We have identified a morphologically distinct type of cancer cell in pancreatic ductal adenocarcinoma (PDAC) characterized by a neurogenic signature and a capacity for
perineurial space invasion. Using in situ transcriptional profiling, 3D image reconstruction, and a combination of in vivo and in vitro patient-derived models, we aim to elucidate the mechanistic connection between pancreatic cancer progression and the activation of neural-like programs.

We are integrating molecular, clinical, and metabolic datasets using advanced machine learning algorithms to identify clinically meaningful subgroups, predict patient outcomes, and stratify therapeutic risk. This effort supports personalized treatment planning, optimization of clinical trial design, and the development of precision medicine pipelines specific to pancreatic cancer.

This project focuses on the discovery of predictive biomarkers and therapeutic vulnerabilities in pancreatic, colorectal, and gastric cancers, building on our ongoing work in gastric and gastroesophageal junction cancers. Through multi-omic profiling, gene-expression analysis, and tumor microenvironment characterization, we aim to guide the selection of patients for upfront surgery, neoadjuvant chemotherapy, or immunotherapy.

We are dissecting the immune and metabolic dialogue between pancreatic tumors and their microenvironment. Leveraging ligand-receptor interaction modeling, spatial transcriptomics, and functional assays, this project focuses on how metabolic competition, immune evasion, and local nutrient dynamics influence tumour progression and therapeutic resistance. Emphasis is placed on uncovering immune cell states and metabolic pathways that mediate cachexia, immunosuppression, and response to immunotherapy.

This project aims to build a comprehensive multi-tissue, multi-omic human atlas of pancreatic cancer cachexia, an underexplored but devastating complication of pancreatic cancer. Through single-cell and spatial transcriptomics, mass spectrometry-based metabolomics, and computational modeling, we seek to characterize the pathogenesis of cachexia at both cellular and systemic levels. This work will uncover key tumor-host communication pathways, identify functional drivers of muscle and fat wasting, and lay the groundwork for biomarker discovery and therapeutic development in PC-cachexia.

Our lab aims to:

1) Reveal the mechanisms underlying oncogenic amplifications and other forms of complex structural variation in pancreatic cancer;

2) Delineate the changes in the architecture of the genome in the different stages of cancer development and evolution;

3) Develop computational tools, sequencing assays and genomic biomarkers for cancer detection, characterisation, diagnosis and targeted therapy.
 

By understanding the spatial distribution of pathological changes, the lab aims to develop models that can predict disease progression, complications, and patient outcomes. This includes understanding how localized changes within the pancreas can affect its function and lead to conditions like pancreatic insufficiency or diabetes.