The marriage of toxicology and genomics has created not only opportunities but also novel informatics challenges. As with the larger field of gene expression analysis, toxicogenomics faces the problems of probe annotation and data comparison across different array platforms. Toxicogenomics studies are generally built on standard toxicology studies generating biological end point data, and as such, one goal of toxicogenomics is to detect relationships between changes in gene expression and in those biological parameters. These challenges are best addressed through data collection into a well-designed toxicogenomics database. A successful publicly accessible toxicogenomics database will serve as a repository for data sharing and as a resource for analysis, data mining, and discussion. It will offer a vehicle for harmonizing nomenclature and analytical approaches and serve as a reference for regulatory organizations to evaluate toxicogenomics data submitted as part of registrations. Such a database would capture the experimental context of in vivo studies with great fidelity such that the dynamics of the dose response could be probed statistically with confidence. This review presents the collaborative efforts between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute, and the National Institute of Environmental Health Sciences National Center for Toxigenomics Chemical Effects in Biological Systems knowledge base. The goal of this collaboration is to establish public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics databases. In this review we discuss several issues common to such databases: the requirement for identifying minimal descriptors to represent the experiment, the demand for standardizing data storage and exchange formats, the challenge of creating standardized nomenclature and ontologies to describe biological data, the technical problems involved in data upload, the necessity of defining parameters that assess and record data quality, and the development of standardized analytical approaches.

Database development in toxicogenomics: issues and efforts.

AIM: To develop a microarray-based prewarning system consisting of gastric cancer chip, prewarning data and analysis software for early detection of gastric cancer and pre-cancerous lesions.

METHODS: Two high-density chips with 8 464 human cDNA sites were used to primarily identify potential genes specific for normal gastric mucosa, pre-cancerous lesion and gastric cancer. The low-density chips, composed of selected genes associated with normal gastric mucosa, precancerous lesion and gastric cancer, were fabricated and used to screen 150 specimens including 60 specimens of gastric cancer, 60 of pre-cancerous tissues and 30 of normal gastric mucosa. CAD software was used to screen out the relevant genes and their critical threshold values of expression levels distinguishing normal mucosa from pre-cancerous lesion and cancer. All data were stored in a computer database to establish a prewarning data library for gastric cancer. Two potential markers brcaa1 and ndr1 were identified by Western blot and immunohistochemistry.

RESULTS: A total of 412 genes associated with three stages of gastric cancer development were identified. There were 216 genes displaying higher expression in gastric cancer, 85 genes displaying higher expression in pre-cancerous lesion and 88 genes displaying higher expression in normal gastric mucosa. Also 15 genes associated with metastasis of gastric cancer and 8 genes associated with risk factors were screened out for target genes of diagnosis chip of early gastric cancer. The threshold values of 412 selected genes to distinguish gastric cancer, pre-cancerous lesion from normal gastric mucosa were defined as 6.01±2.40, 4.86±1.94 and 5.42±2.17, respectively. These selected 412 genes and critical threshold values were compiled into an analysis software, which can automatically provide reports by analyzing the results of 412 genes obtained by examining gastric tissues. All data were compiled into a prewarning database for gastric cancer by CGO software. Northern blot and immunohistochemistry analysis confirmed that gene and protein of brcaa1 displayed lower expression in normal gastric mucosa and higher expression in gastric cancer tissues, conversely, ndr1 displayed lower expression in gastric cancer and higher expression in normal gastric mucosa.

CONCLUSION: The microarray-based prewarning system for gastric cancer was developed. This system consisted of gastric cancer-associated gene chip, prewarning data and analysis software, which has a high potential for applications in the early detection of gastric cancer. The two potential markers brcaa1 and ndr1 identified may be used to distinguish cancer status fand non-cancer status.

A microarray-based gastric carcinoma prewarning system.

PURPOSE/OBJECTIVES To review the changing treatment paradigm for newly diagnosed multiple myeloma and its implications for nursing. DATA SOURCES Journal articles, textbooks, published research data. DATA SYNTHESIS The treatment approaches to newly diagnosed multiple myeloma are varied, and no consensus exists about the best choice of induction therapy prior to high-dose chemotherapy with autologous stem cell transplantation. Novel therapies that have shown strong clinical activity in patients with relapsed or refractory myeloma currently are being explored as first-line therapy with associated higher incidence of serious complications. CONCLUSIONS Novel approaches in the treatment of newly diagnosed multiple myeloma may lead to better overall patient survival. Research is ongoing to find ways to improve progression-free and overall survival in patients with multiple myeloma. IMPLICATIONS FOR NURSING Oncology nurses play vital roles in the assessment and monitoring of serious complications associated with various therapies for patients with newly diagnosed multiple myeloma. Key responsibilities include safe and effective administration of complex chemotherapeutic regimens, management of side effects, patient and family education, and coordination of a multidisciplinary approach.

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The changing treatment paradigm in patients with newly diagnosed multiple myeloma: implications for nursing.

A database for tracking toxicogenomic samples and procedures.

The design and implementation of a computer vision system called DNAScan for the automated analysis of DNA hybridization images is presented. The hybridization of a DNA clone with a radioactively tagged probe manifests itself as a spot on the hybridization membrane. The imaging of the hybridization membranes and the automated analysis of the resulting images are imperative for high-throughput genomics experiments. A recursive segmentation procedure is designed and implemented to extract spotlike features in the hybridization images in the presence of a highly inhomogeneous background. Positive hybridization signals (hits) are extracted from the spotlike features using grouping and decomposition algorithms based on computational geometry. A mathematical model for the positive hybridization patterns and a Bayesian pattern classifier based on shape-based moments are proposed and implemented to distinguish between the clone-probe hybridization signals. Experimental results on real hybridization membrane images are presented.

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Automated analysis of DNA hybridization images for high-throughput genomics

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.

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Julian D. Shaughnessy

Papers

CGO: utilizing and integrating gene expression microarray data in clinical research and data management