Tme Immune Profiling Agent

TME Immune Profiling Agent

The TME Immune Profiling Agent provides comprehensive tumor microenvironment (TME) immune profiling by integrating multiple data modalities including bulk RNA-seq deconvolution, single-cell transcriptomics, spatial transcriptomics, and multiplex immunofluorescence. It enables biomarker discovery for immunotherapy response and TME-based patient stratification.

When to Use This Skill

• When characterizing immune composition of tumor microenvironment.
• For predicting immunotherapy response from TME profiles.
• To identify immune cell states and functional programs.
• When analyzing spatial organization of immune infiltrates.
• For discovering TME-based biomarkers and therapeutic targets.

Core Capabilities

1. Bulk Deconvolution: Estimate immune cell fractions from bulk RNA-seq.

2. Single-Cell Immune Profiling: Deep characterization of immune populations.

3. Spatial Immune Architecture: Map immune cell locations and neighborhoods.

4. Immune Phenotype Classification: Hot/cold/excluded tumor classification.

5. Functional State Analysis: Exhaustion, activation, memory signatures.

6. Response Prediction: Multi-modal immunotherapy response models.

Immune Cell Types Profiled

Deconvolution Methods

Workflow

1. Input: Bulk RNA-seq, scRNA-seq, spatial data, or IHC images.

2. Deconvolution: Estimate cell fractions from bulk data.

3. Single-Cell Analysis: Deep immune phenotyping if available.

4. Spatial Mapping: Localize immune populations in tissue.

5. Integration: Combine modalities for comprehensive profile.

6. Classification: Assign TME phenotype (hot/cold/excluded).

7. Output: Immune profiles, visualizations, response predictions.

Example Usage

User: "Profile the tumor microenvironment of this lung cancer cohort to identify immunotherapy responders."

Agent Action:

python3 Skills/Immunology_Vaccines/TME_Immune_Profiling_Agent/tme_profiling.py \
    --bulk_rna expression_matrix.tsv \
    --scRNA_data scRNA_lung.h5ad \
    --spatial_data visium_tumor.h5ad \
    --cancer_type nsclc \
    --deconvolution_methods cibersortx,epic,mcpcounter \
    --response_labels clinical_response.csv \
    --output tme_profiles/

TME Phenotypes

Output Components

Immune Signatures

AI/ML Components

Deconvolution Enhancement:

• Deep learning deconvolution
• Multi-method ensemble
• Single-cell reference optimization

Response Prediction:

• Multi-modal fusion (bulk + spatial)
• Survival analysis integration
• Transfer learning across cancers

Spatial Analysis:

• Graph neural networks for niches
• Attention for region importance
• Cell-cell interaction networks

Clinical Applications

Performance Benchmarks

Prerequisites

• Python 3.10+
• CIBERSORTx, EPIC, xCell
• Scanpy, Squidpy
• PyTorch for deep learning
• R for certain deconvolution methods

Related Skills

• TCR_Repertoire_Analysis_Agent - T cell specificity
• TCell_Exhaustion_Analysis_Agent - Exhaustion phenotyping
• Spatial_Epigenomics_Agent - Spatial analysis
• Nicheformer_Spatial_Agent - Spatial foundation models

Spatial Immune Metrics

Special Considerations

1. Reference Panel: Use cancer-type specific references
2. Batch Correction: Normalize across samples/platforms
3. Purity Effects: Account for tumor purity in deconvolution
4. Single-Cell Validation: Validate bulk estimates with scRNA
5. Spatial Context: Bulk loses spatial information

Therapeutic Implications

Author

AI Group - Biomedical AI Platform