CiDD

CiDD identifies candidate drug compounds and representative cell lines by deriving gene expression signatures associated with clinical or molecular cancer characteristics and integrating TCGA, Connectivity Map (CMap), and CCLE data to support phenotype-driven drug discovery.

Key Features:

• Association of clinical or molecular characteristics with gene expression signatures: Determines whether specific clinical phenotypes or molecular features correlate with distinct gene expression profiles.
• Identification of candidate drugs: Identifies drugs predicted to repress the derived expression signatures using Connectivity Map (CMap) data.
• Selection of relevant cell lines for in vitro testing: Proposes CCLE cell lines that closely resemble the tumors under study for experimental validation.
• Data integration: Integrates large-scale datasets including TCGA, CMap, and CCLE to enable cross-dataset analyses.
• Annotated outputs: Produces biologically annotated lists of potential drug candidates and suggested cell lines for follow-up experiments.
• Handling dataset-specific challenges: Addresses analytical challenges associated with complex TCGA data and technology differences in CMap.

Scientific Applications:

• Phenotype-driven drug discovery: Enables discovery of drugs linked to clinical or molecular cancer phenotypes using TCGA-derived signatures and CMap connections.
• BRAF-mutant colorectal cancer example: Identified EGFR and proteasome inhibitors as candidate therapeutics and recommended five cell lines for in vitro validation in BRAF-mutant colorectal cancer.

Methodology:

Derives gene expression signatures associated with specified clinical or molecular characteristics, queries CMap to identify drugs that repress those signatures, and selects CCLE cell lines resembling the tumors while integrating TCGA, CMap, and CCLE data.