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Updated: May 5, 2026
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Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
Published on: March 25, 2020
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Turep: Detecting cross-cancer tumor-reactive T cells in single-cell and spatial transcriptomics data
Wendao Liu1,2, Chia-Hao Tung2, Eva M Sevick-Muraca1,3
1The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
Biorxiv : the Preprint Server for Biology
|May 4, 2026
Summary
Identifying tumor-reactive T cells is crucial for cancer immunity. Turep, a new deep learning method, accurately predicts these cells across cancers using transcriptomics, improving immunotherapy strategies.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Tumor-infiltrating lymphocytes (TILs) are key to anti-tumor immunity.
- Distinguishing tumor-reactive T cells from bystander cells is challenging.
- Current prediction methods lack cross-cancer robustness.
Purpose of the Study:
- To develop a robust, cross-cancer method for predicting tumor-reactive T cells.
- To leverage deep learning and transcriptomics for improved T cell identification.
- To assess the clinical utility of predicted tumor-reactive T cells in immunotherapy.
Main Methods:
- Developed Turep, a deep learning model using single-cell RNA and T cell receptor sequencing data.
- Integrated data from seven human malignancies to identify a pan-cancer signature.
- Employed generative data augmentation to handle data imbalance.
- Extended Turep to spatial transcriptomics data.
Main Results:
- Turep achieved a mean AUC of 0.870 across cancer types, outperforming existing biomarkers.
- Predicted tumor-reactive T cell proportions correlated with immunotherapy response.
- Tumor-reactive T cells were found in specific spatial niches with high antigen presentation.
Conclusions:
- Turep offers a robust and generalizable tool for identifying tumor-reactive T cells.
- The method enhances understanding of T cell spatial organization in tumors.
- Turep facilitates personalized cancer immunotherapy by identifying key immune cells.
