Cutaneous Melanoma Gene-Expression Assay for Metastasis and Recurrence Risk
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Solution Overview
Problem
Current methods for predicting metastasis risk in cutaneous melanoma are inaccurate, leading to inappropriate treatment strategies and surgical complications, as they rely on histological techniques with high false negatives and positives, and lack a personalized approach.
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 histological techniques are used for predicting metastasis risk, then the current staging system can be applied, but the accuracy is poor with high false negatives and positives
Solution Approach 1:
The patent transitions from histological parameters (Breslow thickness, mitotic index, ulceration) to molecular parameters (gene expression profiles, protein markers) for predicting metastasis risk. This parameter change enables more precise and reliable classification of melanoma patients into metastasis risk groups, directly addressing the inaccuracy and high false rates of current staging systems.
2Adaptability or versatility
If current staging system is used, then treatment decisions can be made, but personalized treatment approach is lacking leading to inappropriate treatment strategies
Solution Approach 1:
The patent segments melanoma patients into distinct metastasis risk groups (low, intermediate, high risk) based on molecular profiles rather than treating all patients within TNM stages uniformly. This segmentation enables personalized treatment strategies tailored to each patient's actual metastatic potential, improving adaptability while maintaining operational simplicity through clear risk categorization.
3Measurement precision
If SLN biopsy is performed to improve detection accuracy, then regional disease detection is enhanced, but surgical complications and patient morbidity increase
Solution Approach 1:
The patent performs preliminary molecular testing on the primary tumor before proceeding to SLN biopsy. By using gene expression profiles and protein markers to predict metastasis risk in advance, the system can identify low-risk patients who may avoid SLN biopsy entirely, thereby preventing surgical complications and morbidity in patients who would not benefit from the procedure.
4Reliability
If extensive tissue sampling from invasive biopsy is used, then SLN analysis sensitivity is improved, but patient morbidity and procedural complexity increase
Solution Approach 1:
The patent extracts and analyzes molecular markers (gene expression profiles, protein markers) directly from the primary tumor tissue, eliminating the need for extensive SLN biopsy and complex histological analysis. This extraction approach maintains diagnostic accuracy while significantly reducing procedural complexity and patient morbidity associated with invasive lymph node sampling.
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 statistically significant and clinically valuable prediction of metastasis risk, achieving high accuracy and sensitivity in distinguishing low-risk from high-risk tumors, reducing unnecessary treatments and surgical complications.
Implementation Method 1
measuring the gene-expression levels of at least eight genes selected from the group consisting of 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 a sample taken from the primary cutaneous melanoma tumor, wherein measuring gene-expression levels of the at least eight genes comprises measurement of a level of fluorescence by a sequence detection system following RT-PCR
Implementation Method 2
measurement of a level of fluorescence by a sequence detection system following RT-PCR
Implementation Method 3
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.