%0 Journal Article
%T Methods in Mammary Gland Development and Cancer: the second ENDBC meeting - intravital imaging, genomics, modeling and metastasis
%A John Stingl
%A Matthew J Smalley
%A Marina A Glukhova
%A Mohamed Bentires-Alj
%J Breast Cancer Research
%D 2010
%I BioMed Central
%R 10.1186/bcr2630
%X The European Network for Breast Development and Cancer (ENBDC) organized its second meeting to foster interactions and the sharing of protocols between groups working on breast development and cancer. Graduate students, postdocs and research associates were encouraged to attend. The meeting included discussions on genomics, bioinformatics, intravital microscopy, disseminated tumor cells, ex vivo culture and in vivo models for studying breast cancer.Nuno Barbosa-Morais (Cancer Research UK, Cambridge Research Institute) discussed the importance of the correct annotation of microarray probes and of being sure that a probe truly maps to the gene of interest. Further problems to consider are probes that map to intron-exon boundaries, the presence of SNPs, and alternative splicing, which, as is becoming apparent, occurs on a far wider scale than previously appreciated. Splicing may lead to difficulties when summarizing data from multiple probes apparently mapping to the same gene but which in fact detect different splice variants. Overall, it is clear that whatever array platform is being used, application of the latest, most reliable annotation is important. In a test of annotation reliability, it was found that Refseq annotations are a more reliable guide to probe identity than GenBank/UniGene.Britta Weigelt (Cancer Research UK, London Research Institute) spoke on the design of gene expression microarray studies, which fall into three types. The first, class comparison, is a supervised analysis to define molecular differences between predefined groups. The second, class prediction, is a supervised analysis where, after identifying the transcriptional differences between predefined groups, a genomic classifier (signature) is defined to classify new samples. It has become clear that class prediction signatures mainly identify tumors with high proliferation, although they do perform well in identifying poor prognosis tumors of the estrogen receptor-positive type. The third
%U http://breast-cancer-research.com/content/12/5/311