Gene Expression Signatures Predicting Multi-Kinase Inhibitor Response
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Solution Overview
Problem
Current treatment options for hepatocellular carcinoma (HCC) are limited, with Sorafenib being the only approved target therapy, which has modest efficacy and significant side effects, and lacks effective predictive markers to identify responsive patients.
Innovation Solution
A panel of gene markers including SEC14L2, H6PD, TMEM140, SLC2A5, ACTA1, IRF8, STAT2, and UGT2A1 is used to predict a subject's responsiveness or resistance to multi-kinase inhibitors like Sorafenib by measuring their activity profiles before, during, and after treatment, allowing for personalized treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Sorafenib is used as the only approved target therapy for HCC, then treatment options are limited, but efficacy is modest and side effects are significant
Solution Approach 1:
The patent applies preliminary action by identifying predictive biomarkers (gene expression profiles, protein markers) before treatment initiation. This allows patients to be stratified into likely responders vs. non-responders before receiving Sorafenib, enabling selective treatment assignment and improving overall treatment efficacy while reducing unnecessary exposure to toxic patients.
Solution Approach 2:
The patent implements feedback mechanisms through monitoring treatment response over time using multiple biomarkers (e.g., soluble c-KIT, HGF, AFP, VEGF) and adjusting treatment decisions accordingly. This feedback loop allows dynamic adaptation of treatment strategies based on individual patient response patterns.
2Productivity
If Sorafenib is administered to all HCC patients, then treatment coverage is maximized, but toxicity is increased due to lack of patient selection
Solution Approach 1:
The patent applies preliminary action by conducting comprehensive biomarker profiling (gene expression, protein levels) before treatment initiation to identify patients most likely to benefit from Sorafenib. This pre-treatment stratification ensures that only patients with favorable biomarker profiles receive the drug, maximizing treatment coverage for responsive patients while excluding those likely to experience severe toxicity.
Solution Approach 2:
The patent applies local quality by tailoring treatment decisions to individual patient characteristics based on their specific biomarker profiles. Instead of uniform treatment, patients are stratified into subgroups (likely responders vs. non-responders) and receive differentiated treatment strategies, optimizing the benefit-risk ratio for each patient subgroup.
3Ease of operation
If baseline pERK level is used as a predictive marker, then treatment prediction is simplified, but the marker shows inconsistent results across studies
Solution Approach 1:
The patent merges multiple predictive markers (gene expression profiles including SEC14L2, H6PD, TMEM140, SLC2A5, ACTA1, IRF8, STAT2, UGT2A1; and protein markers such as soluble c-KIT, HGF, AFP, VEGF) into a comprehensive predictive model. This combination approach leverages the complementary information from different marker types, achieving both operational feasibility through integrated analysis and improved reliability through multi-parameter validation across diverse patient populations.
Data Source
AI summary
Gene expression signature predictive of cancer patient response to multi-kinase inhibitor is disclosed. Also disclosed are methods predicting the efficacy of the multi-kinase inhibitor for treating cancer in a patient. Also disclosed are methods for distinguishing responders from non-responders to a multi-kinase inhibitor in treating cancer. Also disclosed are methods for treating a cancer patient with a multi-kinase inhibitor.