System and method for hierarchical tumor immune microenvironment epigenetic deconvolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for deconvolving the tumor microenvironment (TME) using DNA methylation lack accuracy, specificity, and detailed cell type resolution, particularly due to the heterogeneity and complexity of the TME, and often use data from cancer cell lines rather than primary cancer cells.
Innovation Solution
A system and method, termed HiTIMED, employs a novel tumor-type-specific hierarchical model and DNA methylation-based process/algorithm that employs a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, identifying the most cell-discriminatory CpG sites for each tumor type, resulting in 12 libraries per tumor type, identifying the most cell-discriminatory CpG sites for each tumor type, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved, resulting in a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing DNA methylation-based deconvolution methods are used, then cell composition can be estimated, but accuracy and measurement precision are limited due to TME heterogeneity and complexity
Solution Approach 1:
The patent segments the TME deconvolution process into multiple hierarchical layers, where each layer resolves specific cell type groups at increasing levels of detail. The first layer resolves broad categories (immune, stromal, endothelial, epithelial, tumor), while subsequent layers progressively subdivide these into more specific cell types. This segmentation approach manages the complexity of TME heterogeneity by breaking down the deconvolution task into manageable stages, thereby improving measurement precision without being overwhelmed by the overall system complexity.
Solution Approach 2:
The patent extends the deconvolution process from a single-layer approach to a multi-dimensional hierarchical structure with six layers. This dimensional expansion allows the system to capture cell type composition at multiple resolutions simultaneously, transforming the problem from estimating a flat list of cell proportions to estimating a hierarchical tree of cell type compositions. This dimensional change enables more accurate measurement of cell composition by accounting for the nested hierarchical relationships among cell types.
2Reliability
If reference-based DNA methylation methods are used, then cell composition estimation improves with larger feature space, but reliability is reduced when using cancer cell lines instead of primary cancer cells
Solution Approach 1:
The patent applies local quality by creating tumor-type-specific reference libraries rather than using a universal reference library. Each of the 20 tumor types has its own customized reference library that reflects the specific epigenetic characteristics and cell type composition of that tumor type. This localized approach to reference library creation significantly improves reliability for each specific tumor type, as the reference profiles are tailored to match the actual biological variability and methylation patterns of primary cancer cells from that particular tumor type.
Solution Approach 2:
The patent changes the parameters of the reference libraries by customizing them for each tumor type based on primary cancer cell data. This involves adjusting the reference methylation profiles to reflect tumor-type-specific epigenetic alterations, cell type proportions, and biological variability. By changing these reference parameters to match each tumor type's unique characteristics, the system achieves both high reliability for specific tumor types and broad adaptability across 20 different carcinoma types.
3Loss of information
If comprehensive TME cell type resolution is pursued, then understanding of cell heterogeneity improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the comprehensive TME cell type resolution into six hierarchical layers, with each layer focusing on resolving specific cell type groups at appropriate levels of detail. This segmentation prevents the need to estimate all cell types simultaneously at maximum resolution, which would create computational intractability. Instead, the system progressively refines cell type estimates across layers, maintaining high information content while managing computational complexity through the hierarchical structure.
Solution Approach 2:
The patent resolves the contradiction by adding the hierarchical layer dimension to the deconvolution model. Rather than attempting to estimate all cell types in a single flat layer, the system distributes the resolution across six hierarchical layers. This dimensional transformation allows comprehensive cell type characterization to emerge from the cumulative information across layers, achieving high information content without requiring a single overly complex model.
Data Source
AI summary
A system and method for determining a cancerous condition based upon at least one DNA sample is provided. An interface provides data related to DNA methylation for the sample, the data including related information about the sample. A processor, responsive to the interface, identifies the data related to the DNA methylation of the sample and accesses a data store containing a library of DNA methylation information related to each of tumor, immune, and angiogenic microenvironment components. A deconvolution process, relative to the DNA sample and the DNA methylation information, then determines association with one or more components from the sample. Illustratively, the library can define a plurality of layers of information associated with aspects of the cancerous condition relative to microenvironment components thereof. One or more components can define a tumor-type-specific hierarchical model related to a plurality of immune cell types that are subject to the deconvolution process.