How Peptide-MHC Complexes Present Cancer Targets

Peptide Cancer Immunotherapy

1-10 neoantigens

The average tumor harbors hundreds of somatic mutations, but only 1-10 produce peptide-MHC complexes that T cells actually recognize as foreign, making neoantigen identification a needle-in-a-haystack problem.

Chen et al., Journal for ImmunoTherapy of Cancer, 2021

Chen et al., Journal for ImmunoTherapy of Cancer, 2021

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Every cell in your body continuously chops its own proteins into short peptide fragments and displays them on the cell surface using MHC (major histocompatibility complex) molecules. This molecular show-and-tell allows T cells to survey what proteins each cell is making. Normal peptides on MHC are ignored. But when a cancer cell displays a mutant peptide, one derived from a somatic mutation unique to the tumor, a CD8+ T cell can recognize it as foreign and kill the cell.^[1] This interaction, the peptide-MHC-T cell receptor (TCR) triplet, is the foundation of cancer immunotherapy. Understanding how it works explains why checkpoint inhibitors succeed, why neoantigen vaccines are being developed, and why some tumors escape immune detection entirely. For how peptides are being engineered as alternatives to antibody checkpoint inhibitors, see our pillar article on peptide-based immune checkpoint inhibitors.

The antigen presentation pathway in cancer

Every nucleated cell presents peptides on MHC class I. The pathway operates identically in normal and cancer cells, with one critical difference: cancer cells contain mutant proteins that generate mutant peptides.

Protein degradation. The proteasome, a barrel-shaped enzyme complex in the cytoplasm, degrades intracellular proteins into peptide fragments of varying lengths. Both normal and mutant proteins are processed. The immunoproteasome, an interferon-inducible variant, generates a slightly different peptide repertoire that favors MHC class I binding.

TAP transport. The transporter associated with antigen processing (TAP) moves peptide fragments from the cytoplasm into the ER lumen. TAP selects peptides of 8-16 amino acids with specific C-terminal residues.

MHC loading. Inside the ER, peptides compete for binding to MHC class I molecules. Only peptides that fit the MHC groove with sufficient affinity (governed by anchor residues at positions 2 and 9) form stable complexes. The peptide loading complex, which includes tapasin, ERp57, and calreticulin, facilitates this quality control process.

Surface display. Stable peptide-MHC-I complexes travel through the Golgi to the cell surface, where they are displayed for T cell surveillance. A single cell presents thousands of different peptide-MHC complexes simultaneously, each representing a different protein being produced inside the cell.^[2]

What makes a cancer peptide a neoantigen

A neoantigen is a peptide derived from a tumor-specific somatic mutation that is presented on MHC and recognized by T cells as foreign. Not every mutation generates a neoantigen. The mutation must:

1. Occur in a protein that is expressed in the tumor cell (many mutations fall in non-expressed regions)
2. Produce a peptide fragment that contains the mutant residue (the proteasome may cleave the protein at sites that exclude the mutation)
3. Bind the patient's HLA alleles with sufficient affinity (HLA alleles vary between individuals, so a mutation that generates a neoantigen in one patient may not in another)
4. Be recognized by a T cell receptor in the patient's immune repertoire

De Mattos-Arruda et al.'s 2020 review noted that the average tumor harbors dozens to hundreds of somatic mutations, but only a fraction produce peptides that pass all four filters. Typically, 1-10 mutations per tumor generate bona fide neoantigens.^[3] Tumors with high mutation burdens (melanoma, lung cancer, microsatellite-unstable colorectal cancer) tend to generate more neoantigens, which partly explains their better response to immunotherapy.

For how computational tools predict which mutations will generate MHC-binding peptides, see our article on immunoinformatics and epitope prediction.

Immunopeptidomics: seeing what tumors actually present

Computational prediction tells you which peptides might bind MHC. Immunopeptidomics tells you which peptides actually do. This experimental approach uses mass spectrometry to strip peptides directly from MHC molecules on tumor cell surfaces and identify them.^[4]

Nelde et al.'s 2021 warehouse-based immunopeptidome approach created a reference database of normally presented peptides from healthy tissues.^[4] By comparing tumor immunopeptidomes against this normal reference, truly tumor-specific peptides can be identified with high confidence. Peptides found on tumor MHC but absent from all normal tissue samples are strong neoantigen candidates.

This approach overcomes a limitation of purely computational neoantigen prediction: not every peptide predicted to bind MHC is actually processed and presented. Immunopeptidomics provides direct evidence of presentation, bridging the gap between prediction and reality.

How tumors escape MHC presentation

If MHC presentation allows T cells to detect cancer, tumors face evolutionary pressure to disrupt this process. Several escape mechanisms have been documented:

MHC class I downregulation. Many tumors reduce MHC-I expression, making them partially invisible to CD8+ T cells. This is one of the most common immune evasion strategies, observed in melanoma, lung cancer, breast cancer, and many other tumor types.^[2]

Antigen processing machinery loss. Mutations or epigenetic silencing of proteasome subunits, TAP, tapasin, or beta-2-microglobulin (essential for MHC-I surface expression) reduce or eliminate peptide presentation. Lee et al.'s 2020 review catalogued defects in antigen processing machinery across multiple cancer types.^[2]

Immunoediting. Over time, immune pressure eliminates tumor cells presenting strong neoantigens, selecting for subclones that have lost those mutations or lost the ability to present them. The remaining tumor is enriched for cells that are invisible to the existing T cell response.

PD-L1 expression. Even when peptide-MHC complexes are displayed, tumors can express PD-L1, which binds PD-1 on T cells and delivers an inhibitory signal that prevents killing. This is the checkpoint that peptide PD-1/PD-L1 inhibitors are designed to block.

Therapeutic strategies built on peptide-MHC biology

Neoantigen vaccines

Personalized neoantigen vaccines identify tumor-specific mutations, predict which will generate MHC-binding peptides, synthesize those peptides, and administer them to train the patient's T cells against the tumor. Ding et al.'s 2021 trial used neoantigen-pulsed dendritic cells as the vaccine vehicle, loading the patient's own dendritic cells with predicted neoantigens ex vivo before reinfusing them.^[5]

Chen et al.'s 2021 neoantigen-based peptide vaccine in pancreatic cancer demonstrated that vaccination could elicit neoantigen-specific T cell responses that correlated with improved outcomes.^[6]

For the clinical history of peptide cancer vaccines, see our articles on gp100 peptide vaccine for melanoma and HER2 peptide vaccines for breast cancer.

Checkpoint inhibitors

Antibodies (and now peptides) that block PD-1/PD-L1 or CTLA-4 checkpoints restore T cell killing of tumor cells that display neoantigens on MHC. These drugs do not work by targeting specific neoantigens. Instead, they remove the brakes that prevent existing neoantigen-recognizing T cells from functioning. Tumors with more neoantigens (higher mutation burden) tend to respond better because there are more peptide-MHC targets for the unblocked T cells to recognize.

Adoptive T cell therapy

TCR-engineered T cells are modified to express a TCR specific for a particular peptide-MHC complex on the tumor. Bispecific peptides that bridge T cells and tumors represent a related approach using peptide engineering rather than cell engineering.

The bottleneck: TCR recognition

MHC binding prediction achieves over 97% accuracy. But predicting which peptide-MHC complexes will actually be recognized by a patient's T cell receptors remains the largest unsolved problem in cancer immunology. The TCR repertoire contains millions of unique receptors, and the structural rules governing TCR-pMHC recognition are far more complex than MHC binding motifs alone.

Current approaches to overcoming this bottleneck include:

• Functional assays: Testing whether patient T cells respond to predicted neoantigens ex vivo
• Deep learning models: Training neural networks on TCR-pMHC binding data to predict recognition
• Multimer screening: Using fluorescent peptide-MHC multimers to identify T cells that bind specific neoantigens from patient blood samples

The Bottom Line

Cancer cells display mutant peptide fragments on MHC class I molecules, creating molecular targets that T cells can recognize as foreign. The antigen presentation pathway (proteasome cleavage, TAP transport, MHC loading, surface display) is identical in normal and cancer cells, but tumor-specific mutations generate neoantigens that mark cancer cells for immune destruction. Only 1-10 mutations per tumor typically produce true neoantigens, and tumors actively evade detection by downregulating MHC, losing processing machinery, or expressing checkpoint ligands. Neoantigen vaccines, checkpoint inhibitors, and adoptive T cell therapy all exploit this peptide-MHC biology, with immunopeptidomics and computational prediction providing increasingly precise methods for identifying which peptide-MHC complexes to target.

Frequently Asked Questions

What is a peptide-MHC complex?

A peptide-MHC complex is a short peptide fragment (8-11 amino acids for class I) bound within the groove of a major histocompatibility complex molecule on a cell's surface. Every nucleated cell displays thousands of these complexes, each representing a different protein being produced inside. T cells scan these complexes to determine if the cell is normal or abnormal (infected or cancerous).

What are neoantigens in cancer?

Neoantigens are peptide fragments derived from proteins with tumor-specific somatic mutations. When these mutant peptides bind MHC molecules and are displayed on the cancer cell surface, T cells can recognize them as foreign because they differ from normal self-peptides. Typically only 1-10 out of hundreds of tumor mutations produce peptides that T cells actually recognize.

How does immunopeptidomics work?

Immunopeptidomics strips peptides directly from MHC molecules on cell surfaces using acid elution, then identifies them by mass spectrometry. Comparing peptides found on tumor cells versus normal tissue reveals tumor-specific peptides that are candidates for immunotherapy targeting. This experimental approach validates computational neoantigen predictions.

Why do some cancers respond better to immunotherapy?

Cancers with high mutation burdens (melanoma, lung cancer, MSI-high colorectal cancer) generate more neoantigens displayed on MHC molecules, giving T cells more targets to recognize. Tumors that maintain MHC class I expression and intact antigen processing machinery are also more visible to the immune system and more responsive to checkpoint inhibitors.

How do tumors hide from the immune system?

Tumors evade immune detection by downregulating MHC class I molecules (reducing surface peptide display), losing antigen processing components (proteasome subunits, TAP, beta-2-microglobulin), expressing PD-L1 to inhibit T cells, and immunoediting (losing the mutations that generate the strongest neoantigens through selective pressure from T cell killing).

What is a neoantigen vaccine?

A neoantigen vaccine identifies a patient's tumor-specific mutations through genomic sequencing, predicts which mutations will generate MHC-binding peptides, synthesizes those peptides, and administers them to train the patient's immune system to recognize and attack the tumor. Each vaccine is personalized because each patient's tumor mutations and HLA alleles are unique.