EcoTyper Deconvolution of Tumor Ecosystems for Personalized Therapy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for characterizing cellular heterogeneity in tumors, such as diffuse large B cell lymphoma, are limited by small sample sizes and technical variations, making it challenging to identify generalizable prognostic cell states and ecosystems that can inform personalized cancer therapies.
Innovation Solution
A computational framework, EcoTyper, is developed to deconvolute tumor ecosystems by using negative matrix factorization and digital cytometry algorithms like CIBERSORTx, which purifies gene expression profiles and identifies tumor subtypes, enabling the characterization of spatially and temporally linked cell states across multiple tissue types and samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If small sample sizes are used for characterizing cellular heterogeneity in tumors, then the complexity of analysis is reduced, but the reliability and generalizability of identified prognostic cell states and ecosystems deteriorates
Solution Approach 1:
The patent combines multiple gene expression datasets from different studies and platforms into a large integrated dataset. This merging approach increases sample size and statistical power while maintaining biological relevance, allowing reliable identification of prognostic cell states and ecosystems that would be undetectable in small individual studies
Solution Approach 2:
The patent develops computational methods that are universally applicable across different tumor types, platforms, and study designs. The deconvolution algorithms and ecosystem characterization frameworks can process diverse gene expression data regardless of source, enabling generalizable findings that transcend individual study limitations
2Ease of operation
If traditional bulk RNA sequencing is used, then the ease of operation is maintained, but the measurement precision of cellular heterogeneity deteriorates
Solution Approach 1:
The patent segments the bulk gene expression signal into distinct cellular components through computational deconvolution. By separating the contributions of different cell types (tumor cells, immune cells, stromal cells) from the bulk signal, the method achieves single-cell resolution precision while maintaining the operational simplicity of bulk RNA sequencing
Solution Approach 2:
The patent introduces computational algorithms as an intermediary between bulk RNA sequencing data and cellular heterogeneity characterization. These algorithms act as a bridge, translating bulk expression profiles into detailed cellular composition and state information without requiring actual single-cell experimental procedures
3Measurement precision
If computational deconvolution methods are applied to purify gene expression profiles, then the measurement precision of cell states is improved, but the device complexity increases
Solution Approach 1:
The computational framework is designed to be self-calibrating and self-validating. It uses internal consistency checks, cross-validation across datasets, and automated quality control measures to ensure accurate cell state identification without requiring extensive manual intervention or complex external validation procedures
Solution Approach 2:
The patent optimizes computational parameters through systematic testing and validation across multiple datasets. By carefully selecting and tuning parameters such as deconvolution algorithms, clustering thresholds, and ecosystem definition criteria, the method achieves high measurement precision while keeping computational complexity manageable through standardized parameter sets
4Quantity of substance
If large-scale characterization of tumor ecosystems is performed, then the quantity of biological information is increased, but the difficulty of detecting and measuring patterns deteriorates
Solution Approach 1:
The patent transforms the analysis from traditional two-dimensional gene-by-sample matrices to multi-dimensional ecosystems defined by cell type compositions, cell states, and spatial relationships. This dimensional transformation organizes the large quantity of biological information into structured ecological communities, making patterns detectable through hierarchical and network-based approaches rather than simple statistical methods
Data Source
AI summary
Methods and systems for deconvoluting tumor ecosystems for personalized cancer therapy are disclosed. Generally, human cancers exhibit large variation in behavior between and within patients, which is in large part related to cellular composition. Identifying cell types can identify specific types of tumors and/or cancers present in an individual. Further embodiments generally describe identifying therapies from clinical trials to which the tumor or cancer ecotypes respond, thus providing personalized therapies based on the identified cancer or tumor type.