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Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer
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 associat...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949646/ https://www.ncbi.nlm.nih.gov/pubmed/20804563 http://dx.doi.org/10.1186/bcr2616 |
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author | Luo, Jingqin Ellis, Matthew J |
author_facet | Luo, Jingqin Ellis, Matthew J |
author_sort | Luo, Jingqin |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2949646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29496462011-02-20 Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer Luo, Jingqin Ellis, Matthew J Breast Cancer Res Editorial 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. BioMed Central 2010 2010-08-20 /pmc/articles/PMC2949646/ /pubmed/20804563 http://dx.doi.org/10.1186/bcr2616 Text en Copyright ©2010 BioMed Central Ltd |
spellingShingle | Editorial Luo, Jingqin Ellis, Matthew J Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer |
title | Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer |
title_full | Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer |
title_fullStr | Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer |
title_full_unstemmed | Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer |
title_short | Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer |
title_sort | microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer |
topic | Editorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949646/ https://www.ncbi.nlm.nih.gov/pubmed/20804563 http://dx.doi.org/10.1186/bcr2616 |
work_keys_str_mv | AT luojingqin microarraydataanalysisinneoadjuvantbiomarkerstudiesinestrogenreceptorpositivebreastcancer AT ellismatthewj microarraydataanalysisinneoadjuvantbiomarkerstudiesinestrogenreceptorpositivebreastcancer |