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Phage-display profiling of CDR3β loops enables machine learning predictions of NY-ESO-1 specific TCRs

ABSTRACT: Phage-display profiling of CDR3β loops enables machine learning predictions of NY-ESO-1 specific TCRs

PROVIDER: PRJEB76298 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR13231000_1.fastq.gz	Fastqsanger.gz
	ERR13231000_2.fastq.gz	Fastqsanger.gz
	ERR13231001_1.fastq.gz	Fastqsanger.gz
	ERR13231001_2.fastq.gz	Fastqsanger.gz
	ERR13231002_1.fastq.gz	Fastqsanger.gz

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Phage display enables machine learning discovery of cancer antigen specific TCRs

Project description:T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. T-cell epitope recognition is mediated by the binding of the T-Cell Receptor (TCR) and TCRs recognizing clinically relevant epitopes are promising for T-cell based therapies. Starting from one of the few known TCRs targeting the cancer-testis antigen NY-ESO-1 (157–165) epitope, we built large phage display libraries of TCRs with randomized Complementary Determining Region 3 of the β chain. The TCR libraries were panned against the NY-ESO-1 epitope, which enabled us to collect thousands of epitope-specific TCR sequences. We then trained a machine learning TCR-epitope interaction predictor with this data and could identify several epitope-specific TCRs directly from TCR repertoires. Cellular binding and functional assays revealed that the predicted TCRs displayed activity towards the NY-ESO-1 epitope and no detectable cross-reactivity with self-peptides. Overall, our work demonstrates how display technologies combined with machine learning models of TCR-epitope recognition can effectively leverage large TCR repertoires for TCR discovery.

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Project description:Phage-display profiling of CDR3a loops of a NY-ESO-1 specific TCR

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TCR-Engineered Lymphocytes Targeting NY-ESO-1: In Vitro Assessment of Cytotoxicity against Tumors

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