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BioTechniques 269 Oncogenomic screening in malignant neoplasias has led to the description of oncogenetic mechanisms and, recently, to the first successful targeted drug development approaches (1). Individual genomic abnormalities are used as diagnostic markers or for the individual prediction of clinical aggressiveness (2). However, most malignancies show nonrandom aberration patterns that may reflect the cooperation of multiple oncoand tumor suppressor genes, according to the multistep model of oncogenesis (3). The complexity of those changes warrants the application of advanced data mining methods for the development of oncogenomic models. A number of cytogenetic and molecular genetic techniques describe chromosomal imbalances or changes in the regional DNA content of tumor cells. Historically, the microscopic inspection of stained metaphase spreads (4) had been most widely applied, and still is the reference method, in many clinical applications. Comparative genomic hybridization (CGH) (5) permits the detection of genomic imbalances from tumor samples with more than 50% tumor cell content as well as from archival material (6). Recently, array or matrix CGH (7,8) has started to overcome the limited spatial resolution (9) of metaphase CGH. An intriguing concept for oncogenomic data mining is the combination of the accumulated cytogenetic data with the molecular cytogenetic data from metaphase and array-based CGH experiments. However, complex annotation formats are used for the description of experimental results. The standards for cytogenetic banding and reverse in situ hybridization (ISH) (e.g., CGH) have been defined in the International System for Cytogenetic Nomenclature (ISCN) (10). The results of genomic microarray experiments usually are stored according to the minimal information about a microarray experiment (MIAME) guidelines (11). The largest publicly accessible resource for molecular cytogenetic screening data in oncology is the Mitelman Database of Chromosome Aberrations in Cancer (cgap.nci.nih. gov/Chromosomes/Mitelman), which describes more than 46,000 samples analyzed by metaphase banding. Utilization of this data has been limited by the lack of a format amenable to data mining procedures, though valuable studies have been published by the database maintainers (12). Another resource is the National Center for Biotechnology Information (NCBI) spectral karyotyping (SKY)/CGH database (www.ncbi.nlm.nih.gov/sky/ skyweb.cgi) (13). It provides wellstructured clinical and experimental information for the included cases, but due to the reliance of the NCBI site on voluntary data submission it is, with currently 1006 included experiments, quantitatively limited. Recently (13), the Mitelman database and the SKY/CGH project have been integrated into NCBI’s Entrez Cancer Chromosomes site (www.ncbi. nlm.nih.gov/entrez/query.fcgi?db= cancerchromosomes) and now offer band-specific search capabilities. By far, the largest collection of casespecific CGH data are presented through the Progenetix web site (www.progenetix.net) (14), on which this article is focused. Online database and bioinformatics toolbox to support data mining in cancer cytogenetics