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Digital pathology and image analysis in tissue biomarker research

Significance statement

Digital pathology and the computerized analysis of tissue samples is providing industry and academic labs with radically new opportunities to accelerate their discovery programmes from biomarker discovery to clinical trials. The Biopharma industry today relies strongly on tissue and cellular image analysis for the discovery and delivery of novel diagnostic biomarkers and associated targeted therapies.   Of particular importance is the high throughput quantitative analysis of tissue samples for molecular profiling of solid tumours in translational research and in clinical trials.   Here manual annotation and visual estimates of tumour sufficiency show gross variation between pathologists, impacting negatively on the discovery process, clinical trials and molecular diagnostics.  Automated computerised annotation of tumour boundaries and precise measurement of % tumor cells, using solutions such as TissueMark™ from PathXL,  ensures the quality of tissue samples for molecular testing, thereby reducing cost in clinical trials and potentially avoids false negative test results.  It also has the potential to accelerate the discovery and validation of novel nucleic acid based biomarkers in cancer. Automated analysis of tumour is also necessary to selectively quantify cellular biomarkers in cancer using imaging software.  For example, immunohistochemistry (IHC) is commonly used in research and diagnostic laboratories across the world to identify candidate predictive and prognostic biomarkers – but is poorly reproducible when assessed by eye.  A range of powerful computerized image analysis software can now give biopharma tremendous capacity in identifying and validating candidate IHC markers in cancer and in translating these into objective clinical tests. The next few years will see an exciting increase in the use of computerized automation for the analysis of cancer tissue samples.  This will impact significantly on conventional pathological review in diagnostic discovery and provide pathologists with powerful new tools for both research and practice.

Figure Legend: Automated annotation and quantitative measurements of tumour content in tissue samples using TissueMark™ for quality molecular profiling and next generation sequencing.


Digital pathology and image analysis in tissue biomarker research. Global Medical Discovery












Journal Reference

Methods. 2014 ;70(1):59-73.

Hamilton PW1, Bankhead P2, Wang Y2, Hutchinson R2, Kieran D2, McArt DG2, James J2, Salto-Tellez M2.

1Centre for Cancer Research & Cell Biology, Queen’s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, Northern Ireland, United Kingdom. Electronic address: [email protected]

2Centre for Cancer Research & Cell Biology, Queen’s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, Northern Ireland, United Kingdom.



Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research  for  biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image  analysis, with a particular focus on research and biomarker  discovery. A variety of image analysis  applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence  analysis of tissue biomarkers.  Digital pathology  and  image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology,  tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high qualitytissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital  pathology  laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological,  clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

Copyright © 2014 Elsevier Inc. All rights reserved.

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