Affinity Ligand Biomarker Panel for Early HCC Blood Detection
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Solution Overview
Problem
Current methods for diagnosing hepatocellular carcinoma (HCC) are inadequate, with limited sensitivity and specificity, particularly in early stages, and there is a need for improved minimal-invasive tests to enhance early detection.
Innovation Solution
A composition and method utilizing nucleic acid and peptide affinity ligands targeting biomarkers such as Prolactin, Alpha Fetoprotein (AFP), Interleukin-6 (IL-6), and optionally Squamous Cell Carcinoma Antigen (SCC), Metallopeptidase Inhibitor 1 (TIMP-1), and CYFRA 21-1, combined with machine learning models, to analyze serum samples for HCC detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If imaging-based techniques are used for HCC diagnosis, then diagnostic capability is improved, but technical challenges in discriminating pre-malignant liver diseases from HCC and limited sensitivity of ultrasound-based surveillance occur
Solution Approach 1:
The patent segments the diagnostic approach by combining multiple biomarkers (AFP, PIVKA-II, DCP, TIMP-1, IL-6, Prolactin) into a panel rather than relying on a single marker. This segmentation allows each biomarker to contribute differently to the overall diagnostic accuracy, improving the ability to discriminate HCC from pre-malignant conditions while maintaining high sensitivity for early detection
Solution Approach 2:
The patent creates a composite diagnostic system by integrating multiple biomarkers with different biological functions and expression patterns. This composite approach combines the advantages of each individual marker (e.g., AFP for classic HCC, PIVKA-II for early detection, TIMP-1 for fibrosis monitoring) to achieve superior diagnostic performance that overcomes the limitations of single-marker or single-modality imaging approaches
2Measurement precision
If a panel of multiple biomarkers is used, then sensitivity and specificity of HCC detection is improved, but device complexity increases
Solution Approach 1:
The patent develops a multi-functional diagnostic system where a single biomarker panel can serve multiple purposes: early HCC detection, discrimination from pre-malignant diseases, monitoring of chronic liver disease progression, and assessment of treatment response. This universal approach eliminates the need for separate diagnostic tests for different clinical scenarios, reducing overall system complexity despite using multiple biomarkers
Solution Approach 2:
The biomarker panel is designed to provide self-sufficient diagnostic information without requiring additional confirmatory tests or complex procedural steps. The combination of biomarkers with complementary expression patterns allows the panel to automatically differentiate between HCC and benign conditions, reducing the need for external validation and simplifying the diagnostic workflow
3Ease of operation
If minimal-invasive tests are used for early detection, then ease of operation is improved, but diagnostic precision may be worsened
Solution Approach 1:
The patent changes the diagnostic parameters by measuring multiple biomarkers simultaneously in a single blood draw rather than performing invasive procedures. The use of biomarkers with different biological half-lives, expression thresholds, and regulatory mechanisms provides redundant information that compensates for the minimal-invasive nature of the test, maintaining high diagnostic precision while improving ease of operation
Data Source
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AI summary
The present invention relates to a composition for diagnosing, detecting, or monitoring a liver cancer disease, comprising nucleic acid affinity ligands and/or peptide affinity ligands for a group of biomarkers comprising at least Prolactin, Alpha Fetoprotein (AFP) and Interleukin-6 (IL-6) and, optionally, further comprising one or more of Squamous Cell Carcinoma Antigen (SCC), Metallopeptidase Inhibitor 1 (TIMP-1) and CYFRA 21-1. The present invention further envisages corresponding methods for diagnosing, detecting or monitoring a cancer disease in a subject, as well as a computer-implemented method for the analysis of a sample of a subject, a computer-implemented method for providing a trained machine learning model, the use of affinity ligands for detecting, diagnosing, or monitoring a liver cancer disease, in particular early forms of liver cancer.