Machine Learning-Based Genome-Wide Interrogation of Somatic Copy Number Aberrations in Circulating Tumor DNA for Early Detection of Hepatocellular Carcinoma
Kaishan Tao 1 , Zhenyuan Bian 2 , Qiong Zhang 3 , Xu Guo 4 , Chun Yin 4 , Yang Wang 1 , Kaixiang Zhou 4 , Shaogui Wan 5 , Meifang Shi 6 , Dengke Bao 7 , Chuhu Yang 8 , Jinliang Xing 9
Affiliations
Affiliations
1
Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
2
Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710032, China; Department of General Surgery, General Hospital of Shenyang Military Area Command, Shenyang, Liaoning 110016, China.
3
Research and Development Division, Oriomics Biotech, Hangzhou, Zhejiang 310018, China.
4
State Key Laboratory of Cancer Biology and Department of Physiology and Pathophysiology, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
5
Center for Molecular Pathology, First Affiliated Hospital, Gannan Medical University, Ganzhou, Jiangxi 341000, China.
6
Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital of Fudan University, Shanghai 200032, China; Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Shanghai 200032, China.
7
Laboratory of Cancer Biomarkers and Liquid Biopsy, School of Pharmacy, Henan University, Kaifeng 475001, China.
8
Research and Development Division, Oriomics Biotech, Hangzhou, Zhejiang 310018, China. Electronic address: [email protected].
9
State Key Laboratory of Cancer Biology and Department of Physiology and Pathophysiology, Fourth Military Medical University, Xi'an, Shaanxi 710032, China. Electronic address: [email protected].
PMID: 32512514 DOI: 10.1016/j.ebiom.2020.102811
Abstract
Background: DNAs released from tumor cells into blood (circulating tumor DNAs, ctDNAs) carry tumor-specific genomic aberrations, providing a non-invasive means for cancer detection. In this study, we aimed to leverage somatic copy number aberration (SCNA) in ctDNA to develop assays to detect early-stage HCCs.
Methods: We conducted low-depth whole-genome sequencing (WGS) to profile SCNAs in 384 plasma samples of hepatitis B virus (HBV)-related HCC and cancer-free HBV patients, using one discovery and two validation cohorts. To fully capture the robust signals of WGS data from the complete genome, we developed a machine learning-based statistical model that is focused on detection accuracy in early-stage HCC.
Findings: We built the model using a discovery cohort of 209 patients, achieving an overall area under curve (AUC) of 0.893, with 0.874 for early-stage (Barcelona clinical liver cancer [BCLC] stage 0-A) and 0.933 for advanced-stage (BCLC stage B-D). The performance of the model was then assessed in two validation cohorts (76 and 99 patients) that only consisted of patients with stage 0-A HCC. Our model exhibited a robust predictive performance, with an AUC of 0.920 and 0.812 for the two validation cohorts. Further analyses showed the impact of tumor sample heterogeneity in model training on detecting early-stage tumors, and a refined model addressing the heterogeneity in the discovery cohort significantly increased model performance in validation.
Interpretation: We developed an SCNA-based, machine learning-driven model in the non-invasive detection of early-stage HCC in HBV patients and demonstrated its performance through strict independent validations.
Keywords: Copy number aberration (CNA); Early detection; Hepatocellular carcinoma (HCC); Machine learning.