Classifying the estrogen receptor status of breast cancers by expression profiles reveals a poor prognosis subpopulation exhibiting high expression of the ERBB2 receptor
| Autor: | Lee Chee How, Amit Aggarwal, Tan Puay Hoon, Wee Siew Bok, Yu Kun, Wong Chow Yin, Tan Sin Lam, Patrick Tan, Hong Ga Sze, Vladimir B. Bajic |
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| Rok vydání: | 2003 |
| Předmět: |
Transcriptional Activation
medicine.medical_specialty Neoplasms Hormone-Dependent Receptor ErbB-2 medicine.drug_class Statistics as Topic Estrogen receptor Breast Neoplasms Biology Breast cancer Internal medicine Gene expression Genetics medicine Humans Molecular Biology Estrogen Receptor Status Genetics (clinical) Regulation of gene expression Gene Expression Profiling General Medicine Prognosis medicine.disease Gene Expression Regulation Neoplastic Survival Rate Gene expression profiling Endocrinology Estrogen Cancer research Female Breast disease Algorithms |
| Zdroj: | Human Molecular Genetics. 12:3245-3258 |
| ISSN: | 1460-2083 0964-6906 |
| DOI: | 10.1093/hmg/ddg347 |
| Popis: | Recent work using expression profiling to computationally predict the estrogen receptor (ER) status of breast tumors has revealed that certain tumors are characterized by a high prediction uncertainty ('low-confidence'). We analyzed these 'low-confidence' tumors and determined that their 'uncertain' prediction status arises as a result of widespread perturbations in multiple genes whose expression is important for ER subtype discrimination. Patients with 'low-confidence' ER+ tumors exhibited a significantly worse overall survival (P=0.03) and shorter time to distant metastasis (P=0.004) compared with their 'high-confidence' ER+ counterparts, indicating that the 'high-' and 'low-confidence' binary distinction is clinically meaningful. We then discovered that elevated expression of the ERBB2 receptor is significantly correlated with a breast tumor exhibiting a 'low-confidence' prediction, and this association was subsequently validated across multiple independently derived breast cancer expression datasets employing a variety of different array technologies and patient populations. Although ERBB2 signaling has been proposed to inhibit the transcriptional activity of ER, a large proportion of the perturbed genes in the 'low-confidence'/ERBB2+ samples are not known to be estrogen responsive, and a recently described bioinformatic algorithm (DEREF) was used to demonstrate the absence of potential estrogen-response elements (EREs) in their promoters. We propose that a significant portion of ERBB2's effects on ER+ breast tumors may involve ER-independent mechanisms of gene activation, which may contribute to the clinically aggressive behavior of the 'low-confidence' breast tumor subtype. |
| Databáze: | OpenAIRE |
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