ssg-MeDIP-Seq and sscf-MeDIP-Seq for DNA methylation detection

This technology is two methods, called ssg-MeDIP-Seq and sscf-MeDIP-Seq, for improved tumor detection using detection of DNA methylation density in genomic DNA, and of hemi-methylated regions in plasma cell-free DNA.

Unmet Need: Improved precision in tumor detection based on accessible biomarkers

Cancer detection is a critical component of the diagnosis and therapeutic pipeline. Biomarker detection offers a promising method of detection, offering an accessible and less-invasive alternative to more conventional diagnostic tools. Still, a highly precise and accessible tool to assess biomarkers that harnesses the full potential of information attainable for DNA samples has yet to emerge. A highly precise biomarker tool for cancer tumor detection, as well as classification will improve on the current standard of diagnosis.

The Technology: Tumor detection via DNA methylation and hemi-methylation biomarkers

This technology detects sDNA methylation density as well as hemi-methylated regions of genomic DNA and plasma cell free (cf) DNA to detect tumors. Quantification of genomic DNA methylation is a commonly used tool for tumor detection, however the use of detection of hemi-methylated DNA has not been robustly used. This technology uses the detection of differentially methylated regions (DMRs) and differentially hemi-methylated regions (DHMRs) as independent biomarkers for tumor detection.

This technology has been validated using human methylomes from 221 cfDNA samples.

Applications:

  • Tumor cancer diagnostic including brain and liver cancer
  • Cancer classification tool
  • Cancer progression detection
  • Diagnostic tool for neurodegenerative diseases including Alzheimer’s Disease

Advantages:

  • Combines data from both differentially methylated regions (DMRs) and differentially hemi-methylated regions (DHMRs) as independent biomarkers for tumor detection
  • Tested in over 221 human samples and outperformed other models trained on only one methylation type
  • Leverages artificial intelligence (machine learning) capabilities
  • Enhances diagnostic precision for tumor detection and classification

Lead Inventor:

Zhiguo Zhang, Ph.D

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