tme-immune-profiling-agent

name	tme-immune-profiling-agent
description	Comprehensive AI-powered tumor microenvironment immune profiling integrating bulk deconvolution, single-cell analysis, and spatial transcriptomics for immunotherapy biomarker discovery.

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

Cell Type	Subtypes	Key Markers
T cells	CD8+, CD4+, Treg, Th1/2/17	CD3, CD8, CD4, FOXP3
B cells	Naive, memory, plasma	CD19, CD20, CD138
NK cells	CD56bright, CD56dim	NKG7, NCAM1
Macrophages	M1, M2, TAM	CD68, CD163, CD206
Dendritic	cDC1, cDC2, pDC	CLEC9A, CD1C, BDCA2
MDSC	M-MDSC, PMN-MDSC	CD33, CD11b, ARG1
CAF	myCAF, iCAF, apCAF	FAP, ACTA2, COL1A1

Deconvolution Methods

Method	Algorithm	Cell Types	Best For
CIBERSORTx	SVR	22	Gold standard
xCell	ssGSEA	64	Comprehensive
EPIC	Constrained regression	8	Tumor/stroma
MCP-counter	Marker genes	10	Robust scores
quanTIseq	Deconvolution	10	Pan-cancer
TIMER2.0	Multiple	Variable	Integrated

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

Phenotype	Characteristics	Immunotherapy Response
Immune Hot	High TIL infiltration, PD-L1+	Favorable
Immune Cold	Low TIL, low inflammation	Poor
Immune Excluded	TILs at margin, not penetrating	Intermediate
Immune Suppressed	TILs + MDSCs/Tregs	Variable

Output Components

Output	Description	Format
Cell Fractions	Per-sample immune estimates	.csv
TME Classification	Hot/cold/excluded labels	.csv
Immune Scores	Composite signatures	.csv
Spatial Maps	Cell type locations	.h5ad
Neighborhood Analysis	Immune niches	.csv
Response Prediction	IO probability	.json
Visualizations	Deconvolution plots	.png, .pdf

Immune Signatures

Signature	Genes	Interpretation
Cytotoxic	PRF1, GZMB, GNLY	T cell killing
Exhaustion	PDCD1, LAG3, HAVCR2, TIGIT	T cell dysfunction
IFN-gamma	IFNG, STAT1, IRF1	Inflammation
TLS	CD20, CD4, BCL6	Tertiary lymphoid
Exclusion	TGFB1, FAP, COL1A1	Stromal barrier

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

Application	TME Feature	Clinical Decision
IO Selection	Immune hot phenotype	Prioritize IO
Combination	Cold + excluded	Consider combo
Prognosis	TLS presence	Favorable outcome
Biomarker	CD8+ density	Response prediction
Resistance	MDSC enrichment	Address suppression

Performance Benchmarks

Task	Dataset	Performance
IO Response	NSCLC	AUC 0.78
IO Response	Melanoma	AUC 0.82
TME Classification	Pan-cancer	Accuracy 85%
Survival	TCGA	C-index 0.72

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

Metric	Definition	Clinical Relevance
Immune Distance	Distance to tumor edge	Exclusion
Clustering Coefficient	Immune aggregation	TLS formation
CD8/Treg Ratio	Spatial ratio	Effector balance
Contact Score	Immune-tumor contacts	Direct killing
Neighborhood Entropy	Mixing vs segregation	TME organization

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

TME State	Therapeutic Strategy
Hot, PD-L1+	Anti-PD-1/PD-L1
Cold	Oncolytic virus, radiation, chemo
Excluded	TGF-beta inhibition, VEGF targeting
Suppressed	Treg depletion, MDSC targeting
TLS+	Excellent IO candidate

Author

AI Group - Biomedical AI Platform