Anja Mösch, Filippo Grazioli, Pierre Machart, Brandon Malone
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NeoAgDT: Optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.
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.
期刊介绍:
ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.