ArticleBioinformatics (Oxford, England)2024
NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- From virtual to reality: innovative practices of digital twins in tumor therapy.Journal of translational medicine · 2025Review
- Article
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4 authors.
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Abstract
motivationNeoantigen 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.
resultsHere, 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. AVAILABILITY AND IMPLEMENTATION: The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.
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