tcell-exhaustion-analysis-agent

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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.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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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

Category	Markers	Role
Exhaustion TFs	TOX, TOX2, NR4A1-3	Exhaustion program drivers
Stem-like	TCF7 (TCF1), LEF1, SLAMF6	Progenitor maintenance
Terminal	HAVCR2 (TIM-3), ENTPD1 (CD39), LAYN	Terminal exhaustion
Checkpoints	PDCD1, CTLA4, LAG3, TIGIT	Inhibitory receptors
Effector	GZMB, PRF1, IFNG	Cytotoxic function

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