Tumor Heterogeneity Agent

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