Biology subjects

Gillett, C.

Publications and source records attributed to Gillett, C..

5 recordsLinked to original sources

bioRxiv2025

Lactate Blocks Tertiary Lymphoid Structure Formation by Inhibiting B Cell Chemotaxis

Tertiary lymphoid structures (TLS) and B cell infiltration are strong predictors of immunotherapy success across cancers, including triple-negative breast cancer (TNBC). However, immune-cold TNBCs often lack both features. Here, we identify a tumor-intrinsic mechanism that actively suppresses B cell recruitment. Despite evidence of B cell responses in cancer-associated lymph nodes (cLNs), B cells fail to infiltrate TNBC tumors or form TLS. This exclusion is not simply due to chemokine deficiency as exogenous chemokine addition fails to restore B cell migration. Using fractionation and metabolic profiling, we identify lactate as a dominant tumor-secreted metabolite that directly impairs B cell chemotaxis by disrupting mitochondrial metabolism. In vivo, combining lactate inhibition with engineered chemokine secretion promotes cLN-derived B cell infiltration and enables TLS formation, particularly when coupled with CD40 stimulation. Transcriptomics analyses across several human cancer datasets strengthen the association between high glycolytic activity with poor B-cell infiltration in chemokine-rich tumors. Together, our findings reveal lactate as a key metabolic barrier to B cell trafficking and TLS induction, suggesting that metabolic reprogramming may provide an avenue to convert "immune-cold" tumors into TLS-rich, immunologically responsive microenvironments.

cancer biology↗

bioRxiv2025

Normal breast tissue classifiers assess large-scale tissue compartments with high accuracy

Cancer research emphasises early detection, yet quantitative methods for normal tissue analysis remain limited. Digitised haematoxylin and eosin (H&E)-stained slides enable computational histopathology, but artificial intelligence (AI)-based analysis of normal breast tissue (NBT) in whole slide images (WSIs) remains scarce. We curated 70 WSIs of NBTs from multiple sources and cohorts with pathologist-guided manual annotations of epithelium, stroma, and adipocytes (https://github.com/cancerbioinformatics/OASIS). We developed robust convolutional neural network (CNN)-based, patch-level classification models, named NBT-Classifiers, to tessellate and classify NBTs at different scales. Across three external cohorts, NBT-Classifiers trained on 128{square}x{square}128{square}{micro}m and 256{square}x{square}256{square}{micro}m patches achieved AUCs of 0.98-1.00. The model learned independent normal features different from those of precancerous and cancerous epithelium, which were further visualised using two explainable AI techniques. When integrated into an end-to-end preprocessing pipeline, NBT-Classifiers facilitate efficient downstream analysis within peri-lobular regions. NBT-Classifiers provide robust compartment-specific analytical tools and enhance our understanding of NBT appearances, which serve as valuable reference points for identifying premalignant changes and guiding early breast cancer prevention strategies.

pathology↗

bioRxiv2024

Development of a Deep Learning model Tailored for HER2 Detection in Breast Cancer to aid pathologists in interpreting HER2-Low cases

IntroductionOver 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study, we evaluated the performance of a deep learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells staining intensity and completeness of membrane staining. MethodsWe trained a DL model on a multi-centric cohort of breast cancer cases with HER2- immunohistochemistry scores (n=299). The model was validated on 2 independent multi- centric validation cohorts (n=369 and n=92), with all cases reviewed by 3 senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. ResultsThe models concordance with the ground truth (ICC = 0.77 [0.68 - 0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the 3 senior pathologists (ICC = 0.45 [0.17 - 0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93%- 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumors respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumor-infiltrating lymphocytes, and necrosis, whilst some artifacts such as non- specific background cytoplasmic stain in the cytoplasm of tumor cells also cause discrepancy. ConclusionDeep learning can support pathologists interpretation of difficult HER2-low cases. Morphological variables and some specific artifacts can cause discrepant HER2-scores between the pathologist and the DL Model.

pathology↗

bioRxiv2020

Hypoxia-Sensing CAR T-Cells Provide Safety and Efficacy in Treating Solid Tumors

There has been significant interest in the prospects of chimeric antigen receptor (CAR) T-cell therapy in the treatment of solid malignancies, and multiple clinical trials are in progress 1. However, the scope of these trials has been restricted by the lack of availability of tumor-specific targets to direct CAR binding. Tumor specificity is crucial as on-target off-tumor activation of CAR T-cells in healthy tissues can result in potentially lethal toxicities due to uncontrolled cytokine release syndrome 2. Here we engineer a stringent hypoxia-sensing CAR T-cell system which achieves selective expression of a pan-ErbB-targeted CAR within a solid tumor, a microenvironment characterized by an inadequate oxygen supply. Using murine xenograft models, we demonstrate that despite widespread expression of ErbB receptors in healthy organs, the approach provides anti-tumor efficacy without off-tumor toxicity. This dynamic on/off oxygen-sensing safety switch has the potential to facilitate the unlimited expansion of the CAR T-cell target repertoire for treating solid malignancies.Competing Interest StatementYes there is potential Competing Interest. J.M. is co-founder and chief scientific officer, T.M. is an employee, and D.M.D., D.L.Y. are consultants to Leucid Bio, which is a spinout company focused on development of cellular therapeutic agents. J.N.A., J.M. and P.K. are named inventors on a patent submitted in relation to this work. All other authors have declared that there are no competing financial interests or conflicts of interest in relation to this study.View Full Text

synthetic biology↗

bioRxiv2020

Cancer-associated hypersialylated MUC1 drives the differentiation of monocytes into macrophages with a pathogenic phenotype

The tumour microenvironment plays a crucial role in the growth and progression of cancer and the presence of tumour-associated macrophages (TAMs) is associated with poor prognosis. Recent studies show that TAMs show transcriptomic, phenotypic, functional and geographical diversity. Here we show that a sialylated tumour-associated glycoform of the mucin MUC1, MUC1-ST, through the engagement of Siglec-9 can specifically and independently induce the differentiation of monocytes into TAMs with a unique phenotype. These TAMs can recruit and maintain neutrophils, inhibit the function of T cells, degrade basement membrane allowing for invasion, are inefficient at phagocytosis, and can induce plasma clotting. This novel macrophage phenotype is enriched in the stroma at the edge of breast cancer nests and their presence is associated with poor prognosis in breast cancer patients.

cancer biology↗