Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers

Brendan F. Miller, Thomas R. Pisanic Ii, Gennady Margolin, Hanna M. Petrykowska, Pornpat Athamanolap, Alexander Goncearenco, Akosua Osei-Tutu, Christina M. Annunziata, Tza-Huei Wang, Laura Elnitski

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Variation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification.
Original languageAmerican English
Article number154
JournalClinical Epigenetics
Volume12
Issue number1
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Cancer diagnostics
  • Cell-free DNA
  • DNA methylation
  • Intermolecular variation
  • Ovarian cancer

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