Fecal Microbiome Profiling for Lower-False-Positive CRC Screening
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Solution Overview
Problem
Current colorectal cancer (CRC) screening methods, particularly fecal immunochemical tests (FIT) followed by colonoscopy, suffer from high false positive rates, leading to unnecessary invasive procedures and high healthcare costs, while existing biomarkers and diagnostic techniques lack sensitivity and specificity for early detection.
Innovation Solution
A two-phase machine learning algorithm combining microbiome profiling of fecal samples with bacterial signatures, age, and sex to classify CRC risk, reducing unnecessary colonoscopies and improving detection sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fecal immunochemical test (FIT) is used as a non-invasive screening method, then accessibility and patient comfort are improved, but false positive rate increases leading to unnecessary colonoscopies
Solution Approach 1:
The patent combines multiple diagnostic parameters (FIT hemoglobin levels, microbiome composition profiles, and patient demographic data) into an integrated risk assessment model. This merging of multiple information sources allows for more accurate CRC risk stratification while maintaining the non-invasive nature of fecal sampling, thereby reducing false positives without sacrificing accessibility.
Solution Approach 2:
The patent introduces microbiome profiling as an intermediary layer between the initial FIT screening and the definitive colonoscopy procedure. This intermediary assessment uses bacterial composition analysis to further stratify risk among FIT-positive individuals, serving as a filter that reduces unnecessary colonoscopies while maintaining high sensitivity for true CRC cases.
2Reliability
If colonoscopy is performed on all FIT-positive individuals, then detection sensitivity is improved, but healthcare costs and procedure burden increase
Solution Approach 1:
The patent segments the FIT-positive population into different risk categories based on microbiome profiling results. Instead of treating all FIT-positive individuals uniformly with colonoscopy, the method creates stratified groups (high risk, intermediate risk, low risk) that can be managed differently, thereby optimizing healthcare resource allocation while maintaining high detection sensitivity for true CRC cases.
Solution Approach 2:
The patent changes the diagnostic parameter from a single binary FIT result to a multi-dimensional risk score incorporating microbiome composition data. This parameter transformation allows for nuanced risk stratification, enabling clinicians to prioritize colonoscopy resources for high-risk individuals while alternative management strategies can be considered for lower-risk groups.
3Difficulty of detecting and measuring
If existing biomarkers are used for early detection, then screening capability is improved, but sensitivity and specificity remain insufficient
Solution Approach 1:
The patent creates a composite diagnostic approach by integrating multiple types of biomarkers (hemoglobin data from FIT, microbiome composition profiles, and patient demographic information) into a unified risk assessment model. This composite methodology leverages the complementary strengths of different biomarker types to achieve superior sensitivity and specificity compared to any single marker alone.
Solution Approach 2:
The patent adds a new dimension to CRC screening by incorporating microbiome composition analysis alongside traditional FIT hemoglobin measurement. This dimensional expansion transforms the diagnostic process from evaluating a single parameter to assessing multiple independent biological dimensions, thereby improving both sensitivity and specificity for early CRC detection.
Data Source
AI summary
The invention relates to a two-phase method for screening for colorectal cancer (CRC) using fecal microbiome profiling. The method comprises determining in a fecal sample isolated from the subjects the levels of two or more bacterial taxa. classifying with a computer algorithm in a first phase CRC samples vs. non-CRC samples and classifying with a computer algorithm in a second phase the samples that are classified as being non-CRC in the first phase into clinically relevant (CR) samples and non-CR samples using two or more bacterial taxa that are differentially abundant in CR samples relative to non-CR samples. The invention also relates to a kit comprising reagents for conducting the method and a computer program.