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Updated: Aug 7, 2026
Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
Published on: March 25, 2020
TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response
Shiwei Cao1,2, Jinyu Cheng2,3, Feng-Ao Wang2,4
1School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
Abstract:
Immune checkpoint blockade (ICB) can produce durable responses in cancer, but reliable predictors of benefit are still lacking. CD8+ tumor-specific T cells (TSTs) are essential for ICB efficacy, yet it remains unclear which functional states of these cells are associated with therapeutic benefit. 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 (pTSTs). Our analyses show that clinical response is associated with the functional state of these cells rather than their abundance alone. We derived the major pathological response (MPR) score, a metric capturing this functional potential. In an independent validation cohort, the MPR score was associated with pathological response and recurrence-free survival and provided complementary information to selected response-associated biomarkers. Collectively, TSTScope identifies a distinct functional state of tumor-specific T cells linked to ICB response, providing an interpretable framework for studying receptor-linked T-cell function in immunotherapy cohorts.
Insights
Predicting cancer immunotherapy success is challenging. A new tool, TSTScope, identifies functional states of CD8+ T cells, revealing that their activity, not just numbers, predicts response to immune checkpoint blockade (ICB).
Area of Science:
- Immunology
- Oncology
- Computational Biology
Background:
- Immune checkpoint blockade (ICB) offers durable cancer responses but lacks reliable predictive biomarkers.
- CD8+ tumor-specific T cells (TSTs) are crucial for ICB efficacy, yet their specific functional states linked to therapeutic benefit remain undefined.
Purpose of the Study:
- To develop an interpretable deep learning framework, TSTScope, for analyzing CD8+ T cell identity.
- To characterize functional states of TSTs associated with ICB response in non-small cell lung cancer (NSCLC).
Main Methods:
- Integrated single-cell transcriptomic and T-cell receptor sequencing data using TSTScope.
- Characterized gene programs for tumor specificity and inferred potential TSTs (pTSTs).
- Derived the major pathological response (MPR) score to quantify TST functional potential.
Main Results:
- Clinical response to ICB in NSCLC correlates with the functional state of pTSTs, not solely their abundance.
- The MPR score, reflecting TST functional potential, predicted pathological response and recurrence-free survival in an independent cohort.
- MPR score provided complementary predictive information to existing biomarkers.
Conclusions:
- TSTScope identifies a distinct, functionally relevant state of tumor-specific T cells associated with ICB response.
- The MPR score serves as a novel, interpretable biomarker for predicting ICB efficacy.
- This framework enables deeper investigation of T-cell receptor-linked function in immunotherapy.
