Your browser doesn't support javascript.
loading
DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
Powe, Desmond G; Dhondalay, Gopal Krishna R; Lemetre, Christophe; Allen, Tony; Habashy, Hany O; Ellis, Ian O; Rees, Robert; Ball, Graham R.
Afiliación
  • Powe DG; The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom ; Department of Cellular Pathology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom.
  • Dhondalay GK; The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
  • Lemetre C; Albert Einstein College of Medicine, Bronx, New York, United States of America.
  • Allen T; Department of Computing and Informatics, Nottingham Trent University, Nottingham, United Kingdom.
  • Habashy HO; Pathology Department, Faculty of Medicine, Mansoura University, Mansoura City, Daqahlia, Egypt.
  • Ellis IO; Department of Cellular Pathology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom.
  • Rees R; The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
  • Ball GR; The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
PLoS One ; 9(1): e84428, 2014.
Article en En | MEDLINE | ID: mdl-24392136
ABSTRACT

BACKGROUND:

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. MATERIALS AND

METHODS:

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.

RESULTS:

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.

CONCLUSION:

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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Factores de Transcripción / Neoplasias de la Mama / Proteínas del Ojo Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2014 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Factores de Transcripción / Neoplasias de la Mama / Proteínas del Ojo Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Humans / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2014 Tipo del documento: Article País de afiliación: Reino Unido
...