Gene DNA Copy Number Research Articles

Abstract 3749: Uses and update for CellMiner, a tool for access to and comparison of molecular data and pharmacological response for the NCI-60

Abstract Integration and understanding of genomic and proteomic data and their relationships with and influences on pharmacology have become key components of cancer research and its translation to therapeutics. Nonetheless, multiple significant obstacles need to be overcome for both their integration and understanding. The NCI-60 cancerous cell lines provide a premier opportunity in this area due to the scope and quality of the available data. CellMiner (http://discover.nci.nih.gov/cellminer) is a web-application that allows the user to rapidly access data for relative levels of transcript expression for 25,772 genes, genetic variants for 16,568 genes, transcript expression for 360 microRNAs, protein levels for 94 genes, and DNA copy number (from aCGH) for 23,413 genes. In addition, it provides concentration-response activities for 20,002 compounds including 114 Food and Drug Administration (FDA)-approved, 55 clinical trial, and 349 known mechanism-of-action drugs. Here we present our latest CellMiner update along with examples of data integration that provide enhanced understanding in the systems biological sense. Included in the tool updates are i) the upcoming “Protein mean value” cell line signature tool, ii) the recently introduced “Gene DNA copy number” tool, iii) introduction of the “Cross-correlations of transcripts, microRNAs, and drugs” tool, iv) the provision of additional forms of data in the standard output for the “Pattern comparisons” tool, and v) upgrades of the “Genetic variant versus drug visualization” tool allowing the user to query for all drugs significantly correlated to genomic variants for the whole human genome (or visa versa). Examples of data integration will include SLFN11 expression versus the activities of DNA-damaging drugs, RAS activation considering three forms of molecular data, and PTEN knockdown and pharmacological connection (also using three forms of molecular data). The data and tools are or shortly will be publicly available at CellMiner. Citation Format: William C. Reinhold, Margot Sunshine, Sudir Varma, Vinodh Rajapakse, James Doroshow, Joel Morris, Yves Pmmier. Uses and update for CellMiner, a tool for access to and comparison of molecular data and pharmacological response for the NCI-60. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3749. doi:10.1158/1538-7445.AM2015-3749

Abstract 3177: Web-based access using CellMiner for gene expression, DNA copy number, microRNA transcript levels, variant status, drug activity, and their pattern comparisons for the NCI-60.

Abstract High-throughput data is increasingly being integrated into the fields of biology, molecular biology, and pharmacology. However, a difficult problem has been the rapid and fluid access to and integration of the data, which tends to reside in huge unwieldy databases. One set of cell lines with substantial potential for benefit from this type of access and integration is the NCI-60 cancerous cell lines. We present here a set of tools within our CellMiner web-application designed to address this need for the areas of transcript expression, microRNA expression, gene DNA copy number, variant status, and drug activity. CellMiner allows the user to rapidly access data for relative levels of transcript expression for 26,065 genes, 360 microRNAs, and 20,602 compounds including 102 Food and Drug Administration (FDA)-approved drugs. These levels in turn create patterns across the NCI-60 that can be compared to one another using our “pattern match” tool. Together, these tools allow one to query the data for potential relationships between these parameters, in a manner specific to a users area of expertise and interest, in a rapid and flexible manner without the need for expertise in computer science or bioinformatics. Comparisons of transcript/drug will be demonstrated with SLFN11/topotecan; variant/drug with BRAF V600E/vemurafinib; colon tissue-of origin specific genes with TRIM15, RNF43, and VIL1; and DNA copy number change with CDKN2A (p16). The data are publicly available at http://discover.nci.nih.gov/cellminer. Citation Format: William C. Reinhold, Margot Sunshine, Sudir Varma, Hongfang Liu, Ogan Abaan, Paul Meltzer, Joel Morris, Kurt Kohn, James Doroshow, Yves Pommier. Web-based access using CellMiner for gene expression, DNA copy number, microRNA transcript levels, variant status, drug activity, and their pattern comparisons for the NCI-60. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3177. doi:10.1158/1538-7445.AM2013-3177

Abstract 2991: Web-based access for whole genome analyses of gene expression, DNA copy number, microRNA transcript levels, drug activity, and their pattern comparisons for the NCI-60

Abstract High-throughput data is increasingly being integrated into the fields of biology, molecular biology, and pharmacology. However, a difficult problem has been the rapid and fluid access to and integration of this data, which tends to reside in huge unwieldy databases. One set of cell lines with substantial potential for benefit from this type of access and integration is the NCI-60 cancerous cell lines. We present here a set of tools within our CellMiner web-application designed to address this need for the areas of transcript expression, microRNA expression, gene DNA copy number, and drug activity. CellMiner allows the user to rapidly access data for relative levels of transcript expression for 18,532 genes, 360 microRNAs, and 16,861 compounds including 91 Food and Drug Administration (FDA)-approved drugs. These levels in turn create patterns across the NCI-60 that can be compared to one another using our “pattern match” tool. Together, these tools allow one to query the data for potential relationships between these parameters, in a manner specific to a users area of expertise and interest, in a rapid and flexible manner without the need for expertise in computer science or bioinformatics. The output will be demonstrated with cancer interesting microRNAs 18a, 21, 29a, 30c-2, 30d, and 31, cancer interesting genes ABCB1, ATM, ATR, BRCA2, and TP53, and the clinically relevant drugs belinostat, crizotinib, pazopanib, selumetinib and tanespimycin. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2991. doi:1538-7445.AM2012-2991