CancerCellNet Computational Tool for Assessing Cancer Model Fidelity
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Solution Overview
Problem
Current methods lack effective tools to determine the fidelity of genetically engineered mouse models (GEMMs) and patient-derived xenografts (PDXs) compared to cancer cell lines (CCLs), and fail to rapidly assess new cancer models, especially with the increasing ease of generating new models.
Innovation Solution
Development of a computational software tool called CancerCellNet (CCN) that uses gene expression data and machine learning techniques, specifically the Random Forest classification method, to classify biological samples and evaluate cancer models' similarity to various tumor types, enabling platform and species-agnostic analysis across different cancer models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to assess cancer model similarity, then the assessment process is simple, but the measurement precision and ability to evaluate multiple model types is insufficient
Solution Approach 1:
The patent introduces gene expression profiles as an intermediary mediator between cancer models and their fidelity assessment. By comparing transcriptomic data from different model types (CCLs, GEMMs, PDXs) against reference tumor profiles, the system achieves precise measurement of model similarity without requiring complex direct experimental validation of each model type
Solution Approach 2:
The patent replaces traditional mechanical/experimental assessment methods with computational analysis of gene expression data. Instead of using complex biological assays to evaluate model fidelity, the system uses bioinformatics approaches to compare transcriptomic profiles, substituting wet-lab complexity with in-silico analysis
2Measurement precision
If comprehensive validation of each new cancer model is performed, then the measurement precision improves, but the time and resources required increase significantly
Solution Approach 1:
The patent performs preliminary action by pre-processing and normalizing gene expression data from multiple model types before comparison. Reference tumor profiles are established in advance, allowing new models to be rapidly assessed against these pre-defined standards without requiring de novo validation experiments for each model
Solution Approach 2:
The patent transforms complex model validation into a parameter-based comparison by focusing on gene expression levels as key parameters. By changing the assessment parameters from comprehensive biological validation to specific transcriptomic measurements, the system achieves high precision validation with reduced time and resource requirements
3Adaptability or versatility
If multiple cancer model types are evaluated using the same method, then the adaptability and versatility improve, but the device complexity and method sophistication required increase
Solution Approach 1:
The patent implements universality by developing a single gene expression analysis platform that can evaluate multiple cancer model types (CCLs, GEMMs, PDXs) against various reference tumor profiles. This multi-functional system allows researchers to assess fidelity across different model types using the same computational framework, eliminating the need for separate validation methods for each model class
Data Source
AI summary
Provided herein are methods of generating training classifiers and/or evaluating cancer models. Related systems and computer program products are also provided.