Citations

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

Abstract CHEK2 codes for a serine/threonine kinase (Chk2) activated by ATM in response to DNA double-strand breaks (DSB). CHEK2 has been described as a tumor suppressor with pro-apoptotic, cell cycle checkpoint and mitotic functions. However, Chk2 is also commonly activated (phosphorylated at T68) in cancers and precancerous lesions. In the present report, we characterized CHEK2 across the panel of 60 established cancer cell lines from the NCI Anticancer Tumor Cell Line panel (the NCI-60) using genomic and proteomic analyses including exon-specific mRNA expression, DNA copy number variation (CNV) by aCGH, exome sequencing, as well as Western blotting analyses for total and activated (pT68-Chk2) Chk2. We demonstrate that the high heterogeneity of Chk2 levels in the NCI-60 is primarily due to its inactivation (owing to low gene expression, alternative splicing, point mutations, copy number alterations and premature truncation) or reduction of protein levels. Notably, we also found that a significant percentage of cancer cells (12% of the NCI-60 and the HeLa cervix cell line) display high endogenous Chk2 activation, always associated with p53 loss of function. Notably, all the 12 cancer cell lines characterized as being p53 wild-type showed almost undetectable levels of pT68-Chk2. The potential impact of endogenous Chk2 activation was analyzed by transcriptome profiling of the five highest pT68-Chk2 cell lines, comparing them with five, tissue-matched low pT68-Chk2 and p53-matched deficient cell lines. Two-sided t tests revealed 176 genes differentially expressed with statistical significance (P < 0.01, uncorrected) in the two groups (high vs. low pT68-Chk2). Among the downregulated expressed genes, a significant number codes for proteins involved in DNA recombination, replication and repair (FANCA, FANCG, FANCL, BRCA2, RAD51C, EME1, and RECQL5 being the most relevant), and centromere organization. Several of these downregulated genes were parts of the Fanconi Anemia pathway or related to homologous recombination (HR, P < 0.05 for statistically significant enrichment using Ingenuity Pathway Analysis®). In conclusion, the biology of Chk2 remains complex, multifaceted and fast evolving. Our study provides information on the relationship between different genomic and proteomic parameters related to Chk2 in cancer, and suggests a possible role for activated Chk2 in p53-deficient or HR/Fanconi Anemia-deficient cells. 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 2116. doi:1538-7445.AM2012-2116

Abstract High-throughput and high-content databases are increasingly important resources in molecular medicine, systems biology, and pharmacology. However, the information usually resides in unwieldy databases, limiting ready data analysis and integration. One resource that offers substantial potential for improvement in this regard is the NCI-60 cell line database compiled by the U.S. National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacologic response platforms. In this report, we introduce a CellMiner (http://discover.nci.nih.gov/cellminer/) web application designed to improve the use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,379 genes and 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 drugs approved by the U.S. Food and Drug Administration. Converting these differential levels into quantitative patterns across the NCI-60 clarified data organization and cross-comparisons using a novel pattern match tool. Data queries for potential relationships among parameters can be conducted in an iterative manner specific to user interests and expertise. Examples of the in silico discovery process afforded by CellMiner were provided for multidrug resistance analyses and doxorubicin activity; identification of colon-specific genes, microRNAs, and drugs; microRNAs related to the miR-17-92 cluster; and drug identification patterns matched to erlotinib, gefitinib, afatinib, and lapatinib. CellMiner greatly broadens applications of the extensive NCI-60 database for discovery by creating web-based processes that are rapid, flexible, and readily applied by users without bioinformatics expertise. Cancer Res; 72(14); 3499–511. ©2012 AACR.

This paper describes the sentiment classification system developed by the Mayo Clinic team for the 2011 I2B2/VA/Cincinnati Natural Language Processing (NLP) Challenge. The sentiment classification task is to assign any pertinent emotion to each sentence in suicide notes. We have implemented three systems that have been trained on suicide notes provided by the I2B2 challenge organizer–-a machine learning system, a rule-based system, and a system consisting of a combination of both. Our machine learning system was trained on re-annotated data in which apparently inconsistent emotion assignment was adjusted. Then, the machine learning methods by RIPPER and multinomial Naïve Bayes classifiers, manual pattern matching rules, and the combination of the two systems were tested to determine the emotions within sentences. The combination of the machine learning and rule-based system performed best and produced a micro-average F-score of 0.5640.