Gene Expression Algorithm for Colorectal Cancer Recurrence Prediction
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Solution Overview
Problem
Current methods for diagnosing colorectal cancer recurrence and predicting chemotherapy response in colorectal cancer patients are inadequate, as they do not accurately identify individual patient benefits or risks, leading to unnecessary treatments and missed opportunities for beneficial therapy.
Innovation Solution
Algorithm-based molecular assays that measure expression levels of prognostic and predictive genes, including co-expressed genes, to calculate Recurrence Scores and Treatment Scores, providing personalized risk and treatment benefit assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adjuvant chemotherapy is administered to all colorectal cancer patients, then some patients benefit from reduced recurrence risk, but many patients receive unnecessary treatment with associated toxicities
Solution Approach 1:
The patent segments the diagnostic assessment into distinct molecular components by measuring expression levels of multiple specific genes (e.g., BGN, FAP, INHBA from stromal group; MYBL2, Ki-67, cMYC, MAD2L1 from cell cycle group). This segmentation allows the complex diagnostic problem to be broken down into measurable molecular parameters that can be combined algorithmically to predict recurrence risk and chemotherapy benefit.
Solution Approach 2:
The patent creates a universal molecular assay system that can predict both recurrence risk and chemotherapy response using the same gene expression profile. The Recurrence Score and Treatment Score are derived from the same molecular data, providing multi-functional diagnostic capability from a single test platform.
2Measurement precision
If molecular gene expression assays are implemented to predict recurrence and chemotherapy response, then treatment decisions become more personalized and accurate, but the complexity and cost of the diagnostic test increase
Solution Approach 1:
The patent divides the gene expression analysis into specific functional groups (stromal group, cell cycle group, apoptosis group, angiogenesis group, cell signaling group) with designated marker genes in each group. This segmentation allows for standardized, reproducible measurement protocols and facilitates the use of established molecular biology techniques for each gene group.
Solution Approach 2:
The patent transforms complex gene expression data into simplified numerical scores (Recurrence Score and Treatment Score) that can be interpreted clinically. By converting molecular measurements into standardized score parameters, the system maintains measurement precision while making the results more accessible for clinical decision-making.
3Ease of operation
If standard tumor staging alone is used to guide treatment, then treatment decisions are simple to implement, but many patients are misclassified regarding their actual recurrence risk and chemotherapy benefit
Solution Approach 1:
The patent introduces molecular gene expression profiles as an intermediary between standard tumor staging and treatment decision-making. The molecular assay serves as a mediator that refines the crude classification from tumor stage alone, providing more accurate risk stratification while maintaining a structured decision process through algorithmic score calculation.
Solution Approach 2:
The patent performs preliminary molecular assessment before finalizing treatment decisions. By obtaining gene expression profiles and calculating Recurrence Scores and Treatment Scores in advance, clinicians can make more informed decisions about whether to recommend adjuvant chemotherapy, avoiding unnecessary treatments for low-risk patients.
Data Source
AI summary
Algorithm-based molecular assays that involve measurement of expression levels of prognostic and/or predictive genes, or co-expressed genes thereof, from a biological sample obtained from a cancer patient, and analysis of the measured expression levels to provide information concerning the likelihood of recurrence of colorectal cancer and/or the likelihood of a beneficial response to chemotherapy for the patient are provided herein. Methods of analysis of gene expression values of prognostic and/or predictive genes, as well as methods of identifying gene expression-tumor region ratios, tumor-associated stromal surface area, and gene cliques, i.e. genes that co-express with a validated biomarker and thus may be substituted for that biomarker in an assay, are also provided.