Abstract
ABSTRACT: Screening for colorectal cancer with blood-based testing should detect advanced adenomas (AA), facilitating more effective cancer prevention. We evaluated four different methods to detect AAs in plasma: (i) a machine learning algorithm, Signatures of fragment Length (SignaL), based on cell-free DNA (cfDNA) fragmentation; (ii) a “Protein-17” assay measuring 17 cancer-associated proteins; (iii) a global aneuploidy score (GAS); and (iv) cfDNA mutation analysis querying 15 genes commonly mutated in colorectal cancer. Existing data from study populations with and without cancer were utilized to determine 99.5% specificity thresholds. We studied 40 AA cases and 32 colonoscopy-negative controls. SignaL detected 9/40 AAs [22.5%, 95% confidence interval (CI), 12.3%–37.5%] at 100% specificity (95% CI, 89.3%–100%). Protein-17 detected 5/40 AAs (12.5%, 95% CI, 5.5%–26.1%) including three cases not identified by SignaL, at 100% specificity (95% CI, 89.3%–100%). GAS detected 11/40 AAs (27.5%, 95% CI, 16.1%–42.8%) but resulted in 2/32 positive controls (93.8% specificity, 95% CI, 79.9%–98.3%). Combining SignaL, the Protein-17 assay, and GAS at the 99.5% specificity thresholds resulted in detection of 16/40 AAs (40%, 95% CI, 26.3%–55.4%) at 93.8% specificity (95% CI, 79.9%–98.3%). Of 32/40 evaluable AA plasma samples, only one was cfDNA mutation positive at level of significance of 0.01. A blood-based assay based on the analysis of repeated sequence elements plus proteins seems to be able to detect a considerable fraction of patients with AA at relatively high specificity. Large, prospective studies are required to determine whether this approach can add to the options currently available for screening patients for premalignant lesions of the colon. SIGNIFICANCE: Blood-based screening for colorectal cancer could improve testing uptake and outcomes. We propose novel methods to detect AAs in plasma using cfDNA fragmentation patterns, cancer-associated proteins, and aneuploidy with high specificity. Larger studies are needed to validate clinical utility.