Melanoma Gene Expression Assay for Metastasis Risk Stratification
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Solution Overview
Problem
Current methods for predicting metastasis in cutaneous melanoma are inaccurate, leading to over-treatment or under-treatment of patients, and invasive biopsies like sentinel lymph node biopsies have low positivity rates and significant clinical complications.
Innovation Solution
A gene expression profile (GEP) assay measuring the levels of at least eight genes (BAP1_varA, BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6) in primary cutaneous melanoma tumors using RT-PCR, followed by comparison to a predictive training set to classify metastatic risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentinel lymph node biopsy is performed to detect metastasis, then metastasis detection capability is improved, but false negative rates remain high and clinical complications increase
Solution Approach 1:
The patent replaces the mechanical/invasive lymph node biopsy system with a molecular diagnostic system based on gene expression profiling. The GEP assay analyzes gene expression patterns in primary tumor tissue to predict metastatic risk, substituting the need for invasive lymph node sampling while improving detection accuracy and eliminating false negatives associated with histological analysis
Solution Approach 2:
The patent introduces gene expression profiles as an intermediary indicator to predict metastatic risk. Instead of directly examining lymph nodes, the system uses gene expression patterns in primary tumor tissue as a mediator to infer metastatic potential, thereby avoiding the limitations of direct lymph node biopsy
2Ease of operation
If current TNM staging system is used to guide treatment, then treatment decisions are made, but over-treatment and under-treatment occur due to poor prognostic accuracy
Solution Approach 1:
The patent changes the parameters used for prognosis from anatomical/histological parameters (tumor size, lymph node involvement) to molecular parameters (gene expression levels). The GEP assay measures expression levels of multiple genes to generate a molecular signature that provides continuous, granular prognostic information, enabling more precise risk stratification and personalized treatment decisions
3Measurement precision
If invasive biopsy methods are used to improve detection accuracy, then metastasis detection is enhanced, but patient exposure to clinical complications increases
Solution Approach 1:
The patent extracts the diagnostic information needed for metastasis prediction directly from primary tumor tissue through gene expression analysis, eliminating the need to extract and examine lymph node tissue. This extraction approach obtains the necessary diagnostic data without subjecting patients to invasive lymph node biopsy procedures and their associated complications
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The GEP assay provides a more accurate prediction of metastasis risk, with 5-year metastasis-free survival rates of 93-97% for low-risk tumors and 31-33% for high-risk tumors, improving prognostic accuracy and reducing unnecessary treatments.
Implementation Method 1
measuring gene-expression levels of at least eight genes... comprises measurement of a level of fluorescence by a sequence detection system following RT-PCR
Data Source
AI summary
The invention as disclosed herein in encompasses a method for predicting the risk of metastasis of a primary cutaneous melanoma tumor, the method encompassing measuring the gene-expression levels of at least eight genes selected from a specific gene set in a sample taken from the primary cutaneous melanoma tumor; determining a gene-expression profile signature from the gene expression levels of the at least eight genes; comparing the gene-expression profile to the gene-expression profile of a predictive training set; and providing an indication as to whether the primary cutaneous melanoma tumor is a certain class of metastasis or treatment risk when the gene expression profile indicates that expression levels of at least eight genes are altered in a predictive manner as compared to the gene expression profile of the predictive training set.