DSCN

DSCN integrates CRISPR-Cas9 genome-wide screening with a spectral-clustered protein-protein interaction (PPI) network to identify and prioritize combinatorial cancer therapy targets by matching patient gene expression with cell-line gene essentialities.

Key Features:

• CRISPR Screening Integration: Incorporates CRISPR-Cas9 genome-wide screening data to identify candidate essential genes for target combination selection.
• Spectral-Clustered PPI Network: Uses a spectral-clustered PPI network to model gene interactions, simulate first-target knockdowns, and assess downstream network effects for secondary target selection.
• Sub-Sampling Approach: Implements a sub-sampling technique to predict differential gene expression after target inhibition, demonstrating correlation with observed changes in pancreatic cell lines (R² = 0.75 for MAP2K1/MAP2K2 inhibition).
• Scoring Schemes: Evaluates multiple scoring schemes to rank target pairs, with the diffusion-path method showing significant power to distinguish known synthetic lethal (SL) versus non-SL gene pairs in pancreatic cancer (P = 0.001).
• Comparative Performance: Calculates combinations for any gene pair and reports higher computational efficiency and broader coverage than network-based algorithms OptiCon and VIPER, with at least a tenfold speed advantage.
• Translational Application (DSCNi): Extends the framework to individual samples (DSCNi) to predict sample-specific target combinations and drug combinations, with high correlation to observed synergistic combinations in pancreatic cell lines (P = 1e-5).

Scientific Applications:

• Combination target identification: Prioritizes combinatorial targets for cancer therapy by integrating genetic essentiality and patient gene expression data.
• Synthetic lethal discovery: Distinguishes synthetic lethal versus non-synthetic lethal gene pairs, demonstrated in pancreatic cancer datasets.
• Personalized prediction and drug combination selection: Predicts individualized target and drug combinations for single samples using the DSCNi extension, facilitating translation from cell lines to patient data.

Methodology:

Integrates CRISPR-Cas9 genome-wide screening with a spectral-clustered PPI network, applies a sub-sampling approach to simulate first-target knockdowns and predict differential gene expression, and evaluates scoring schemes including diffusion-path to rank target pairs; DSCNi applies this framework at the individual-sample level.