tailieunhanh - báo cáo khoa học: "Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? | Iwamoto and Pusztai Genome Medicine 2010 2 81 http content 2 11 81 Genome Medicine COMMENTARY L__ Predicting prognosis of breast cancer with gene signatures are we lost in a sea of data Takayuki Iwamoto and Lajos Pusztai Abstract A large number of prognostic and predictive signatures have been proposed for breast cancer and a few of these are now available in the clinic as new molecular diagnostic tests. However several other signatures have not fared well in validation studies. Some investigators continue to be puzzled by the diversity of signatures that are being developed for the same purpose but that share few or no common genes. The history of empirical development of prognostic gene signatures and the unique association between molecular subsets and clinical phenotypes of breast cancer explain many of these apparent contradictions in the literature. Three features of breast cancer gene expression contribute to this the large number of individually prognostic genes differentially expressed between good and bad prognosis cases the unstable rankings of differentially expressed genes between datasets and the highly correlated expression of informative genes. Introduction Gene-expression profiling allows simultaneous semi-quantitative measurements of thousands of different mRNA species in a single experiment. It was considered logical to assume that different cancers will have distinct gene-expression patterns and that the expression of many genes will be associated with clinically relevant disease outcomes in particular cancer types. Consequently it was assumed these associations might be exploited to develop a new generation of multi-gene diagnostic tests in particular prognostic and treatment response predictors. Correspondence lpusztai@ Department of Breast Medical Oncology MD Anderson Cancer Center University of Texas Houston TX 77230-1439 USA Full list of author information is available at the end of the article .