Genomic Read Compression by Functional Categories for Immune Profiling
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Solution Overview
Problem
Current methods are inadequate for efficiently determining genes essential for immune profiling of tumor environments and predicting responses to immune-oncology treatments, particularly due to the large volume of nucleic acid sequence data requiring significant memory and computational resources.
Innovation Solution
A method and system for compressing nucleic acid sequence read data into functional categories, allowing for reduced memory storage and efficient detection of enrichment or loss of signatures associated with immune-oncology biomarkers, using next-generation sequencing and compression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive nucleic acid sequencing data is collected for immune profiling, then the accuracy of tumor microenvironment characterization is improved, but the memory requirements and computational resources increase significantly
Solution Approach 1:
The patent segments the comprehensive nucleic acid sequencing data into functional gene categories (e.g., immune response, cell cycle, metabolism) and further into gene sets representing specific biological pathways or processes. This hierarchical segmentation reduces the data volume by grouping individual gene expressions into category-level summaries while preserving the essential immunological information needed for accurate tumor microenvironment profiling.
Solution Approach 2:
The patent extracts and retains only the most informative features from the comprehensive sequencing data - specifically, the expression levels of pre-selected gene sets that are known to be predictive of immune response. By extracting these key gene sets and using them as compressed representations, the method discards redundant information while maintaining the accuracy needed for immune profiling and treatment response prediction.
2Measurement precision
If comprehensive nucleic acid sequencing data is collected for immune profiling, then the accuracy of treatment response prediction is improved, but the computational resources required increase significantly
Solution Approach 1:
The computational analysis is segmented into multiple hierarchical levels: individual gene expression → gene set enrichment → functional category analysis → immune profile classification. This multi-level segmentation allows the system to perform comprehensive immune profiling by analyzing data at appropriate granularities, reducing the computational burden of processing individual gene expressions while maintaining prediction accuracy through aggregated functional insights.
Solution Approach 2:
The patent performs preliminary actions by pre-defining gene sets and functional categories based on existing biological knowledge and literature. These pre-curated gene sets represent known immune-related pathways and processes. By having these analytical frameworks prepared in advance, the system can quickly map sequencing data to relevant biological functions without performing de novo analysis, significantly reducing computational resources while maintaining accurate treatment response prediction.
3Quantity of substance
If gene expression data is compressed into functional categories, then the memory requirements are reduced, but the detail level of gene expression information is lost
Solution Approach 1:
The patent applies local quality by maintaining different levels of detail for different analytical purposes. At the gene set level, aggregated expression values provide compressed representation for memory-efficient storage and processing. However, when detailed gene-level analysis is needed for specific genes of interest, the original fine-grained expression data can be retrieved and analyzed. This allows the system to optimize memory usage for routine immune profiling while preserving access to detailed information when clinically necessary.
Data Source
AI summary
The method includes compressing numbers of reads data for targeted genes of a gene expression assay performed on a test sample. The targeted genes are organized into categories. Each category represents a functional context associated with the targeted genes in that category. The numbers of reads corresponding to targeted genes each category is compressed to form a compressed value for the category. The compressed value is compared to a baseline value for the category to determine an enrichment or a loss of a signature corresponding to the functional context of the category. The method may include analyzing information from multiple assays performed on the test sample, assigning a score value to each assay result and predicting a response to immune-oncology treatment based on the assigned scores.