Novel prognostic marker for survival in myeloid malignancies
This technology is a set of markers for terminal erythroid differentiation (TED) for prognosis of myelodysplastic syndrome (MDS) before progression to acute myeloid leukemia (AML).
Accurate prognostic tool to predict patient progression from MDS to AML
In patients with MDS, accurate prediction of progression to AML is critical for determining the appropriate course of treatment and improving patient survival. Current prognostic tools are technically complex and rely on observer skill, which often results in low accuracy predictions. Additional testing tools and prognostic markers for progression to AML are needed to increase the accuracy of these predictions.
The Technology: Gene expression signatures for accurate prognosis of myelodysplastic syndrome
This technology is a multi-gene prognostic classifier that accurately predicts progression of MDS to AML using transcriptomic analysis of bone marrow samples. The classifier uses transcriptomic signatures and TED to accurately predict fast progression (<2 years) to AML, which is critical for determining the appropriate course of treatment for patients. The analysis can be done using standard biomedical assays, which reduces the observer bias of current prognostic tools. This technology can be further utilized to understand the pathology of disease progression and may lead to the identification of therapeutic targets.
This technology has been validated in human patient samples (n=221).
Applications:
- Prognostic assay for acute myeloid leukemia in individuals with myelodysplastic syndromes
- Prognostic assay for blood cancers
- Diagnostic assay for blood cancers
- Biomarkers for assessing viability of therapeutics
- Identification of targets for blood cancer therapeutics
- Research tool for studying blood cancers
Advantages:
- Increased accuracy of Myelodysplastic Syndromes prognosis
- Utilizes standard biomedical assays
- Highly personalized patient assessment
- Provides insight to disease pathology
Lead Inventor:
Patent Information:
Related Publications:
Tech Ventures Reference:
IR CU17287, CU18296
Licensing Contact: Joan Martinez
