DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.

<h4>Background</h4>Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified...

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Autores principales:	Desmond G Powe, Gopal Krishna R Dhondalay, Christophe Lemetre, Tony Allen, Hany O Habashy, Ian O Ellis, Robert Rees, Graham R Ball
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/65a8a8ef87a7433b922d346ad74caebd
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spelling	oai:doaj.org-article:65a8a8ef87a7433b922d346ad74caebd2021-11-18T08:39:05ZDACH1: its role as a classifier of long term good prognosis in luminal breast cancer.1932-620310.1371/journal.pone.0084428https://doaj.org/article/65a8a8ef87a7433b922d346ad74caebd2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24392136/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified but clinically these can be distilled into two prognostic groups in which Luminal A are accorded good prognosis and Luminal B correlate with poor prognosis. Further biomarkers are needed to attain classification consensus. Machine learning approaches like Artificial Neural Networks (ANNs) have been used for classification and identification of biomarkers in breast cancer using high throughput data. In this study, we have used an artificial neural network (ANN) approach to identify DACH1 as a candidate luminal marker and its role in predicting clinical outcome in breast cancer is assessed.<h4>Materials and methods</h4>A reiterative ANN approach incorporating a network inferencing algorithm was used to identify ER-associated biomarkers in a publically available cDNA microarray dataset. DACH1 was identified in having a strong influence on ER associated markers and a positive association with ER. Its clinical relevance in predicting breast cancer specific survival was investigated by statistically assessing protein expression levels after immunohistochemistry in a series of unselected breast cancers, formatted as a tissue microarray.<h4>Results</h4>Strong nuclear DACH1 staining is more prevalent in tubular and lobular breast cancer. Its expression correlated with ER-alpha positive tumours expressing PgR, epithelial cytokeratins (CK)18/19 and 'luminal-like' markers of good prognosis including FOXA1 and RERG (p<0.05). DACH1 is increased in patients showing longer cancer specific survival and disease free interval and reduced metastasis formation (p<0.001). Nuclear DACH1 showed a negative association with markers of aggressive growth and poor prognosis.<h4>Conclusion</h4>Nuclear DACH1 expression appears to be a Luminal A biomarker predictive of good prognosis, but is not independent of clinical stage, tumour size, NPI status or systemic therapy.Desmond G PoweGopal Krishna R DhondalayChristophe LemetreTony AllenHany O HabashyIan O EllisRobert ReesGraham R BallPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e84428 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Desmond G Powe Gopal Krishna R Dhondalay Christophe Lemetre Tony Allen Hany O Habashy Ian O Ellis Robert Rees Graham R Ball DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
description	<h4>Background</h4>Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified but clinically these can be distilled into two prognostic groups in which Luminal A are accorded good prognosis and Luminal B correlate with poor prognosis. Further biomarkers are needed to attain classification consensus. Machine learning approaches like Artificial Neural Networks (ANNs) have been used for classification and identification of biomarkers in breast cancer using high throughput data. In this study, we have used an artificial neural network (ANN) approach to identify DACH1 as a candidate luminal marker and its role in predicting clinical outcome in breast cancer is assessed.<h4>Materials and methods</h4>A reiterative ANN approach incorporating a network inferencing algorithm was used to identify ER-associated biomarkers in a publically available cDNA microarray dataset. DACH1 was identified in having a strong influence on ER associated markers and a positive association with ER. Its clinical relevance in predicting breast cancer specific survival was investigated by statistically assessing protein expression levels after immunohistochemistry in a series of unselected breast cancers, formatted as a tissue microarray.<h4>Results</h4>Strong nuclear DACH1 staining is more prevalent in tubular and lobular breast cancer. Its expression correlated with ER-alpha positive tumours expressing PgR, epithelial cytokeratins (CK)18/19 and 'luminal-like' markers of good prognosis including FOXA1 and RERG (p<0.05). DACH1 is increased in patients showing longer cancer specific survival and disease free interval and reduced metastasis formation (p<0.001). Nuclear DACH1 showed a negative association with markers of aggressive growth and poor prognosis.<h4>Conclusion</h4>Nuclear DACH1 expression appears to be a Luminal A biomarker predictive of good prognosis, but is not independent of clinical stage, tumour size, NPI status or systemic therapy.
format	article
author	Desmond G Powe Gopal Krishna R Dhondalay Christophe Lemetre Tony Allen Hany O Habashy Ian O Ellis Robert Rees Graham R Ball
author_facet	Desmond G Powe Gopal Krishna R Dhondalay Christophe Lemetre Tony Allen Hany O Habashy Ian O Ellis Robert Rees Graham R Ball
author_sort	Desmond G Powe
title	DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
title_short	DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
title_full	DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
title_fullStr	DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
title_full_unstemmed	DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
title_sort	dach1: its role as a classifier of long term good prognosis in luminal breast cancer.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/65a8a8ef87a7433b922d346ad74caebd
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DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.

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