Molecular-Functional Profiles for Gene-Group Cancer Classification
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Solution Overview
Problem
Existing technologies lack the ability to accurately characterize cancer types in patients and select effective therapies based on molecular-functional profiles, which is crucial for personalized care and prognosis.
Innovation Solution
Systems and methods for generating molecular-functional (MF) profiles by determining gene group expression levels from RNA expression data or whole exome sequencing data, clustering these profiles into distinct clusters, and associating them with specific cancer types, visualizing the profiles in a graphical user interface (GUI).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular-functional profiles are generated using RNA expression data and whole exome sequencing data to characterize cancer types, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex molecular-functional profile analysis into distinct gene groups (tumor-promoting immune microenvironment, anti-tumor immune microenvironment, angiogenesis, fibroblast, and malignancy gene groups). Each gene group is analyzed separately to determine specific expression levels, which are then integrated to generate the overall MF profile. This segmentation reduces the complexity of handling all genes simultaneously while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary computational system that processes raw RNA expression data and whole exome sequencing data through multiple analysis layers. The system uses intermediate representations (gene group expression levels) as mediators between the raw data and final cancer type characterization, simplifying the overall process while maintaining accuracy.
2Measurement precision
If multiple gene group expression levels are determined for each subject, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining the gene groups and their associated genes before analysis. The five gene groups (tumor-promoting immune microenvironment, anti-tumor immune microenvironment, angiogenesis, fibroblast, and malignancy) are established in advance with their respective gene members identified. This preliminary organization allows for more efficient processing during the actual analysis phase, reducing the time required to determine expression levels for each gene group.
3Reliability
If MF profile clusters are generated and stored in databases for comparison, then reliability is improved, but loss of substance increases
Solution Approach 1:
The system extracts and stores only the essential clustered MF profile data in databases, rather than storing complete raw data for all subjects. The clustering process identifies representative profile patterns (four main clusters: inflamed/vascularized, inflamed/non-vascularized, non-inflamed/vascularized, non-inflamed/non-vascularized), and only these clustered representations are stored for comparison. This extraction approach maintains reliability for prognosis prediction while significantly reducing data storage requirements.
Data Source
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AI summary
Various methods, systems, computer-readable storage media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer-readable storage media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer.