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 language | American English |
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Article number | 154 |
Journal | Clinical Epigenetics |
Volume | 12 |
Issue number | 1 |
DOIs | |
State | Published - 2020 |
Externally published | Yes |
Keywords
- Cancer diagnostics
- Cell-free DNA
- DNA methylation
- Intermolecular variation
- Ovarian cancer
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