Serial circulating tumor DNA to predict early recurrence in patients with hepatocellular carcinoma: a prospective study.
Gui-Qi ZhuWei-Ren LiuZheng TangWei-Feng QuYuan FangXi-Fei JiangShu-Shu SongHan WangChen-Yang TaoPei-Yun ZhouRun HuangJun GaoHai-Xiang SunZhen-Bin DingYuan-Fei PengZhi DaiJian ZhouJia FanYing-Hong ShiPublished in: Molecular oncology (2021)
We studied the value of circulating tumor DNA (ctDNA) in predicting early postoperative tumor recurrence and monitoring tumor burden in patients with hepatocellular carcinoma (HCC). Plasma-free DNA, germline DNA, and tissue DNA were isolated from 41 patients with HCC. Serial ctDNAs were analyzed by next-generation sequencing before and after operation. Whole-exome sequencing was used to detect the DNA of HCC and adjacent tissues. In total, 47 gene mutations were identified in the ctDNA of the 41 patients analyzed before surgery. ctDNA was detected in 63.4% and 46% of the patient plasma pre- and postoperation, respectively. The preoperative ctDNA positivity rate was significantly lower in the nonrecurrence group than in the recurrence group. With a median follow-up of 17.7 months, nine patients (22%) experienced tumor recurrence. ctDNA positivity at two time-points was associated with significantly shorter recurrence-free survival (RFS). Tumors with NRAS, NEF2L2, and MET mutations had significantly shorter times to recurrence than those without mutations and showed high recurrence prediction performance by machine learning. Multivariate analyses showed that the median variant allele frequency (VAF) of mutations in preoperative ctDNA was a strong independent predictor of RFS. ctDNA is a real-time monitoring indicator that can accurately reflect tumor burden. The median VAF of baseline ctDNA is a strong independent predictor of RFS in individuals with HCC.
Keyphrases
- circulating tumor
- free survival
- cell free
- circulating tumor cells
- end stage renal disease
- machine learning
- chronic kidney disease
- patients undergoing
- ejection fraction
- newly diagnosed
- prognostic factors
- gene expression
- patient reported outcomes
- oxidative stress
- deep learning
- big data
- patient reported
- tyrosine kinase