| name | tumor-clonal-evolution-agent |
| description | AI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data. |
Tumor Clonal Evolution Agent
The Tumor Clonal Evolution Agent analyzes intratumoral heterogeneity (ITH), reconstructs tumor phylogenies, and tracks clonal dynamics over time. It integrates multi-region sequencing data, longitudinal liquid biopsies, and mathematical modeling to predict treatment response and resistance emergence.
When to Use This Skill
- When analyzing multi-region tumor sequencing to map spatial heterogeneity.
- To reconstruct tumor phylogenetic trees and identify ancestral mutations.
- For tracking clonal evolution through serial liquid biopsy samples.
- To predict time to treatment failure using evolutionary modeling.
- When identifying resistance-conferring subclones before clinical progression.
Core Capabilities
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Clonal Deconvolution: Identifies tumor subpopulations and estimates their cellular fractions using variant allele frequencies (VAF) from bulk sequencing.
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Phylogenetic Reconstruction: Builds tumor evolutionary trees showing relationships between subclones and their mutational acquisition order.
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Longitudinal Tracking: Monitors subclone dynamics over time using ctDNA variant frequencies from serial blood draws.
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Resistance Prediction: Applies Bayesian evolutionary frameworks to forecast emergence of resistant clones and time to progression.
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Spatial ITH Mapping: Integrates multi-region data to visualize spatial distribution of subclones across tumor sites.
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Fitness Estimation: Calculates subclone fitness parameters to identify aggressive populations driving tumor progression.
Workflow
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Input: Multi-region or longitudinal mutation data (VCF/MAF), tumor purity estimates, copy number profiles.
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Clustering: Cluster mutations into subclones using PyClone, SciClone, or MOBSTER.
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Phylogeny: Reconstruct evolutionary trees using CITUP, PhyloWGS, or CALDER.
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Modeling: Apply mathematical models (Lotka-Volterra, birth-death) to estimate dynamics.
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Prediction: Forecast treatment response and resistance timeline.
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Output: Phylogenetic trees, subclone trajectories, resistance predictions, actionable insights.
Example Usage
User: "Analyze the clonal evolution from these 6 longitudinal ctDNA samples and predict time to progression."
Agent Action:
python3 Skills/Oncology/Tumor_Clonal_Evolution_Agent/clonal_evolution.py \
--input longitudinal_ctdna_variants.maf \
--timepoints 0,4,8,12,16,20 \
--tumor_burden cea_values.csv \
--method bayesian_evolution \
--predict_ttp true \
--output evolution_analysis/
Key Methods and Algorithms
| Tool/Method | Application | Reference |
|---|
| PyClone-VI | Bayesian clustering of mutations | Nature Methods 2014 |
| MOBSTER | Subclonal deconvolution with selection | Nature Genetics 2020 |
| PhyloWGS | Phylogenetic tree reconstruction | Genome Biology 2015 |
| CALDER | Copy-number aware phylogeny | Nature Methods 2019 |
| CHESS | Cancer heterogeneity from single samples | Cell Systems 2019 |
Mathematical Framework
The agent applies evolutionary dynamics models:
Lotka-Volterra Competition:
dNi/dt = ri * Ni * (1 - sum(aij * Nj) / Ki)
Where:
- Ni = population of subclone i
- ri = growth rate (fitness)
- aij = competition coefficient
- Ki = carrying capacity
VAF Dynamics Modeling:
- Serial ctDNA VAF measurements enable real-time fitness estimation
- Bayesian inference updates subclone parameters with each sample
- Monte Carlo simulations generate prediction intervals
Prerequisites
- Python 3.10+
- PyClone-VI, PhyloWGS, or MOBSTER
- Copy number calling tools (ASCAT, Sequenza)
- Statistical modeling (PyMC, Stan)
Related Skills
- ctDNA_Analysis - For cfDNA variant calling
- Liquid_Biopsy_Analysis - For blood-based biomarker detection
- Variant_Interpretation - For mutation annotation
Clinical Applications
- Treatment Selection: Identify dominant subclones to target
- Resistance Monitoring: Detect emerging resistant populations early
- Prognosis: Predict time to treatment failure
- Combination Therapy: Design strategies targeting multiple subclones
Author
AI Group - Biomedical AI Platform
