Reliable Clinical MLH1 Promoter Hypermethylation Assessment Using a High-Throughput Genome-Wide Methylation Array Platform

Abstract

Clinical testing for MLH1 promoter hypermethylation status is important in the workup of patients with MLH1-deficient colorectal and uterine carcinomas when evaluating patients for Lynch syndrome. Current assays use single gene-based methods to assess promoter hypermethylation. Herein, we describe the development and report the performance of a clinical assay for MLH1 promoter hypermethylation using the Infinium methylationEPIC (850k) bead-array platform. Using four cytosine-guanine dinucleotide (CpG) sites within the MLH1 gene promoter, a qualitative MLH1 promoter hypermethylation assay was developed and validated using 63 gastrointestinal and uterine carcinoma samples of known hypermethylation status based on a pyrosequencing reference test. The array-based method achieves clinically robust and reproducible results at an analytical sensitivity level of 8%. Of importance, the 850k array contains probes targeting >850,000 additional CpG sites across the genome, covering sites in most known genes as well as important enhancer regions provided by the Encyclopedia of DNA Elements and Functional Annotation of The Mammalian Genome projects. Thus, the testing modality presented may also be applied to determine the methylation status of other clinically relevant genes or regulatory regions, potentially providing a single laboratory testing workflow for all clinical methylation assays. Furthermore, the concomitant acquisition of genome-wide methylation information provides a workflow that seamlessly enables wider translational epigenetic research.

Figure 1

Performance characteristics study using cell lines at varying methylation levels run in triplicate. The four cytosine-guanine dinucleotide (CpG) sites from the triplicate samples are represented with different colors. Each CpG site demonstrates a unique β distribution. Variance of the β distributions is largest for intermediate methylation levels and smallest for 0% and 100% levels. A small amount of jitter is added to methylation level for visualization purposes.

Figure 2

Results of training (A), validation (B), and analytical sensitivity (C) studies. In each graph, the four MLH1 cytosine-guanine dinucleotide (CpG) sites are represented on the x axis (with a small amount of jitter for ease of visualization) and their respective β values are represented on the y axis. Line segments connect β values from the same sample. A: The training study was used to establish qualitative cutoffs (blue line segments in A and B; black line segments in C) and interpretive criteria. B: The interpretive criteria were tested in the validation study. Box-and-whisker plots are shown for the negative samples only for each CpG site in the training and validation studies (A and B). C: Analytical sensitivity study using a sample with 66% methylation by quantitative pyrosequencing diluted into a negative sample shows consistent hypermethylation detection when the methylation level is ≥8%. The >8% category contains triplicates of samples with methylation levels of 66%, 33%, and 17%.

Figure 3

Interassay reproducibility (A), intra-assay reproducibility (B), and limit of detection (C) studies. In each graph, the four MLH1 cytosine-guanine dinucleotide (CpG) sites are represented on the x axis and their respective β values are represented on the y axis. Line segments connect β values from the same sample. Black line segments designate the CpG-specific qualitative cutoff, as defined by the training study. A and B: Samples with methylation levels near the limit of sensitivity were run across three runs (A) and in triplicate on the same run (B). Results demonstrate accurate and reproducible results across and within experimental runs. C: Limit of detection study demonstrated the ability to detect hypermethylation in a positive sample using as little as 25 ng of DNA input before bisulfite conversion.