PlGF Biomarker Detection for Anti-Angiogenic Therapy Prediction
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Solution Overview
Problem
Current methods lack effective, objective, and reproducible ways to predict the clinical outcome of cancer treatment and monitor progression in patients, particularly in determining the suitability of anti-angiogenic therapy, as the predictive value of gene expression for treatment outcomes in cancer is not well understood.
Innovation Solution
Detecting the expression levels of placental growth factor (PlGF) in cancer patients to identify those who may benefit from anti-cancer therapies other than or in addition to anti-angiogenic therapy, or who are less likely to respond to anti-angiogenic therapy alone, by comparing PlGF levels in patient samples to reference samples, and adjusting treatment regimens accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-angiogenic therapy is administered to cancer patients, then tumor growth may be inhibited, but treatment efficacy varies significantly between patients and current methods cannot reliably predict individual response
Solution Approach 1:
The patent introduces PlGF (placental growth factor) expression level as an intermediary biomarker that mediates between the anti-angiogenic therapy and the patient's tumor response. By measuring PlGF expression in patient samples, the system provides predictive information about treatment efficacy without directly measuring treatment outcome, thus resolving the contradiction between needing reliable prediction and lacking sufficient predictive data.
Solution Approach 2:
The patent performs preliminary measurement of PlGF expression levels in patient samples before or during anti-angiogenic therapy administration. This preliminary action provides advance knowledge about likely treatment response, allowing clinicians to predict efficacy before full treatment commitment, thereby improving reliability of treatment selection while avoiding loss of predictive information.
2Adaptability or versatility
If multiple treatment options are explored for cancer patients, then treatment personalization may improve outcomes, but diagnostic complexity and cost increase
Solution Approach 1:
The patent extracts and focuses on a single specific biomarker (PlGF expression) from the complex landscape of cancer diagnostics. By taking out only the most relevant predictive marker rather than measuring all possible genes and proteins, the system achieves treatment personalization capability while maintaining diagnostic simplicity and avoiding excessive complexity.
Solution Approach 2:
The patent changes the diagnostic parameter from complex multi-parameter gene expression profiling to a single quantitative measure of PlGF expression level. This parameter simplification maintains the ability to personalize treatment decisions while significantly reducing diagnostic complexity and making the system more versatile for different cancer types.
3Measurement precision
If gene expression analysis is performed to identify cancer types, then cancer classification improves, but predictive value for clinical outcome remains insufficient
Solution Approach 1:
The patent applies local quality by focusing measurement attention on a specific local aspect (PlGF expression) rather than comprehensive global gene profiling. This localized measurement approach provides sufficient precision for cancer classification while simultaneously delivering reliable predictive value for treatment outcome, resolving the contradiction between classification accuracy and outcome prediction.
Data Source
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AI summary
Disclosed herein are methods and compositions useful for identifying therapies likely to confer optimal clinical benefit for patients with cancer.