tailieunhanh - Báo cáo y học: "A fuzzy gene expression-based computational approach improves breast cancer prognostication"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: A fuzzy gene expression-based computational approach improves breast cancer prognostication. | Haibe-Kains et al. Genome Biology 2010 11 R18 http 2010 11 2 R18 w Genome Biology METHOD Open Access Afuzzy gene expression-based computational approach improves breast eaneer prognostication Benjamin Haibe-Kains 1 2 Christine Desmedt 1 Frangoise Rothé 1 Martine Piccart1 Christos Sotiriou 1 and Gianluca Bontempi2 Abstract Early gene expression studies classified breast tumors into at least three clinically relevant subtypes. Although most current gene signatures are prognostic for estrogen receptor ER positive human epidermal growth factor receptor 2 HER2 negative breast cancers few are informative for ER negative HER2 negative and HER2 positive subtypes. Here we present Gene Expression Prognostic Index Using Subtypes GENIUS a fuzzy approach for prognostication that takes into account the molecular heterogeneity of breast cancer. In systematic evaluations GENIUS significantly outperformed current gene signatures and clinical indices in the global population of patients. Background Early gene expression studies 1-6 classify breast cancer into at least three clinically relevant molecular subtypes basal-like predominantly estrogen receptor ER negative and human epidermal growth factor receptor 2 HER2 negative HER2-positive and luminal-like ER-positive tumors. Although this classification has changed the way clinicians perceive the disease it has been difficult to use the initial microarray-based clustering models in clinical practice. The reason is that these models suffer from the drawbacks of the hierarchical clustering method itself namely its instability and the difficulty associated with using it for new data 7 . To address these concerns we recently used model-based clustering to introduce an alternative model able to identify different molecular subtypes 8 9 . We have shown that this model is capable of fuzzy classification 10 11 a patient s tumor belongs simultaneously to each molecular subtype with some probability degree of membership in a