AI Article Synopsis
- Discovering T-cell receptors (TCRs) for cancer therapies is often slow and costly due to the need for a lot of patient samples.
- To improve efficiency and reduce the reliance on these samples, researchers are using prediction models to identify TCRs specific to cancer epitopes through computational methods.
- This chapter outlines a protocol for training a prediction model using the TCRex webtool, focusing on the WT1 antigen, which is commonly overexpressed in various cancers, and provides a method to compile TCR data from healthy donors for model training.
Article Abstract
Discovery of epitope-specific T-cell receptors (TCRs) for cancer therapies is a time consuming and expensive procedure that usually requires a large amount of patient cells. To maximize information from and minimize the need of precious samples in cancer research, prediction models have been developed to identify in silico epitope-specific TCRs. In this chapter, we provide a step-by-step protocol to train a prediction model using the user-friendly TCRex webtool for the nearly universal tumor-associated antigen Wilms' tumor 1 (WT1)-specific TCR repertoire. WT1 is a self-antigen overexpressed in numerous solid and hematological malignancies with a high clinical relevance. Training of computational models starts from a list of known epitope-specific TCRs which is often not available for new cancer epitopes. Therefore, we describe a workflow to assemble a training data set consisting of TCR sequences obtained from WT1-reactive CD8 T cell clones expanded and sorted from healthy donor peripheral blood mononuclear cells.
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| http://dx.doi.org/10.1016/bs.mcb.2023.08.001 | DOI Listing |
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