Neural Analyses Validate and Emphasize the Role of Progesterone Receptor in Breast Cancer Progression and Prognosis.

Oestrogen receptor (ER) expression is routinely measured in breast cancer management, but the clinical merits of measuring progesterone receptor (PR) expression have remained controversial. Hence the major objective of this study was to assess the potential of PR as a predictor of response to endocrine therapy. We report on analyses of the relative importance of ER and PR for predicting prognosis using robust multilayer perceptron artificial neural networks. Receptor determinations use immunohistochemical (IHC) methods or radioactive ligand binding assays (LBA). In view of the heterogeneity of intratumoral receptor distribution, we examined the relative merits of the IHC and LBA methods. Our analyses reveal a more significant correlation of IHC-determined PR than ER with both nodal status and 5-year disease-free survival (DFS). In LBA, PR displayed higher correlation with survival and ER with nodal status. There was concordance of correlation of PR with DFS by both IHC and LBA. This study suggests a clear distinction between PR and ER, with PR displaying greater correlation than ER with disease progression and prognosis, and emphasizes the marked superiority of the IHC method over LBA. These findings may be valuable in the management of patients with breast cancer.

Prediction of nodal metastasis and prognosis of breast cancer by ANN-based assessment of tumour size and p53, Ki-67 and steroid receptor expression.

BACKGROUND: Tumour stage and the appropriate course of treatment in patients with breast cancer are primarily characterized by the state of metastasis in the axillary lymph nodes. In recent years, substantial research has focused on the prediction of lymph node status based on various pathological and molecular markers in order to obviate the necessity to carry out axillary dissection. In the present study, artificial neural network (ANN) is employed as the analysis platform to examine the prognostic significance of a group of well-established prognostic markers for breast cancer outcome prediction in terms of nodal status. Furthermore, we investigated existing interactions between these markers. PATIENTS AND METHODS: The data set contained 66 patient records, where 5 pathological and molecular markers including tumour size, oestrogen receptor status (ER), progesterone receptor status (PR), Ki-67 and p53 expression had been assessed for each patient. The spread of metastasis to the axillary lymph nodes was clinically diagnosed and patients were accordingly categorized into node-positive and node-negative groups. The aforementioned markers were analyzed using a probabilistic neural network (PNN) for nodal status prediction which was considered as the network output. Furthermore, the interactions between these markers were evaluated using different marker combinations as the network input for finding the best marker arrangement for nodal predication. RESULTS: The best prediction accuracy was obtained by a 3-marker combination including tumour size, PR and p53 with 71% accuracy for nodal prediction. Leaving out ER and PR from the full marker set showed approximately the same variations in the results, which is an indication of the direct correlation of these two markers. Furthermore, tumour size was proved to be the most significant individual marker for predicting nodal metastasis. However, when used in combination with Ki-67 the prediction results drop significantly. CONCLUSION: The results presented here indicate that molecular and pathological markers can provide useful information for early-stage prognosis. However, the interactions between these markers must be considered in order to achieve accurate and reliable prediction.