97-Gene Bayesian Classifier for Multiple Myeloma Subtyping
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Solution Overview
Problem
Current molecular classification schemes for multiple myeloma (MM) fail to predict treatment response and do not correlate with plasma cell development or MM pathogenesis.
Innovation Solution
The use of 97 specific genes to classify MM into MCL1-M high and MCL1-M low subtypes through Bayesian classification, enabling prediction of prognosis and response to bortezomib treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing molecular classification schemes are used, then MM subtypes can be identified, but treatment response prediction fails
Solution Approach 1:
The patent changes the parameters used for classification from traditional gene expression profiles (e.g., UAMS-70, Millennium-100) to a novel 97-gene signature that includes specific pathways related to plasma cell development and bortezomib response. This parameter change enables both accurate classification and treatment response prediction simultaneously.
Solution Approach 2:
The patent introduces a Bayesian classifier as an intermediary computational tool that integrates the 97-gene expression data to predict both molecular subtype and treatment response. This intermediary system bridges the gap between gene expression data and clinical treatment outcomes.
2Measurement precision
If traditional gene expression profiles are used, then MM subtypes can be classified, but correlation with plasma cell development is lost
Solution Approach 1:
The patent selects 97 genes that are specifically enriched in plasma cell development pathways and bone marrow microenvironment interactions. This parameter selection preserves the biological correlation with plasma cell development while maintaining classification accuracy.
Solution Approach 2:
The 97-gene signature is segmented into functional modules including plasma cell development genes, bone marrow interaction genes, and treatment response genes. This segmentation preserves the biological information about plasma cell development while enabling comprehensive classification.
Data Source
AI summary
Disclosed are molecular typing of multiple myeloma and application thereof. Specifically, disclosed is a product comprising a substance for obtaining or detecting 97 gene expressions in multiple myeloma patients to be detected and an apparatus for operating a multiple myeloma Bayesian classifier. By using the product, the present invention identifies a gene module co-expressed with the MCL1 gene, thereby distinguishing molecular subtypes of multiple myeloma having different prognoses and bortezomib sensitivities.