Abstract
Microarray data have been widely utilized to discover biomarkers predictive of response to endocrine therapy in estrogen receptor-positive breast cancer. Typically, these data have focused on analyses conducted on the diagnostic specimen. However, dynamic temporal changes in gene expression associated with treatment may deliver significant improvements to the current generation of predictive models. We present and discuss some statistical issues relevant to the paper by Taylor and colleagues, who conducted studies to model the prognostic potential of gene expression changes that occur after endocrine treatment.
Highlights
Microarray data have been widely utilized to discover biomarkers predictive of response to endocrine therapy in estrogen receptor-positive breast cancer
Expressed genes at some early time points following treatment were found to be prognostic of survival in clinical data sets, but not those identified at other time points
Adjuvant endocrine treatment in estrogen receptorpositive (ER+) breast cancer patients reduces the risk of relapse and death from breast cancer [2], but large numbers of patients still die of endocrine therapyresistant disease [3]
Summary
Microarray data have been widely utilized to discover biomarkers predictive of response to endocrine therapy in estrogen receptor-positive breast cancer. Taylor and colleagues [1] examined the time course of gene expression profile changes in estrogen (E2)-treated and E2 and tamoxifen-treated mouse xenografts. The authors presented three distinct categories of gene expression temporal profiles, each characterized by two sets of genes. Expressed genes at some early time points following treatment were found to be prognostic of survival in clinical data sets, but not those identified at other time points.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
