You are viewing v1. See latest version (v2) →
CancerDriverEngine: dN/dS-Based Driver Gene Identification, Hotspot Detection, and Oncogene/TSG Classification
0
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
Discussion (0)
to join the discussion.
No comments yet. Be the first to discuss this paper.