NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population

• Published in: Bioinformatics (Oxford, England) Vol. 40; no. 5
• Main Authors: Mösch, Anja, Grazioli, Filippo, Machart, Pierre, Malone, Brandon
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 02.05.2024; Oxford Publishing Limited (England)
• Subjects: Algorithms; Antigen presentation; Antigens, Neoplasm - immunology; Cancer; Cancer vaccines; Cancer Vaccines - immunology; Composition; Computational Biology - methods; Computer Simulation; Digital twins; Heterogeneity; Humans; Immune system; Integer programming; Linear programming; Mutation; Neoantigens; Neoplasms - immunology; Original Paper; Software; Tumors; Vaccines
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btae205

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: (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.