--- name: tumor-clonal-evolution-agent description: "---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. license: MIT metadata: author: AI Group version: \"1.0.0\" created: \"2026-01-19\" c..." --- --- 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 --- # 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**: ```bash 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 --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Oncology/Tumor_Clonal_Evolution_Agent/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2025 MD BABU MIA , PHD Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Oncology/Tumor_Clonal_Evolution_Agent/SKILL.md Attribution basis: Recovered original package and original project license at a pinned revision Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences Roles: Business Intelligence Analyst, Clinical Research, Data Scientist, Health Informatics, Researcher Specialties: API Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: a1f30cb62cf573a3e90f56280bc7003deff8778a3ed0ac5bd20e68f38e9537cf Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.