pMTnet Helps Predict T Cell Receptor–Antigen Binding Specificity

Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface neoantigens are recognized by the immune system. The technique, called pMTnet, was published in Nature Machine Intelligence.

“For the immune system, the presence of neoantigens is one of the biggest differences between normal and tumor cells,” said Tianshi Lu, first co-author with Ze Zhang. “If we can figure out which neoantigens stimulate an immune response, then we may be able to use this knowledge in a variety of different ways to fight cancer,” Lu said. 

The team trained a deep learning-based algorithm that they named pMTnet using data from known binding or nonbinding combinations of three different components: neoantigens, major histocompatibility complexes (MHCs), and the T cell receptors (TCRs). They then tested the algorithm against a dataset developed from 30 different studies that had experimentally identified binding or nonbinding neoantigen T cell-receptor pairs. This experiment showed that the new algorithms had a high level of accuracy. The researchers used this new tool to gather insights on neoantigens cataloged in The Cancer Genome Atlas, a public database that holds information from more than 11,000 primary tumors. pMTnet showed that neoantigens generally trigger a stronger immune response compared with tumor-associated antigens. It also predicted which patients had better responses to immune checkpoint blockade therapies and had better overall survival rates.

“As an immunologist, the most significant hurdle currently facing immunotherapy is the ability to determine which antigens are recognized by which T cells in order to leverage these pairings for therapeutic purposes,” said corresponding author Alexandre Reuben.