MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer

Introduction Breast cancer is a heterogeneous disease encompassing a number of phenotypically diverse tumours. Expression levels of the oestrogen, progesterone and HER2/neu receptors which characterize clinically distinct breast tumours have been shown to change during disease progression and in res...

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Veröffentlicht in:Breast cancer research : BCR 2009-05, Vol.11 (3), Article R27
Hauptverfasser: Lowery, Aoife J, Miller, Nicola, Devaney, Amanda, McNeill, Roisin E, Davoren, Pamela A, Lemetre, Christophe, Benes, Vladimir, Schmidt, Sabine, Blake, Jonathon, Ball, Graham, Kerin, Michael J
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container_end_page
container_issue 3
container_start_page
container_title Breast cancer research : BCR
container_volume 11
creator Lowery, Aoife J
Miller, Nicola
Devaney, Amanda
McNeill, Roisin E
Davoren, Pamela A
Lemetre, Christophe
Benes, Vladimir
Schmidt, Sabine
Blake, Jonathon
Ball, Graham
Kerin, Michael J
description Introduction Breast cancer is a heterogeneous disease encompassing a number of phenotypically diverse tumours. Expression levels of the oestrogen, progesterone and HER2/neu receptors which characterize clinically distinct breast tumours have been shown to change during disease progression and in response to systemic therapies. Mi(cro)RNAs play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as regulators of tumour behaviour and progression. The aims of this study were to identify miRNA signatures that accurately predict the oestrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status of breast cancer patients to provide insight into the regulation of breast cancer phenotypes and progression. Methods Expression profiling of 453 miRNAs was performed in 29 early-stage breast cancer specimens. miRNA signatures associated with ER, PR and HER2/neu status were generated using artificial neural networks (ANN), and expression of specific miRNAs was validated using RQ-PCR. Results Stepwise ANN analysis identified predictive miRNA signatures corresponding with oestrogen (miR-342, miR-299, miR-217, miR-190, miR-135b, miR-218), progesterone (miR-520g, miR-377, miR-527-518a, miR-520f-520c) and HER2/neu (miR-520d, miR-181c, miR-302c, miR-376b, miR-30e) receptor status. MiR-342 and miR-520g expression was further analysed in 95 breast tumours. MiR-342 expression was highest in ER and HER2/neu-positive luminal B tumours and lowest in triple-negative tumours. MiR-520g expression was elevated in ER and PR-negative tumours. Conclusions This study demonstrates that ANN analysis reliably identifies biologically relevant miRNAs associated with specific breast cancer phenotypes. The association of specific miRNAs with ER, PR and HER2/neu status indicates a role for these miRNAs in disease classification of breast cancer. Decreased expression of miR-342 in the therapeutically challenging triple-negative breast tumours, increased miR-342 expression in the luminal B tumours, and downregulated miR-520g in ER and PR-positive tumours indicates that not only is dysregulated miRNA expression a marker for poorer prognosis breast cancer, but that it could also present an attractive target for therapeutic intervention.
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Expression levels of the oestrogen, progesterone and HER2/neu receptors which characterize clinically distinct breast tumours have been shown to change during disease progression and in response to systemic therapies. Mi(cro)RNAs play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as regulators of tumour behaviour and progression. The aims of this study were to identify miRNA signatures that accurately predict the oestrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status of breast cancer patients to provide insight into the regulation of breast cancer phenotypes and progression. Methods Expression profiling of 453 miRNAs was performed in 29 early-stage breast cancer specimens. miRNA signatures associated with ER, PR and HER2/neu status were generated using artificial neural networks (ANN), and expression of specific miRNAs was validated using RQ-PCR. Results Stepwise ANN analysis identified predictive miRNA signatures corresponding with oestrogen (miR-342, miR-299, miR-217, miR-190, miR-135b, miR-218), progesterone (miR-520g, miR-377, miR-527-518a, miR-520f-520c) and HER2/neu (miR-520d, miR-181c, miR-302c, miR-376b, miR-30e) receptor status. MiR-342 and miR-520g expression was further analysed in 95 breast tumours. MiR-342 expression was highest in ER and HER2/neu-positive luminal B tumours and lowest in triple-negative tumours. MiR-520g expression was elevated in ER and PR-negative tumours. Conclusions This study demonstrates that ANN analysis reliably identifies biologically relevant miRNAs associated with specific breast cancer phenotypes. The association of specific miRNAs with ER, PR and HER2/neu status indicates a role for these miRNAs in disease classification of breast cancer. Decreased expression of miR-342 in the therapeutically challenging triple-negative breast tumours, increased miR-342 expression in the luminal B tumours, and downregulated miR-520g in ER and PR-positive tumours indicates that not only is dysregulated miRNA expression a marker for poorer prognosis breast cancer, but that it could also present an attractive target for therapeutic intervention.</description><identifier>ISSN: 1465-542X</identifier><identifier>ISSN: 1465-5411</identifier><identifier>EISSN: 1465-542X</identifier><identifier>DOI: 10.1186/bcr2257</identifier><language>eng</language><publisher>BioMed Central Ltd</publisher><subject>Analysis ; Breast cancer ; Estrogen ; MicroRNA ; Neural networks ; Progesterone</subject><ispartof>Breast cancer research : BCR, 2009-05, Vol.11 (3), Article R27</ispartof><rights>COPYRIGHT 2009 BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1707-d16f44df4494d716e7384a2e36b8f7cd6e0fc9d8dc0d5bbb7f531087d425d4e43</citedby><cites>FETCH-LOGICAL-c1707-d16f44df4494d716e7384a2e36b8f7cd6e0fc9d8dc0d5bbb7f531087d425d4e43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids></links><search><creatorcontrib>Lowery, Aoife J</creatorcontrib><creatorcontrib>Miller, Nicola</creatorcontrib><creatorcontrib>Devaney, Amanda</creatorcontrib><creatorcontrib>McNeill, Roisin E</creatorcontrib><creatorcontrib>Davoren, Pamela A</creatorcontrib><creatorcontrib>Lemetre, Christophe</creatorcontrib><creatorcontrib>Benes, Vladimir</creatorcontrib><creatorcontrib>Schmidt, Sabine</creatorcontrib><creatorcontrib>Blake, Jonathon</creatorcontrib><creatorcontrib>Ball, Graham</creatorcontrib><creatorcontrib>Kerin, Michael J</creatorcontrib><title>MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer</title><title>Breast cancer research : BCR</title><description>Introduction Breast cancer is a heterogeneous disease encompassing a number of phenotypically diverse tumours. Expression levels of the oestrogen, progesterone and HER2/neu receptors which characterize clinically distinct breast tumours have been shown to change during disease progression and in response to systemic therapies. Mi(cro)RNAs play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as regulators of tumour behaviour and progression. The aims of this study were to identify miRNA signatures that accurately predict the oestrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status of breast cancer patients to provide insight into the regulation of breast cancer phenotypes and progression. Methods Expression profiling of 453 miRNAs was performed in 29 early-stage breast cancer specimens. miRNA signatures associated with ER, PR and HER2/neu status were generated using artificial neural networks (ANN), and expression of specific miRNAs was validated using RQ-PCR. Results Stepwise ANN analysis identified predictive miRNA signatures corresponding with oestrogen (miR-342, miR-299, miR-217, miR-190, miR-135b, miR-218), progesterone (miR-520g, miR-377, miR-527-518a, miR-520f-520c) and HER2/neu (miR-520d, miR-181c, miR-302c, miR-376b, miR-30e) receptor status. MiR-342 and miR-520g expression was further analysed in 95 breast tumours. MiR-342 expression was highest in ER and HER2/neu-positive luminal B tumours and lowest in triple-negative tumours. MiR-520g expression was elevated in ER and PR-negative tumours. Conclusions This study demonstrates that ANN analysis reliably identifies biologically relevant miRNAs associated with specific breast cancer phenotypes. The association of specific miRNAs with ER, PR and HER2/neu status indicates a role for these miRNAs in disease classification of breast cancer. Decreased expression of miR-342 in the therapeutically challenging triple-negative breast tumours, increased miR-342 expression in the luminal B tumours, and downregulated miR-520g in ER and PR-positive tumours indicates that not only is dysregulated miRNA expression a marker for poorer prognosis breast cancer, but that it could also present an attractive target for therapeutic intervention.</description><subject>Analysis</subject><subject>Breast cancer</subject><subject>Estrogen</subject><subject>MicroRNA</subject><subject>Neural networks</subject><subject>Progesterone</subject><issn>1465-542X</issn><issn>1465-5411</issn><issn>1465-542X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNptUcFKAzEQDaJgreIvBDx4cW2STTbpsZRqhapQFLwt2WRSIm22JOnBvzfSUhRkGGZ4M-_BzEPompJ7SlUz6kxkTMgTNKC8EZXg7OP0V3-OLlL6JIRKJdQAxWdvYr98meDkV0HnXYSEtxGsNxn3kHLsVxBwBAPb3Me7MitAyhD7AEcY62DxfLZkowC7I5hy0UvYB9xF0Cljo4OBeInOnF4nuDrUIXp_mL1N59Xi9fFpOllUhkoiK0sbx7ktOeZW0gZkrbhmUDedctLYBogzY6usIVZ0XSedqClR0nImLAdeD9HNXnel19D64Poctdn4ZNoJV6QhgtG6bN3_s1XCwsabcqTzBf9DuN0Tyt9SiuDabfQbHb9aStofB9qDA_U3kFF6Aw</recordid><startdate>20090511</startdate><enddate>20090511</enddate><creator>Lowery, Aoife J</creator><creator>Miller, Nicola</creator><creator>Devaney, Amanda</creator><creator>McNeill, Roisin E</creator><creator>Davoren, Pamela A</creator><creator>Lemetre, Christophe</creator><creator>Benes, Vladimir</creator><creator>Schmidt, Sabine</creator><creator>Blake, Jonathon</creator><creator>Ball, Graham</creator><creator>Kerin, Michael J</creator><general>BioMed Central Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20090511</creationdate><title>MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer</title><author>Lowery, Aoife J ; Miller, Nicola ; Devaney, Amanda ; McNeill, Roisin E ; Davoren, Pamela A ; Lemetre, Christophe ; Benes, Vladimir ; Schmidt, Sabine ; Blake, Jonathon ; Ball, Graham ; Kerin, Michael J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1707-d16f44df4494d716e7384a2e36b8f7cd6e0fc9d8dc0d5bbb7f531087d425d4e43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Analysis</topic><topic>Breast cancer</topic><topic>Estrogen</topic><topic>MicroRNA</topic><topic>Neural networks</topic><topic>Progesterone</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lowery, Aoife J</creatorcontrib><creatorcontrib>Miller, Nicola</creatorcontrib><creatorcontrib>Devaney, Amanda</creatorcontrib><creatorcontrib>McNeill, Roisin E</creatorcontrib><creatorcontrib>Davoren, Pamela A</creatorcontrib><creatorcontrib>Lemetre, Christophe</creatorcontrib><creatorcontrib>Benes, Vladimir</creatorcontrib><creatorcontrib>Schmidt, Sabine</creatorcontrib><creatorcontrib>Blake, Jonathon</creatorcontrib><creatorcontrib>Ball, Graham</creatorcontrib><creatorcontrib>Kerin, Michael J</creatorcontrib><collection>CrossRef</collection><jtitle>Breast cancer research : BCR</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lowery, Aoife J</au><au>Miller, Nicola</au><au>Devaney, Amanda</au><au>McNeill, Roisin E</au><au>Davoren, Pamela A</au><au>Lemetre, Christophe</au><au>Benes, Vladimir</au><au>Schmidt, Sabine</au><au>Blake, Jonathon</au><au>Ball, Graham</au><au>Kerin, Michael J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer</atitle><jtitle>Breast cancer research : BCR</jtitle><date>2009-05-11</date><risdate>2009</risdate><volume>11</volume><issue>3</issue><artnum>R27</artnum><issn>1465-542X</issn><issn>1465-5411</issn><eissn>1465-542X</eissn><abstract>Introduction Breast cancer is a heterogeneous disease encompassing a number of phenotypically diverse tumours. Expression levels of the oestrogen, progesterone and HER2/neu receptors which characterize clinically distinct breast tumours have been shown to change during disease progression and in response to systemic therapies. Mi(cro)RNAs play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as regulators of tumour behaviour and progression. The aims of this study were to identify miRNA signatures that accurately predict the oestrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status of breast cancer patients to provide insight into the regulation of breast cancer phenotypes and progression. Methods Expression profiling of 453 miRNAs was performed in 29 early-stage breast cancer specimens. miRNA signatures associated with ER, PR and HER2/neu status were generated using artificial neural networks (ANN), and expression of specific miRNAs was validated using RQ-PCR. Results Stepwise ANN analysis identified predictive miRNA signatures corresponding with oestrogen (miR-342, miR-299, miR-217, miR-190, miR-135b, miR-218), progesterone (miR-520g, miR-377, miR-527-518a, miR-520f-520c) and HER2/neu (miR-520d, miR-181c, miR-302c, miR-376b, miR-30e) receptor status. MiR-342 and miR-520g expression was further analysed in 95 breast tumours. MiR-342 expression was highest in ER and HER2/neu-positive luminal B tumours and lowest in triple-negative tumours. MiR-520g expression was elevated in ER and PR-negative tumours. Conclusions This study demonstrates that ANN analysis reliably identifies biologically relevant miRNAs associated with specific breast cancer phenotypes. The association of specific miRNAs with ER, PR and HER2/neu status indicates a role for these miRNAs in disease classification of breast cancer. Decreased expression of miR-342 in the therapeutically challenging triple-negative breast tumours, increased miR-342 expression in the luminal B tumours, and downregulated miR-520g in ER and PR-positive tumours indicates that not only is dysregulated miRNA expression a marker for poorer prognosis breast cancer, but that it could also present an attractive target for therapeutic intervention.</abstract><pub>BioMed Central Ltd</pub><doi>10.1186/bcr2257</doi><oa>free_for_read</oa></addata></record>
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subjects Analysis
Breast cancer
Estrogen
MicroRNA
Neural networks
Progesterone
title MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer
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