Gene Expression Analysis for Cancer Recurrence Prediction
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Solution Overview
Problem
Current methods fail to accurately identify patients with non-small cell lung cancer at high risk of recurrence, leading to inadequate treatment and poor survival rates, and there is a need for additional diagnostic and treatment options tailored to individual tumors in breast cancer.
Innovation Solution
A method for characterizing cancer by identifying altered gene expression associated with recurrence-free survival, specifically targeting genes such as ADK, AP2B1, and NAMPT-influenced genes, using nucleic acid primers and probes to determine a risk score for recurrence in breast or lung cancer patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If histopathological assessment and standard clinical factors are used to stratify patients, then the diagnostic process is simple and widely applicable, but the precision in identifying high-risk patients is insufficient
Solution Approach 1:
The patent changes the parameter being measured from standard clinical factors (tumor size, stage, differentiation) to gene expression levels of specific genes (ADK, AP2B1, and NAMPT-influenced genes). This parameter change enables more precise identification of high-risk patients by detecting molecular signatures that correlate with recurrence-free survival, thereby resolving the contradiction between diagnostic precision and method complexity.
2Measurement precision
If gene expression analysis of multiple genes is performed, then the precision of recurrence prediction is improved, but the complexity and cost of the diagnostic method increases
Solution Approach 1:
The patent extracts and focuses on a specific subset of genes (ADK, AP2B1, and NAMPT-influenced genes) that have been identified as particularly relevant to recurrence-free survival in NSCLC. By selecting only these key genes rather than analyzing the entire genome or all differentially expressed genes, the method achieves high predictive precision while reducing the complexity and cost of the diagnostic assay compared to comprehensive genomic analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively predicts recurrence-free survival independently of standard clinical factors, providing a personalized prognosis and potential therapeutic targets for cancer treatment.
Implementation Method 1
using nucleic acid primers and probes to determine a risk score for recurrence
Data Source
AI summary
A method of identifying gene expression associated with recurrence free survival in a subject with cancer, comprising: a) assaying a sample from a subject diagnosed with cancer for the presence of altered gene expression of one or more genes selected from the group consisting of ADK, AP2B1, AVL9, CANX, DBT, DHRS7, DONSON, FAM190B, FGFR1, FOXN3, FZD5, GGH, GM2A, IGFBP5, ITSN2, LAMC1, LIFR, METTL7A, MT1F, MT1G, MT1P2, MT1X MT2A, NAB1, NCOA1, NCOR1, PAPOLA, PPME1, PPP1R13L, PRKAR2A, RABEP1, RBBP8, SGPL1, SIRT1, SNX2, SREK1, TAF1B, TMED5, and ZMIZ2; and b) identifying an outcome of decreased likelihood of recurrence free survival when altered gene expression relative to the level in a non-cancerous sample is present.