Screening of early-staged colorectal neoplasia by clonal hematopoiesis-based liquid biopsy and machine-learning.

Liquid biopsy test has a better uptake for colorectal cancer (CRC) screening. However, suboptimal detection of early-staged colorectal neoplasia (CRN) limits its application. Here, we established an early-staged CRN blood test using error-corrected sequencing by comparing clonal hematopoiesis (CH) of 63 CRN patients and that of 32 controls. We identified 1,446 variants and classified the uniqueness in CRN patients. There was no significance difference in the amount of variant between CRNs and controls, but the uniqueness of variants with defective DNA mismatch repair-related mutational signature was addressed from peripheral blood in early-staged CRN patients. By machine learning approach, the early-staged CRNs was discriminated from controls with an AUC of 0.959 and an accuracy of 0.937 (95% CI, 0.863 to 0.968). The CRN predictive model was further validated by additional 20 CRNs and 10 controls and showed the accuracy, sensitivity, specificity, positive prediction value (PPV) and negative prediction value (NPV) of 0.933 (95% CI: 0.779 to 0.992), 0.95, 0.90, 0.95 and 0.90, respectively. In summary, we develop a CH-based liquid biopsy test with machine learning approach, which not only increase screening uptake but also improve the detection rate of early-staged CRN. AJCR