NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.
Bioinformatics
; 40(5)2024 05 02.
Article
in En
| MEDLINE
| ID: mdl-38614133
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.
Full text:
1
Collection:
01-internacional
Health context:
1_ASSA2030
Database:
MEDLINE
Main subject:
Software
/
Cancer Vaccines
/
Antigens, Neoplasm
/
Neoplasms
Limits:
Humans
Language:
En
Journal:
Bioinformatics
Year:
2024
Document type:
Article