TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response

Area of Science:

• Immunology
• Computational Biology
• Oncology

Background:

• Immune checkpoint blockade (ICB) offers durable cancer responses, but predictors of efficacy are limited.
• CD8+ tumor-specific T cells (TSTs) are critical for ICB, yet their specific functional states linked to therapy 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 correlated 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 functional state of TSTs crucial for ICB efficacy.
• The MPR score serves as a novel, interpretable biomarker for predicting ICB response in NSCLC.
• This framework advances the study of T-cell receptor-linked function in immunotherapy.