Characterisation of HER2‐driven morphometric signature in breast cancer and prediction of risk of recurrence

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.

Publication Details

Journal
White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Published
2025-04-01
Primary Topic
Theoretical and Computational Physics
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article
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article

Characterisation of HER2‐driven morphometric signature in breast cancer and prediction of risk of recurrence

White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Theoretical and Computational Physics
article

Characterisation of HER2‐driven morphometric signature in breast cancer and prediction of risk of recurrence

article en

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.

White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Good health and well-being
Openalex Percentile: Top 61%
Theoretical and Computational Physics
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Characterisation of HER2‐driven morphometric signature in breast cancer and prediction of risk of recurrence · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (2025) | TGRS Research Map | TGRS