Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma

作者：Kaishan Tao, Zhenyuan Bian, Qiong Zhang, Xu Guo, Chun Yin, Yang Wang, Kaixiang Zhou, Shaogui Wan, Meifang Shi, Dengke Bao, Chuhu Yang, Jinliang Xing · 发表于：EBioMedicine · 年份：2020 · DOI：10.1016/j.ebiom.2020.102811 · 被引用次数：79 · 研究领域：Cancer Genomics and Diagnostics、Genomic variations and chromosomal abnormalities、Hepatocellular Carcinoma Treatment and Prognosis

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 develo...