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

Yin-Chen Hsu; Sin-Ming Huang; Li-Chun Chang; Yan-Ming Chen; Ya-Hsuan Chang; Jing-Wei Lin; Chien-Chia Lin; Ching-Wen Chen; Hsuan-Yu Chen; Han-Mo Chiu; Sung-Liang Yu

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

Am J Cancer Res. 2022 Mar 15;12(3):1088-1101. eCollection 2022.

Authors

Yin-Chen Hsu¹, Sin-Ming Huang¹, Li-Chun Chang^{2

3

4}, Yan-Ming Chen¹, Ya-Hsuan Chang⁵, Jing-Wei Lin¹, Chien-Chia Lin¹, Ching-Wen Chen¹, Hsuan-Yu Chen⁵, Han-Mo Chiu^{2

3

4}, Sung-Liang Yu^{1

4

6

7

8}

Affiliations

¹ Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University Taipei, Taiwan.
² Department of Internal Medicine, National Taiwan University Hospital Taipei, Taiwan.
³ Health Management Center, National Taiwan University Hospital Taipei, Taiwan.
⁴ Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University Taipei, Taiwan.
⁵ Institute of Statistical Science, Academia Sinica Taipei, Taiwan.
⁶ Department of Laboratory Medicine, National Taiwan University Hospital Taipei, Taiwan.
⁷ Graduate Institute of Pathology, College of Medicine, National Taiwan University Taipei, Taiwan.
⁸ Institute of Medical Device and Imaging, College of Medicine, National Taiwan University Taipei, Taiwan.

PMID: 35411222
PMCID: PMC8984904

Abstract

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.

Keywords: Colorectal neoplasia; clonal hematopoiesis; early diagnosis; machine learning.