CancerDriverEngine: dN/dS-Based Driver Gene Identification, Hotspot Detection, and Oncogene/TSG Classification
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Identifying cancer driver genes from the background of passenger mutations is a central challenge in cancer genomics. We present CancerDriverEngine, a pure-Python pipeline for cancer driver gene analysis. The engine implements dN/dS ratio calculation per branch (PAML-style), hotspot mutation detection (recurrence above background), functional impact scoring, oncogene vs tumor suppressor classification (gain-of-function vs loss-of-function pattern), and pathway enrichment of driver genes. Applied to 500 genes × 200 tumors, the pipeline identifies 73 genes with dN/dS>2, 54 hotspot mutations, 13 oncogenes, 17 TSGs, and top driver BRCA1 (62% mutation frequency).
Introduction
Cancer driver genes are distinguished from passenger genes by positive selection: driver mutations confer growth advantage and are observed more frequently than expected under neutral evolution. dN/dS>1 indicates positive selection.
Methods
dN/dS
Expected rates from trinucleotide context and codon structure. dN/dS = (obs_NS/exp_NS) / (obs_S/exp_S).
Hotspot Detection
Recurrence > Poisson expectation (p<0.001, BH FDR).
Oncogene/TSG
Oncogenes: enrichment of missense at hotspots. TSGs: enrichment of truncating mutations.
Results
73 genes dN/dS>2. 54 hotspots. 13 oncogenes, 17 TSGs. Top driver: BRCA1 (62%).
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: cancer-driver-engine description: dN/dS-based cancer driver gene identification, hotspot detection, and oncogene/TSG classification allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/CancerDriverEngine cd CancerDriverEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python cancer_driver_engine.py ``` 4. Output: `cancer_driver_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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