A premier tumor model database to empower cancer research
Meritudio’s Tumor Model database provides an extensive repository of genomic and pharmacological data, currently covering 2,000 cancer cell lines and 1,800 oncology drugs. With its intuitive search features and advanced analytics tools, the platform empowers researchers to effortlessly access and analyze critical datasets. From selecting optimal tumor models and identifying therapeutic targets to unraveling molecular mechanisms and evaluating drug response patterns, the database serves as a transformative resource for advancing cancer research and driving impactful discoveries. Licensing is also available for both internal and commercial use, with options for customized development.
Interactive figures complemented by detailed, easy-to-read tables
Information is structured into key sections like Overview, Genomics, and Pharmacology
Seamless comparison of multiple genes, pathways, drugs, or tumor models
Dynamic, real-time visualizations powered by over 10 advanced open-source and proprietary plugins
Dose-response curves visualized with raw and fitted data for quick and precise comparison
2000 cancer cell lines characterized for gene expression, mutations, copy number variations, fusions, and pathway activities
1800 oncology drugs categorized by targets, mechanism of action (MoA), and signaling pathways
Over 1 million dose-response curves systematically fitted for reliable comparability
2300+ signaling pathways integrated from leading databases like Hallmark, KEGG, Reactome, WikiPathways, and Biocarta
Approximately 120 million data points, including 100 million genomic and 20 million pharmacological data points
Intuitive interface designed for effortless navigation without prior training
Dedicated modules for targeted exploration of Models, Genes, and Drugs
Comprehensive, single-page summaries for every model, gene, pathway, and drug
Advanced filters and search tools tailored to specific user needs
Integrated links to external databases such as NCBI, Ensembl, OpenTargets, and OncoKB
Normalized datasets for consistent and reliable cross-comparison of genomic and drug data
Advanced annotation tools like AlphaMissense for classifying missense mutations
High-efficiency workflows delivering 90% of analyses within 10 seconds
Cell line and drug similarities evaluated based on shared drug or cell line efficacy profiles
Biomarker discovery tools linking drug response to genomic features, such as gene expression
