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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
MIB1/Ki-67 labelling index can classify grade 2 breast cancer into two clinically distinct subgroups
Breast Cancer Research and Treatment, Volume 127, No. 3, Year 2011
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Description
Histological grade is recognized as one of the strongest prognostic factors in operable breast cancer (BC). Although grade 1 and grade 3 tumours are biologically and clinically distinct, grade 2 tumours bear considerable difficulty in outcome prediction and planning therapies. Several attempts such as genomic grade index have been performed to subclassify grade 2 into two subgroups with clinical relevance. Here, we present evidence that the routinely evaluable immunohistochemical MIB1/Ki67 labelling index (MIB-LI) can classify grade 2 tumours into two clinically distinct subgroups. In this study, growth fractions of 1,550 primary operable invasive breast carcinomas were immunohistochemically assayed on full-face tissue sections using the MIB1 clone of Ki-67. Growth fractions were assessed as number of MIB1 positive nuclei in 1,000 tumour nuclei at high-power magnification and expressed as MIB1-LI. Using a 10% cut-point of MIB1-LI, grade 2 BCs were classified into low (49.8%) and high (50.2%) proliferative subgroups. Univariate and multivariate survival analysis revealed statistically significant differences between these subgroups regarding patients' BC specific survival (P < 0.001), and metastasis free survival (P < 0.001) which was independent of the well-established prognostic factors (HR = 2.944, 95% CI = 1.634-5.303, P < 0.001). In conclusion, our results further demonstrate that grade 2 BCs may represent at least two biological or behaviourally different entities. Assay of growth fraction in BC using MIB1/Ki67 immunohistochemistry is a robust cost-effective diagnostic tool that subdivides grade 2 tumours into low and high risk populations providing additional prognostic information in planning therapies and outcome prediction. © 2010 Springer Science+Business Media, LLC.
Authors & Co-Authors
Aleskandarany, Mohammed A.
United Kingdom, Nottingham
University of Nottingham
Egypt, Shibin el Kom
Menoufia University Faculty of Medicine
Rakha, Emad A.
United Kingdom, Nottingham
Nottingham University Hospitals Nhs Trust
MacMillan, Robert Douglas
United Kingdom, Nottingham
Nottingham University Hospitals Nhs Trust
Powe, Desmond George
United Kingdom, Nottingham
Nottingham University Hospitals Nhs Trust
Ellis, Ian O.
United Kingdom, Nottingham
University of Nottingham
United Kingdom, Nottingham
Nottingham University Hospitals Nhs Trust
Green, Andew R.
United Kingdom, Nottingham
University of Nottingham
Statistics
Citations: 111
Authors: 6
Affiliations: 3
Identifiers
Doi:
10.1007/s10549-010-1028-3
ISSN:
01676806
e-ISSN:
15737217
Research Areas
Cancer