BMC Cancer 2007, 7:226 http://www.biomedcentral.com/1471-2407/7/226 FCilguustreerin5g of different histologies in carcinomas by their overall imbalance pattern Clustering of different histologies in carcinomas by their overall imbalance pattern. Here, the most frequent histological types were automatically grouped for their overall imbalance profiles. Since considerable differences had been found for adenocarci- noma cases from the prostate (most notably lack of 1q gains), this group was separeted from the overall adenocarcinoma group. lection of case-specific chromosomal imbalance data from published chromosomal CGH experiments. The most striking observations were the high frequency of a limited set of changes (+1q, +3q, +7, +8q, -13q, -17p, - 18q, +20q ...), of which different subsets occurred in most entities. The iterative involvement of certain genomic regions in either gains or losses is a strong argument for the non-random incorporation of these changes into the tumor cell genomes, with common as well as disease spe- cific changes becoming apparent. One may speculate that the recurring combination of a limited set of imbalance hot-spots is concordant with the multistep process of can- cer development [78], and that these regions point towards preferential oncogenetic targets. For a detailed discussion of the region-specific changes and patterns related to single disease entities, only examples could be given in the context of this article. The existing literature should be considered for additional information. This descriptive analysis of local genomic imbalance fre- quencies cannot be able to address some important ques- tions, e.g. regarding the biological significance of the observed changes. Although certain genomic copy number amplifications and segmental deletions have been shown to involve oncogenetically relevant genes in various malignancies (see Table 2 and for reference e.g. [79-82]), some of the recurring genomic imbalances might be as well pure epi-phenomena of defects in the molecular maintenance of the genome. In respect to the limited spatial resolution and phenomenological nature of the data, the discussion and validation of specific target genes should be left to meticulously crafted molecular- biological experiments. Cluster analysis of case specific chromosomal CGH data has been used previously to identify subsets of cases in single clinico-pathological entities [45,83,84]. Current methodology appears more suited for limited data sets, while new algorithms have to be developed for automatic Page 11 of 15 (page number not for citation purposes)