Early detection and prognosis prediction for colorectal cancer by circulating tumour DNA methylation haplotypes: A multicentre cohort study

Abstract

Background: Early detection and prognosis prediction of colorectal cancer (CRC) can significantly reduce CRC-related mortality. Recently, circulating tumour DNA (ctDNA) methylation has shown good application foreground in the early detection and prognosis prediction of multiple tumours.

Methods: This multicentre cohort study evaluated ctDNA methylation haplotype patterns based on archived plasma samples (collected between 2010 and 2018) from 1138 individuals at two medical centres: Fudan University Shanghai Cancer Center (Shanghai, China) and Southern Medical University Nanfang Hospital (Guangzhou, Guangdong, China), including 366 healthy individuals, 182 patients with advanced adenoma (AA), and 590 patients with CRC. Samples were processed using the ColonES assay, a targeted bisulfite sequencing method that detects ctDNA methylation haplotype patterns in 191 genomic regions. Among these 1138 samples, 748 were used to develop a classification model, and 390 served as a blinded cohort for independent validation. The study is registered at https://register.clinicaltrials.gov with the unique identifier NCT03737591.

Results: The model obtained from unblinded samples discriminated patients with CRC or AA from normal controls with high accuracy. In the blinded validation set, the ColonES assay achieved sensitivity values of 79.0% (95% confidence interval (CI), 66%-88%) in AA patients and 86.6% (95% CI, 81%-91%) in CRC patients with a specificity of 88.1% (95% CI, 81%-93%) in healthy individuals. The model area under the curve (AUC) for the blinded validation set was 0.903 for AA samples and 0.937 for CRC samples. Additionally, the prognosis of patients with high preoperative ctDNA methylation levels was worse than that of patients with low ctDNA methylation levels (p = 0.001 for relapse-free survival and p = 0.004 for overall survival).

Interpretation: We successfully developed and validated an accurate, noninvasive detection method based on ctDNA methylation haplotype patterns that may enable early detection and prognosis prediction for CRC.

Funding: The Grant of National Natural Science Foundation of China (No.81871958), National Natural Science Foundation of China (No. 82203215), Shanghai Science and Technology Committee (No. 19140902100), Scientific Research Fund of Fudan University (No.IDF159052), Shanghai Municipal Health Commission (SHWJRS 2021-99), and Shanghai Sailing Program (22YF1408800).

Keywords: Colorectal cancer; Early detection; Precancerous adenomas; Prognosis prediction; ctDNA methylation.

Fig. 1

An outline of the study design.

Fig. 2

Development of a Methylation Haplotype-based Classification Model. (A) The top 20 absolute value of coefficient methylation regions identified from tissue samples. (B) Heatmap of methylation regions in model building of training data set including control, advanced adenoma and CRC plasmas. (C) ROC curve of training data set with LR score of 191 methylation haplotype markers. The AUCs were 0.951 for AA samples and 0.963 for CRC samples. (D) Heatmap of methylation regions in model building of validation data set including control, advanced adenoma and CRC plasmas. (E) ROC curve of validation data set with LR score of 191 methylation haplotype markers. The AUCs were 0.902 for AA samples and 0.919 for CRC samples.

Fig. 3

ColonES Detects Early-Stage Neoplasms with High Accuracy. (A) ROC curve of blind test data set. AUC for the blinded validation set was 0.903 for adenoma samples and 0.937 for CRC samples. (B) Logistic regression model score of control, advanced adenoma and CRC groups. The prediction table were also included. The sensitivity is 86.6% for CRC and 79.0% for AA with 88.1% specificity. (C) Bar plot with 95% confidence interval of the comparison between CEA and ColonES for different disease stages. (D) Bar plot with 95% confidence interval of the comparison between FIT and ColonES for different disease stages.

Fig. 4

Influence of Covariates on ColonES Assay Performance. Covariate analysis of ColonES model for disease stages (A), tumor size of CRC patients (B), age of CRC patients (C), gender of CRC patients (D) and tumor location of CRC patients (E). The statical analysis of detection rate or sensitivity was done with z-test, Z-value was computed by the difference of two ratios and divided by the standard error of the overall ratio. The significant difference between any groups were labelled in figures. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

Fig. 5

Prognosis Prediction for Stage I–III CRC Patients by ColonES Assay. (A) LR scores between relapse (n = 72) and non-relapse (n = 356) groups. (B) LR scores between early relapse group (within 12 months, n = 39) and late relapse group (after 12 months, n = 33). (C) LR scores between alive (n = 366) and dead (n = 62) groups. (D) Kaplan–Meier curves of RFS between LR score-High and -Low groups (p = 0.001). (E) Comparison of relapse rate for 358 stage I–III CRC patients stratified by pre-LR score status on average. (F) Kaplan–Meier curves of OS between LR score-High and -Low groups (p = 0.004). (G) Comparison of overall survival rate for 358 stage I–III CRC patients stratified by pre-LR score status on average.