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Updated: May 12, 2026

Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection
Published on: June 8, 2020
Low-coverage whole genome sequencing of low-grade dysplasia strongly predicts advanced neoplasia risk in ulcerative
Ibrahim Al Bakir1,2,3, Kit Curtius4,5,6,7, George D Cresswell8,9
1Barts Cancer Institute, Queen Mary University of London, London, UK.
Background:
The risk of developing advanced neoplasia (AN; colorectal cancer and/or high-grade dysplasia) in ulcerative colitis (UC) patients with a low-grade dysplasia (LGD) lesion is variable and difficult to predict. This is a major challenge for effective clinical management.
Objective:
We aimed to provide accurate AN risk stratification in UC patients with LGD. We hypothesised that the pattern and burden of somatic genomic copy number alterations (CNAs) in LGD lesions could predict future AN risk.
Design:
We performed a retrospective multicentre validated case-control study using n=270 LGD samples from n=122 patients with UC. Patients were designated progressors (n=40) if they had a diagnosis of AN in the ~5 years following LGD diagnosis or non-progressors (n=82) if they remained AN-free during follow-up. DNA was extracted from the baseline LGD lesion, low-coverage whole genome sequencing performed and data processed to detect CNAs. Survival analysis was used to evaluate CNAs as predictors of future AN risk.
Results:
CNA burden was significantly higher in progressors than non-progressors (p=2×10-6 in discovery cohort) and was a very significant predictor of AN risk in univariate analysis (OR=36; p=9×10-7), outperforming existing clinical risk factors such as lesion size, shape and focality. Optimal risk prediction was achieved with a multivariate model combining CNA burden with the known clinical risk factor of incomplete LGD resection. Within-LGD lesion genetic heterogeneity did not confound risk prediction.
Conclusion:
Measurement of CNAs in LGD is an accurate predictor of AN risk in inflammatory bowel disease and is likely to support clinical management.
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