Atallah, N.M. orcid.org/0000-0001-6845-5608, Makhlouf, S., Nabil, M. et al. (4 more authors) (2025) Characterisation of HER2‐driven morphometric signature in breast cancer and prediction of risk of recurrence. Cancer Medicine, 14 (8). e70852. ISSN 2045-7634
Abstract
Introduction Human epidermal growth factor receptor 2-positive (HER2-positive) breast cancer (BC) is a heterogeneous disease. In this study, we hypothesised that the degree of HER2 oncogenic activity, and hence response to anti-HER2 therapy is translated into a morphological signature that can be of prognostic/predictive value.
Methods We developed a HER2-driven signature based on a set of morphometric features identified through digital image analysis and visual assessment in a sizable cohort of BC patients. HER2-enriched molecular sub-type (HER2-E) was used for validation, and pathway enrichment analysis was performed to assess HER2 pathway activity in the signature-positive cases. The predictive utility of this signature was evaluated in post-adjuvant HER2-positive BC patients.
Results A total of 57 morphometric features were evaluated; of them, 22 features were significantly associated with HER2 positivity. HER2 IHC score 3+/oestrogen receptor-negative tumours were significantly associated with HER2-related morphometric features compared to other HER2 classes including HER2 IHC 2+ with gene amplification, and they showed the least intra-tumour morphological heterogeneity. Tumours displaying HER2-driven morphometric signature showed the strongest association with PAM50 HER2-E sub-type and were enriched with ERBB signalling pathway compared to signature-negative cases. BC patients with positive HER2 morphometric signature showed prolonged distant metastasis-free survival post-adjuvant anti-HER2 therapy (p = 0.007). The clinico-morphometric prognostic index demonstrated an 87% accuracy in predicting recurrence risk.
Conclusion Our findings underscore the strong prognostic and predictive correlation between HER2 histo-morphometric features and response to targeted anti-HER2 therapy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | artificial neural network; digital image analysis; HER2 oncogenic activity; PAM50 gene assay; response to therapy; risk of recurrence |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Apr 2025 08:36 |
Last Modified: | 29 Apr 2025 08:36 |
Published Version: | https://doi.org/10.1002/cam4.70852 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/cam4.70852 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:225828 |