shinyDepMap

shinyDepMap provides exploration and analysis of the Cancer Dependency Map (DepMap) dataset using CRISPR and shRNA screening data to assess gene dependencies across cancer cell lines.

Key Features:

• Data Integration: Integrates CRISPR and shRNA screening data to assess the impact of gene knockout or knockdown on cellular growth.
• Gene Dependency Analysis: Calculates growth reduction metrics for gene perturbations and evaluates selectivity of these effects across different cell lines.
• Functional Clustering: Clusters genes with similar dependency profiles to reveal functional relationships and implicate pathways and networks.
• Drug Target Prediction: Predicts efficacy and selectivity of potential drugs against specific genes and identifies cell lines most sensitive to those perturbations.
• Target Hopping: Identifies more druggable targets with dependency profiles similar to undruggable proteins (e.g., activated oncogenes) to enable alternative targeting strategies.
• Pathway Discovery: Supports identification of novel pathways driving cancer cell growth and survival.

Scientific Applications:

• Drug Discovery: Prioritizes candidate targets and predicts selective vulnerabilities for development of targeted therapies.
• Preclinical Testing: Identifies maximally sensitive cell lines for testing compound efficacy and selectivity in preclinical studies.
• Therapeutic Strategy Design: Guides selection of alternative, more druggable targets for undruggable oncogenes to inform therapeutic strategies.
• Pathway Analysis: Reveals pathways implicated in cancer progression to support identification of new drug targets.

Methodology:

Combines CRISPR and shRNA screening results to assess gene dependencies, calculates growth reduction metrics for each gene perturbation, and clusters genes based on dependency profiles to uncover functional relationships.