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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.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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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

1. Clonal Deconvolution: Identifies tumor subpopulations and estimates their cellular fractions using variant allele frequencies (VAF) from bulk sequencing.

2. Phylogenetic Reconstruction: Builds tumor evolutionary trees showing relationships between subclones and their mutational acquisition order.

3. Longitudinal Tracking: Monitors subclone dynamics over time using ctDNA variant frequencies from serial blood draws.

4. Resistance Prediction: Applies Bayesian evolutionary frameworks to forecast emergence of resistant clones and time to progression.

5. Spatial ITH Mapping: Integrates multi-region data to visualize spatial distribution of subclones across tumor sites.

6. Fitness Estimation: Calculates subclone fitness parameters to identify aggressive populations driving tumor progression.

Workflow

1. Input: Multi-region or longitudinal mutation data (VCF/MAF), tumor purity estimates, copy number profiles.

2. Clustering: Cluster mutations into subclones using PyClone, SciClone, or MOBSTER.

3. Phylogeny: Reconstruct evolutionary trees using CITUP, PhyloWGS, or CALDER.

4. Modeling: Apply mathematical models (Lotka-Volterra, birth-death) to estimate dynamics.

5. Prediction: Forecast treatment response and resistance timeline.

6. 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

1. Treatment Selection: Identify dominant subclones to target
2. Resistance Monitoring: Detect emerging resistant populations early
3. Prognosis: Predict time to treatment failure
4. Combination Therapy: Design strategies targeting multiple subclones

Author

AI Group - Biomedical AI Platform

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