Gene Expression Algorithm for Kidney Cancer Recurrence Scoring
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Solution Overview
Problem
Current methods for evaluating kidney cancer prognosis are subjective and lack concordance among pathology laboratories, leading to inaccurate prognostic information and treatment decisions.
Innovation Solution
A molecular diagnostic assay measuring the expression levels of specific genes (APOLD1, EDNRB, NOS3, PPA2B, EIF4EBP1, LMNB1, TUBB2A, CCL5, CEACAM1, CX3CL1, and IL-6) to calculate a recurrence score, normalizing and weighting these levels to predict clinical outcomes and cancer recurrence in kidney cancer patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective clinical and pathological evaluation methods are used to assess kidney cancer prognosis, then the evaluation process is simple and accessible, but the prognostic information accuracy and concordance among laboratories deteriorate
Solution Approach 1:
The patent replaces subjective mechanical/pathological evaluation methods with an automated molecular diagnostic assay that measures gene expression levels. This substitution eliminates inter-laboratory variability and subjectivity while providing standardized, objective prognostic information through molecular biology techniques rather than traditional pathological assessment.
Solution Approach 2:
The patent changes the assessment parameters from subjective clinical/pathological features to quantifiable gene expression levels. By measuring specific genes (APOLD1, EDNRB, NOS3, PPA2B, EIF4EBP1, LMNB1, TUBB2A, CCL5, CEACAM1, CX3CL1, and IL-6) and calculating a recurrence score, the system transforms qualitative prognostic assessment into quantitative molecular measurement, improving precision and concordance.
2Reliability
If traditional staging and grading methods are used for kidney cancer, then the classification system is easy to implement, but the prognostic information reliability deteriorates due to lack of concordance among pathology laboratories
Solution Approach 1:
The patent replaces traditional pathological classification methods with a molecular diagnostic assay that measures gene expression levels. This substitution standardizes the classification process across laboratories, eliminating subjectivity and improving reliability through automated molecular measurement rather than manual pathological assessment.
Solution Approach 2:
The patent changes classification parameters from subjective pathological features to objective gene expression measurements. By quantifying specific genes and calculating a standardized recurrence score, the system transforms qualitative pathological classification into quantitative molecular classification, enhancing reliability and inter-laboratory concordance.
3Measurement precision
If gene expression profiling is implemented to improve prognostic accuracy, then the prognostic information quality improves, but the diagnostic assay complexity and cost increase
Solution Approach 1:
The patent segments the prognostic assessment into specific measurable gene components (APOLD1, EDNRB, NOS3, PPA2B, EIF4EBP1, LMNB1, TUBB2A, CCL5, CEACAM1, CX3CL1, and IL-6). By dividing the complex prognostic evaluation into discrete gene expression measurements, the system achieves high precision while making the assay more manageable and standardized compared to comprehensive molecular profiling.
Solution Approach 2:
The patent changes from comprehensive molecular analysis to a focused panel of specific genes with proven prognostic value. By selecting and measuring only the most relevant genes and calculating a standardized recurrence score, the system achieves high prognostic precision with a more streamlined and less complex assay compared to whole-genome or transcriptome analysis.
Data Source
AI summary
The present invention provides algorithm-based molecular assays that involve measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The present invention also provides methods of obtaining a quantitative score for a patient with kidney cancer based on measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The genes may be grouped into functional gene subsets for calculating the quantitative score and the gene subsets may be weighted according to their contribution to cancer recurrence.