Targeted methylation sequencing of plasma cell-free DNA for cancer detection and classification

Abstract

Background: Targeted methylation sequencing of plasma cell-free DNA (cfDNA) has a potential to expand liquid biopsies to patients with tumors without detectable oncogenic alterations, which can be potentially useful in early diagnosis.

Patients and methods: We developed a comprehensive methylation sequencing assay targeting 9223 CpG sites consistently hypermethylated according to The Cancer Genome Atlas. Next, we carried out a clinical validation of our method using plasma cfDNA samples from 78 patients with advanced colorectal cancer, non-small-cell lung cancer (NSCLC), breast cancer or melanoma and compared results with patients' outcomes.

Results: Median methylation scores in plasma cfDNA samples from patients on therapy were lower than from patients off therapy (4.74 versus 85.29; P = 0.001). Of 68 plasma samples from patients off therapy, methylation scores detected the presence of cancer in 57 (83.8%), and methylation-based signatures accurately classified the underlying cancer type in 45 (78.9%) of these. Methylation scores were most accurate in detecting colorectal cancer (96.3%), followed by breast cancer (91.7%), melanoma (81.8%) and NSCLC (61.1%), and most accurate in classifying the underlying cancer type in colorectal cancer (88.5%), followed by NSCLC (81.8%), breast cancer (72.7%) and melanoma (55.6%). Low methylation scores versus high were associated with longer survival (10.4 versus 4.4 months, P < 0.001) and longer time-to-treatment failure (2.8 versus 1.6 months, P = 0.016).

Conclusions: Comprehensive targeted methylation sequencing of 9223 CpG sites in plasma cfDNA from patients with common advanced cancers detects the presence of cancer and underlying cancer type with high accuracy. Methylation scores in plasma cfDNA correspond with treatment outcomes.

Figures

Figure 1.

Pan-cancer methylation panel target sites: (A) heat map of methylation levels of 9223 CpG sites in 32 cancer types and subtypes based on the TCGA database. Distribution of targeted CpG sites relative to CpG islands (B), gene feature categories (C), and known regulatory feature groups (D) based on the UCSC Genome Browser Database. Targeted methylation profiling for cancer/normal classification: (E) heat map displaying measured methylation levels at each targeted CpG site of the methylation panel for all samples tested. The bar chart above the heat map indicates calculated sample methylation scores: black bars indicate classification of cancer and white bars indicate classification of normal. (F) ROC curve of cancer/normal classification for colorectal cancer (AUC = 0.969).

Figure 2.

Cancer type classification accuracy on TCGA tumor tissue samples and correlation in methylation profiles between cancer plasma and cancer tissue samples. (A) The cancer type classification algorithm was applied on the TCGA tumor tissue testing sets. Classification accuracy was shown for 32 cancer types. (B) The correlation between methylation profiles from an individual plasma sample and each TCGA cancer type was calculated. The average correlation of each plasma cancer type (horizontal) and TCGA tissue cancer type (vertical) are displayed as a heat map. Black to white indicates correlation from strong to weak.

Figure 3.

Clinical validation of pan-cancer methylation profiling in plasma cfDNA from patients with advanced cancers. (A) Boxplot of 82 patients including 68 patients off therapy during sample collection (baseline) and 14 patients on therapy during sample collection (on therapy) shows that patients before systemic therapy had higher methylations scores (median 85.29) compared with methylation scores for patients on therapy (median 4.7). (B) In 68 patients, whose cfDNA samples were tested and methylation scores were calculated, the median OS for patients with high methylation scores (> 267.54, red) was significantly shorter with 4.4 months (95% CI 3.3–5.4) compared with patients with low methylation scores (≤267.54, blue) who had a median OS of 10.4 months (95% CI 7.1–13.7; P < 0.001). (C) For 55 patients who received systemic therapy after cfDNA samples were drawn and methylation scores were calculated, patients with high methylation scores (> 267.54, red) had a shorter TTF of 1.6 months (95% CI 1.3–1.9), while in patients with low methylation scores (≤267.54, blue) TTF was significantly longer with 2.8 months (95% CI 1.6–4.0; P = 0.016). (D) Linear regression (blue line) between methylation score and best RECIST change of 45 patients who received systemic therapy after sample collection shows a positive correlation (r = 0.32, P = 0.034).