Astronomical algorithms for automated analysis of tissue protein expression in breast cancer
AuthorAli, HR; Irwin, M; Morris, L; Dawson, S-J; Blows, FM; Provenzano, E; Mahler-Araujo, B; Pharoah, PD; Walton, NA; Brenton, JD; ...
Source TitleBritish Journal of Cancer
PublisherNATURE PUBLISHING GROUP
University of Melbourne Author/sDawson, Sarah-Jane
AffiliationSir Peter MacCallum Department of Oncology
Document TypeJournal Article
CitationsAli, H. R., Irwin, M., Morris, L., Dawson, S. -J., Blows, F. M., Provenzano, E., Mahler-Araujo, B., Pharoah, P. D., Walton, N. A., Brenton, J. D. & Caldas, C. (2013). Astronomical algorithms for automated analysis of tissue protein expression in breast cancer. BRITISH JOURNAL OF CANCER, 108 (3), pp.602-612. https://doi.org/10.1038/bjc.2012.558.
Access StatusOpen Access
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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