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Cross-linking breast tumor transcriptomic states and tissue histology
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Dawood, Muhammad, Eastwood, Mark, Jahanifar, Mostafa, Young, Lawrence S., Ben-Hur, Asa, Branson, Kim, Jones, Louise, Rajpoot, Nasir M. and Minhas, Fayyaz ul Amir Afsar (2023) Cross-linking breast tumor transcriptomic states and tissue histology. Cell Reports Medicine, 4 (12). 101313. doi:10.1016/j.xcrm.2023.101313 ISSN 2666-3791.
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Official URL: http://dx.doi.org/10.1016/j.xcrm.2023.101313
Abstract
Identification of the gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from WSIs using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinical and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining biological insights from routine pathology imaging directly.
Item Type: | Journal Article | ||||||
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Subjects: | R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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Library of Congress Subject Headings (LCSH): | Breast cancer, Diagnosis, Laboratory -- Data processing, Pathology -- Technological innovations, Human gene mapping, Oncology, Diagnostic imaging, Histology, Pathological, Genomics -- Statistical methods, Genetic transcription, RNA -- Analysis | ||||||
Journal or Publication Title: | Cell Reports Medicine | ||||||
Publisher: | Elsevier | ||||||
ISSN: | 2666-3791 | ||||||
Official Date: | 19 December 2023 | ||||||
Dates: |
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Volume: | 4 | ||||||
Number: | 12 | ||||||
Article Number: | 101313 | ||||||
DOI: | 10.1016/j.xcrm.2023.101313 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 29 January 2024 | ||||||
Date of first compliant Open Access: | 30 January 2024 | ||||||
RIOXX Funder/Project Grant: |
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