TCR Repertoire Analysis Agent

The TCR Repertoire Analysis Agent provides comprehensive T-cell receptor repertoire analysis for cancer immunology applications. It leverages deep learning and multi-layer machine learning approaches to analyze TCR diversity, predict immunotherapy response, identify tumor-reactive TCRs, and support therapeutic TCR selection for cancer immunotherapy.

When to Use This Skill

• When analyzing TCR repertoire for cancer diagnosis and staging.
• For predicting immunotherapy (anti-PD-1/PD-L1) response from TCR profiles.
• To identify tumor-reactive TCRs for adoptive cell therapy.
• When monitoring treatment response through TCR clonality changes.
• For selecting therapeutic TCRs for TCR-T cell therapy development.

Core Capabilities

1. Repertoire Diversity Analysis: Quantify TCR diversity, clonality, and convergence.

2. Cancer Diagnosis: Distinguish cancer types from TCR signatures.

3. Immunotherapy Response Prediction: Predict checkpoint inhibitor response.

4. Tumor-Reactive TCR Identification: Find neoantigen-specific TCRs.

5. TCR-pMHC Binding Prediction: Predict TCR epitope specificity.

6. Clonal Dynamics Tracking: Monitor TCR clones during treatment.

TCR Repertoire Metrics

Workflow

1. Input: TCR-seq data (bulk or single-cell), clinical metadata.

2. Preprocessing: CDR3 extraction, error correction, clustering.

3. Repertoire Analysis: Calculate diversity, clonality, convergence.

4. ML Classification: Cancer type, stage, response prediction.

5. TCR Prioritization: Rank tumor-reactive TCR candidates.

6. TCR-pMHC Prediction: Predict epitope specificity.

7. Output: Repertoire metrics, predictions, therapeutic candidates.

Example Usage

User: "Analyze the TCR repertoire from this melanoma patient's tumor and blood to predict immunotherapy response and identify tumor-reactive TCRs."

Agent Action:

python3 Skills/Immunology_Vaccines/TCR_Repertoire_Analysis_Agent/tcr_repertoire_analysis.py \
    --tumor_tcr tumor_tils.tsv \
    --blood_tcr pbmc_tcrs.tsv \
    --cancer_type melanoma \
    --hla_type HLA-A*02:01,HLA-B*07:02 \
    --neoantigens patient_neoantigens.fasta \
    --task response_prediction,tcr_identification \
    --output tcr_analysis/

Input Formats

Output Components

Response Prediction Features

AI/ML Components

Cancer Classification:

• Multi-layer ensemble (XGBoost, RF, SVM)
• TCR embedding networks
• Attention-based sequence models

Response Prediction:

• Cox regression with TCR features
• Deep survival analysis
• Multi-task learning (response + survival)

TCR-pMHC Prediction:

• AlphaFold3-based structural prediction
• Transformer models (TCR-BERT)
• Contrastive learning embeddings

Clinical Applications

Performance Benchmarks

Prerequisites

• Python 3.10+
• MiXCR, TRUST4 for TCR calling
• immunarch, tcrdist3
• PyTorch, transformers
• AlphaFold3 (optional, for structure)

Related Skills

• TCR_pMHC_Prediction_Agent - Detailed TCR-epitope prediction
• Neoantigen_Prediction_Agent - Neoantigen identification
• TME_Immune_Profiling_Agent - Broader immune context
• TCell_Exhaustion_Analysis_Agent - T cell phenotyping

TCR Sequence Analysis

Therapeutic TCR Selection Criteria

Special Considerations

1. Sample Quality: Fresh samples preferred for TIL analysis
2. Sequencing Depth: Sufficient depth for rare clones
3. Batch Effects: Normalize across sequencing runs
4. HLA Context: TCR analysis requires HLA typing
5. Paired Chains: Single-cell for alpha-beta pairing

Cancer-Specific TCR Signatures

Author

AI Group - Biomedical AI Platform