Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes

Abstract

Improving early cancer detection has the potential to substantially reduce cancer-related mortality. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a highly sensitive assay capable of detecting early-stage tumors. We report accurate classification of patients across all stages of renal cell carcinoma (RCC) in plasma (area under the receiver operating characteristic (AUROC) curve of 0.99) and demonstrate the validity of this assay to identify patients with RCC using urine cell-free DNA (cfDNA; AUROC of 0.86).

Figures

Extended Data Fig. 1. Flow chart of study samples.

Flow chart outlining the number of cfDNA samples collected, excluded per previously established pre-defined criteria, and included in the analytical cohorts.

Extended Data Fig. 2. Differentially methylated regions in cfDNA between RCC and control samples.

Representative sites of plasma cfDNA methylation gain (left) and loss (right) in RCC relative to controls. Each panel is a 300 base pair window. Each row represents one sample. Row height represents amount of cfDNA methylation at that site.

Extended Data Fig. 3. Correlation of differential methylation in plasma cfDNA and tumor DNA.

Density plot showing the overlap of plasma cfMeDIP-seq DMRs between 69 RCC and 13 control samples with differentially methylated CpGs in tissue from 450K methylation arrays between 93 peripheral blood mononuclear cell and 324 RCC tumor samples. Each observation (n=102,852) represents a 450K methylation array probe (x-axis) that overlaps a region covered by the cfMeDIP-seq assay (y-axis) where the color of each hex bin corresponds to the number of observations in that bin. A simple linear regression line is shown in red. The spearman rank correlation was computed and a 2-sided hypothesis of whether this correlation is equal to zero was carried out.

Figure 1:

Summary figure describing the analytical methods used to classify samples based on differential methylation in cfDNA.

Figure 2:

Classification of RCC using cfDNA methylomes. The top 300 DMRs between groups were used to generate principal component (PC) plots for plasma RCC versus control samples (a), plasma RCC versus UBC samples (b), and urine RCC versus control samples (c). Boxplot of methylation scores of individual plasma RCC and control samples from n=100 training-test sets (d). Boxplots are displayed with a median center line, box range of 25th to 75th percentile, and whiskers extending to the most extreme observation within 1.5 times the interquartile range (IQR); points outside the IQR are shown individually. AUROC curves for classifiers generated from n=100 training-test sets comparing plasma RCC versus control samples (e), plasma RCC versus UBC samples (f), and urine RCC versus control samples (g). Individual AUROC curves are shown in light grey with more overlapping curves indicated by darker shades of grey/black along with a smoothed mean curve in blue.