Article ; Online: NeoAgDT: Optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.
Bioinformatics (Oxford, England)
2024
Abstract: Motivation: Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying ...
Abstract | Motivation: Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity. Results: Here, we present NeoAgDT, a two-step approach consisting of: (1) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (2) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally-validated neoantigens over ranking-based approaches in a study of seven patients. Availability: The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt. Supplementary information: Supplementary data are available at Bioinformatics online. |
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Language | English |
Publishing date | 2024-04-13 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 1422668-6 |
ISSN | 1367-4811 ; 1367-4803 |
ISSN (online) | 1367-4811 |
ISSN | 1367-4803 |
DOI | 10.1093/bioinformatics/btae205 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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