Prioritizing candidate peptides for cancer vaccines through predicting peptide presentation by HLA-I proteins.

ABSTRACT: Cancer (treatment) vaccines that are made of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising method to reinvigorate the immune response against cancer. A key step to prioritizing neoantigens for cancer vaccines is computationally predicting which neoantigens are presented on the cell surface by a human leukocyte antigen (HLA). We propose to address this challenge by training a neural network using mass spectrometry (MS) data composed of peptides presented by at least one of several HLAs of a subject. We embed the neural network within a mixture model and train the neural network by maximizing the likelihood of the mixture model. After evaluating our method using data sets where the peptide presentation status was known, we applied it to

SUBMITTER: Zhou LY 

PROVIDER: S-EPMC10548401 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature