Data & ReproClinical MedicineFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

tumor-clonal-evolution-agent

Maintainer FreedomIntelligence · Last updated April 1, 2026

Model tumor clonal evolution: phylogenetic trees, clonal dynamics, branching patterns from somatic variants.

OpenClawNanoClawAnalysisReproductiontumor-clonal-evolution-agent🧠 bioos extended suiteoncology & precision medicine agentsmodel

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tumor-clonal-evolution-agent

Skill Snapshot

2 min

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 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.

Source Doc

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:

Use tumor-clonal-evolution-agent for clinical, translational, or medical research tasks.
Apply tumor-clonal-evolution-agent when healthcare-specific guidance is required.