Author

Valerie Speirs

University of AberdeenORCID 0000-0002-0602-4666Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Assay:imaging· 2 Assay:bulk RNA-seq· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1

Frequent Co-Authors

Rasha Abu EidiD· 2 shared Abigail DodsoniD· 1 shared Akash PatnaikiD· 1 shared Alexander T. PearsoniD· 1 shared Amelie EchleiD· 1 shared Andrew M. Hanby· 1 shared Andrew SrisuwananukorniD· 1 shared Beatrix ElsbergeriD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersCancer Biology

Rasha Abu EidORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Sexual Dimorphism of Cancer-Associated Fibroblasts Governs Matrix and Vascular Organisation in Breast Cancer

Peng Liu, Fiona R. Saunders, Matthew Everest, Natthaya Eiamampai, Matthew P. Humphries, Camilla Coulson‐Gilmer, Giulia Conti, Lucy F. Stead, Rasha Abu Eid, Valerie Speirs

This study shows that male and female cancer-associated fibroblasts in breast cancer have distinct transcriptional programs and functional behaviors affecting matrix and vascular organization.

bioRxiv · Aug 28, 2026 · PreprintCancer Biology

A compendium of adipocyte morphologies across different breast pathologies

Abigail Dodson, Katie Hanna, K PALMER, H. Ibrahim, Katharina Kusserow, Rasha Abu Eid, Gerald Lip, Nicola Spence, Ehab A. Husain, Beatrix Elsberger, Justin J. Rochford, Valerie Speirs

This study quantifies adipocyte morphology changes in breast tissue across cancer pathologies and risk factors, finding size differences in cancer-associated adipocytes (CAAs) linked to menopausal status, BMI, and survival.

Adipocyte · Oct 7, 2025Peer-reviewedCancer Biology

Pan-cancer image-based detection of clinically actionable genetic alterations

Jakob Nikolas Kather, Lara Rosaline Heij, Heike Irmgard Grabsch, Chiara Maria Lavinia Loeffler, Amelie Echle, Hannah Sophie Muti, Jeremias Krause, Jan Niehues, Kai A. J. Sommer, Peter Bankhead, Loes F. S. Kooreman, Jefree J. Schulte, Nicole A. Cipriani, Roman David Buelow, Peter Boor, , Tom Luedde

This paper develops a deep learning method to predict genetic alterations from histological images across multiple cancer types.

Nature Cancer · Jul 27, 2020Peer-reviewedGenetics