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

• Published in: PloS one Vol. 9; no. 1; p. e84428
• Main Authors: Powe, Desmond G, Dhondalay, Gopal Krishna R, Lemetre, Christophe, Allen, Tony, Habashy, Hany O, Ellis, Ian O, Rees, Robert, Ball, Graham R
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02-01-2014; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Algorithms; Artificial neural networks; Bioindicators; Biomarkers; Biomarkers, Tumor; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Cancer; Cancer metastasis; Cancer research; Cancer therapies; Cell adhesion & migration; Cell cycle; Cell growth; Classification; Computer Science; Datasets; Disease control; DNA microarrays; Drosophila; Eye Proteins - genetics; Eye Proteins - metabolism; Females; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Growth factors; Humans; Immunohistochemistry; Insects; Learning algorithms; Learning theory; Machine learning; Mathematics; Medical prognosis; Medical research; Medical treatment; Medicine; Metastases; Middle Aged; Neoplasm Grading; Neoplasm Staging; Neural networks; Neural Networks, Computer; Pathology; Patient Outcome Assessment; Prognosis; Protein Binding; Protein Interaction Maps; Proteins; Quality control; Receptors, Estrogen - genetics; Receptors, Estrogen - metabolism; Risk Factors; Survival; Transcription Factors - genetics; Transcription Factors - metabolism; Tumor Burden; Tumors; Young Adult; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0084428

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.
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.
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.
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.