloproliferative Neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFN$α$ is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFN$α$ acts on mutated stem cells by inducing their differentiation into progenitor cells; the higher the dose, the higher the effect. We found that the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_10688 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFN$α$ therapy against Myeloproliferative Neoplasms Hermange, Gurvan Vainchenker, William Plo, Isabelle Cournède, Paul-Henry Quantitative Methods Applications Computation Myeloproliferative Neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFN$α$ is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFN$α$ acts on mutated stem cells by inducing their differentiation into progenitor cells; the higher the dose, the higher the effect. We found that the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured. |
| title | Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFN$α$ therapy against Myeloproliferative Neoplasms |
| topic | Quantitative Methods Applications Computation |
| url | https://arxiv.org/abs/2112.10688 |