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

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.