Multiomics Target Identification for Personalized GBM Therapy
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Solution Overview
Problem
Current methods for developing therapeutics for complex and heterogeneous diseases like glioblastoma multiforme (GBM) are slow and inefficient due to the need for tools that can navigate the large space of possible drug combinations and prioritize specific drug combinations based on molecular signatures of a patient's tumor.
Innovation Solution
A method and system for identifying treatment targets using multiomics data, including transcriptomics and genomics data, to determine biclusters, bicluster eigengenes, causal transcription factors, and causal miRNAs, which are then used to select treatment targets and combinations for personalized therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiomics data is analyzed to identify treatment targets, then therapeutic efficacy and selectivity are improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex multiomics data into distinct modules: transcriptomics data, genomics data, and clinical data. Each data type is processed separately through dedicated filtering and analysis pipelines, then integrated to identify treatment targets. This segmentation reduces the complexity of handling the entire multiomics dataset as a single complex structure.
Solution Approach 2:
The patent extracts and filters specific subsets of data relevant to treatment target identification. From the transcriptomics data, highly expressed genes are extracted and organized into biclusters. From genomics data, somatically mutated genes are extracted and mapped to pathways. This extraction process isolates meaningful signals from the complex multiomics data, simplifying the analysis while maintaining therapeutic relevance.
2Measurement precision
If biclustering analysis is performed on highly expressed genes, then treatment target identification is improved, but computational time increases
Solution Approach 1:
The patent performs preliminary filtering of transcriptomics data to identify highly expressed genes before conducting biclustering analysis. This preliminary action reduces the dataset size to only the most relevant genes, significantly decreasing the computational time required for subsequent biclustering while maintaining the precision of treatment target identification.
Solution Approach 2:
The patent applies biclustering analysis to a partial subset of genes (highly expressed genes) rather than the entire transcriptome. This partial action approach focuses computational resources on the most promising gene candidates, reducing overall computational time while maintaining sufficient precision for identifying treatment targets.
3Measurement precision
If multiple data filters are applied to transcriptomics and genomics data, then treatment target accuracy is improved, but analysis complexity increases
Solution Approach 1:
The patent segments the filtering process into distinct stages: first filtering transcriptomics data for highly expressed genes, then filtering genomics data for somatically mutated genes. Each filtering stage is independent and can be optimized separately, reducing the overall complexity compared to applying multiple filters simultaneously in a single complex analysis.
Solution Approach 2:
The patent uses biclusters as intermediary structures that organize and bridge the filtered gene sets from transcriptomics and genomics data. These biclusters serve as mediators that integrate information from multiple data types and filters, simplifying the final analysis by providing structured groupings of genes that meet multiple criteria.
Data Source
AI summary
The invention includes methods and systems for identifying targets for therapeutic intervention for various diseases and conditions; and provides specific materials and methods for treatment of specific diseases and conditions.