Powe Desmond G, Dhondalay Gopal Krishna R, Lemetre Christophe, Allen Tony, Habashy Hany O, Ellis Ian O, Rees Robert, Ball Graham R
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.
The John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
PLoS One. 2014 Jan 2;9(1):e84428. doi: 10.1371/journal.pone.0084428. eCollection 2014.
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.
雌激素受体（ER）阳性（管腔型）肿瘤在女性乳腺癌患者中占比最大。这是一种异质性疾病，在治疗管理方面存在临床挑战。已确定了三个主要的生物学管腔型亚组，但临床上可将其归纳为两个预后组，其中管腔A型预后良好，管腔B型预后较差。需要更多生物标志物以达成分类共识。诸如人工神经网络（ANN）等机器学习方法已被用于利用高通量数据对乳腺癌中的生物标志物进行分类和识别。在本研究中，我们采用人工神经网络（ANN）方法来鉴定DACH1作为候选管腔型标志物，并评估其在预测乳腺癌临床结局中的作用。
采用一种包含网络推理算法的迭代ANN方法，在一个公开可用的cDNA微阵列数据集中鉴定与ER相关的生物标志物。发现DACH1对ER相关标志物有强烈影响且与ER呈正相关。通过对一系列未经选择的乳腺癌组织微阵列进行免疫组织化学检测后，对蛋白质表达水平进行统计学评估，研究其在预测乳腺癌特异性生存方面的临床相关性。
强核DACH1染色在乳腺小管癌和小叶癌中更为常见。其表达与表达PgR、上皮细胞角蛋白（CK）18/19以及包括FOXA1和RERG在内的预后良好的“管腔样”标志物的ER-α阳性肿瘤相关（p<0.05）。在癌症特异性生存时间较长、无病间期较长且转移形成减少的患者中，DACH1水平升高（p<0.001）。核DACH1与侵袭性生长和预后不良的标志物呈负相关。
核DACH1表达似乎是一种预测预后良好的管腔A型生物标志物，但并非独立于临床分期、肿瘤大小、NPI状态或全身治疗。
