癌症反应性T细胞和新抗原特异性T细胞受体的表征

Not all tumor-infiltrating T cells are cancer-reactive (CR). Several studies have reported gene expression signatures associated with CR-T cells. To integrate these findings, 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. The efficacy of Neo-TCR is validated by cross-validation studies and replications in two independent datasets. Together, our findings suggest a new direction for developing biomarkers for cancer detection and monitoring: integrating CR gene expression signatures with neoantigen-specific TCRs.