T Cell Epitope Prediction in Cancer Immunotherapy

Effective cancer immunotherapy depends on the identification of peptide fragments, or epitopes, that are presented on tumour cells by human leukocyte antigen (HLA) molecules and recognised by cytotoxic T cells. Recent advances combine high-throughput mass spectrometry, computational modelling and machine-learning algorithms to predict which peptides will bind to HLA alleles and elicit a T cell response. These methods enable the prioritisation of neoantigens arising from tumour-specific mutations as well as unmutated, tumour-associated peptides, broadening the repertoire of targetable antigens. By integrating peptide-processing rules, HLA binding affinities and data on naturally eluted ligands, researchers now achieve higher sensitivity and specificity in epitope prediction. This progress has global significance, informing personalised vaccine design, adoptive T cell therapies and peptide-centric chimeric antigen receptors to attack a diversity of malignancies.

Research from Nature Portfolio

One study demonstrated the feasibility of targeting intracellular oncoproteins by engineering chimeric antigen receptors that recognise specific peptides from a key transcriptional regulator in neuroblastoma. Computational modelling guided the selection of peptide-HLA surfaces and counter-panning strategies to avoid cross-reactivity, resulting in potent in vitro tumour cell killing and complete regression in murine models. Another investigation employed deep immunopeptidomic profiling of lung cancer specimens, combining genomics and spatial transcriptomics to compare the peptide repertoires of inflamed versus non-inflamed tumour regions. This work revealed clusters of predicted neoantigens within HLA-I presentation hotspots in T cell-excluded areas, implicating these epitopes in immune editing and suggesting tailored combination therapies based on the patient’s mutational and immune landscape.

T Cell Epitope Prediction in Cancer Immunotherapy publication trend

The graph below shows the total number of articles in t cell epitope prediction in cancer immunotherapy across all publications each year (not limited to Nature Index journals).

Technical terms

Epitope: A short peptide fragment that is bound by HLA molecules and recognised by T cell receptors.

Neoantigen: An epitope arising from a tumour-specific somatic mutation, absent from normal tissues.

HLA (Human Leukocyte Antigen): A family of cell-surface proteins that present peptide epitopes to T cells.

Immunopeptidome: The complete set of peptides naturally presented by HLA molecules on the cell surface.

Chimeric Antigen Receptor (CAR): A synthetic receptor combining a peptide-binding domain with T cell signalling modules to redirect T cells to target cells.

T Cell Receptor (TCR): A membrane-bound protein complex on T cells that recognises peptide-HLA complexes and initiates T cell activation.

References

1. Targeting of intracellular oncoproteins with peptide-centric CARs. Nature (2023).
2. The immunopeptidome landscape associated with T cell infiltration, inflammation and immune editing in lung cancer. Nature Cancer (2023).
3. NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. The Journal of Immunology (2017).
4. Targeting of multiple tumor-associated antigens by individual T cell receptors during successful cancer immunotherapy. Cell (2023).
5. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Research (2018).