Autoantibody Panels and Machine Learning for Early Colorectal Cancer Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Colorectal cancer is often diagnosed late due to inadequate screening methods, leading to high mortality rates, and existing diagnostic tests like colonoscopy are invasive and have low patient compliance.

Innovation Solution

A panel of autoantibodies targeting specific antigens (NME5, USP16, UBE2S, RNF41, etc.) is used to distinguish between healthy individuals and those with colon cell proliferative disorders, combined with machine learning models for accurate classification and early detection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If colonoscopy is used for screening, then detection capability is improved, but patient compliance deteriorates due to invasiveness

Engineering Contradiction:

Improvedetection capabilityVSAvoidpatient compliance

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent replaces the mechanical/invasive colonoscopy procedure with an immunological detection system using autoantibody panels and machine learning analysis of blood samples, eliminating the need for direct mechanical examination of the colon while maintaining high detection accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces autoantibodies as intermediary biomarkers that indirectly indicate the presence of colon cell proliferative disorders, allowing detection without direct observation or mechanical intervention in the colon

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If screening is delayed, then false positives are reduced, but mortality rate increases due to late diagnosis

Engineering Contradiction:

Improvefalse positive rateVSAvoiddiagnosis timing

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary detection of colon cell proliferative disorders at early stages through autoantibody profiling before symptoms develop or disease progresses, enabling early intervention and reducing the need for delayed confirmatory testing

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the detection parameter from direct visualization of lesions (colonoscopy) to measurement of autoantibody levels and patterns, enabling detection at earlier disease stages when antibody responses are first generated

Inventive Principle:

Principle #35Parameter changes

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

The autoantibody panel and machine learning approach provides a highly sensitive and specific method for early detection of colorectal cancer and adenomas, reducing false positives and negatives, and allowing for timely intervention.

Implementation Method 1

A panel of autoantibodies targeting specific antigens (NME5, USP16, UBE2S, RNF41, etc.) is used to distinguish between healthy individuals and those with colon cell proliferative disorders

Methodology Applied

Scientific EffectAutoantibody-antigen binding:

Data Source

PatentUS12372528B2Markers for the early detection of colon cell proliferative disorders

Publication Date: 2025.07.29 FREENOME HOLDINGS INC

AI summary

Systems, media, compositions, methods, and kits disclosed herein relate to a panel of autoantibody biomarkers for the early detection of colon cell proliferative disorders, including colorectal cancer. The presence or levels of the autoantibodies in a biological sample for the autoantibody panels described herein may be used for classifier generation, and as inputs in machine learning models useful to classify subjects in a population for the detection of colon cell proliferative disorders.