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Howat WJ, Blows FM, Provenzano E, Brook MN, Morris L, Gazinska P, Johnson N, McDuffus LA, Miller J, Sawyer EJ, Pinder S, van Deurzen CHM, Jones L, Sironen R, Visscher D, Caldas C, Daley F, Coulson P, Broeks A, Sanders J, Wesseling J, Nevanlinna H, Fagerholm R, Blomqvist C, Heikkilä P, Ali HR, Dawson SJ, Figueroa J, Lissowska J, Brinton L, Mannermaa A, Kataja V, Kosma VM, Cox A, Brock IW, Cross SS, Reed MW, Couch FJ, Olson JE, Devillee P, Mesker WE, Seyaneve CM, Hollestelle A, Benitez J, Perez JIA, Menéndez P, Bolla MK, Easton DF, Schmidt MK, Pharoah PD, Sherman ME, García-Closas M. Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium. J Pathol Clin Res 2015; 1:18-32. [PMID: 27499890 PMCID: PMC4858117 DOI: 10.1002/cjp2.3] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Accepted: 05/28/2014] [Indexed: 01/02/2023]
Abstract
Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value = 30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.
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Affiliation(s)
- William J Howat
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge UK
| | - Fiona M Blows
- Centre for Cancer Genetic Epidemiology, Department of Oncology University of Cambridge Cambridge UK
| | | | - Mark N Brook
- Division of Genetics and Epidemiology The Institute of Cancer Research London UK
| | - Lorna Morris
- Cancer Research UK Cambridge Institute, University of CambridgeCambridgeUK; Department of OncologyUniversity of CambridgeCambridgeUK
| | - Patrycja Gazinska
- Breakthrough Breast Cancer Research Unit, Division of Cancer Studies King's College London, Guy's Hospital London UK
| | - Nicola Johnson
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge UK
| | - Leigh-Anne McDuffus
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge UK
| | - Jodi Miller
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge UK
| | - Elinor J Sawyer
- Division of Cancer Studies, NIHR Comprehensive Biomedical Research Centre Guy's & St. Thomas' NHS Foundation Trust in partnership with King's College London London UK
| | - Sarah Pinder
- Research Oncology, Division of Cancer Studies King's College London, Guy's Hospital London UK
| | | | - Louise Jones
- Centre for Tumour BiologyBarts Institute of CancerBartsUK; The London School of Medicine and DentistryLondonUK
| | - Reijo Sironen
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic MedicineCancer Center of Eastern Finland, University of Eastern FinlandKuopioFinland; Imaging Center, Department of Clinical PathologyKuopio University HospitalKuopioFinland
| | - Daniel Visscher
- Department of Laboratory Medicine and Pathology Mayo Clinic Rochester MN USA
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge UK
| | - Frances Daley
- Breakthrough Breast Cancer Research Centre, Division of Breast Cancer Research The Institute of Cancer Research London UK
| | - Penny Coulson
- Division of Genetics and Epidemiology The Institute of Cancer Research London UK
| | - Annegien Broeks
- Core Facility for Molecular Pathology and Biobanking Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital Amsterdam The Netherlands
| | - Joyce Sanders
- Department of Pathology, Division of Diagnostic Oncology Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital Amsterdam The Netherlands
| | - Jelle Wesseling
- Department of Pathology, Division of Diagnostic Oncology Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital Amsterdam The Netherlands
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology University of Helsinki and Helsinki University Central Hospital Helsinki Finland
| | - Rainer Fagerholm
- Department of Obstetrics and Gynecology University of Helsinki and Helsinki University Central Hospital Helsinki Finland
| | - Carl Blomqvist
- Department of Oncology Helsinki University Central Hospital Helsinki Finland
| | - Päivi Heikkilä
- Department of Pathology Helsinki University Central Hospital Helsinki Finland
| | - H Raza Ali
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge UK
| | - Sarah-Jane Dawson
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge UK
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville Maryland USA
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology Warsaw Poland
| | - Louise Brinton
- Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville Maryland USA
| | - Arto Mannermaa
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic MedicineCancer Center of Eastern Finland, University of Eastern FinlandKuopioFinland; Imaging Center, Department of Clinical PathologyKuopio University HospitalKuopioFinland
| | - Vesa Kataja
- Kuopio University Hospital, Cancer CenterKuopioFinland; School of Medicine, Institute of Clinical MedicineUniversity of Eastern Finland, Oncology and Central Hospital of Central Finland, Central Finland Hospital DistrictKuopioFinland
| | - Veli-Matti Kosma
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic MedicineCancer Center of Eastern Finland, University of Eastern FinlandKuopioFinland; Imaging Center, Department of Clinical PathologyKuopio University HospitalKuopioFinland
| | - Angela Cox
- CRUK/YCR Sheffield Cancer Research Centre, Department of Oncology University of Sheffield Sheffield UK
| | - Ian W Brock
- CRUK/YCR Sheffield Cancer Research Centre, Department of Oncology University of Sheffield Sheffield UK
| | - Simon S Cross
- Academic Unit of Pathology, Department of Neuroscience University of Sheffield Sheffield UK
| | - Malcolm W Reed
- CRUK/YCR Sheffield Cancer Research Centre, Department of Oncology University of Sheffield Sheffield UK
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology Mayo Clinic Rochester MN USA
| | - Janet E Olson
- Department of Health Sciences Research Mayo Clinic Rochester MN USA
| | - Peter Devillee
- Department of Human Genetics & Department of Pathology Leiden University Medical Center Leiden The Netherlands
| | - Wilma E Mesker
- Department of Surgical Oncology Leiden University Medical Center RC Leiden The Netherlands
| | - Caroline M Seyaneve
- Family Cancer Clinic, Department of Medical Oncology Erasmus MC Cancer Institute Rotterdam The Netherlands
| | - Antoinette Hollestelle
- Family Cancer Clinic, Department of Medical Oncology Erasmus MC Cancer Institute Rotterdam The Netherlands
| | - Javier Benitez
- Human Genetics Group, Human Cancer Genetics ProgramSpanish National Cancer Research Centre (CNIO)MadridSpain; Centro de Investigación en Red de Enfermedades Raras (CIBERER)ValenciaSpain
| | | | | | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care University of Cambridge Cambridge UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of OncologyUniversity of CambridgeCambridgeUK; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Marjanka K Schmidt
- Division of Molecular Pathology Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital Amsterdam The Netherlands
| | - Paul D Pharoah
- Centre for Cancer Genetic Epidemiology, Department of OncologyUniversity of CambridgeCambridgeUK; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Mark E Sherman
- Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville Maryland USA
| | - Montserrat García-Closas
- Division of Genetics and EpidemiologyThe Institute of Cancer ResearchLondonUK; Breakthrough Breast Cancer Research Centre, Division of Breast Cancer ResearchThe Institute of Cancer ResearchLondonUK
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Ali HR, Irwin M, Morris L, Dawson SJ, Blows FM, Provenzano E, Mahler-Araujo B, Pharoah PD, Walton NA, Brenton JD, Caldas C. Astronomical algorithms for automated analysis of tissue protein expression in breast cancer. Br J Cancer 2013; 108:602-12. [PMID: 23329232 PMCID: PMC3593538 DOI: 10.1038/bjc.2012.558] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2012] [Revised: 11/15/2012] [Accepted: 11/19/2012] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. METHODS We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. RESULTS All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to 'positive' or 'negative' categories with agreement rates of up to 96%. CONCLUSION The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.
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Affiliation(s)
- H R Ali
- Department of Oncology, University of Cambridge, Cambridge CB1 9RN, UK
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 ORE, UK
- Cambridge Breast Unit, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK
| | - M Irwin
- Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
| | - L Morris
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 ORE, UK
| | - S-J Dawson
- Department of Oncology, University of Cambridge, Cambridge CB1 9RN, UK
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 ORE, UK
- Cambridge Breast Unit, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK
| | - F M Blows
- Strangeways Research Laboratories, University of Cambridge, Cambridge CB1 9RN, UK
| | - E Provenzano
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 ORE, UK
- Cambridge Breast Unit, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK
- Cambridge Experimental Cancer Medicine Centre (ECMC), Cambridge, UK
- Department of Histopathology, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust, Cambridge CB2 2QQ, UK
| | - B Mahler-Araujo
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 ORE, UK
- Cambridge Breast Unit, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK
- Cambridge Experimental Cancer Medicine Centre (ECMC), Cambridge, UK
- Department of Histopathology, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust, Cambridge CB2 2QQ, UK
| | - P D Pharoah
- Department of Oncology, University of Cambridge, Cambridge CB1 9RN, UK
- Strangeways Research Laboratories, University of Cambridge, Cambridge CB1 9RN, UK
- Cambridge Experimental Cancer Medicine Centre (ECMC), Cambridge, UK
| | - N A Walton
- Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
| | - J D Brenton
- Department of Oncology, University of Cambridge, Cambridge CB1 9RN, UK
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 ORE, UK
| | - C Caldas
- Department of Oncology, University of Cambridge, Cambridge CB1 9RN, UK
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 ORE, UK
- Cambridge Breast Unit, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK
- Cambridge Experimental Cancer Medicine Centre (ECMC), Cambridge, UK
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Hunter AA, Macgregor AB, Szabo TO, Wellington CA, Bellgard MI. Yabi: An online research environment for grid, high performance and cloud computing. SOURCE CODE FOR BIOLOGY AND MEDICINE 2012; 7:1. [PMID: 22333270 PMCID: PMC3298538 DOI: 10.1186/1751-0473-7-1] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Accepted: 02/15/2012] [Indexed: 12/02/2022]
Abstract
BACKGROUND There is a significant demand for creating pipelines or workflows in the life science discipline that chain a number of discrete compute and data intensive analysis tasks into sophisticated analysis procedures. This need has led to the development of general as well as domain-specific workflow environments that are either complex desktop applications or Internet-based applications. Complexities can arise when configuring these applications in heterogeneous compute and storage environments if the execution and data access models are not designed appropriately. These complexities manifest themselves through limited access to available HPC resources, significant overhead required to configure tools and inability for users to simply manage files across heterogenous HPC storage infrastructure. RESULTS In this paper, we describe the architecture of a software system that is adaptable to a range of both pluggable execution and data backends in an open source implementation called Yabi. Enabling seamless and transparent access to heterogenous HPC environments at its core, Yabi then provides an analysis workflow environment that can create and reuse workflows as well as manage large amounts of both raw and processed data in a secure and flexible way across geographically distributed compute resources. Yabi can be used via a web-based environment to drag-and-drop tools to create sophisticated workflows. Yabi can also be accessed through the Yabi command line which is designed for users that are more comfortable with writing scripts or for enabling external workflow environments to leverage the features in Yabi. Configuring tools can be a significant overhead in workflow environments. Yabi greatly simplifies this task by enabling system administrators to configure as well as manage running tools via a web-based environment and without the need to write or edit software programs or scripts. In this paper, we highlight Yabi's capabilities through a range of bioinformatics use cases that arise from large-scale biomedical data analysis. CONCLUSION The Yabi system encapsulates considered design of both execution and data models, while abstracting technical details away from users who are not skilled in HPC and providing an intuitive drag-and-drop scalable web-based workflow environment where the same tools can also be accessed via a command line. Yabi is currently in use and deployed at multiple institutions and is available at http://ccg.murdoch.edu.au/yabi.
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Affiliation(s)
- Adam A Hunter
- Centre for Comparative Genomics, Murdoch, Western Australia, 6150
| | | | - Tamas O Szabo
- Centre for Comparative Genomics, Murdoch, Western Australia, 6150
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Sreenivasaiah PK, Kim DH. Current trends and new challenges of databases and web applications for systems driven biological research. Front Physiol 2010; 1:147. [PMID: 21423387 PMCID: PMC3059952 DOI: 10.3389/fphys.2010.00147] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2010] [Accepted: 10/18/2010] [Indexed: 12/17/2022] Open
Abstract
Dynamic and rapidly evolving nature of systems driven research imposes special requirements on the technology, approach, design and architecture of computational infrastructure including database and Web application. Several solutions have been proposed to meet the expectations and novel methods have been developed to address the persisting problems of data integration. It is important for researchers to understand different technologies and approaches. Having familiarized with the pros and cons of the existing technologies, researchers can exploit its capabilities to the maximum potential for integrating data. In this review we discuss the architecture, design and key technologies underlying some of the prominent databases and Web applications. We will mention their roles in integration of biological data and investigate some of the emerging design concepts and computational technologies that are likely to have a key role in the future of systems driven biomedical research.
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Affiliation(s)
- Pradeep Kumar Sreenivasaiah
- Systems Biology Research Center and College of Life Science, Gwangju Institute of Science and TechnologyGwangju, Republic of Korea
| | - Do Han Kim
- Systems Biology Research Center and College of Life Science, Gwangju Institute of Science and TechnologyGwangju, Republic of Korea
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