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.
MeSH terms
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Animals
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Antineoplastic Combined Chemotherapy Protocols / therapeutic use
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Breast Neoplasms / drug therapy*
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Breast Neoplasms / genetics*
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Breast Neoplasms / metabolism
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Drug Resistance, Neoplasm / genetics
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Estradiol / administration & dosage
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Estradiol / pharmacology
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Estrogen Antagonists / administration & dosage
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Estrogen Antagonists / pharmacology*
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Estrogens / administration & dosage
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Estrogens / pharmacology
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Female
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Gene Expression Profiling*
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Gene Expression Regulation, Neoplastic / drug effects
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Humans
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Mice
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Mice, Nude
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Oligonucleotide Array Sequence Analysis
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Prognosis
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Receptors, Estrogen / metabolism
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Tamoxifen / administration & dosage
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Tamoxifen / pharmacology
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Xenograft Model Antitumor Assays
Substances
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Estrogen Antagonists
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Estrogens
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Receptors, Estrogen
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Tamoxifen
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Estradiol