Microbial Nucleic Acid Profiling for Cancer Site Classification

Resolve Bottlenecks,
Find Innovative Solutions
Generate Solutions

Solution Overview

Problem

Current cancer diagnostics fail to accurately identify the tissue/body site location and detect somatic mutations associated with cancer, lacking sensitivity and specificity, especially in early stages, and do not provide comprehensive data for medical intervention.

Innovation Solution

A method using nucleic acids from non-human origin in combination with human somatic mutations, employing machine learning to diagnose cancer location and predict therapeutic responses by analyzing k-mers and somatic mutations in biological samples.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If liquid biopsy-based diagnostics detect cancer-associated somatic mutations, then sensitivity for detecting cancer presence is improved, but ability to identify tissue/body site location deteriorates

Engineering Contradiction:
Improvedetection sensitivityVSAvoidtissue location information
Core Design Contradiction:
Measurement precisionVSLoss of information

Solution Approach 1:

The patent combines two previously separate diagnostic approaches into a single integrated system: (1) detection of cancer-associated somatic mutations and (2) identification of tissue/body site location through microbial nucleic acid patterns. By simultaneously analyzing both mutation data and microbial signature data from the same liquid biopsy sample, the system recovers both sensitivity for cancer detection and information about tissue location, eliminating the trade-off that existed when these functions were separated into different diagnostic categories

Inventive Principle:
Principle #5Merging (Combining)

2Loss of information

If liquid biopsy-based diagnostics detect tissue-unique molecular patterns, then ability to identify tissue/body site location is improved, but detection of somatic mutations deteriorates

Engineering Contradiction:
Improvetissue location informationVSAvoidsomatic mutation detection
Core Design Contradiction:
Loss of informationVSMeasurement precision

Solution Approach 1:

The integrated diagnostic system merges the analysis of tissue-unique molecular patterns (for location identification) with simultaneous detection of somatic mutations (for cancer characterization). By processing both types of molecular information from the same sequencing data and integrating them through a unified analytical framework, the system maintains high precision in somatic mutation detection while also recovering tissue location information that would otherwise be lost

Inventive Principle:
Principle #5Merging (Combining)

3Measurement precision

If existing diagnostics provide specialized cancer detection, then sensitivity for specific cancer type is improved, but comprehensiveness of diagnostic data deteriorates

Engineering Contradiction:
Improvecancer detection sensitivityVSAvoidcomprehensive diagnostic data
Core Design Contradiction:
Measurement precisionVSLoss of information

Solution Approach 1:

The patent creates a universal diagnostic platform that performs multiple functions simultaneously: detecting cancer presence, identifying tissue/body site location, characterizing somatic mutations, and providing data for treatment selection. This multi-functional system eliminates the need for separate specialized tests by integrating all these diagnostic capabilities into a single comprehensive analysis of microbial nucleic acids and somatic mutations from liquid biopsy samples

Inventive Principle:
Principle #6Universality (Multi-functionality)

Data Source

PatentEP4268232B1Taxonomy-independent cancer diagnostics and classification using microbial nucleic acids and somatic mutations
Publication Date: 2026.03.25 LIQUID BIOPSY HOLDCO LLC
  • EP4268232B1 patent drawingFigure 1A
  • EP4268232B1 patent drawingFigure 1B
  • EP4268232B1 patent drawingFigure 1C

AI summary

Provided are systems and methods for the diagnosis and classification of cancer by taxonomy-independent classifications of microbial nucleic acids and somatic mutations.