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.