APSiC

APSiC analyzes deep shRNA perturbation screens (e.g., Project DRIVE) to systematically identify genetic and non-genetic cancer driver genes and relate perturbation effects to gene expression profiles.

Key Features:

• Comprehensive analysis: Analyzes large-scale deep shRNA perturbation screens to identify established pan-cancer genetic drivers and novel putative genetic drivers dysregulated in specific cancer types.
• Contextual insights: Detects context-dependent phenomena such as mRNA splicing variations and cancer type–specific dysregulation.
• Discovery of non-genetic drivers: Identifies non-genetic oncogenes and tumor suppressor genes, reporting a median of 28 non-genetic oncogenes and 35 non-genetic tumor suppressors per cancer type in benchmark analyses.
• Functional validation support: Uses a statistical framework that prioritizes candidates for downstream functional validation, exemplified by identification of LRRC4B as a putative non-genetic tumor suppressor affecting breast cancer proliferation via cell cycle and apoptosis.
• Data integration: Integrates comprehensive rank profiles from the DRIVE shRNA screen with corresponding TCGA gene expression data for cross-referencing perturbation effects and expression patterns.
• Biological process annotation: Highlights genes involved in processes such as genome stability maintenance and cell cycle regulation.

Scientific Applications:

• Discovery of rarely mutated drivers: Enables investigation of genes with low somatic mutation rates that may act as cancer drivers via non-genetic mechanisms.
• Investigation of non-genetic oncogenicity: Facilitates exploration of epigenetic and expression-based mechanisms of oncogenesis.
• Cross-cancer comparisons: Supports comparison of perturbation effects and splicing or expression patterns across cancer types to identify context-specific drivers.
• Candidate prioritization for validation: Prioritizes genes for experimental follow-up, as demonstrated by LRRC4B's prioritization and downstream characterization.

Methodology:

APSiC analyzes deep shRNA perturbation screens (e.g., Project DRIVE), leverages comprehensive rank profiles from DRIVE integrated with TCGA gene expression data, and applies a robust statistical framework tailored for perturbation screens to handle many gene perturbations across relatively few cell lines.