Presented by Wei Sun
Biostatistics Program
Fred Hutch Cancer Center

T cells are critical components of the human immune system. A human cell presents peptides on its surface using human leukocyte antigen (HLA) proteins. These peptide-HLA complexes are recognized by T cells through interactions with T cell receptors (TCRs). Foreign peptides (e.g., those from virus or cancer somatic mutations) will trigger immune response. A human blood sample can contain millions of unique TCRs, which is a sample from the individual’s TCR repertoire. Understanding the relationship between TCRs and HLAs is essential to elucidate the specificity of the immune response, uncover mechanisms of autoimmunity, and advance targeted immunotherapies. We will first introduce a deep learning method that predicts the associations between TCRs and HLAs. Next, we will use TCRs and T cell gene expression data to study cancer-reactive (CR) T cells. We developed a computational workflow, CAT (Cancer-Associated T cells), which harmonizes existing CR-T cell signatures and applies them to identify CR-T cells across an atlas of one million T cells. Our findings reveal that the abundance of CR-T cells varies across cancer types and that baseline levels of CR-T cells predict patients’ responses to immunotherapy. In parallel, we established a high-throughput computational platform, Neo-TCR, for systematic screening of neoantigen-specific TCRs and their cognate neoantigens. Together, our findings suggest a new direction for developing biomarkers for cancer detection and monitoring: integrating CR gene expression signatures with neoantigen-specific TCRs.

A seminar tea will be held at 2:45 p.m. in University Office Plaza, Room 240. All are Welcome.