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Background
Colorectal cancer (CRC) is the second leading cause of cancer‑related mortality in the UK, accounting for approximately 10% of all cancer deaths. The majority of CRC cases are sporadic and arise from premalignant colorectal polyps, most commonly adenomatous polyps (adenomas) or sessile serrated lesions [1]. CRC development is driven by the progressive accumulation of genetic and epigenetic alterations that enable the transformation of normal colonic epithelium into polyps and, ultimately, invasive carcinoma [2].
The progression from a benign polyp to malignancy typically occurs over 7–15 years, providing a critical window for early detection and intervention. Endoscopic resection of precancerous polyps via polypectomy is highly effective in preventing CRC development [3]. However, despite successful polypectomy, 20–50% of patients go on to develop metachronous polyps [4]. As many of these patients will never progress to further polyps or cancer, universal surveillance colonoscopy is neither clinically appropriate nor sustainable, given procedural risks to patients and the substantial resource burden placed on the NHS. Improving risk stratification following polypectomy is therefore a major unmet clinical need.
The INCISE project (INtegrated TeChnologies for Improved Polyp SurveillancE) aims to address this challenge by enhancing risk stratification for metachronous polyp development through the integration of molecular and morphological data beyond conventional histopathological assessment. By identifying and validating novel biomarkers predictive of future polyp risk, INCISE seeks to refine existing surveillance protocols—reducing unnecessary procedures while ensuring high‑risk patients receive appropriately intensive follow‑up.
To support this objective, we have established a well‑characterised cohort of 2,642 patients with archival polyp samples available for further molecular profiling, including analysis using state‑of‑the‑art spatial ‘omic and proteomic platforms. By identifying molecular features within a spatial context that are associated with future risk of metachronous polyp development, this project aims to generate a robust risk stratification score to identify patients at increased risk of developing metachronous polyps.
This multidisciplinary PhD project will be undertaken in collaboration with the primary supervisors Dr Stephen McSorley (Consultant Colorectal Surgeon, University of Glasgow; specialist in colorectal cancer screening), Professor Joanne Edwards (Professor of Translational Cancer Pathology, University of Glasgow), Dr Hayley Morris (Consultant Pathologist, University of Glasgow; specialist in colorectal pathology) and Dr Philip Dunne (Specialist in Translational Bioinformatics, Queen’s University Belfast).
References
Strum WB. (2016) Colorectal Adenomas. New England Journal of Medicine. 374(11):1065–75.
De Palma FDE, D’argenio V, Pol J, Kroemer G, Maiuri MC, Salvatore F. (2019) The Molecular Hallmarks of the Serrated Pathway in Colorectal Cancer. Cancers (Basel). 11(7).
Simon K. (2016) Colorectal cancer development and advances in screening. Clin. Interv. Aging. 11:967–76.
Hao Y, et al. (2020) Risk Factors for Recurrent Colorectal Polyps. Gut Liver;14(4): 399-411. 4. Løberg M, et al. (2014) Long-term colorectal-cancer mortality after adenoma removal. N Engl J Med;371(9): 799-807.
Aims
Using the INCISE cohort and its associated datasets, this project aims to:
Identify and validate morphological and molecular features associated with the risk of developing metachronous colorectal polyps
Integrate cutting‑edge spatial ‘omic and proteomic technologies—including the Lunaphore COMET and Bruker CosMx Spatial Molecular Imager—to uncover novel biomarkers of risk of metachronous polyps.
Develop predictive models to support personalised surveillance strategies following polypectomy
Training Outcomes
The PhD student will receive comprehensive interdisciplinary training in:
The biology of colorectal cancer development and progression
Histopathological assessment of colorectal polyps
Immunohistochemistry and quantitative tissue analysis
The use of advanced spatial ‘omic and proteomic platforms, including Lunaphore COMET and Bruker CosMx Spatial Molecular Imager
Statistical and computational analysis of spatial ‘omic and proteomic datasets
In addition, the student will contribute to the translational positioning of candidate biomarkers, supporting their evaluation for future clinical utility and impact on colorectal cancer surveillance practice.
Funding Notes
UK Student fees covered.
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