drugZ

drugZ identifies chemogenetic interactions from genome-scale CRISPR screens to reveal genes that enhance or suppress the activity of chemical compounds, elucidating drug mechanisms of action and resistance pathways.

Key Features:

• Identification of Chemogenetic Interactions: Detects both synergistic and suppressive gene–drug interactions from CRISPR screening data.
• Genome-scale CRISPR Screen Analysis: Analyzes genome-scale CRISPR screens to identify genetic perturbations that modify drug activity.
• Implementation: Implemented in Python for computational analysis of screening datasets.
• Insights into Resistance Mechanisms: Recapitulates and discovers resistance factors such as KEAP1 loss conferring resistance to ERK inhibitors in oncogenic KRAS backgrounds.
• Synthetic Lethal Interaction Detection: Identifies synthetic lethal interactions involving PARP inhibitors and DNA damage repair pathway genes.
• Tumor Suppressor Genes as Resistance Factors: Reveals that tumor suppressor genes can act as drug-agnostic resistance genes across contexts.
• Patient Stratification Data: Provides genetic-vulnerability and resistance information applicable to stratifying patient populations for treatment strategies.

Scientific Applications:

• Chemogenetic Interaction Discovery: Mapping gene–drug interactions from pooled CRISPR screening data.
• Mechanism of Action and Resistance Studies: Elucidating drug mechanisms and resistance pathways in oncogenic contexts, including KRAS-driven models.
• Synthetic Lethality Identification: Detecting synthetic lethal relationships, for example between PARP inhibitors and DNA damage repair genes.
• Patient Stratification for Precision Medicine: Informing patient stratification based on genetic vulnerabilities and resistance signatures.

Methodology:

Analyzes CRISPR screen data files that require the "gene" column to be the first non-index column, uses column headers in command-line operations, and accepts specification of replicates via command-line options.