Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• T-cell receptor (TCR) repertoire analysis is crucial for diagnosing immune diseases and developing personalized therapies.
• Current sequence-based and structure-based methods for TCR analysis have limitations, especially with scarce data or for large-scale predictions.

Purpose of the Study:

• To introduce TCRpcDist, a 3D-based computational approach for analyzing TCR similarities based on physico-chemical properties.
• To evaluate TCRpcDist's performance against existing methods in clustering TCRs with similar antigen specificities.

Main Methods:

• TCRpcDist calculates TCR similarities using a metric based on the physico-chemical properties of loop residues predicted to interact with epitopes.
• The approach was validated using private and public datasets, comparing its performance with competing methods.

Main Results:

• TCRpcDist accurately identifies groups of TCRs likely to bind the same epitopes.
• The method was experimentally validated for determining antigen specificities of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients.

Conclusions:

• TCRpcDist is a promising tool for TCR repertoire analysis and TCR deorphanization.
• This approach supports the development of individualized treatments, including cancer immunotherapies.