A large fraction of human tumor mutations are effectively patient-specific. Therefore, neoantigens may also be based on individual tumor genomes. Deep-sequencing technologies can identify mutations within the protein-coding part of the genome (the exome) and predict potential neoantigens. In mice models, for all novel protein sequences, potential MHC-binding peptides were predicted. The resulting set of potential neoantigens was used to assess T cell reactivity. Exome–based analyses were exploited in a clinical setting, to assess reactivity in patients treated by either tumor-infiltrating lymphocyte (TIL) cell therapy or checkpoint blockade. Neoantigen identification was successful for multiple experimental model systems and human malignancies.
The false-negative rate of cancer exome sequencing is low—i.e.: the majority of neoantigens occur within exonic sequence with sufficient coverage. However, the vast majority of mutations within expressed genes do not produce neoantigens that are recognized by autologous T cells.
As of 2015 mass spectrometry resolution is insufficient to exclude many false positives from the pool of peptides that may be presented by MHC molecules. Instead, algorithms are used to identify the most likely candidates. These algorithms consider factors such as the likelihood of proteasomal processing, transport into the endoplasmic reticulum, affinity for the relevant MHC class I alleles and gene expression or protein translation levels.
The majority of human neoantigens identified in unbiased screens display a high predicted MHC binding affinity. Minor histocompatibility antigens, a conceptually similar antigen class are also correctly identified by MHC binding algorithms. Another potential filter examines whether the mutation is expected to improve MHC binding. The nature of the central TCR-exposed residues of MHC-bound peptides is associated with peptide immunogenicity.