TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response
Cao, S.; Cheng, J.; Wang, F.; Yi, C.; Chen, J.; Wang, K.; Liu, L.; Liu, J.; Li, Y.
Show abstract
Immune checkpoint blockade (ICB) can produce durable responses in cancer, but reliable predictors of benefit are still lacking. CD8 tumor-specific T cells are essential for ICB efficacy, yet it remains unclear which functional states of these cells determine therapeutic success. To address this, we developed TSTScope, an interpretable deep learning framework that integrates single-cell transcriptomic and T-cell receptor sequencing data to generate unified representations of CD8 T-cell identity. By applying TSTScope to non-small cell lung cancer (NSCLC) datasets, we characterized the gene programs defining tumor specificity and computationally inferred a population of potential TSTs. Crucially, we demonstrate that clinical response is not a product of TST abundance, but is instead governed by their functional state. We derived the MPR score, a metric capturing this functional potential, which proved to be a robust predictor of treatment outcomes. In an independent validation cohort, the MPR score significantly outperformed established biomarkers. Collectively, TSTScope identifies a distinct functional state of tumor-specific T cells as a primary determinant of ICB efficacy, providing both a mechanistic framework and a potent tool for precision immunotherapy.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses 97%
- Two-Stage CD8+ CAR T-Cell Differentiation in Patients with Large B-Cell Lymphoma 97%
- APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence 97%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Simultaneous analysis of pMHC binding and reactivity unveils virus-specific CD8 T cell immunity to a concise epitope set 96%
- Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning 95%
- MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.