Multi-Gene RNA Analysis for Chemotherapy Response Prediction
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Solution Overview
Problem
Current diagnostic tests for cancer, particularly breast cancer, are limited in predicting patient response to chemotherapy due to their reliance on single analyte measurements and subjective interpretations, and they often cannot utilize RNA-based tests effectively because of RNA degradation issues and the need for fresh tissue samples, which restricts the analysis of multiple genes from small samples.
Innovation Solution
A method using multi-gene RNA analysis to predict chemotherapy response in cancer patients, specifically identifying gene sets such as TBP, ILT.2, ABCC5, and others, which can be analyzed from archived paraffin-embedded biopsy material, allowing for the prediction of increased or decreased likelihood of response to chemotherapy based on expression levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RNA-based tests are used to analyze multiple genes, then prediction accuracy for chemotherapy response is improved, but RNA degradation and difficulty in obtaining fresh tissue samples worsen the feasibility
Solution Approach 1:
The patent applies preliminary action by extracting and stabilizing RNA from tissue samples at the time of biopsy, before the tissue is processed into paraffin-embedded blocks. This preliminary RNA extraction and stabilization preserves the molecular information long-term, allowing future analysis without requiring fresh tissue. The RNA is processed and stored in a state that prevents degradation while maintaining analytical utility years later.
Solution Approach 2:
The patent creates a molecular copy of the RNA information by extracting RNA from fresh tissue at biopsy and preserving it in paraffin-embedded blocks. This copy process allows the tissue to be archived indefinitely while the RNA information remains accessible through subsequent extraction and analysis, eliminating the need for repeated fresh tissue acquisition.
2Device complexity
If single analyte diagnostic tests are used, then test simplicity is maintained, but the ability to capture relationships between multiple markers is lost
Solution Approach 1:
The patent merges multiple gene expression analyses into a single integrated test platform. By combining the expression profiling of numerous genes (including TBP, ILT.2, ABCC5, and other markers) within one comprehensive assay system, the patent captures relationships between multiple markers simultaneously while maintaining a unified test structure that can be applied to archived tissue samples.
3Quantity of substance
If immunohistochemistry methods are used for diagnostic testing, then protein detection is achieved, but quantitative accuracy and inter-laboratory consistency worsen due to non-standardized reagents and subjective interpretation
Solution Approach 1:
The patent replaces the mechanical/chemical immunohistochemistry system with a molecular biology-based RNA expression analysis system. Instead of using antibodies and protein detection methods that suffer from subjectivity and lack of standardization, the patent uses RNA extraction, amplification, and expression profiling techniques that provide quantitative, objective, and reproducible measurements. This substitution eliminates the need for subjective interpretation while maintaining the ability to detect and quantify molecular markers.
Data Source
AI summary
The present invention provides sets of genes the expression of which is important in the prognosis of cancer. In particular, the invention provides gene expression information useful for predicting whether cancer patients are likely to have a beneficial treatment response to chemotherapy FHIT; MTA1; ErbB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; STAT3; ERK1; SGCB; DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND1; PRKCD; Hepsin; AK055699; ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2; Wnt.5a; PTPD1; RAB6C; GSTM1, BCL2, ESR1; or the corresponding expression product, is determined, said report includes a prediction that said subject has a decreased likelihood of response to chemotherapy.