Gene Expression Microarray for Breast Cancer Recurrence Prediction
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Solution Overview
Problem
Current methods for predicting cancer recurrence and treatment outcomes in breast cancer patients are inadequate, as they fail to accurately identify patients who can safely avoid chemotherapy, leading to unnecessary treatments and lack of personalized treatment strategies.
Innovation Solution
A method using microarray technology to assess the expression levels of specific 'multi-state' genes (such as MKI67, SPAG5, and CDC6) to classify patients into good or poor prognosis groups, determining the need for chemotherapy based on bimodal expression patterns, allowing for personalized treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard clinical traits are used to identify patients who can avoid chemotherapy, then treatment simplicity is maintained, but prediction accuracy of cancer recurrence is insufficient
Solution Approach 1:
The patent segments the complex problem of cancer recurrence prediction by focusing on a specific subset of genes (3-10 genes) rather than analyzing all genes or using broad clinical traits. This segmentation allows for improved prediction accuracy while maintaining manageable test complexity by dividing the genomic analysis into targeted gene panels.
Solution Approach 2:
The patent extracts specific genetic information (expression levels of selected genes) from the complex genomic data to create a simplified yet accurate predictive model. By taking out only the most relevant gene expression data needed for prediction, the method achieves high accuracy without requiring complete genomic analysis.
2Measurement precision
If multi-gene expression analysis is used to improve recurrence prediction, then prediction accuracy improves, but treatment decision complexity increases
Solution Approach 1:
The patent transforms complex multi-gene expression data into a simplified risk score or classification category (e.g., low vs. high recurrence risk). By changing the parameter representation from continuous gene expression levels to discrete risk categories, the method maintains high predictive accuracy while making treatment decisions easier and more straightforward for clinicians.
3Reliability
If chemotherapy is administered to all patients to reduce recurrence risk, then recurrence risk decreases, but toxic effects and unnecessary treatment increase
Solution Approach 1:
The patent uses gene expression analysis to provide feedback about individual patient recurrence risk, enabling personalized treatment decisions. This feedback mechanism allows clinicians to identify patients who truly need chemotherapy versus those who can safely avoid it, thereby reducing unnecessary toxic effects while maintaining disease-free survival rates for high-risk patients.
4Adaptability or versatility
If existing classification methods are used to stratify patients, then treatment guidelines are established, but a significant number of patients receive unnecessary chemotherapy
Solution Approach 1:
The patent replaces traditional mechanical/classical classification methods (based on clinical traits) with a molecular biology-based gene expression analysis system. This substitution enables more precise patient stratification by detecting molecular differences that are not apparent through conventional clinical assessment, thereby reducing misclassification and unnecessary chemotherapy exposure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a more accurate prediction of cancer recurrence and treatment necessity, reducing unnecessary chemotherapy and improving treatment outcomes by identifying patients with low or high expression levels of key genes, thereby optimizing patient care.
Implementation Method 1
The level of expression of a gene of interest is determined by hybridization of a sample cDNA or cRNA to a microarray
Data Source
AI summary
An Accelerated Progression Relapse Test (APRT) and method is provided for use in the prognosis of a patient having an ER+ breast cancer. The APRT provides a determination of when a patient in a particular diseased state is likely to benefit from further disease treatment, or does not have a high probability of benefit with additional treatment. Four genetic probes are disclosed that target the MKI67, CDC6 and SPAG5 gene and gene products. The ER+ breast cancer patient population is stratified into two groups, with the low gene expression group identifying the patient/patient group that is less likely to benefit from additional treatment measures, and a high gene expression group identifying the patient group that is more likely to benefit from additional treatment measures.