RERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in breast cancer: a marker of ER-positive luminal-like subtype
Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with...
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Published in: | Breast cancer research and treatment Vol. 128; no. 2; pp. 315 - 326 |
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Abstract | Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER− non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (
P
= 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (
P
= 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups. |
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AbstractList | Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER- non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups. Keywords Breast carcinoma * RERG * Gene expression * Luminal * Oestrogen receptor * Immunohistochemistry Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER- non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups. Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterized by estrogen receptor (ER) expression, appears to contain distinct subgroups with differing behavior. In this study, we analyzed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumors. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER- non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterize ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups.[PUBLICATION ABSTRACT] Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER− non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 ( P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 ( P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups. Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER− non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 ( = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 ( = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups. |
Audience | Academic |
Author | Habashy, Hany Onsy Rakha, Emad A. Garibaldi, Jonathan M. Green, Andrew R. Caldas, C. Ball, Graham Powe, Desmond G. Spiteri, Inmaculada Ellis, Ian O. Glaab, Enrico Krasnogor, Natalio |
Author_xml | – sequence: 1 givenname: Hany Onsy surname: Habashy fullname: Habashy, Hany Onsy organization: Department of Pathology, School of Molecular Medical Sciences, Nottingham University Hospitals NHS Trust, University of Nottingham, Department of Histopathology, Faculty of Medicine, Mansoura University – sequence: 2 givenname: Desmond G. surname: Powe fullname: Powe, Desmond G. organization: Department of Cellular Pathology, Queen’s Medical Centre, Nottingham University Hospitals NHS Trust – sequence: 3 givenname: Enrico surname: Glaab fullname: Glaab, Enrico organization: School of Computer Science, University of Nottingham – sequence: 4 givenname: Graham surname: Ball fullname: Ball, Graham organization: John Van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University – sequence: 5 givenname: Inmaculada surname: Spiteri fullname: Spiteri, Inmaculada organization: Department of Oncology, University of Cambridge, Cancer Research UK Cambridge Research Institute – sequence: 6 givenname: Natalio surname: Krasnogor fullname: Krasnogor, Natalio organization: School of Computer Science, University of Nottingham – sequence: 7 givenname: Jonathan M. surname: Garibaldi fullname: Garibaldi, Jonathan M. organization: School of Computer Science, University of Nottingham – sequence: 8 givenname: Emad A. surname: Rakha fullname: Rakha, Emad A. organization: Department of Pathology, School of Molecular Medical Sciences, Nottingham University Hospitals NHS Trust, University of Nottingham – sequence: 9 givenname: Andrew R. surname: Green fullname: Green, Andrew R. organization: Department of Pathology, School of Molecular Medical Sciences, Nottingham University Hospitals NHS Trust, University of Nottingham – sequence: 10 givenname: C. surname: Caldas fullname: Caldas, C. organization: Department of Oncology, University of Cambridge, Cancer Research UK Cambridge Research Institute – sequence: 11 givenname: Ian O. surname: Ellis fullname: Ellis, Ian O. email: Ian.Ellis@nottingham.ac.uk organization: Department of Pathology, School of Molecular Medical Sciences, Nottingham University Hospitals NHS Trust, University of Nottingham, Department of Histopathology, Molecular Medical Sciences, Nottingham City Hospital NHS Trust, University of Nottingham |
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Keywords | Immunohistochemistry Breast carcinoma Gene expression Luminal RERG Oestrogen receptor Growth inhibitor Estrogen receptor Breast disease Biological marker Breast cancer Malignant tumor Mammary gland diseases Anatomic pathology C-Onc gene Ras gene Sex steroid hormone Subtype Protooncogene Cancer Hormonal receptor |
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PublicationTitle | Breast cancer research and treatment |
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SubjectTerms | Adult Analysis Biological and medical sciences Biomarkers Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Breast cancer Breast Neoplasms - genetics Breast Neoplasms - metabolism Breast Neoplasms - pathology Cancer research Cancer therapies Carcinoma, Ductal, Breast - genetics Carcinoma, Ductal, Breast - metabolism Carcinoma, Ductal, Breast - secondary Carcinoma, Lobular - genetics Carcinoma, Lobular - metabolism Carcinoma, Lobular - secondary Criminal investigation Estrogen Female Gene expression Gene Expression Profiling Genes GTP Phosphohydrolases - genetics GTP Phosphohydrolases - metabolism Gynecology. Andrology. Obstetrics Humans Immunoenzyme Techniques Immunohistochemistry Lymphatic Metastasis Mammary gland diseases Medical prognosis Medical sciences Medicine Medicine & Public Health Middle Aged Neoplasm Invasiveness Neoplasm Recurrence, Local - genetics Neoplasm Recurrence, Local - metabolism Neoplasm Staging Neural networks Oligonucleotide Array Sequence Analysis Oncology Preclinical Study Prognosis Receptor, ErbB-2 - genetics Receptor, ErbB-2 - metabolism Receptors, Estrogen - genetics Receptors, Estrogen - metabolism Receptors, Progesterone - genetics Receptors, Progesterone - metabolism Survival Rate Tumor proteins Tumors |
Title | RERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in breast cancer: a marker of ER-positive luminal-like subtype |
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