Single Cell and Plasma RNA Sequencing for RNA Liquid Biopsy for Hepatocellular Carcinoma
Joaquim S. L. Vong, Lu Ji, Macy M. S. Heung, Suk Hang Cheng, John Wong, Paul B.S. Lai, Vincent Wai‐Sun Wong, Stephen L. Chan, Henry Lik‐Yuen Chan, Peiyong Jiang, Kelvin Chan, Rossa W. K. Chiu, Yuk Ming Dennis Lo
Abstract
BACKGROUND: Human plasma contains RNA transcripts released by multiple cell types within the body. Single-cell transcriptomic analysis allows the cellular origin of circulating RNA molecules to be elucidated at high resolution and has been successfully utilized in the pregnancy context. We explored the application of a similar approach to develop plasma RNA markers for cancer detection. METHODS: Single-cell RNA sequencing was performed to decipher transcriptomic profiles of single cells from hepatocellular carcinoma (HCC) samples. Cell-type-specific transcripts were identified and used for deducing the cell-type-specific gene signature (CELSIG) scores of plasma RNA from patients with and without HCC. RESULTS: Six major cell clusters were identified, including hepatocyte-like, cholangiocyte-like, myofibroblast, endothelial, lymphoid, and myeloid cell clusters based on 4 HCC tumor tissues as well as their paired adjacent nontumoral tissues. The CELSIG score of hepatocyte-like cells was significantly increased in preoperative plasma RNA samples of patients with HCC (n = 14) compared with non-HCC participants (n = 49). The CELSIG score of hepatocyte-like cells declined in plasma RNA samples of patients with HCC within 3 days after tumor resection. Compared with the discriminating power between patients with and without HCC using the abundance of ALB transcript in plasma [area under curve (AUC) 0.72)], an improved performance (AUC: 0.84) was observed using the CELSIG score. The hepatocyte-specific transcript markers in plasma RNA were further validated by ddPCR assays. The CELSIG scores of hepatocyte-like cell and cholangiocyte trended with patients' survival. CONCLUSIONS: The combination of single-cell transcriptomic analysis and plasma RNA sequencing represents an approach for the development of new noninvasive cancer markers.