AI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data.
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.
Clonal Deconvolution: Identifies tumor subpopulations and estimates their cellular fractions using variant allele frequencies (VAF) from bulk sequencing.
Phylogenetic Reconstruction: Builds tumor evolutionary trees showing relationships between subclones and their mutational acquisition order.
Longitudinal Tracking: Monitors subclone dynamics over time using ctDNA variant frequencies from serial blood draws.
Resistance Prediction: Applies Bayesian evolutionary frameworks to forecast emergence of resistant clones and time to progression.
Spatial ITH Mapping: Integrates multi-region data to visualize spatial distribution of subclones across tumor sites.
Fitness Estimation: Calculates subclone fitness parameters to identify aggressive populations driving tumor progression.
Input: Multi-region or longitudinal mutation data (VCF/MAF), tumor purity estimates, copy number profiles.
Clustering: Cluster mutations into subclones using PyClone, SciClone, or MOBSTER.
Phylogeny: Reconstruct evolutionary trees using CITUP, PhyloWGS, or CALDER.
Modeling: Apply mathematical models (Lotka-Volterra, birth-death) to estimate dynamics.
Prediction: Forecast treatment response and resistance timeline.
Output: Phylogenetic trees, subclone trajectories, resistance predictions, actionable insights.
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/
| 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 |
The agent applies evolutionary dynamics models:
Lotka-Volterra Competition:
dNi/dt = ri * Ni * (1 - sum(aij * Nj) / Ki)
Where:
VAF Dynamics Modeling:
AI Group - Biomedical AI Platform