Rakha, Emad A., Alsaleem, Mansour, ElSharawy, Khloud A., Toss, Michael S., Raafat, Sara, Mihai, Raluca, Minhas, Fayyaz ul Amir Afsar, Green, Andrew R., Rajpoot, Nasir M. (Nasir Mahmood), Dalton, Les W. and Mongan, Nigel P. (2020) Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer : a morpho‐molecular study. Histopathology, 77 (4). pp. 631-645. doi:10.1111/his.14199 ISSN 0309-0167.

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Abstract

Background:
Tumour genotype and phenotype are related and can predict outcome. In this study, we hypothesised that the visual assessment of breast cancer (BC) morphological features can provide valuable insight into underlying molecular profiles.

Methods:
The Cancer Genome Atlas (TCGA) BC cohort was used (n=743) and morphological features including Nottingham grade and its components and nucleolar prominence were assessed utilising whole slide images (WSIs). Two independent scores were assigned, and discordant cases were utilised to represent cases with intermediate morphological features. Differentially expressed genes (DEGs) were identified for each feature, compared among concordant/discordant cases and tested for specific pathways.

Results:
Concordant grading was observed in 467/743 (63%) of cases. Among concordant case groups, 8 common DEGs (UGT8, DDC, RGR, RLBP1, SPRR1B, CXorf49B, PSAPL1, and SPRR2G) were associated with overall tumour grade and its components. These genes are related mainly to cellular proliferation, differentiation and metabolism. The number of DEGs in cases with discordant grading was larger than those identified in concordant cases. The largest number of DEGs was observed in discordant grade 1:3 cases (n=1185). DEGs were identified for each discordant component. Some DEGs were uniquely associated with well‐defined specific morphological features, whereas expression/co‐expression of other genes was identified across multiple features and underlined intermediate morphological features.

Conclusion:
Morphological features are likely related to distinct underlying molecular profiles that drive both morphology and behaviour. This study provides further evidence to support the use of image‐based analysis of WSIs, including artificial intelligence algorithms, to predict tumour molecular profiles and outcome.

Item Type:	Journal Article
Subjects:	R Medicine > RC Internal medicine
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Breast -- Cancer, Breast -- Cancer -- Histopathology, Breast -- Cancer -- Molecular aspects, Breast -- Cancer -- Imaging
Journal or Publication Title:	Histopathology
Publisher:	Wiley-Blackwell Publishing Ltd.
ISSN:	0309-0167
Official Date:	October 2020
Dates:	Date Event October 2020 Published 2 July 2020 Available 26 June 2020 Accepted
Volume:	77
Number:	4
Page Range:	pp. 631-645
DOI:	10.1111/his.14199
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	15 July 2020
Date of first compliant Open Access:	7 January 2021

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