DCIS Biomarker Profiling for Recurrence Risk Stratification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cancer diagnosis and treatment methods lack specificity, as they do not account for the diverse genetic aberrations that can lead to similar pathologic phenotypes, resulting in inadequate tailored therapies and drug discovery.
Innovation Solution
The use of biomarkers such as Ki67, COX-2, p16, ER, and ERBB2 to analyze cell signatures from DCIS lesions for risk categorization, enabling personalized treatment strategies by assessing the likelihood of DCIS recurrence or progression to invasive cancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current cancer diagnosis methods are used, then diagnosis can be performed, but specificity is insufficient due to not accounting for diverse genetic aberrations
Solution Approach 1:
The diagnostic approach segments cancer diagnosis into multiple independent biomarker assessments (Ki67, COX-2, p16, ER, ERBB2). Each biomarker is evaluated separately and then integrated to provide a comprehensive risk assessment, allowing the system to handle genetic diversity without overwhelming complexity
Solution Approach 2:
The patent transitions from traditional single-parameter diagnosis to a multi-dimensional assessment framework. By adding biomarker layers (molecular, cellular, genetic) to traditional diagnostic dimensions, the system achieves higher specificity while maintaining manageable complexity through structured evaluation
2Adaptability or versatility
If traditional cancer treatment methods are used, then treatment can be administered, but tailoring for specific cancer types is inadequate
Solution Approach 1:
The system performs preliminary biomarker profiling (Ki67, COX-2, p16, ER, ERBB2) before treatment decision-making. This advance characterization allows treatment strategies to be pre-tailored based on predicted cancer behavior and patient risk, reducing the complexity of real-time treatment adaptation
Solution Approach 2:
The patent uses changes in biomarker expression parameters (presence/absence, expression level) to define different cancer subtypes and risk categories. These parameter-based classifications enable adaptable treatment protocols that can be selected based on specific biomarker profiles without requiring complex real-time adjustments
3Measurement precision
If comprehensive biomarker analysis is performed, then risk assessment precision improves, but the complexity of the assessment process increases
Solution Approach 1:
The comprehensive biomarker analysis is segmented into five distinct components (Ki67, COX-2, p16, ER, ERBB2), each representing a specific biological pathway or function. This segmentation allows the complex assessment to be performed systematically and interpreted through established frameworks, reducing the perceived complexity while maintaining precision
Solution Approach 2:
The biomarker panel is designed to serve multiple functions simultaneously: risk stratification, treatment selection, prognosis prediction, and monitoring. This multi-functionality consolidates what would otherwise require separate assessment systems into a single comprehensive panel, reducing overall system complexity
Data Source
AI summary
Embodiments are provided for characterizing a biological sample. In some embodiments, one can estimate the risk that a subject with ductal carcinoma in situ will have a subsequent DCIS event and/or invasive cancers.