Biology subjects

Humphries, M. P.

Publications and source records attributed to Humphries, M. P..

3 recordsLinked to original sources

bioRxiv2026

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

Breast cancer (BC) shows greatest sexual diversity. Increased diagnosis and poorer outcomes in men highlights the need to better understand its biology. We hypothesised that cancer-associated fibroblasts (CAFs), the most abundant cell type in the tumour microenvironment, might define sex-related differences. Using phenotypically matched male and female CAFs generated from breast cancer tissues, we demonstrate distinct transcriptional programmes and functional behaviours associated with extracellular matrix remodelling, cell adhesion, migration and vascular development. Compared to CAFs generated from females BC, those from males generated denser, more complex matrices promoting stronger tumour and endothelial cell adhesion, vascular growth, but less organised capillary network formation. Findings reveal fundamental sex-related variations in CAF phenotype and biology in BC. These findings highlight the need to integrate biological sex into precision oncology to identify opportunities for sex-specific therapeutic strategies in BC.

cancer biology↗

bioRxiv2021

HistoClean: Open-source Software for Histological Image Pre-processing and Augmentation to Improve Development of Robust Convolutional Neural Networks

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

cancer biology↗

bioRxiv2020

The clinical and molecular significance associated with STING signaling in estrogen receptor-positive early breast cancer

STING signaling in cancer is a crucial component of response to immunotherapy and other anti-cancer treatments. Conversely, STING signaling can promote tumor invasion and metastasis. Currently, there is no robust method of measuring STING activation in cancer. Here, we describe an immunohistochemistry-based assay with digital pathology assessment of STING in tumor cells. Using this novel approach, we identify perinuclear-localized expression of STING (pnSTING) in estrogen receptor-positive (ER+) breast cancer as an independent predictor of good prognosis, associated with immune cell infiltration and upregulation of immune checkpoints. Tumors with low pnSTING are immunosuppressed with increased infiltration of "M2" -polarised macrophages. In ER-disease, pnSTING does not have a significant prognostic role, and STING appears to be uncoupled from interferon responses. Importantly, a gene signature defining low pnSTING expression in ER+ disease is predictive of poor prognosis in independent datasets. Low pnSTING is associated with chromosomal instability, MYC amplification and mTOR signaling, suggesting novel therapeutic approaches for this subgroup.

cancer biology↗