TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification.

ABSTRACT:

Background

Neoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Algorithms for the accurate prediction of neoantigens have played a pivotal role in such studies. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. However, the predictive accuracy of immunogenicity of these methods has been shown to be low. Thus, a ranking algorithm to select highly immunogenic neoantigens of patients is needed urgently in research and clinical practice.

Results

We develop TruNeo, an integrated computational pipeline to identify and select highly immunogenic neoantigens based on multiple biological processes.

SUBMITTER: Tang Y 

PROVIDER: S-EPMC7672179 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature