title: Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups.
abstract: The 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.
  
  
  
  We 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.
  
  
  
  Patients 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.
  
  
  
  Findings 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.
authors.affiliation: Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK. Mustapha.Abubakar@icr.ac.uk.
authors.forename: Mustapha
authors.initials: M
authors.lastname: Abubakar
authors.affiliation: Breast Cancer Now Toby Robins Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
authors.forename: Nick
authors.initials: N
authors.lastname: Orr
authors.affiliation: Breast Cancer Now Toby Robins Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
authors.forename: Frances
authors.initials: F
authors.lastname: Daley
authors.affiliation: Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.
authors.forename: Penny
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authors.lastname: Coulson
authors.affiliation: Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
authors.forename: H Raza
authors.initials: HR
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authors.affiliation: Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
authors.forename: Fiona
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authors.affiliation: Human Genetics Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Centro de Investigacion en Red de Enfermedades Raras (CIBERER), Valencia, Spain.
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authors.affiliation: Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.|Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global health, The University of Melbourne, Melbourne, Australia.
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authors.affiliation: Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.|Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.|German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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authors.affiliation: Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.|University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors.forename: Jenny
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authors.affiliation: Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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authors.affiliation: Department of Pathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
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authors.affiliation: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
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authors.affiliation: Department of Human Genetics and Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
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authors.affiliation: Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Rob A E M
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authors.affiliation: Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
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authors.affiliation: Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
authors.forename: Jonine
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authors.affiliation: Divisions of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
authors.forename: Mark E
authors.initials: ME
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authors.affiliation: Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland.
authors.forename: Jolanta
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authors.affiliation: Faculty of Medicine Academic Unit of Cancer Sciences, Southampton General Hospital, Southampton, UK.
authors.forename: Diana
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authors.affiliation: Family Cancer Clinic, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
authors.forename: Maartje J
authors.initials: MJ
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authors.affiliation: Family Cancer Clinic, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
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authors.affiliation: Family Cancer Clinic, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
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authors.initials: JW
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authors.affiliation: Department of Pathology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
authors.forename: Carolien H M
authors.initials: CH
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authors.affiliation: Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
authors.affiliation: Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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authors.initials: MK
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authors.affiliation: Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: Qin
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authors.affiliation: Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.
authors.forename: Michael
authors.initials: M
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authors.affiliation: Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.
authors.forename: Minouk
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authors.affiliation: Division of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
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authors.affiliation: Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
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authors.affiliation: Division of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
authors.forename: Laura
authors.initials: L
authors.lastname: Van 't Veer
authors.affiliation: Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: Douglas
authors.initials: D
authors.lastname: Easton
authors.affiliation: Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, SM2 5NG, UK.|Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.
authors.forename: Anthony J
authors.initials: AJ
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authors.affiliation: Breast Cancer Now Toby Robins Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.|Academic Department of Biochemistry, Royal Marsden Hospital, Fulham Road, London, UK.
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authors.affiliation: Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.|Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Pharoah
authors.affiliation: Division of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.|Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
authors.forename: Marjanka K
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authors.affiliation: Divisions of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
authors.forename: Montserrat
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mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000453:epidemiology / Q000378:metabolism* / Q000401:mortality* / Q000473:pathology; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D053208:Kaplan-Meier Estimate; D019394:Ki-67 Antigen / Q000378:metabolism*; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D011159:Population Surveillance; D011379:Prognosis; D016016:Proportional Hazards Models; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen
keywords: Automated KI67; Breast cancer; Prognostication; Visual KI67