DNA Methylation Analysis for Brain Tumor Classification
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Solution Overview
Problem
Current diagnostic tools for brain tumors lack specificity and sensitivity, as they often cannot distinguish between histologically identical tumors with different molecular groups and prognostic values, leading to inadequate treatment planning.
Innovation Solution
A method involving DNA methylation analysis using the Illumina HumanMethylation450 BeadChip to classify tumor samples by determining methylation levels at multiple CpG positions, employing random forest analysis to create a classification rule based on pre-classified tumor data, allowing for more precise stratification and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic tools are used for brain tumors, then the diagnostic process is simple, but the classification accuracy and ability to distinguish between histologically identical tumors with different molecular groups is insufficient
Solution Approach 1:
The diagnostic approach segments the classification process into multiple stages: initial histological examination followed by molecular characterization using DNA methylation analysis. This segmentation allows simple initial screening while providing options for more precise classification when needed, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
DNA methylation status serves as an intermediary marker that bridges histological appearance and molecular classification. By measuring methylation levels at specific CpG positions, the method provides an intermediate layer of information that enables accurate tumor classification without requiring direct observation of molecular characteristics, thus improving accuracy while maintaining practical feasibility.
2Measurement precision
If DNA methylation analysis at multiple CpG positions is performed, then the classification accuracy improves, but the complexity and cost of the diagnostic procedure increases
Solution Approach 1:
The method employs partial action by selecting and analyzing only specific CpG positions that are most informative for tumor classification, rather than examining the entire genome. This targeted approach achieves high classification accuracy while reducing the complexity and cost compared to comprehensive genomic analysis.
Solution Approach 2:
The invention changes the parameter being measured from general histological features to specific DNA methylation levels at selected CpG positions. This parameter change enables more precise classification because methylation status provides molecular-level information that is not visible through conventional histology, while the selective measurement of specific positions keeps the methodology practical.
3Reliability
If more extensive methylation data analysis is performed, then the ability to distinguish between different brain tumor subtypes improves, but the time and resources required for diagnosis increase
Solution Approach 1:
The method performs preliminary action by using DNA methylation analysis as an early diagnostic tool that can quickly provide reliable tumor classification. By establishing methylation profiles at selected CpG positions, the method enables rapid differentiation of tumor subtypes before more time-consuming treatment planning begins, thus improving reliability without excessive time loss.
Data Source
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AI summary
The present invention pertains to methods for classifying tumorous diseases based on their specific genomic DNA methylation profile. The invention provides a method that allows for a classification of a tumor sample obtained from a patient by analysing a multitude, preferably genome wide, collection of CpG positions by comparison to a classification rule derived from a set of methylation data acquired from pre-classified tumor species. The invention is in particular useful for classifying brain tumor samples since brain tumors are characterized by a large variety of distinct tumor species which have different prognostic values and require in the clinic a for each species developed treatment regime.