Gene Expression Classifier for Cancer Progression Risk Stratification
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Solution Overview
Problem
Current cancer treatment options are hindered by the heterogeneity of cancer presentation at the individual patient level, leading to a lack of robust therapeutic effectiveness due to incomplete characterization of cancer types and inadequate stratification of patients within a population.
Innovation Solution
Development of methods to determine a cancer progression risk score by detecting expression levels of specific gene signatures, such as glioblastoma or non-small cell lung cancer progression gene signatures, and using classification methods like artificial neural networks to stratify patients into high or low risk groups, facilitating personalized treatment approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cancer treatment is applied to general patient populations, then treatment coverage is maximized, but treatment effectiveness decreases due to cancer heterogeneity
Solution Approach 1:
The patent segments the general patient population into distinct subgroups based on gene expression profiles. By dividing patients into high-risk and low-risk progression groups using classifiers trained on gene expression data, the treatment approach transitions from a one-size-fits-all model to targeted subpopulation-specific treatments, thereby improving treatment effectiveness while managing complexity through systematic classification
Solution Approach 2:
The patent changes the parameter used for patient classification from clinical characteristics to gene expression levels. By measuring and analyzing the expression levels of specific genes (e.g., EEF2, CTSB, HSP90B1) and using these molecular parameters to stratify patients, the invention enables more precise treatment selection that accounts for biological heterogeneity, improving treatment reliability
2Measurement precision
If gene expression analysis is performed to characterize cancer at individual patient level, then treatment precision is improved, but diagnostic complexity and cost increase
Solution Approach 1:
The patent extracts a specific set of key genes (such as EEF2, CTSB, HSP90B1, and others listed in the embodiments) from the entire genome to create a focused progression gene signature. By selecting only the most relevant genes that contribute to cancer progression and using this reduced signature for classification, the invention achieves precise cancer characterization while reducing diagnostic complexity compared to analyzing all genes
Solution Approach 2:
The patent develops a universal classifier system that can be applied across different cancer types (glioblastoma, non-small cell lung cancer, etc.) by training on gene expression data from multiple cancer types. This multi-functional classifier can stratify patients in various cancer types using the same underlying approach, reducing the need for cancer-type-specific diagnostic systems and thereby managing complexity
Data Source
AI summary
Described herein are compositions, methods, and techniques to generate a cancer signature and uses thereof. The cancer signature can be used to determine a cancer progression risk of a subject based upon expression levels of genes of a progression gene signature in a sample. The methods can be used to predict a prognosis, to select an appropriate treatment regimen, to identify or screen for an agent effective against a cancer, or a combination thereof. Computer implemented methods and systems that implement those methods are also provided. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present disclosure.