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Biology subjects

Redmond, K. L.

Publications and source records attributed to Redmond, K. L..

2 recordsLinked to original sources

Uridine Phosphorylase-1 supports metastasis of mammary cancer by altering immune and extracellular matrix landscapes of the lung

Understanding the mechanisms that facilitate early events in metastatic seeding is key to developing therapeutic approaches to reduce metastasis - the leading cause of cancer-related death. Using whole animal screens in genetically engineered mouse models of cancer we have identified circulating metabolites associated with metastasis. Specifically, we highlight the pyrimidine uracil as a prominent metastasis-associated metabolite. Uracil is generated by neutrophils expressing the enzyme uridine phosphorylase-1 (UPP1), and neutrophil specific Upp1 expression is increased in cancer. Altered UPP1 activity influences expression of adhesion molecules on the surface of neutrophils, leading to decreased neutrophil motility in the pre-metastatic lung. Furthermore, we find that UPP1-expressing neutrophils suppress T-cell proliferation, and the UPP1 product uracil can increase fibronectin deposition in the extracellular microenvironment. Consistently, knockout or inhibition of UPP1 in mice with mammary tumours increases the number of T-cells and reduces fibronectin content in the lung and decreases the proportion of mice that develop lung metastasis. These data indicate that UPP1 influences neutrophil behaviour and extracellular matrix deposition in the lung and suggest that pharmacological targeting of this pathway could be an effective strategy to reduce metastasis.

cancer biology↗

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.

cancer biology↗