Targeted Diagnostics - AllCaN Oesophageal Project Title

  • Malignant Potential of Barrett’s Oesophagus: Morphomolecular Pathology Insights Using Multiomic Approaches.

Specific Project Team

Dr Stephaine Craig

AllCaN Oesophageal Principal Investigator

Dr Richard Turkington

AllCaN Oesophageal Principal Investigator

Prof. Helen Coleman

AllCaN Oesophageal Co-Lead & Principal Investigator

Mr Richard Murray

AllCaN Oesophageal PhD Candidate

Project Start Date and Duration

  • October 2024 (3 years)

Project Collaborators

  • Clinical Collaborators

Dr Damian McManus

AllCaN Graphical Abstract_Mr-Richard-Murray-Dr-Craig

Project Lay Summary

This project will be part of work package 3 (targeted diagnostics). This project will focus on identifying biomarkers to stratify Barrett’s oesophagus patient’s risk of developing oesophageal adenocarcinoma. It will utilise numerous techniques to determine morphological and molecular features which are associated with disease progression. These will include artificial intelligence and genomic sequencing analysis techniques. Identification of novel image or transcriptomic signatures will provide improved surveillance strategies for Barret’s patients including targeted interventions for high-risk individuals and a reduction of unnecessary procedures for those considered low risk.

Scientific Overview

Can we improve the identification of survivors who most need dietary support using personalised biomarkers, and is it visceral adiposity or dietary saturated fatty acids that augment the risk of transition from BO to OAC?

For this PhD project we will make use of matched case-control H&E stained biopsies of Barrett’s Oesophagus (BO) patients who do and do not progress to oesophageal adenocarcinoma (OAC) according to the Northern Ireland Barret’s register. The biopsies have been pre-classified by a digital pathologist and we will undertake digital image analysis to identify aberrant morphological features associated with BO progression to OAC. Feature engineering will be performed to train predictive models for machine and deep learning techniques. Bulk RNA-sequencing data will be used to draw genetic and molecular links to the morphological features identified during image analysis. We will also use spatial transcriptomic data to identify individual cell genetic niches which are involved in dysplastic progression of BO to OAC. Finally DNA methylation profiles will be used to determine if any gene expression observed has been influenced by methylated regions.

The integrated analysis of gene expression and methylation data can be applied to develop biomarkers to predict which non-dysplastic BO patients will progress to HGD/OAC. This will enable high-risk BO lesions to be eradicated endoscopically, while low-risk patients can have the frequency of endoscopic screening reduced or even discontinued.

AllCaN Oesophageal

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