This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
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This project aims to define how the tumour microenvironment shapes stromal and immune cell behaviour in liver cancer, with the goal of identifying new therapeutic vulnerabilities. Hepatocellular carcinoma (HCC) develops within a fibrotic and metabolically disordered liver, creating regions of hypoxia, acidosis and nutrient stress that profoundly influence both cancer-associated fibroblasts (CAFs) and tumour-infiltrating immune cells. These microenvironmental pressures drive immunosuppression, tumour progression and resistance to current therapies, including immune checkpoint blockade.
We will analyse large single-cell, spatial transcriptomic, proteomic and metabolomic datasets generated from patient tumours and advanced preclinical models. These analyses will map how CAFs and diverse immune populations—including neutrophils, macrophages, T cells and myeloid-derived suppressor cells—respond to hypoxic and acidic niches, and how their interactions contribute to therapeutic resistance. Computational modelling and network inference will be employed to identify signalling pathways, metabolic dependencies and cell–cell communication circuits that could be targeted to reprogramme the tumour microenvironment.
This work will generate a systems-level understanding of stromal–immune–cancer interactions in HCC and inform the development of combination therapies aimed at overcoming immune exclusion and restoring anti-tumour immunity.
We will use the HPC facility to analyse large multi-omic datasets, including single-cell sequencing, spatial transcriptomics, and spatial proteomics from human liver tumours and mouse models. The work will involve standard bioinformatic tools in R and Python for data processing, integration, and statistical modelling, alongside image-analysis software for spatial proteomic data.
High-performance compute resources are required to handle large imaging files, run parallelised workflows, perform machine-learning–based cell segmentation and spatial mapping, and integrate multi-modal datasets to define stromal–immune interactions in liver cancer.