What guidance is recommended for DNA (Deoxyribonucleic Acid) methylation testing in patients?

DNA Methylation Testing Guidance in Clinical Practice

DNA methylation testing should only be used in specific diagnostic contexts for certain hereditary syndromes, cancer diagnostics, or as part of comprehensive genetic evaluations, as it has limited clinical utility in general medical practice. 1

Clinical Applications of DNA Methylation Testing

Cancer Diagnostics and Classification

• Medulloblastoma subtyping: DNA methylation with next-generation sequencing is considered the gold standard for medulloblastoma classification, helping distinguish between molecular subtypes that impact prognosis and treatment 2, 1
• Ovarian cancer: BRCA mutation tests have good clinical validity for identifying patients who benefit from PARP inhibitor therapy, but there is insufficient evidence to determine the clinical validity of BRCA1 or RAD51C promoter methylation for predicting treatment response 2
• Non-small cell lung cancer (NSCLC): DNA methylation biomarkers may facilitate early detection, provide insights into epigenetic alterations, and predict prognosis, though this is still evolving 2

Genetic Disorders

• Fragile X syndrome: Methylation analysis is essential for detecting full mutations and showing X-inactivation patterns in females with distinguishable alleles 2, 1
• Prader-Willi syndrome: Methylation testing confirms the absence of paternally imprinted genes 1

Technical Considerations for Methylation Testing

Sample Types and Preparation

• Tissue types suitable for methylation analysis include:
  • Cell-based samples (blood, tumor material)
  • Cell-free DNA samples (plasma)
  • Tissue biopsies

Methodological Approaches

• Sequencing-based methods: Bisulfite sequencing remains the gold standard for DNA methylation analysis 2
• Array-based platforms: Illumina 450k or EPIC 850k arrays are commonly used 1
• PCR-based methods: For targeted analysis of specific regions 3

Quality Control Considerations

• Sample quality assessment: Essential for interpreting findings, with clear documentation of limitations 2
• Methylation bias correction: M-bias plotting helps identify and correct for sequencing and base-calling errors 2
• SNP correction: "CG-to-TG" SNPs can cause bias in methylation calling and should be accounted for 2

Important Limitations and Challenges

Technical Limitations

• Current platforms assess only ~3% of all CpG sites in the human genome 1
• Strong bias toward populations of European ancestry in research studies 1
• Lack of standardization across laboratories 1

Clinical Interpretation Challenges

• Tissue-specific methylation patterns: Methylation profiles vary across different tissues 1
• Age-related methylation changes: Methylation patterns change with aging 1
• Environmental influences: Environmental exposures can impact methylation patterns 2, 1

Specific Clinical Scenarios

• Ovarian cancer: Zygosity of BRCA1 methylation is critical for predicting PARP inhibitor response, with all copies needing to be methylated for treatment response 2
• Fragile X testing: Skewed X-inactivation can complicate detection of females with small premutations 2
• Fetal or newborn cases: FMR1 methylation status should not be used to predict disease severity 2

Best Practices for Implementation

For Cancer Testing

• Use multiplexed genetic sequencing panels rather than multiple single-gene tests when available 2
• Ensure unexpected, discordant, or equivocal results are confirmed using alternative methods 2
• Consider molecular testing in non-adenocarcinoma tumors when clinical features suggest higher probability of an oncogenic driver 2

For Methylation Analysis in Research

• Stay within one well-documented analysis pipeline for both alignment and methylation calling 2
• Combine read counts from both strands for CpG methylation as DNA methylation between strands should be highly correlated 2
• Consider the impact of SNPs, insertions, and deletions on methylation calling 2

Future Directions

• Development of "epigenetic clocks" to measure biological aging 1
• Integration with genetic data for enhanced disease prediction models 1
• Serial methylation testing for disease/longitudinal monitoring of genetic alterations 2
• Assessment of minimal residual disease based on plasma tumor genetic material 2

DNA methylation testing shows promise as a biomarker for various conditions, but its clinical application should be limited to specific contexts where evidence supports its utility for improving patient outcomes related to morbidity, mortality, and quality of life.