Lymphoma Subtyping via Gene Expression Signatures
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Solution Overview
Problem
Current lymphoma classification methods, such as the WHO classification, do not accurately account for molecular differences, leading to varying clinical outcomes among patients assigned to the same diagnostic category, necessitating more precise methods for identifying and classifying lymphomas based on molecular characteristics.
Innovation Solution
A method involving the isolation of gene expression products from biopsy samples, obtaining digital gene expression data, generating a weighted average of gene expression signature values, calculating a predictor score, and classifying subjects into specific lymphoma types to select appropriate treatment options, using a gene expression signature comprising specific genes like ASB13, CCDC50, and others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If WHO classification based on morphology and immunophenotype is used, then classification simplicity is maintained, but measurement precision of molecular differences deteriorates
Solution Approach 1:
The patent segments the lymphoma classification problem into multiple molecular dimensions by analyzing expression levels of multiple individual genes (such as BCL6, IRF4, MAF, etc.) separately rather than as a single composite marker, allowing precise characterization of molecular subtypes while maintaining systematic organization
Solution Approach 2:
The patent changes the classification parameters from traditional morphology-based single parameters to multiple molecular expression parameters, where each gene's expression level serves as an independent parameter for distinguishing lymphoma subtypes, thereby improving measurement precision without excessive complexity
2Loss of time
If traditional morphological classification is used, then diagnostic speed is maintained, but reliability of treatment prediction deteriorates
Solution Approach 1:
The patent performs preliminary molecular expression analysis on biopsy samples to pre-classify lymphoma subtypes before treatment decisions are made, using established gene expression thresholds that can be rapidly assessed, thereby improving prediction reliability without significantly increasing diagnostic time
Solution Approach 2:
The patent substitutes traditional morphological examination (mechanical/visual assessment) with molecular expression profiling (biological measurement), replacing subjective visual classification with objective quantitative gene expression data to improve treatment prediction reliability
3Measurement precision
If comprehensive molecular profiling is implemented, then measurement precision of lymphoma subtyping is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and focuses on a specific subset of key genes (such as BCL6, IRF4, MAF, MYC, etc.) that are most discriminative for lymphoma subtyping, rather than analyzing the entire transcriptome, thereby improving classification precision while reducing the complexity of the diagnostic system
Solution Approach 2:
The patent develops a multi-functional gene expression panel that can simultaneously classify multiple lymphoma subtypes (germinal center B-cell-like, activated B-cell-like, Burkitt, mantle cell) using the same set of genes and analytical approach, achieving comprehensive classification without proportionally increasing system complexity
Data Source
AI summary
The invention is directed to methods for selecting a treatment option for an activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) subject, a germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL) subject, a primary mediastinal B cell lymphoma (PMBL) subject, a Burkitt lymphoma (BL) subject, or a mantle cell lymphoma (MCL) subject by analyzing digital gene expression data obtained from the subject, e.g., from a biopsy sample.