title: Neural Analyses Validate and Emphasize the Role of Progesterone Receptor in Breast Cancer Progression and Prognosis.
abstract: 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.
authors.affiliation: College of Computer Studies, De La Salle University, Manila, Philippines.
authors.forename: Arturo
authors.initials: A
authors.lastname: Caronongan
authors.affiliation: Oncology Department, Romagna Local Health Unit, Rimini, Italy.
authors.forename: Barbara
authors.initials: B
authors.lastname: Venturini
authors.affiliation: Cancer Screening Centre, Romagna Local Health Unit, Rimini, Italy.
authors.forename: Debora
authors.initials: D
authors.lastname: Canuti
authors.affiliation: School of Electrical and Electronic Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, U.K.
authors.forename: Satnam
authors.initials: S
authors.lastname: Dlay
authors.affiliation: BIOCORE Research & Consultancy International, Liverpool, U.K. Faculty of Science, Liverpool Hope University, Liverpool, U.K.
authors.forename: Raouf N G
authors.initials: RN
authors.lastname: Naguib
authors.affiliation: School of Electrical and Electronic Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, U.K. Institute for Molecular Medicine, Huntington Beach, CA, U.S.A. gajanan.sherbet@ncl.ac.uk.
authors.forename: Gajanan V
authors.initials: GV
authors.lastname: Sherbet
mesh_terms: D001943:Breast Neoplasms / Q000378:metabolism* / Q000473:pathology; D018450:Disease Progression; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D016571:Neural Networks, Computer; D011379:Prognosis; D011869:Radioligand Assay; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism*; D015203:Reproducibility of Results
chemical_list: D011960:Receptors, Estrogen; D011980:Receptors, Progesterone
keywords: Breast cancer; multilayer perceptron artificial neural networks; oestrogen receptor; progesterone receptor; prognosis; progression