NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 38614133.
- Also identified by DOI 10.1093/bioinformatics/btae205 and PMC identifier 11076149.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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. Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) 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. The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.
Medical subject headings
- Cancer Vaccines
- Neoplasms
- Antigens, Neoplasm
- Software