KDR Kinase Domain Variant Classification for Targeted Cancer Therapy
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Solution Overview
Problem
Current methods fail to accurately classify the pathogenicity of KDR gene variants, which hinders the prediction of patient response to targeted therapies like bevacizumab and kinase domain inhibitors, necessitating a need for precise classification of KDR variants as pathogenic or non-pathogenic to guide treatment decisions.
Innovation Solution
Methods are developed to identify and label non-frameshift alterations in the KDR gene, specifically affecting amino acids 630-753, as pathogenic, using techniques such as sequencing and labeling these alterations to determine the effectiveness of kinase domain inhibitors like sorafenib, sunitinib, and apatinib for treating cancers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to classify KDR gene variants, then the classification process is simple, but the accuracy of pathogenicity prediction is insufficient
Solution Approach 1:
The patent segments the KDR gene into specific regions (amino acid positions 630-753) and identifies particular alteration types (non-frameshift insertions and deletions) within these regions as pathogenic. This segmentation approach allows precise classification by focusing on specific gene portions rather than treating the entire gene uniformly, thereby improving prediction accuracy while maintaining a manageable classification framework.
Solution Approach 2:
The patent applies local quality by assigning different pathogenicity classifications to different regions and alteration types within the KDR gene. Specifically, non-frameshift insertions and deletions in amino acid positions 630-753 are classified as pathogenic, while other alterations may be classified differently. This region-specific classification strategy enhances prediction accuracy by recognizing that not all gene alterations have equal clinical significance.
2Reliability
If comprehensive KDR variant analysis is performed to improve treatment prediction, then treatment efficacy can be optimized, but the time and resources required for analysis increase
Solution Approach 1:
The patent establishes predetermined classification criteria for KDR variants before clinical testing. By pre-defining which alteration types (non-frameshift insertions and deletions in specific regions) are pathogenic, the system enables rapid classification without requiring complex real-time analysis. This preliminary establishment of classification rules reduces analysis time while maintaining high prediction reliability for treatment response.
Solution Approach 2:
The patent changes the classification parameters from considering all possible KDR alterations to focusing specifically on non-frameshift insertions and deletions in amino acid positions 630-753. This parameter refinement simplifies the analysis process by reducing the number of variants that require detailed evaluation, thereby decreasing analysis time while improving the reliability of treatment response predictions for kinase domain inhibitors.
Data Source
AI summary
The present disclosure provides non-frameshift deletions of KDR methods for evaluating, identifying, assessing, and/or treating an individual having a cancer. The present disclosure also provides method of classifying one or more regions of KDR as pathogenic in a preselected group of cancer ontologies.