The Meritudio Tumor Models Database is a leading resource for researchers, offering comprehensive insights into over 2,000 commonly used cell lines. This article explores the powerful features of the Model Page, using the HeLa cell line as an example to demonstrate how the database can enhance research efficiency and decision-making.
The Model Page is a centralized hub for all information related to a specific cell line. For the HeLa cell line, the page is intuitively organized into four key sections: Overview, Genomics, Pharmacology, and Analytics. This structured layout ensures researchers can quickly access the data they need without navigating through multiple pages.
The Overview section provides a concise yet comprehensive summary of the HeLa cell line. It includes:
● Basic Information: Origin, disease type, subtype, and relevant clinical data.
● Model Genomics: Key genomic characteristics and data availability.
This section serves as a quick reference for researchers to understand the fundamental properties of the cell line.
This section displays genetic alterations of near 800 cancer driver genes in the Driver Genes tab. This visual representation provides a quick overview of the complex genetic landscape change, for instance, it shows the copy number loss of RSPH10B2 and the mutation of EGFR. Detailed mutation information is shown in the lollipop graph and a table gives all relevant genomic information. Users can also search for any gene in the All Genes tab.
The section is a treasure trove for researchers interested in drug responses. It offers a detailed list of drug with information on target, MOA, signaling pathway, and efficacy (AUC, IC50, EC50, Hill slope etc.). This information is invaluable for understanding how different compounds affect the cell line and can guide the development of new treatment strategies.
More informative is the comparison of dose-response curves between drugs, and the actual drug response data.
The Analytics section on the Model page offers a suite of advanced tools to deeply analyze and interpret cell line data, enabling researchers to optimize experimental design and resource allocation. Below are key insights derived from its analytical functions:
1. Genetic Similarity Analysis
● HELA229 and HELA exhibit 95.79% genetic overlap, indicating near-identical genomic profiles. This high similarity suggests functional redundancy, meaning researchers could streamline studies by selecting one line without compromising genetic relevance.
2. Efficacy Similarity Analysis
● KYSE450 and HELA share strikingly similar drug response patterns. To avoid duplication in drug efficacy experiments, prioritizing one cell line (e.g., based on availability or secondary characteristics) is recommended.
3. Pathway Activation Analysis
● The KEGG Glyoxylate and Dicarboxylate Metabolism pathway shows pronounced activation in HELA. This metabolic pathway’s activity may influence cellular responses to therapies targeting energy metabolism, highlighting its potential as a biomarker or therapeutic target.
