---name: tcell-exhaustion-analysis-agent description: AI-powered analysis of T-cell exhaustion states, epigenetic scarring, stem-like T-cell populations, and checkpoint blockade response prediction in cancer immunotherapy. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• tcell-exhaustion-analysis-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

T-Cell Exhaustion Analysis Agent

The T-Cell Exhaustion Analysis Agent provides comprehensive profiling of T-cell dysfunction states in cancer and chronic infection. It analyzes exhaustion signatures, identifies stem-like progenitor populations, characterizes epigenetic scarring, and predicts checkpoint immunotherapy response.

When to Use This Skill

• When profiling tumor-infiltrating lymphocyte (TIL) exhaustion states from scRNA-seq data.
• To identify stem-like exhausted T-cells (Tex-prog) that predict checkpoint blockade response.
• For analyzing epigenetic exhaustion programs via ATAC-seq or CUT&Tag.
• To assess exhaustion reversal potential and re-exhaustion risk.
• When designing combination immunotherapy strategies.

Core Capabilities

1. Exhaustion State Classification: Distinguishes progenitor exhausted (Tex-prog), intermediate, and terminally exhausted (Tex-term) populations using transcriptional signatures.

2. Stem-like T-Cell Detection: Identifies TCF1+ stem-like exhausted cells that sustain anti-tumor immunity and respond to PD-1 blockade.

3. Epigenetic Scarring Analysis: Characterizes chromatin accessibility patterns that maintain exhaustion programs despite checkpoint blockade.

4. Checkpoint Expression Profiling: Quantifies inhibitory receptors (PD-1, TIM-3, LAG-3, TIGIT, CTLA-4) at single-cell resolution.

5. Response Prediction: Machine learning models predict checkpoint blockade response based on exhaustion profiles.

6. TME Interaction Analysis: Maps suppressive cell interactions (Tregs, MDSCs, TAMs) promoting exhaustion.

Exhaustion Signatures

Progenitor Exhausted (Tex-prog):

• TCF1+, SLAMF6+, PD-1+
• Self-renewal capacity
• Proliferative burst upon checkpoint blockade
• Good prognosis marker

Terminal Exhausted (Tex-term):

• TCF1-, TIM-3+, CD39+
• Effector-like but dysfunctional
• Limited proliferative potential
• Epigenetically fixed exhaustion

Workflow

1. Input: scRNA-seq, CITE-seq, or scATAC-seq data from TILs or PBMCs.

2. Preprocessing: Quality control, normalization, batch correction.

3. Clustering: Identify T-cell subsets and exhaustion states.

4. Signature Scoring: Apply exhaustion gene signatures (TOX, NR4A, NFAT targets).

5. Epigenetic Analysis: Assess chromatin accessibility at exhaustion loci.

6. Prediction: Model checkpoint response from exhaustion profiles.

7. Output: Exhaustion state proportions, stem-like cell fractions, response predictions.

Example Usage

User: "Analyze T-cell exhaustion states in this TIL scRNA-seq dataset and predict anti-PD-1 response."

Agent Action:

python3 Skills/Immunology_Vaccines/TCell_Exhaustion_Analysis_Agent/exhaustion_analyzer.py \
    --input til_scrnaseq.h5ad \
    --tcells CD8A+CD3E+ \
    --signatures exhaustion_signatures.gmt \
    --epigenetic til_scatacseq.h5ad \
    --predict_response true \
    --output exhaustion_report/

Key Markers and Genes

Epigenetic Exhaustion Program

The exhaustion epigenetic landscape is largely resistant to checkpoint blockade:

• Stable open chromatin at exhaustion-associated genes (TOX, NR4A, checkpoint loci)
• Epigenetic scars maintained even after PD-1 therapy
• Re-exhaustion occurs upon cessation of checkpoint blockade
• Therapeutic implications: Epigenetic modifiers may enhance durability

Prerequisites

• Python 3.10+
• Scanpy/Seurat for scRNA-seq
• ArchR/Signac for scATAC-seq
• CellTypist or custom classifiers

Related Skills

• CAR_T_Design - For engineering exhaustion-resistant CAR-T cells
• Immune_Repertoire_Analysis - For TCR clonotype tracking
• Tumor_Microenvironment - For TIL context analysis

Clinical Implications

1. Patient Selection: High stem-like Tex predicts checkpoint response
2. Combination Therapy: TIGIT + PD-1 for resistant tumors
3. Epigenetic Therapy: DNMT/HDAC inhibitors to reprogram exhausted cells
4. CAR-T Engineering: TOX knockout to prevent CAR-T exhaustion

Author

AI Group - Biomedical AI Platform