DCIS Marker Panel for Recurrence Risk Stratification
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Solution Overview
Problem
Current methods for predicting and treating ductal carcinoma in situ (DCIS) recurrence and invasive breast cancer are inadequate, leading to overtreatment and potential side effects due to the inability to accurately assess individual patient risk, particularly for lumpectomy-eligible patients.
Innovation Solution
Utilizing marker sets, including analysis of PR, HER2, SIAH2, and FOXA1, to assess individual patient risk for DCIS recurrence and invasive breast cancer, allowing for tailored treatment approaches such as more aggressive or less aggressive therapies based on specific marker combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for predicting DCIS recurrence and invasive breast cancer are used, then treatment decisions can be made, but the prediction accuracy is inadequate leading to overtreatment and side effects
Solution Approach 1:
The patent changes the parameters used for prediction by introducing a multi-marker panel including proliferation markers (Ki-67, MCM2), apoptosis markers (cleaved caspase-3), and differentiation markers (p63, E-cadherin) instead of relying on single markers or clinical factors alone. This comprehensive parameter set enables more precise risk stratification, allowing clinicians to accurately identify high-risk patients who need aggressive treatment while avoiding overtreatment of low-risk patients
Solution Approach 2:
The patent segments patients into distinct risk categories (low risk, intermediate risk, high risk) based on marker expression patterns and combines this with clinical factors. This segmentation allows for personalized treatment recommendations tailored to each risk group, preventing both overtreatment of low-risk patients and undertreatment of high-risk patients, thereby reducing unnecessary side effects while maintaining treatment efficacy
2Reliability
If aggressive treatment is applied to all DCIS patients, then recurrence risk is reduced, but unnecessary surgeries and side effects increase
Solution Approach 1:
The patent implements a dynamic treatment decision framework where treatment aggressiveness is adjusted based on the patient's specific marker profile and clinical characteristics. High-risk patients with multiple positive markers receive aggressive multimodal treatment (surgery, radiation, endocrine therapy), while low-risk patients with negative markers receive conservative management or observation. This dynamic approach ensures recurrence prevention is optimized for each patient without subjecting anyone to unnecessary treatments and their associated harms
3Adaptability or versatility
If individual patient risk assessment is implemented, then personalized treatment is enabled, but the complexity of analysis increases
Solution Approach 1:
The patent develops a universal risk assessment model that integrates multiple marker types (proliferation, apoptosis, differentiation) with clinical factors into a single comprehensive framework. This multi-functional system can evaluate various aspects of tumor biology and patient characteristics simultaneously, providing personalized risk assessment without requiring separate analyses for each factor. The model delivers actionable treatment recommendations across diverse patient scenarios, making personalized medicine practical and accessible
Data Source
AI summary
The present technology generally relates to methods and compositions relevant to the prediction that a subject with and/or after treatment for DCIS will experience a subsequent ipsilateral breast event that is a DCIS recurrence, an invasive breast cancer, both a DCIS recurrence and invasive cancer, or neither. The technology can assist one with how to treat such subjects.