title: DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.
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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 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.
authors.forename: Desmond G
authors.initials: DG
authors.lastname: Powe
authors.affiliation: The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
authors.forename: Gopal Krishna R
authors.initials: GK
authors.lastname: Dhondalay
authors.affiliation: Albert Einstein College of Medicine, Bronx, New York, United States of America.
authors.forename: Christophe
authors.initials: C
authors.lastname: Lemetre
authors.affiliation: Department of Computing and Informatics, Nottingham Trent University, Nottingham, United Kingdom.
authors.forename: Tony
authors.initials: T
authors.lastname: Allen
authors.affiliation: Pathology Department, Faculty of Medicine, Mansoura University, Mansoura City, Daqahlia, Egypt.
authors.forename: Hany O
authors.initials: HO
authors.lastname: Habashy
authors.affiliation: Department of Cellular Pathology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom.
authors.forename: Ian O
authors.initials: IO
authors.lastname: Ellis
authors.affiliation: The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
authors.forename: Robert
authors.initials: R
authors.lastname: Rees
authors.affiliation: The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
authors.forename: Graham R
authors.initials: GR
authors.lastname: Ball
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000401:mortality / Q000473:pathology*; D005136:Eye Proteins / Q000235:genetics* / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D007150:Immunohistochemistry; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D063868:Patient Outcome Assessment; D011379:Prognosis; D011485:Protein Binding; D060066:Protein Interaction Maps; D011960:Receptors, Estrogen / Q000235:genetics / Q000378:metabolism; D012307:Risk Factors; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism; D047368:Tumor Burden; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor; C486747:DACH1 protein, human; D005136:Eye Proteins; D011960:Receptors, Estrogen; D014157:Transcription Factors