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Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification

ABSTRACT: Neoantigens, which are expressed on tumor cells, are one of the main targets of an effective anti-tumor T-cell response. Cancer immunotherapies to target neoantigens are of growing interest, and are currently in early human trials, but methods to identify neoantigens either require invasive or difficult-to-obtain clinical specimens, the screening of hundreds to thousands of synthetic peptides or tandem minigenes or are only relevant to specific human leukocyte antigen (HLA) alleles. We apply deep learning to a large (N=74 patients) HLA peptide and genomic dataset from various human tumors to create a computational model of antigen presentation for neoantigen prediction. We show that our model, named EDGE, increases the positive predictive value of HLA antigen prediction by up to 9 fold. We apply EDGE to enable identification of neoantigens and neoantigen-reactive T cells using routine clinical specimens and small numbers of synthetic peptides for most common HLA alleles. EDGE could enable an improved ability to develop neoantigen-targeted immunotherapies for cancer patients.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Roman Yelensky

PROVIDER: MSV000082648 | MassIVE | Sat Jul 21 13:10:00 BST 2018

REPOSITORIES: MassIVE

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