Owen James Sansom

This study identifies mitochondrial metabolism as a key factor in 5-fluorouracil resistance in colorectal cancer and shows that targeting mitochondrial Complex I can enhance chemotherapy effectiveness.

This paper shows that pulsed narmafotinib priming improves chemotherapy response in pancreatic cancer by reducing fibrosis and enhancing treatment efficacy.

This study identifies Podocalyxin as a regulator of glandular architecture in colorectal cancer, showing its role in promoting tumour growth and metastasis.

This study shows that poly(I:C) treatment in CMS4 colorectal tumors induces a pro-inflammatory, CMS1-like regenerative state and identifies biomarkers for predicting therapeutic response.

This study identifies SOX2 as a key driver of rapid tumorigenesis from a previously resistant skin cell population in mice, with implications for human cutaneous squamous cell carcinoma.

This study identifies epithelial state transitions driven by inflammation that promote tumorigenesis, showing that targeting NOTCH signaling can prevent pre-neoplastic to neoplastic transition.

Pathological epithelial GREM1 expression induces therapeutically reversible ectopic stem cell niches through stromal remodelling.

This study shows that oncogenic KRAS cells in the adult pancreas evade elimination by using Wnt signaling and cell dormancy.

This study identifies how eIF4A1 differentially activates translation based on mRNA features, revealing distinct mechanisms for mRNA selection and inhibition by eFT226 and hippuristanol.

This study identifies a druggable PREX2/RAC1/PI3Kβ signalling axis in BRAF-mutant melanoma that confers resistance to therapies and can be targeted to improve treatment outcomes.

The paper identifies two neutrophil subtypes in cancer using single-cell RNA sequencing, showing their conservation across species and tumor types, and links them to immunosuppressive pathways.

MouSR is an open-source tool for rapid transcriptomic analysis of human or mouse data, enabling biological discovery without programming expertise.

The paper uses deep learning with t-SNE to analyze large datasets and overcome size limitations in bioinformatics.