High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium

Mustapha Abubakar, William J. Howat, Frances Daley, Lila Zabaglo, Leigh Anne McDuffus, Fiona Blows, Penny Coulson, H. Raza Ali, Javier Benitez, Roger Milne, Herman Brenner, Christa Stegmaier, Arto Mannermaa, Jenny Chang-Claude, Anja Rudolph, Peter Sinn, Fergus J Couch, Rob A.E.M. Tollenaar, Peter Devilee, Jonine FigueroaMark E. Sherman, Jolanta Lissowska, Stephen Hewitt, Diana Eccles, Maartje J. Hooning, Antoinette Hollestelle, John Martens, Carolien HM van Deurzen, k. Con Fab Investigators, Manjeet K. Bolla, Qin Wang, Michael Jones, Minouk Schoemaker, Annegien Broeks, Flora E. van Leeuwen, Laura Van't Veer, Anthony J. Swerdlow, Nick Orr, Mitch Dowsett, Douglas Easton, Marjanka K. Schmidt, Paul D. Pharoah, Montserrat Garcia-Closas

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.

Original languageEnglish (US)
Pages (from-to)138-153
Number of pages16
JournalJournal of Pathology: Clinical Research
Volume2
Issue number3
DOIs
StatePublished - Jul 1 2016

Fingerprint

Breast Neoplasms
Quality Control
Multicenter Studies
Area Under Curve
Research Design
Cell Count
Neoplasms

Keywords

  • automated algorithm
  • breast cancer
  • immunohistochemistry
  • Ki67
  • tissue microarrays

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

Cite this

Abubakar, M., Howat, W. J., Daley, F., Zabaglo, L., McDuffus, L. A., Blows, F., ... Garcia-Closas, M. (2016). High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium. Journal of Pathology: Clinical Research, 2(3), 138-153. https://doi.org/10.1002/cjp2.42

High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium. / Abubakar, Mustapha; Howat, William J.; Daley, Frances; Zabaglo, Lila; McDuffus, Leigh Anne; Blows, Fiona; Coulson, Penny; Raza Ali, H.; Benitez, Javier; Milne, Roger; Brenner, Herman; Stegmaier, Christa; Mannermaa, Arto; Chang-Claude, Jenny; Rudolph, Anja; Sinn, Peter; Couch, Fergus J; Tollenaar, Rob A.E.M.; Devilee, Peter; Figueroa, Jonine; Sherman, Mark E.; Lissowska, Jolanta; Hewitt, Stephen; Eccles, Diana; Hooning, Maartje J.; Hollestelle, Antoinette; Martens, John; HM van Deurzen, Carolien; Investigators, k. Con Fab; Bolla, Manjeet K.; Wang, Qin; Jones, Michael; Schoemaker, Minouk; Broeks, Annegien; van Leeuwen, Flora E.; Van't Veer, Laura; Swerdlow, Anthony J.; Orr, Nick; Dowsett, Mitch; Easton, Douglas; Schmidt, Marjanka K.; Pharoah, Paul D.; Garcia-Closas, Montserrat.

In: Journal of Pathology: Clinical Research, Vol. 2, No. 3, 01.07.2016, p. 138-153.

Research output: Contribution to journalArticle

Abubakar, M, Howat, WJ, Daley, F, Zabaglo, L, McDuffus, LA, Blows, F, Coulson, P, Raza Ali, H, Benitez, J, Milne, R, Brenner, H, Stegmaier, C, Mannermaa, A, Chang-Claude, J, Rudolph, A, Sinn, P, Couch, FJ, Tollenaar, RAEM, Devilee, P, Figueroa, J, Sherman, ME, Lissowska, J, Hewitt, S, Eccles, D, Hooning, MJ, Hollestelle, A, Martens, J, HM van Deurzen, C, Investigators, KCF, Bolla, MK, Wang, Q, Jones, M, Schoemaker, M, Broeks, A, van Leeuwen, FE, Van't Veer, L, Swerdlow, AJ, Orr, N, Dowsett, M, Easton, D, Schmidt, MK, Pharoah, PD & Garcia-Closas, M 2016, 'High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium', Journal of Pathology: Clinical Research, vol. 2, no. 3, pp. 138-153. https://doi.org/10.1002/cjp2.42
Abubakar, Mustapha ; Howat, William J. ; Daley, Frances ; Zabaglo, Lila ; McDuffus, Leigh Anne ; Blows, Fiona ; Coulson, Penny ; Raza Ali, H. ; Benitez, Javier ; Milne, Roger ; Brenner, Herman ; Stegmaier, Christa ; Mannermaa, Arto ; Chang-Claude, Jenny ; Rudolph, Anja ; Sinn, Peter ; Couch, Fergus J ; Tollenaar, Rob A.E.M. ; Devilee, Peter ; Figueroa, Jonine ; Sherman, Mark E. ; Lissowska, Jolanta ; Hewitt, Stephen ; Eccles, Diana ; Hooning, Maartje J. ; Hollestelle, Antoinette ; Martens, John ; HM van Deurzen, Carolien ; Investigators, k. Con Fab ; Bolla, Manjeet K. ; Wang, Qin ; Jones, Michael ; Schoemaker, Minouk ; Broeks, Annegien ; van Leeuwen, Flora E. ; Van't Veer, Laura ; Swerdlow, Anthony J. ; Orr, Nick ; Dowsett, Mitch ; Easton, Douglas ; Schmidt, Marjanka K. ; Pharoah, Paul D. ; Garcia-Closas, Montserrat. / High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium. In: Journal of Pathology: Clinical Research. 2016 ; Vol. 2, No. 3. pp. 138-153.
@article{7550fc50c94e4fdaa8a76f9f1584988c,
title = "High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium",
abstract = "Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85{\%}) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.",
keywords = "automated algorithm, breast cancer, immunohistochemistry, Ki67, tissue microarrays",
author = "Mustapha Abubakar and Howat, {William J.} and Frances Daley and Lila Zabaglo and McDuffus, {Leigh Anne} and Fiona Blows and Penny Coulson and {Raza Ali}, H. and Javier Benitez and Roger Milne and Herman Brenner and Christa Stegmaier and Arto Mannermaa and Jenny Chang-Claude and Anja Rudolph and Peter Sinn and Couch, {Fergus J} and Tollenaar, {Rob A.E.M.} and Peter Devilee and Jonine Figueroa and Sherman, {Mark E.} and Jolanta Lissowska and Stephen Hewitt and Diana Eccles and Hooning, {Maartje J.} and Antoinette Hollestelle and John Martens and {HM van Deurzen}, Carolien and Investigators, {k. Con Fab} and Bolla, {Manjeet K.} and Qin Wang and Michael Jones and Minouk Schoemaker and Annegien Broeks and {van Leeuwen}, {Flora E.} and {Van't Veer}, Laura and Swerdlow, {Anthony J.} and Nick Orr and Mitch Dowsett and Douglas Easton and Schmidt, {Marjanka K.} and Pharoah, {Paul D.} and Montserrat Garcia-Closas",
year = "2016",
month = "7",
day = "1",
doi = "10.1002/cjp2.42",
language = "English (US)",
volume = "2",
pages = "138--153",
journal = "Journal of Pathology: Clinical Research",
issn = "2056-4538",
publisher = "Wiley-Blackwell Publishing Ltd",
number = "3",

}

TY - JOUR

T1 - High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium

AU - Abubakar, Mustapha

AU - Howat, William J.

AU - Daley, Frances

AU - Zabaglo, Lila

AU - McDuffus, Leigh Anne

AU - Blows, Fiona

AU - Coulson, Penny

AU - Raza Ali, H.

AU - Benitez, Javier

AU - Milne, Roger

AU - Brenner, Herman

AU - Stegmaier, Christa

AU - Mannermaa, Arto

AU - Chang-Claude, Jenny

AU - Rudolph, Anja

AU - Sinn, Peter

AU - Couch, Fergus J

AU - Tollenaar, Rob A.E.M.

AU - Devilee, Peter

AU - Figueroa, Jonine

AU - Sherman, Mark E.

AU - Lissowska, Jolanta

AU - Hewitt, Stephen

AU - Eccles, Diana

AU - Hooning, Maartje J.

AU - Hollestelle, Antoinette

AU - Martens, John

AU - HM van Deurzen, Carolien

AU - Investigators, k. Con Fab

AU - Bolla, Manjeet K.

AU - Wang, Qin

AU - Jones, Michael

AU - Schoemaker, Minouk

AU - Broeks, Annegien

AU - van Leeuwen, Flora E.

AU - Van't Veer, Laura

AU - Swerdlow, Anthony J.

AU - Orr, Nick

AU - Dowsett, Mitch

AU - Easton, Douglas

AU - Schmidt, Marjanka K.

AU - Pharoah, Paul D.

AU - Garcia-Closas, Montserrat

PY - 2016/7/1

Y1 - 2016/7/1

N2 - Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.

AB - Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.

KW - automated algorithm

KW - breast cancer

KW - immunohistochemistry

KW - Ki67

KW - tissue microarrays

UR - http://www.scopus.com/inward/record.url?scp=85056746527&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056746527&partnerID=8YFLogxK

U2 - 10.1002/cjp2.42

DO - 10.1002/cjp2.42

M3 - Article

AN - SCOPUS:85056746527

VL - 2

SP - 138

EP - 153

JO - Journal of Pathology: Clinical Research

JF - Journal of Pathology: Clinical Research

SN - 2056-4538

IS - 3

ER -