A circulating cell-free DNA methylation signature for the detection of hepatocellular carcinoma

Abstract

To address the shortcomings of current hepatocellular carcinoma (HCC) surveillance tests, we set out to find HCC-specific methylation markers and develop a highly sensitive polymerase chain reaction (PCR)-based method to detect them in circulating cell-free DNA (cfDNA). The analysis of large methylome data revealed that Ring Finger Protein 135 (RNF135) and Lactate Dehydrogenase B (LDHB) are universally applicable HCC methylation markers with no discernible methylation level detected in any other tissue types. These markers were used to develop Methylation Sensitive High-Resolution Analysis (MS-HRM), and their diagnostic accuracy was tested using cfDNA from healthy, at-risk, and HCC patients. The combined MS-HRM RNF135 and LDHB analysis detected 57% of HCC, outperforming the alpha-fetoprotein (AFP) test's sensitivity of 45% at comparable specificity. Furthermore, when used with the AFP test, the methylation assay can detect 70% of HCC. Our findings suggest that the cfDNA methylation assay could be used for HCC liquid biopsy.

Keywords: Biomarker; Cancer diagnosis; Cell-free DNA; DNA methylation.; Hepatocellular carcinoma; Liquid biopsy; Methylation-sensitive high-resolution melting analysis.

Fig. 1

Study Overview. The overall strategy used to develop HCC-specific MS-HRM assay is shown. The first panel displays the number of normal (N) and HCC (T) methylome data from CGRC and TCGA cohorts obtained using Infinium microarray (top). The scatter plot of the Random Forest classification importance rank for markers in two cohorts is shown, with the common markers labeled with a red dotted circle (bottom). The second panel depicts the random forest models used to select HCC-specific markers and the number of samples used to build the model (top), as well as a t-SNE plot that distinguishes HCC from other cell types when clustered with the selected methylation markers (bottom). The third panel shows the CpG sites differentially methylated and their melting curves from MS-HRM analysis (top). The formulas for the methylation score and the sum of methylation score are shown below. The last panel shows the number of blood samples used for the HCC liquid biopsy test and the test results in bar graph along with the sensitivity of the assays shown

Fig. 2

Clinical Performance of Methylation Markers for Detecting HCC. (A) AUC analysis of HCC-specific diagnostic markers across four different HCC validation datasets (GSE54503, GSE56588, GSE60753, and GSE89852). The x-axis denotes 1-specificity, the y-axis indicates sensitivity, and the line represents the receiver operating characteristic (ROC) curve for each dataset. (B) Boxplot illustrating left) the combined methylation scores for RNF135 and LDHB, as well as right) log20(AFP), across four groups: Healthy, At-risk, Early-stage HCC, and Late-stage HCC. A dotted line marks the 90th quantile of the at-risk group’s sum of methylation score. Log₂₀(AFP) set at 1, based on an AFP cutoff of 20 ng/mL, is used as the cutoff value for the AFP tests. (Statistical P values are shown as *, P ≤ 0.05 and ****, P ≤ 0.0001). (C) Assay performance table for the MS-HRM and AFP tests. Sensitivity, specificity, and accuracy metric for cancers in the early (BCLC stage 0-A), late (BCLC stage B-D), and any stage are shown. (D) ROC curve for the dual-marker combination for 304 HCC patients versus 207 at-risk subjects. The color represents the following combinations: (1) AFP + AFP-L3 (black), (2) AFP + glypican-3 (GPC3) (orange), (3) AFP + MS-HRM (green), (4) AFP-L3 + GPC3 (purple), (5) AFP-L3 + MS-HRM (blue), (6) GPC3 + MS-HRM (red). The x-axis and y-axis represent 1-specificity and sensitivity, respectively