Despite breakthroughs in cancer immunotherapy, most T cells reactive to tumor targets cannot persist in immunosuppressive solid tumors. Identifying molecular programs of T cells sustaining effective antitumor immunity is the center of cancer research. We developed a computational framework named the tumor-resilient T cell (Tres) model. Tres utilizes single-cell transcriptomic data from solid tumors to identify signatures of T cells that are resilient to immunosuppressive signals, including TGF-beta, TRAIL, and PGE2. Analyzing single-cell data cohorts, the Tres model can predict the clinical efficacies of T cells in immune checkpoint blockade and adoptive cell transfer.


  • Search: Query whether a gene is a positive or negative marker of tumor-resilient T cells.
  • Predict: Input gene expression profiles of T cells or T-cell enriched samples, and predict their resilience in solid tumors.
  • Prioritize: Input a gene set and prioritize markers of tumor-resilient T cells for follow ups.
  • Evaluate: Test whether an input gene signature can predict T cell clinical efficacies in immune checkpoint blockade and adoptive cell therapy.