name: 'tumor-heterogeneity-agent' description: 'AI-powered intratumor heterogeneity analysis for clonal architecture reconstruction, subclonal evolution tracking, and therapy resistance prediction using multi-region and longitudinal sequencing.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

Tumor Heterogeneity Agent

The Tumor Heterogeneity Agent provides comprehensive analysis of intratumor heterogeneity (ITH) for understanding clonal architecture, tracking subclonal evolution, and predicting therapy resistance. It integrates multi-region sequencing, single-cell data, and longitudinal samples to reconstruct tumor phylogenies and identify actionable subclones.

When to Use This Skill

• When analyzing multi-region tumor sequencing for clonal architecture.
• For tracking clonal evolution under treatment pressure.
• To predict resistance emergence from subclonal populations.
• When assessing tumor heterogeneity impact on treatment response.
• For integrating single-cell and bulk sequencing for ITH analysis.

Core Capabilities

1. Clonal Deconvolution: Infer clonal populations and their frequencies.

2. Phylogeny Reconstruction: Build tumor evolutionary trees from variants.

3. Subclonal Tracking: Monitor subclone dynamics over time.

4. Resistance Prediction: Identify pre-existing resistant subclones.

5. Multi-Region Integration: Combine spatial heterogeneity data.

6. Single-Cell ITH: Integrate scDNA-seq for ground-truth clones.

Heterogeneity Metrics

Workflow

1. Input: Multi-region/longitudinal WES/WGS, copy number, tumor purity.

2. Preprocessing: Variant calling, CNV calling, purity estimation.

3. CCF Estimation: Calculate cancer cell fraction for each mutation.

4. Clustering: Group mutations into clonal populations.

5. Phylogeny: Reconstruct evolutionary tree.

6. Temporal Analysis: Track clone dynamics over time.

7. Output: Clone structures, phylogenies, heterogeneity metrics.

Example Usage

User: "Analyze the clonal architecture of this multi-region lung tumor sequencing to understand heterogeneity and identify resistant subclones."

Agent Action:

python3 Skills/Oncology/Tumor_Heterogeneity_Agent/ith_analysis.py \
    --multi_region_vcfs region1.vcf,region2.vcf,region3.vcf \
    --cnv_segments cnv_calls.seg \
    --purity 0.7,0.65,0.72 \
    --sample_names Primary,Met1,Met2 \
    --method pyclone-vi \
    --phylogeny_method citup \
    --output ith_analysis/

Deconvolution Methods

Input Requirements

Output Components

Clonal Classification

AI/ML Components

Clone Inference:

• Variational autoencoders for CCF estimation
• Dirichlet process mixture models
• Graph neural networks for phylogeny

Resistance Prediction:

• Time-series models for clone trajectories
• Classification of resistant signatures
• Drug-clone interaction prediction

Multi-Region Integration:

• Multi-task learning across regions
• Spatial models for regional patterns
• Transfer learning across cancers

Clinical Applications

Cancer-Specific Patterns

Prerequisites

• Python 3.10+
• PyClone-VI, SciClone
• CITUP, Clonevol
• CNVkit/FACETS for CNV
• R with clonal evolution packages

Related Skills

• ctDNA_Dynamics_MRD_Agent - Liquid biopsy tracking
• Single_Cell_CNV_Agent - scDNA-seq analysis
• HRD_Analysis_Agent - Genomic instability
• Pan_Cancer_MultiOmics_Agent - Multi-omic integration

Phylogeny Visualization

Special Considerations

1. Sampling Bias: Multi-region captures more heterogeneity
2. Purity Effects: Low purity reduces clone resolution
3. CNV Complexity: High CNV burden complicates CCF
4. Single-Cell Validation: Ground truth from scDNA-seq
5. Temporal Resolution: Frequent sampling improves tracking

Resistance Mechanisms

Author

AI Group - Biomedical AI Platform