DNA methylation biomarkers for blood-based colorectal cancer screening

Sensitive detection of colorectal cancer in peripheral blood by septin 9 DNA methylation assay

Background

Colorectal cancer (CRC) is the second leading cause of cancer deaths despite the fact that detection of this cancer in early stages results in over 90% survival rate. Currently less than 45% of at-risk individuals in the US are screened regularly, exposing a need for better screening tests. We performed two case-control studies to validate a blood-based test that identifies methylated DNA in plasma from all stages of CRC.

Methodology/Principal Findings

Using a PCR assay for analysis of Septin 9 (SEPT9) hypermethylation in DNA extracted from plasma, clinical performance was optimized on 354 samples (252 CRC, 102 controls) and validated in a blinded, independent study of 309 samples (126 CRC, 183 controls). 168 polyps and 411 additional disease controls were also evaluated. Based on the training study SEPT9-based classification detected 120/252 CRCs (48%) and 7/102 controls (7%). In the test study 73/126 CRCs (58%) and 18/183 control samples (10%) were positive for SEPT9 validating the training set results. Inclusion of an additional measurement replicate increased the sensitivity of the assay in the testing set to 72% (90/125 CRCs detected) while maintaining 90% specificity (19/183 for controls). Positive rates for plasmas from the other cancers (11/96) and non-cancerous conditions (41/315) were low. The rate of polyp detection (>1 cm) was ∼20%.

Conclusions/Significance

Analysis of SEPT9 DNA methylation in plasma represents a straightforward, minimally invasive method to detect all stages of CRC with potential to satisfy unmet needs for increased compliance in the screening population. Further clinical testing is warranted.

Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles

Background

DNA methylation patterns have been shown to significantly correlate with different tissue types and disease states. High-throughput methylation arrays enable large-scale DNA methylation analysis to identify informative DNA methylation biomarkers. The identification of disease-specific methylation signatures is of fundamental and practical interest for risk assessment, diagnosis, and prognosis of diseases.

Results

Using published high-throughput DNA methylation data, a two-stage feature selection method was developed to select a small optimal subset of DNA methylation features to precisely classify two sample groups. With this approach, a small number of CpG sites were highly sensitive and specific in distinguishing lung cancer tissue samples from normal lung tissue samples.

Conclusion

This study shows that it is feasible to identify DNA methylation biomarkers from high-throughput DNA methylation profiles and that a small number of signature CpG sites can suffice to classify two groups of samples. The computational method we developed in the study is efficient to identify signature CpG sites from disease samples with complex methylation patterns.

DNA methylation analysis by digital bisulfite genomic sequencing and digital MethyLight

Alterations in cytosine-5 DNA methylation are frequently observed in most types of human cancer. Although assays utilizing PCR amplification of bisulfite-converted DNA are widely employed to analyze these DNA methylation alterations, they are generally limited in throughput capacity, detection sensitivity, and or resolution. Digital PCR, in which a DNA sample is analyzed in distributive fashion over multiple reaction chambers, allows for enumeration of discrete template DNA molecules, as well as sequestration of non-specific primer annealing templates into negative chambers, thereby increasing the signal-to-noise ratio in positive chambers. Here, we have applied digital PCR technology to bisulfite-converted DNA for single-molecule high-resolution DNA methylation analysis and for increased sensitivity DNA methylation detection. We developed digital bisulfite genomic DNA sequencing to efficiently determine single-basepair DNA methylation patterns on single-molecule DNA templates without an interim cloning step. We also developed digital MethyLight, which surpasses traditional MethyLight in detection sensitivity and quantitative accuracy for low quantities of DNA. Using digital MethyLight, we identified single-molecule, cancer-specific DNA hypermethylation events in the CpG islands of RUNX3, CLDN5 and FOXE1 present in plasma samples from breast cancer patients.