CMS Gene Probe Classifier for FFPE Colorectal Cancer Typing

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Solution Overview

Problem

Current assays for classifying colorectal cancer, such as Oncotype DX and Coloprint, require fresh frozen tissue and struggle with sample degradation in formalin-fixed paraffin-embedded (FFPE) samples, limiting their practical application and accuracy in clinical settings.

Innovation Solution

A gene set and customized list of gene probes are provided for classifying colorectal cancer into four consensus molecular subtypes (CMS) using either FF or FFPE samples, utilizing a weighted support vector machine for accurate classification and nanostring probes to measure gene expression.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If fresh frozen tissue is used for gene expression analysis, then measurement precision is improved, but ease of operation deteriorates due to tissue handling requirements and limited clinical applicability

Engineering Contradiction:
Improvegene expression measurement precisionVSAvoidtissue handling and clinical applicability
Core Design Contradiction:
Measurement precisionVSEase of operation

Solution Approach 1:

The patent changes the physical state parameter of the tissue sample from fresh frozen to formalin-fixed paraffin-embedded (FFPE), which is more stable and easier to handle in clinical settings. This parameter change allows the tissue to maintain integrity without requiring specialized freezing and storage infrastructure, thereby improving ease of operation while preserving sufficient gene expression information for classification.

Inventive Principle:
Principle #35Parameter changes

2Ease of operation

If existing classification assays are used, then ease of operation is improved, but measurement precision deteriorates due to limited prognostic accuracy and inability to identify high-risk patients

Engineering Contradiction:
Improveassay usabilityVSAvoidprognostic accuracy
Core Design Contradiction:
Ease of operationVSMeasurement precision

Solution Approach 1:

The patent segments the patient population into distinct molecular subtypes (CMS1, CMS2, CMS3, CMS4) based on gene expression patterns. This segmentation allows for more precise prognostic classification compared to existing assays, enabling identification of high-risk patients who would benefit from intensified therapy while maintaining ease of operation through a standardized assay protocol.

Inventive Principle:
Principle #1Segmentation

3Ease of operation

If simplified treatment regimens are used for stage III patients, then ease of operation is improved, but reliability deteriorates due to inadequate treatment of high-risk patients

Engineering Contradiction:
Improvetreatment regimen simplicityVSAvoidtreatment effectiveness
Core Design Contradiction:
Ease of operationVSReliability

Solution Approach 1:

The patent applies local quality by tailoring treatment intensity to specific molecular subtypes rather than applying uniform treatment to all stage III patients. High-risk subtypes receive intensified adjuvant chemotherapy while lower-risk subtypes receive standard or reduced therapy, optimizing treatment effectiveness for each patient group while maintaining operational simplicity through clear classification criteria.

Inventive Principle:
Principle #3Local quality

Data Source

PatentUS12626784B2Colorectal cancer consensus molecular subtype classifier codesets and methods of use thereof
Publication Date: 2026.05.12 BOARD OF RGT THE UNIV OF TEXAS SYST
  • US12626784B2 patent drawing
  • US12626784B2 patent drawing
  • US12626784B2 patent drawing

AI summary

Provided herein is a consensus molecular subtype (CMS) classifier for colorectal cancer patients. Also provided are methods of using the classifier to identify a clinically beneficial therapeutic regime for each patient as well as methods of treating a patient accordingly Custom Nanostring code sets, which work on formalin-fixed, paraffin-embedded samples, are provide for use in determining the CMS for a colorectal cancer patient.