Data from Risk Prediction of Metachronous Colorectal Cancer from Molecular Features of Adenomas: A Nested Case–Control Study

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Abstract
Current morphologic features defining advanced adenomas (size ≥10 mm, high-grade dysplasia or ≥25% villous component) cannot optimally distinguish individuals at high risk or low risk of metachronous colorectal cancer (me-CRC), which may result in suboptimal surveillance. Certain DNA copy-number alterations (CNAs) are associated with adenoma-to-carcinoma progression. We aimed to evaluate whether these molecular features can better predict an individual's risk of me-CRC than the morphologic advanced adenoma features.In this nested case–control study, 529 individuals with a single adenoma at first colonoscopy were selected from a Norwegian adenoma cohort. DNA copy-number profiles were determined, by low-coverage whole-genome sequencing. Prevalence of CNAs in advanced and non-advanced adenomas and its association (OR) with me-CRC was assessed. For the latter, cases (with me-CRC) were matched to controls (without me-CRC) on follow-up, age and sex.CNAs associated with adenoma-to-carcinoma progression were observed in 85/267 (32%) of advanced adenomas and in 27/262 (10%) of non-advanced adenomas. me-CRC was statistically significantly associated, also after adjustment for other variables, with age at baseline [OR, 1.14; 95% confidence interval CI), 1.03–1.26; P = 0.012], advanced adenomas (OR, 2.46; 95% CI, 1.50–4.01; P < 0.001) and with the presence of ≥3 DNA copy-number losses (OR, 1.90; 95% CI. 1.02–3.54; P = 0.043).Molecularly-defined high-risk adenomas were associated with me-CRC, but the association of advanced adenoma with me-CRC was stronger.Significance:Identifying new biomarkers may improve prediction of me-CRC for individuals with adenomas and optimize surveillance intervals to reduce risk of colorectal cancer and reduce oversurveillance of patients with low risk of colorectal cancer. Use of DNA CNAs alone does not improve prediction of me-CRC. Further research to improve risk classification is required.