Evidence map›Paper›PMID 28478613›Full record

ArticleBreast cancer research and treatment2017

Ki67 expression in invasive breast cancer: the use of tissue microarrays compared with whole tissue sections.

Abir A Muftah, Mohammed A Aleskandarany, Methaq M Al-Kaabi, Sultan N Sonbul, Maria Diez-Rodriguez, Chris C Nolan, Carlos Caldas, Ian O Ellis, Emad A Rakha, Andrew R Green

Open access · hybridAbstract readComparative Study
In one paragraph

Article in Breast cancer research and treatment, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
2.0field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 58 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 3 countries.

Abir A MuftahDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK.
Mohammed A AleskandaranyDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK.
Methaq M Al-KaabiDepartment of Pathology, College of Medicine, Al Mustansiriyah University, Baghdad, Iraq.
Sultan N SonbulDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK.
Maria Diez-RodriguezDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK.
Chris C NolanDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK.
Carlos CaldasCancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.
Ian O EllisDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK.
Emad A RakhaDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK.
Andrew R GreenDepartment of Histopathology, Division of Cancer and Stem Cells, School of Medicine, Nottingham City Hospital, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham, UK. Andrew.Green@nottingham.ac.uk.
Nottingham University Hospitals NHS Trust · GBUniversity of Nottingham · GBCancer Research UK Cambridge Center · GBMustansiriyah University · IQNottingham City Hospital · GB

Funding

Cancer Research UK 16942
6 · The paper itself

Abstract

backgroundAlthough the prognostic value of Ki67 in breast cancer is well documented, using optimal cut-points for patient stratification, reproducibility of the scoring and interpretation of the results remains a matter of debate particularly when using tissue microarrays (TMAs). This study aims to assess Ki67 expression assessed on TMAs and their matched whole tissue sections (WTS). Moreover, whether the cut-off used for WTS is reproducible on TMA in BC molecular classes and the association between Ki67 expression cut-off, assessed on TMAs and WTS, and clinicopathological parameters and patient outcome were tested.

methodA large series (n = 707) of primary invasive breast tumours were immunostained for Ki67 using both TMA and WTS and assessed as percentage staining and correlated with each other, clinicopathological parameters and patient outcome. In addition, MKI67 mRNA expression was correlated with Ki67 protein levels on WTS and TMAs in a subset of cases included in the METABRIC study.

resultsThere was moderate concordance in Ki67 expression between WTS and TMA when analysed as a continuous variable (Intraclass correlation coefficient = 0.61) and low concordance when dichotomised (kappa value = 0.3). TMA showed low levels of Ki67 with mean percentage of expression of 35 and 22% on WTS and TMA, respectively. MKI67 mRNA expression was significantly correlated with protein expression determined on WTS (Spearman Correlation, r = 0.52) and to a lesser extent on TMA (r = 0.34) (p < 0.001). Regarding prediction of patient outcome, statistically significant differences were detected upon stratification of patients with tumours expressing Ki67 at 10, 15, 20, 25 or 30% in TMA. Using TMA, ≥20% Ki67 provided the best prognostic cut-off particularly in triple-negative and HER2-positive classes.

conclusionKi67 expression in breast cancer can be evaluated using TMA although different cut-points are required to emulate results from WTS. A cut-off of ≥20% for Ki67 expression in BC provides the best prognostic correlations when TMAs are used.

Indexed as

Biomarkers, TumorBreast NeoplasmsFemaleHistocytological Preparation TechniquesHumansImmunohistochemistryKi-67 AntigenNeoplasm InvasivenessPrognosisSurvival AnalysisTissue Array AnalysisBiomarkers, TumorKi-67 AntigenBreast CancerImmunohistochemistryKi67METABRICPrognosis

Identifiers

PMID28478613
PMCPMC5487701
OpenAlexW2611837547

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.