Evidence map›Paper›PMID 27756439›Full record

ArticleBreast cancer research : BCR2016

Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups.

Mustapha Abubakar, Nick Orr, Frances Daley, Penny Coulson, H Raza Ali, Fiona Blows, Javier Benitez, Roger Milne, Herman Brenner, Christa Stegmaier and 31 more

Abstract readMulticenter Study
In one paragraph

Article in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

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47citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

47 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

41 authors.

Mustapha AbubakarDivision of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK. Mustapha.Abubakar@icr.ac.uk.
Nick OrrBreast Cancer Now Toby Robins Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
Frances DaleyBreast Cancer Now Toby Robins Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
Penny CoulsonDivision of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.
H Raza AliCancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
Fiona BlowsCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
Javier BenitezHuman Genetics Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
Roger MilneCancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.
Herman BrennerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Christa StegmaierSaarland Cancer Registry, Saarland, Germany.
Arto MannermaaSchool of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, Cancer Center of Eastern Finland, University of Eastern Finland, Kuopio, Finland.
Jenny Chang-ClaudeDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Anja RudolphDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Peter SinnDepartment of Pathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
Fergus J CouchDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
Peter DevileeDepartment of Human Genetics and Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
Rob A E M TollenaarDepartment of Surgery, Leiden University Medical Center, Leiden, The Netherlands.
Caroline SeynaeveDepartment of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Jonine FigueroaUsher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
Mark E ShermanDivisions of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Jolanta LissowskaDepartment of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland.
Stephen HewittLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
Diana EcclesFaculty of Medicine Academic Unit of Cancer Sciences, Southampton General Hospital, Southampton, UK.
Maartje J HooningFamily Cancer Clinic, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Antoinette HollestelleFamily Cancer Clinic, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
John W M MartensFamily Cancer Clinic, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Carolien H M van DeurzenDepartment of Pathology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
kConFab InvestigatorsDepartment of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Manjeet K BollaCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Qin WangCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Michael JonesDivision of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.
Minouk SchoemakerDivision of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.
Jelle WesselingDivision of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
Flora E van LeeuwenDivision of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
Laura Van 't VeerDivision of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
Douglas EastonCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
Anthony J SwerdlowDivision of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.
Mitch DowsettBreast Cancer Now Toby Robins Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
Paul D PharoahCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
Marjanka K SchmidtDivision of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
Montserrat Garcia-ClosasDivisions of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.

Funding

Women's Cancer ProgramP30CA015083 · NCI · MAYO CLINIC ROCHESTER · PI Felicity Enders · 1985 to 2026
$151.3M
Hormone-Related CancersZIACP010126 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI GIERACH, GRETCHEN · 2009 to 2025
$103.5M
The Role of CHFR in Tumorigenesis and Paclitaxel-Sensitivity in Breast CancerP50CA116201 · NCI · MAYO CLINIC ROCHESTER · PI PETER C LUCAS · 2005 to 2026
$49.9M
Cancer Research UK 10119Cancer Research UK 10124Cancer Research UK 16561Cancer Research UK C490/A16561NCI NIH HHS P30 CA015083NCI NIH HHS P50 CA116201
6 · The paper itself

Abstract

backgroundThe value of KI67 in breast cancer prognostication has been questioned due to concerns on the analytical validity of visual KI67 assessment and methodological limitations of published studies. Here, we investigate the prognostic value of automated KI67 scoring in a large, multicentre study, and compare this with pathologists' visual scores available in a subset of patients.

methodsWe utilised 143 tissue microarrays containing 15,313 tumour tissue cores from 8088 breast cancer patients in 10 collaborating studies. A total of 1401 deaths occurred during a median follow-up of 7.5 years. Centralised KI67 assessment was performed using an automated scoring protocol. The relationship of KI67 levels with 10-year breast cancer specific survival (BCSS) was investigated using Kaplan-Meier survival curves and Cox proportional hazard regression models adjusted for known prognostic factors.

resultsPatients in the highest quartile of KI67 (>12 % positive KI67 cells) had a worse 10-year BCSS than patients in the lower three quartiles. This association was statistically significant for ER-positive patients (hazard ratio (HR) (95 % CI) at baseline = 1.96 (1.31-2.93); P = 0.001) but not for ER-negative patients (1.23 (0.86-1.77); P = 0.248) (P-heterogeneity = 0.064). In spite of differences in characteristics of the study populations, the estimates of HR were consistent across all studies (P-heterogeneity = 0.941 for ER-positive and P-heterogeneity = 0.866 for ER-negative). Among ER-positive cancers, KI67 was associated with worse prognosis in both node-negative (2.47 (1.16-5.27)) and node-positive (1.74 (1.05-2.86)) tumours (P-heterogeneity = 0.671). Further classification according to ER, PR and HER2 showed statistically significant associations with prognosis among hormone receptor-positive patients regardless of HER2 status (P-heterogeneity = 0.270) and among triple-negative patients (1.70 (1.02-2.84)). Model fit parameters were similar for visual and automated measures of KI67 in a subset of 2440 patients with information from both sources.

conclusionsFindings from this large-scale multicentre analysis with centrally generated automated KI67 scores show strong evidence in support of a prognostic value for automated KI67 scoring in breast cancer. Given the advantages of automated scoring in terms of its potential for standardisation, reproducibility and throughput, automated methods appear to be promising alternatives to visual scoring for KI67 assessment.

Indexed as

Biomarkers, TumorAdultAgedAged, 80 and overBreast NeoplasmsFemaleHumansImmunohistochemistryKaplan-Meier EstimateKi-67 AntigenMiddle AgedNeoplasm GradingNeoplasm StagingPopulation SurveillancePrognosisProportional Hazards ModelsBiomarkers, TumorKi-67 AntigenAutomated KI67Breast cancerPrognosticationVisual KI67

Identifiers

PMID27756439
PMCPMC5070183

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