Office of Technology Transfer – University of Michigan

Utilizing biomarkers to predict MDS patient sensitivity to DNA methyltransferase inhibitors

Technology #6380

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Maria Figueroa
Managed By
Janani Ramaswamy
Licensing Specialist, Medical Deviceses 734-763-9081
Patent Protection
US Patent Pending

Myelodysplastic syndromes (MDS) and MDS/myeloproliferative (MDS/MPN) overlap syndromes are marrow stem-cell disorders characterized by ineffective haematopoiesis leading to a reduction in blood cells, with one-third of cases progressing to acute myeloid leukemia (AML). MDS can be a primary disease involving gene mutations and widespread gene hypermethylation at advanced stages, or can be a secondary disease following exposure to radiation or chemotherapy. MDS currently affects between 35,000 and 55,000 people in the US, with an increasing incidence attributed to the growing elderly population. The natural course of MDS is highly variable, with survival ranging from a few weeks to several years. The standard care for MDS patients is constantly changing. The only FDA approved drugs for treatment of MDS are the DNA methyltransferase inhibitors (DNMTi) azacitidine and decitabine. Only 50% of MDS patients treated with DNMTi show hematological improvement. Six months of treatment may be required for the therapeutic benefit of DNMTis to become apparent, forcing half of patients to undergo long treatments before being deemed resistant.

Predicting DNMTi sensitivity utilizing DNA methylation biomarkers

Using a next-generation bisulfite sequencing approach, MDS patients DNA methylation status was identified prior to DNMTi treatment. A set of 21 differentially methylated regions was identified between DNMTi responders versus non-responders. Using these novel biomarkers, a classification system was created that accurately predicts patient responsiveness to DNMTi at the time of diagnosis.


  • Diagnostic for MDS patient sensitivity to DNMTis
  • Identification of epigenetic differences for non-responders for future therapeutic targets


  • Accurate prediction of DNMTi sensitivity
  • First tool to predict responsiveness rather than association
  • Rapid analysis