Introduction

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.

Structure of the Model Page

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.

Overview 

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.

Genomics 

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.

Pharmacology

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.

Analytics

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.

Introduction

Gene expression data is crucial for understanding the molecular mechanisms underlying tumor development and progression. By analyzing the expression levels of various genes, researchers can identify key pathways involved in tumorigenesis, discover potential biomarkers for diagnosis and prognosis, and uncover novel therapeutic targets. This information is essential for advancing cancer research and developing more effective treatments. In Meritudio Tumor Models Database, there are two ways to obtain mRNA expression data for multiple genes.

Pathway-Based Search

This method allows users to select a specific biological pathway and view the expression and mutation information of all genes within that pathway. The animated graph shows the operations to obtain pathway activity score, gene expression, and mutation data for the Autophagy pathway in the WikiPathways database :

Some key points:

● Gene expression by default is displayed in z-transformed scores, users can select log₂TPM as well. 

● Rows and columns in the heatmap can be arranged in multiple ways.

● MTOR and PRKAA1 genes are mutated in this pathway and their mutations are mutually exclusive.

Manual Gene Search

This method involves manually entering the names of specific genes to perform a search. This approach is useful when researchers have a predefined list of genes they are interested in and want to obtain detailed expression data for those particular genes. The animated graph shows the operations to obtain gene expression data for four key genes (MDM2, MDM4, CDKN2A, TP53) in the TP53 pathway:

Some key points:

● Gene expression by default is displayed in z-transformed scores, users can select log₂TPM as well. 

● Gray grids in the heatmap indicate data unavailable.

● Gene expression can be viewed, ranked, compared and downloaded from the table below the heatmap.

Introduction

In cancer research, understanding the interplay between specific genetic mutations and pathway activations is crucial for uncovering disease mechanisms and developing targeted therapies. Combination searches allow researchers to identify cell lines that exhibit multiple molecular features, such as mutations in key genes (e.g., BRCA1/2) and activation of critical signaling pathways (e.g., WNT). These insights can reveal potential biomarkers, therapeutic targets, and resistance mechanisms.

This is the second article in a series that demonstrates how to perform combination searches in Meritudio’s Tumor Models Database. In this guide, we will focus on identifying cell lines with:

● BRCA mutations (BRCA1 and/or BRCA2 mutations), which are commonly associated with DNA repair deficiencies and cancer susceptibility.

● WNT pathway activation, a key signaling pathway involved in cell proliferation, differentiation, and cancer progression.

The animated graph shows the operations to identify 16 cell lines satisfying the search criteria:

Some key points:

● The combination search is accomplished by the use of logic operators OR and AND. 

● The three filters form the search criteria: ((BRCA1: & mutation = Somatic) OR (BRCA2: & mutation = Somatic)) AND (KEGG_WNT_SIGNALING_PATHWAY: MetaScore = 1.5~2.8), which means (BRCA1_mutation OR BRCA2_mutation) AND WNT_pathway_activation.

● There are several WNT pathways, we used KEGG_WNT_SIGNALING_PATHWAY in the search, but others can be used as well, such as HALLMARK_WNT_BETA_CATENIN_SIGNALING, BIOCARTA_WNT_PATHWAY, REACTOME_SIGNALING_BY_WNT, WP_WNT_SIGNALING_PATHWAY.

Introduction

In cancer research, combination searches are essential for understanding the complex genetic landscape of tumors and identifying potential therapeutic targets. BRAF and KRAS are two critical oncogenes frequently mutated in various cancers, and their mutations often drive tumor growth through the activation of the MAPK/ERK signaling pathway. While BRAF and KRAS mutations are typically mutually exclusive—meaning they rarely occur together in the same tumor—studying cell lines with either mutation provides valuable insights into their distinct roles in cancer biology and treatment responses.

Note: We are using BRAF and KRAS as an example to demonstrate how to perform a combination search for multiple genes. This method can be applied to other gene combinations to explore their co-occurrence or mutual exclusivity in cancer cell lines, providing a powerful tool for uncovering new insights into cancer biology and therapy development.

This combination search focuses on identifying cell lines with either BRAF or KRAS mutations, enabling researchers to:

● Compare the molecular and phenotypic differences between BRAF- and KRAS-driven cancers.

● Explore targeted therapies specific to each mutation (e.g., BRAF inhibitors for BRAF-mutant cancers and KRAS G12C inhibitors for KRAS-mutant cancers).

● Investigate the broader implications of MAPK/ERK pathway activation in cancer progression.

The animated graph shows the operations to identify 392 cell lines satisfying the search criteria:

Some key points:

● The combination search is accomplished by the use of logic operators OR. 

● The two filters form the search criteria: (BRAF: & mutation = Driver) OR (BRAF: & mutation = Driver), which means either BRAF or KRAS carries a driver mutation.

● We observe that BRAF and KRAS mutations are mutually exclusive from the heatmap, and mutation details are in the table below the heatmap.

Introduction

In cancer research, identifying cell lines with specific mutations is crucial for studying disease mechanisms, developing targeted therapies, and advancing drug discovery. Mutations like KRAS G12C, a common oncogenic driver in cancers such as non-small cell lung cancer (NSCLC) and colorectal cancer, are of particular interest due to their role in tumor growth and resistance to treatment. Researchers often need to find cell lines harboring such mutations to conduct experiments that mimic the genetic landscape of tumors.

Meritudio's Tumor Models Database is an invaluable resource for this purpose, offering a comprehensive collection of genomics data for approximately 2,000 cancer cell lines. Most of these cell lines are annotated with detailed mutation data, enabling researchers to quickly identify models that match their experimental needs. In this tutorial, we will walk you through the process of searching for cell lines with a specific mutation, using KRAS G12C as an example. Screenshots will guide you step-by-step to ensure a seamless experience.

Step-by-Step Guide

Step 1: Log in to Meritudio Bioinformatics Cloud and Access the Tumor Models Database

● Open your web browser and navigate to Meritudio Bioinformatics Cloud.

● Enter your credentials (username and password) to log in to the platform.

● Once logged in, locate and click on the Tumor Models Database from the top Menu.

Step 2: Enter KRAS in the Quick Search Box

● On the Tumor Models Database homepage, locate the Quick Search box, as shown in the image.

● Type KRAS into the Quick Search box. The database will display relevant results, including gene, pathways, and associated tumor models (cell lines).

● Click on KRAS (KRS1, K-Ras4B) to continue to the KRAS gene page.

Step 3: View the Search Results

● On the KRAS gene page, you will see a graph displaying cell lines. Each dot in the graph represents a cell line.

● Red dots indicate cell lines with KRAS mutations, while blue represents wild-type.

● The graph also provides insights into the frequency of KRAS mutations across different cancer types. For example, you may observe that pancreatic cancer and colorectal cancer have a high frequency of KRAS mutations, which aligns with known oncogenic roles of KRAS in these cancers.

● The graph below also shows that there are 177 cell lines carrying mutations at position 12, of which 25 are G12C mutation.

Step 4: Use Filters to Get G12C Mutation Cell Lines

● On the KRAS gene page, locate the Gene Mutation filter section.

● Click the last bullet point in the filter section to expand the mutation options.

● From the dropdown menu, select p.G12C and click Filter to filter for cell lines with the KRAS G12C mutation.

● The graph and results will update to display only 24 cell lines with the G12C mutation and KRAS expression data, the remaining one cell with G12C mutation has no KRAS expression data so is not in the boxplot, but can be found in the table below the boxplot.

● The table displays detailed information about 25 cell lines that carry the KRAS G12C mutation. We observe that (a) Lung adenocarcinoma and colorectal adenocarcinoma are prominently represented, consistent with the high prevalence of KRAS mutations in these cancers. (b) The mRNA expression and copy number values provide insights into the molecular characteristics of each cell line, which can help researchers select appropriate models for their studies. (c) The mutation frequency and pathogenicity classification (e.g., "likely_pathogenic") underscore the functional significance of the KRAS G12C mutation in driving cancer progression.

Conclusion

By following these steps, you can efficiently identify cell lines with specific mutations like KRAS G12C using Meritudio’s Tumor Models Database. This tool provides detailed genomics data, enabling researchers to explore mutation frequencies, expression levels, and copy number variations across various cancers. It’s an invaluable resource for advancing cancer research and drug development. Happy researching!