Gene Expression Profiling for High-Risk Multiple Myeloma
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Solution Overview
Problem
Current laboratory parameters, such as beta-2-microglobulin and albumin levels, fail to adequately account for the survival variability of multiple myeloma patients, and there is a need for a method to identify high-risk disease that may develop drug resistance and an aggressive clinical course.
Innovation Solution
Gene expression profiling is used to determine the expression levels of specific genes (KIF14, SLC19A1, CKS1B, YWHAZ, MPHOSPH1, TMPO, NADK, LARS2, TBRG4, AIM2, ASPM, AHCYL1, CTBS, MCLC, and others) in plasma cells to identify genomic signatures associated with high-risk multiple myeloma, allowing for prognosis and prediction of clinical outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current laboratory parameters (beta-2-microglobulin and albumin levels) are used for prognosis, then the method is simple and widely available, but it fails to adequately account for survival variability and cannot identify high-risk disease
Solution Approach 1:
The patent transitions from using traditional laboratory parameters (beta-2-microglobulin and albumin levels) to measuring gene expression levels of specific genes (KIF14, SLC19A1, CKS1B, YWHAZ, MPHOSPH1, TMPO, NADK, LARS2, TBRG4, AIM2, ASPM, AHCYL1, CTBS, MCLC, LTBP1). This parameter change enables accurate identification of high-risk multiple myeloma patients and prediction of clinical outcome, directly resolving the contradiction between prognosis accuracy and method simplicity by providing superior diagnostic capability despite increased complexity.
2Reliability
If gene expression profiling of multiple genes is performed, then high-risk disease can be accurately identified, but the complexity and cost of the method increases
Solution Approach 1:
The patent identifies and profiles a specific subset of 15 genes (KIF14, SLC19A1, CKS1B, YWHAZ, MPHOSPH1, TMPO, NADK, LARS2, TBRG4, AIM2, ASPM, AHCYL1, CTBS, MCLC, LTBP1) rather than performing genome-wide analysis. This segmentation approach maintains high reliability in identifying high-risk disease while reducing the complexity compared to comprehensive genomic profiling, as it focuses only on the most prognostically relevant genes.
Data Source
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AI summary
The present invention discloses a method of gene expression profiling to identify genomic signatures linked to survival specific for a disease and a kit that can be used for performing such a method. Also disclosed herein is the use of such a method in classifying the disease into subsets, predicting clinical outcome and survival of an individual, selecting treatment for an individual suffering from a disease, predicting post-relapse risk and survival of an individual, correlating molecular classification of a disease with genomic signature defining the risk group or a combination thereof.