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Using CellMiner 1.6 for Systems Pharmacology and Genomic Analysis of the NCI-60
William C Reinhold1, Margot Sunshine2, Sudhir Varma3
1Developmental Therapeutics Branch and Laboratory of Molecular Pharmacology, Center for Cancer Research, NCI, NIH, Bethesda, Maryland. wcr@mail.nih.gov pommier@nih.gov.
Abstract:
The NCI-60 cancer cell line panel provides a premier model for data integration, and systems pharmacology being the largest publicly available database of anticancer drug activity, genomic, molecular, and phenotypic data. It comprises gene expression (25,722 transcripts), microRNAs (360 miRNAs), whole-genome DNA copy number (23,413 genes), whole-exome sequencing (variants for 16,568 genes), protein levels (94 genes), and cytotoxic activity (20,861 compounds). Included are 158 FDA-approved drugs and 79 that are in clinical trials. To improve data accessibility to bioinformaticists and non-bioinformaticists alike, we have developed the CellMiner web-based tools. Here, we describe the newest CellMiner version, including integration of novel databases and tools associated with whole-exome sequencing and protein expression, and review the tools. Included are (i) "Cell line signature" for DNA, RNA, protein, and drugs; (ii) "Cross correlations" for up to 150 input genes, microRNAs, and compounds in a single query; (iii) "Pattern comparison" to identify connections among drugs, gene expression, genomic variants, microRNA, and protein expressions; (iv) "Genetic variation versus drug visualization" to identify potential new drug:gene DNA variant relationships; and (v) "Genetic variant summation" designed to provide a synopsis of mutational burden on any pathway or gene group for up to 150 genes. Together, these tools allow users to flexibly query the NCI-60 data for potential relationships between genomic, molecular, and pharmacologic parameters in a manner specific to the user's area of expertise. Examples for both gain- (RAS) and loss-of-function (PTEN) alterations are provided.
Insights
The NCI-60 cancer cell line panel, integrated with CellMiner tools, offers a comprehensive resource for analyzing anticancer drug activity, genomic, and molecular data. This platform enhances data accessibility for researchers exploring drug-gene relationships.
Area of Science:
- Bioinformatics
- Systems Pharmacology
- Cancer Research
Background:
- The NCI-60 cancer cell line panel is a unique resource for systems pharmacology, offering extensive anticancer drug activity, genomic, molecular, and phenotypic data.
- Existing data accessibility challenges for bioinformaticists and non-bioinformaticists hinder comprehensive analysis of this valuable resource.
Purpose of the Study:
- To introduce the newest version of the CellMiner web-based tools, enhancing accessibility to the NCI-60 data.
- To integrate novel databases and tools for whole-exome sequencing and protein expression analysis.
- To facilitate flexible querying of the NCI-60 data for relationships between genomic, molecular, and pharmacologic parameters.
Main Methods:
- Development and description of the CellMiner web-based tools, including new features for whole-exome sequencing and protein expression.
- Integration of diverse datasets: gene expression, microRNAs, DNA copy number, whole-exome sequencing, protein levels, and drug cytotoxic activity.
- Implementation of specific tools: "Cell line signature," "Cross correlations," "Pattern comparison," "Genetic variation versus drug visualization," and "Genetic variant summation."
Main Results:
- The updated CellMiner provides enhanced integration of whole-exome sequencing and protein expression data with the NCI-60 panel.
- New tools enable detailed analysis, including cross-correlations, pattern comparisons, and visualization of genetic variation-drug relationships.
- Demonstration of identifying potential drug-gene DNA variant relationships and summarizing mutational burden.
Conclusions:
- The enhanced CellMiner tools significantly improve accessibility and analytical capabilities for the NCI-60 cancer cell line panel data.
- Researchers can now more effectively explore complex relationships between genomic alterations, molecular profiles, and drug responses.
- This resource empowers diverse users to investigate potential therapeutic strategies based on specific cancer characteristics.
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