Turep: Detecting cross-cancer tumor-reactive T cells in single-cell and spatial transcriptomics data

Abstract

Tumor-infiltrating lymphocytes are essential for anti-tumor immunity, yet distinguishing tumor-reactive T cells from non-reactive bystander cells remains a significant challenge. Existing signatures, often derived from single cohorts, lack robustness in cross-cancer prediction. We present Turep, a deep learning method designed for robust, cross-cancer prediction of tumor-reactive T cells using single-cell or spatial transcriptomics data. By integrating paired single-cell RNA and T cell receptor sequencing data from seven human malignancies, we identified a pan-cancer tumor-reactive gene signature and leveraged generative data augmentation to address data imbalance. Turep consistently outperformed existing biomarkers, achieving a mean area under the receiver operating characteristic curve of 0.870 across cancer types. In validation across diverse cohorts, we found that Turep-predicted tumor-reactive T cell proportions could predict clinical response to immunotherapy. Furthermore, extending Turep to spatial transcriptomics revealed that tumor-reactive T cells preferentially resided in spatial niches where target cells exhibited elevated antigen presentation. Overall, Turep provides a powerful, generalizable tool for identifying tumor-reactive T cells and their spatial architectures, facilitating personalized cancer immunotherapy strategies.