Single nucleotide polymorphisms provide a Dimesna high-density method for large-scale analyses of polymorphic markers. SNP arrays provide a feasible means of conducting high-throughput, genomewide screens for allelic imbalance. SNP array analysis enables detection of genotypic alterations in the tumors of individual patients and, in principle, identification of new areas with common allelic imbalance that could harbor potential tumor suppressors. Additionally, this method allows for simultaneous measurement of DNA copy number. SNP array analysis shows high concordance with microsatellite methods, but allows detection of smaller regions of LOH that maybe missed by microsatellite mapping. In addition, because LOH can occur without change in DNA copy number, SNP arrays offer more potential than comparative genomic hybridization in detecting such events. This type of analysis has been used successfully to assess different cancers. Studies of neuroblastoma cells consistently identify LOH at Atractylodin several chromosomal regions, some of which correlate with a poor outcome, but tumor suppressor genes in these regions remain to be identified. An example is the deletion of chromosome band 1p36, which is strongly correlated with MYCN gene amplification, a very poor prognostic marker in neuroblastoma, and which by itself is also commonly associated with an advanced disease stage and a poor outcome. Chromosome band 11q23 LOH is inversely related to MYCN amplification but also reliably identifies patients at high risk for disease relapse. Additionally, unbalanced gain of chromosome 17q is associated with high-risk disease features, such as advanced disease stage and age at diagnosis, MYCN amplification, and 1p36 LOH, and a decreased survival probability. Thus, the available data clearly indicate that DNA copy number aberrations are significant predictors of disease phenotype and clinical behavior in neuroblastoma, including likelihood of response to chemotherapy and/or eventual disease relapse. A chip-based method for whole-genome evaluation of DNA alterations has the potential to streamline gene discovery efforts and provide genomic signatures that may be useful in predicting prognosis.