armored-cart-design-agent
Design armored CAR-T cells with cytokine payloads and resistance mechanisms.
Maintainer FreedomIntelligence · Last updated April 1, 2026
TCR repertoire analysis: V(D)J usage, clonotype dynamics, antigen specificity.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tcr-repertoire-analysis-agent
Skill Snapshot
Source Doc
Repertoire Diversity Analysis: Quantify TCR diversity, clonality, and convergence.
Cancer Diagnosis: Distinguish cancer types from TCR signatures.
Immunotherapy Response Prediction: Predict checkpoint inhibitor response.
Tumor-Reactive TCR Identification: Find neoantigen-specific TCRs.
TCR-pMHC Binding Prediction: Predict TCR epitope specificity.
Clonal Dynamics Tracking: Monitor TCR clones during treatment.
| Metric | Definition | Clinical Significance |
|---|---|---|
| Clonality | Gini coefficient of clone sizes | Immune focusing |
| Shannon Entropy | Diversity measure | Immune breadth |
| Richness | Unique clonotypes | Repertoire depth |
| Top Clone % | Largest clone fraction | Dominant response |
| Convergent TCRs | Shared across patients | Public epitope response |
| Tumor-Infiltrating % | TIL-derived TCRs | Tumor reactivity |
Input: TCR-seq data (bulk or single-cell), clinical metadata.
Preprocessing: CDR3 extraction, error correction, clustering.
Repertoire Analysis: Calculate diversity, clonality, convergence.
ML Classification: Cancer type, stage, response prediction.
TCR Prioritization: Rank tumor-reactive TCR candidates.
TCR-pMHC Prediction: Predict epitope specificity.
Output: Repertoire metrics, predictions, therapeutic candidates.
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