Gene Expression Algorithm for Taxane Benefit Prediction in Breast Cancer
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Solution Overview
Problem
Current breast cancer therapies lack reliable predictive markers to identify patients who benefit from taxane addition to conventional chemotherapy, leading to over-treatment of low-risk patients and undertreatment of high-risk patients, with no effective test available to determine the optimal therapeutic regimen for individual patients.
Innovation Solution
A method involving the determination of gene expression levels of AKR1C3, SPP1, PTGER3, VEGFC, and CXCL9, combined using an algorithm to yield a score indicative of benefit from taxane-based therapy, allowing for personalized chemotherapy decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If taxane-containing regimens are used to improve disease-free survival, then 5 years DFS increases by 4-7%, but toxicity increases significantly limiting benefit to only a small percentage of patients
Solution Approach 1:
The invention segments patients into distinct subgroups based on gene expression profiles (AKR1C3, SPP1, and combination scores) to identify which specific segment will benefit from taxane therapy. This segmentation allows personalized treatment decisions, administering taxanes only to the segment with high likelihood of benefit while avoiding toxicity in segments that would not respond.
Solution Approach 2:
The invention changes the parameter used for treatment selection from conventional clinical/pathological parameters to molecular gene expression parameters. By measuring expression levels of AKR1C3, SPP1, and their combination, the system identifies patients who will benefit from taxanes, transforming the selection criterion from anatomical/clinical features to molecular characteristics.
2Reliability
If conventional chemotherapy is administered to all high-risk patients, then some patients receive adequate treatment, but 70% of early stage patients receive over-treatment with unnecessary side effects
Solution Approach 1:
The invention segments the broad category of 'high-risk patients' into molecular subgroups based on gene expression patterns. Patients are classified as having high likelihood of benefit, intermediate likelihood, or low likelihood of benefit from taxane-based therapy, enabling precise identification of who truly needs aggressive chemotherapy versus who would benefit from less toxic regimens.
Solution Approach 2:
The gene expression profile essentially allows the tumor itself to indicate its sensitivity to taxane therapy through the expression pattern of AKR1C3, SPP1, and other genes. The biological system provides its own predictive information, eliminating the need for trial-and-error treatment approaches.
3Ease of operation
If no predictive markers are used, then treatment decisions are made uniformly for all patients, but reliable identification of patients who benefit from taxanes remains impossible
Solution Approach 1:
The invention replaces the mechanical/clinical assessment system (physical exams, imaging, pathological grading) with a molecular biology-based system using gene expression measurement. Instead of relying on anatomical and clinical parameters, the system uses molecular markers (AKR1C3, SPP1 expression levels) to predict treatment response, substituting one diagnostic paradigm for another more precise one.
Data Source
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AI summary
The invention relates to a method or predicting an outcome of cancer in a patient suffering from cancer, said method comprising : (a) determining in a biological sample from said patient the expression level of at least one marker gene selected from AKR1C3, MAP4, SPP1, CXCL9, PTGER3, and VEGFC; (b) comparing said expression level to a reference pattern of expression, wherein an increased expression of said at least one marker gene is indicative of said patient having a benefit from microtubule stabilizing agent-based cytotoxic chemotherapy.