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Neural network models for sequence-based TCR and HLA association prediction.

Si Liu¹, Philip Bradley^2,3, Wei Sun^1,4,5

¹Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.

Plos Computational Biology

|November 20, 2023

Summary

Understanding T cell receptor (TCR) and human leukocyte antigen (HLA) interactions is crucial for immune research. A new deep learning method, DePTH, accurately predicts TCR-HLA associations and reveals HLA similarities linked to cancer patient survival.

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Streamlined Single Cell TCR Isolation and Generation of Retroviral Vectors for In Vitro and In Vivo Expression of Human TCRs

Published on: September 10, 2017

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

T cells use T cell receptors (TCRs) to recognize foreign antigens presented by human leukocyte antigen (HLA) proteins.
TCR repertoire data holds potential biomarkers for immune-related diseases, but characterizing TCRs requires understanding TCR-HLA associations.
The high diversity and rarity of HLA alleles pose significant challenges for existing methods.

Purpose of the Study:

To develop a novel method for predicting TCR-HLA associations using amino acid sequences.
To overcome the limitations of existing methods in generalizing to unseen HLAs.
To leverage predicted TCR-HLA associations to quantify functional similarities among HLA alleles.

Main Methods:

Proposed a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH).
DePTH predicts TCR-HLA associations directly from amino acid sequences of TCRs and HLAs.
Evaluated DePTH's ability to generalize to novel TCR-HLA pairs not present in the training data.

Main Results:

DePTH accurately predicts TCR-HLA associations, even for unseen HLA and TCR sequences.
The method successfully quantifies functional similarities between different HLA alleles.
These quantified HLA similarities correlate with survival outcomes in cancer patients undergoing immune checkpoint blockade therapy.

Conclusions:

DePTH offers a powerful deep learning approach for predicting TCR-HLA associations.
The method advances our ability to analyze TCR repertoire data and understand immune responses.
Functional HLA similarities derived from DePTH have implications for cancer immunotherapy and patient prognostics.