SYNERGxDB

SYNERGxDB integrates large-scale drug-combination and molecular profiling data to identify synergistic drug combinations and predictive biomarkers for precision oncology.

Key Features:

• Integration of Diverse Data Sources: Aggregates nine major drug-combination datasets comprising 22,507 unique drug combinations involving 1,977 distinct compounds tested across 151 cancer cell lines.
• Comprehensive Molecular Profiling: Includes metabolomics, gene expression, copy number variations, and mutation profiles for each cancer cell line.
• Unified Computational Analysis: Applies harmonized computational methodologies to enable cross-study analysis and identification of drug synergy.
• Analytical Tools for Biomarker Discovery: Provides analytical approaches to discover therapeutic combinations and predictive biomarkers specific to cancer types.
• Exploration of Drug Synergism Predictors: Systematically investigates a broad spectrum of univariate predictors for drug synergism by combining molecular and pharmacological profiles.

Scientific Applications:

• Therapeutic Combination Discovery: Enables identification and prioritization of synergistic drug combinations across thousands of tested pairs.
• Predictive Biomarker Identification: Associates metabolomics, gene expression, copy number variation, and mutation features with combination response to nominate predictive biomarkers.
• Mechanistic Exploration of Drug Synergy: Facilitates analysis of univariate predictors to probe molecular determinants and potential mechanisms underlying synergistic interactions.

Methodology:

Integrates nine high-throughput drug-combination datasets and harmonizes computational analyses across studies; performs systematic univariate predictor analysis of drug synergism using combined molecular (metabolomics, gene expression, copy number variations, mutation profiles) and pharmacological profiles.