Tumor Phenotype Prediction via Gene Expression Clustering
Solution Overview
Problem
Current methods struggle to systematically define tumor-immune phenotypes in cancer patients due to the complex and heterogeneous nature of immune cell infiltration, and the molecular features shaping CD8+ T cell distribution in the tumor microenvironment are not well understood, which hampers the effectiveness of cancer immunotherapy.
Innovation Solution
A machine-learning approach is employed to classify and molecularly characterize tumor-immune phenotypes by using gene expression levels to identify specific genes associated with CD8+ T cell characteristics, enabling the prediction of immune phenotypes such as immune-desert, immune-excluded, and inflamed/infiltrated phenotypes, and informing treatment candidates like anti-TGFβ agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional histological methods are used to define tumor-immune phenotypes, then visual assessment of CD8+ T cell distribution is possible, but systematic definition and molecular characterization remain challenging due to heterogeneity and complexity
Solution Approach 1:
The patent replaces manual histological assessment with a machine learning-based computational system that processes gene expression data. The classifier automatically identifies tumor-immune phenotypes by analyzing molecular features, substituting the mechanical/visual assessment process with an automated information processing system that handles the complexity of heterogeneous immune cell infiltration patterns.
Solution Approach 2:
The patent transforms the phenotype classification approach by changing from visual histological parameters to molecular gene expression parameters. By measuring expression levels of specific genes (e.g., HLA-DOB, PSMB10, TDO2) that are informative of CD8+ T cell characteristics, the system achieves systematic and reproducible phenotype definition that overcomes the subjectivity and complexity limitations of traditional methods.
2Loss of information
If gene expression data from multiple genes are analyzed to predict CD8+ T cell characteristics, then molecular characterization of tumor-immune phenotypes is achieved, but data processing complexity increases
Solution Approach 1:
The patent extracts and focuses on a specific subset of genes from the entire transcriptome that are most informative for predicting CD8+ T cell characteristics. By selecting only the relevant genes (such as HLA-DOB, PSMB10, TDO2, and others identified through training data), the system reduces the dimensionality of the data processing task while retaining the essential molecular features needed for accurate phenotype classification.
Solution Approach 2:
The patent develops a universal classifier that can process gene expression data from multiple different cancer types and systematically define tumor-immune phenotypes across diverse contexts. The machine learning model trained on comprehensive training data achieves multi-functional applicability, enabling the same computational approach to characterize CD8+ T cell distribution patterns in various tumor microenvironments without requiring type-specific adjustments.
3Reliability
If machine learning classifier is used to predict tumor-immune phenotypes from gene expression data, then treatment candidate identification is improved, but computational resources and analysis time increase
Solution Approach 1:
The patent performs preliminary action by training the machine learning classifier on comprehensive training data that includes gene expression profiles and corresponding digital pathology annotations. This pre-training phase captures the relationships between gene expression patterns and CD8+ T cell characteristics, enabling the classifier to rapidly and accurately predict tumor-immune phenotypes in new patients without requiring time-consuming manual assessment or extensive computational optimization during clinical application.
Data Source
AI summary
A machine-learning model (e.g., a clustering model) may be used to predict a phenotype of a tumor based on expression levels of a set of genes. The set of genes may have been identified using a same or different machine-learning model. The phenotype may include an immune-excluded, immune-desert or an inflamed/infiltrated phenotype. A treatment strategy and/or treatment recommendation may be identified based on the predicted phenotype.