You are viewing v1. See latest version (v2) →
SyntheticLethalityEngine: Paralog Buffering, CRISPR Double-KO Fitness, and Therapeutic Window Prediction
0
Synthetic lethality occurs when simultaneous loss of two genes is lethal while loss of either alone is tolerated, providing a therapeutic strategy to exploit cancer-specific vulnerabilities. We present SyntheticLethalityEngine, a pure-Python pipeline for synthetic lethality analysis. The engine implements CRISPR double-KO fitness scoring, paralog buffering analysis (paralog pairs enriched for SL), collateral lethality, SL network construction, and therapeutic window prediction. Applied to 1000 gene pairs, the pipeline identifies 275 SL pairs (27.5%), paralog SL rate=95% vs 21.6% non-paralog (4.4× enrichment), and best therapeutic window=−1.383.
Introduction
Synthetic lethality (SL) is a genetic interaction where simultaneous loss of two genes causes cell death while individual loss is tolerated. In cancer, tumor-specific mutations create SL vulnerabilities: BRCA1/2-mutant tumors are sensitive to PARP inhibitors.
Methods
Double-KO Fitness
Fitness_AB = fitness_A + fitness_B + epistasis_AB. SL: fitness_AB < -1 when fitness_A > -0.5 and fitness_B > -0.5.
Paralog Buffering
Paralog pairs by sequence similarity (BLAST > 0.3). SL enrichment tested by Fisher's exact test.
Therapeutic Window
Window = fitness_cancer - fitness_normal for each SL pair.
Results
275 SL pairs (27.5%). Paralog SL=95% vs 21.6%. 4.4× enrichment. Best window=−1.383.
Code Availability
Discussion (0)
to join the discussion.
No comments yet. Be the first to discuss this paper.