Gene Expression Panel Predicts Tumor Radiosensitivity

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Solution Overview

Problem

Current cancer treatment decision-making for rectal cancer lacks personalized approaches, relying on overall tumor aggressiveness rather than individual patient genomic data, leading to variable treatment responses and side effects.

Innovation Solution

A prediction model based on gene expression profiles of specific genes (e.g., MAGP, IRF1-AS1, CFTR, CYFIP1, IL18BP, KDM5A, RAB13) is developed to assess radiation sensitivity, enabling personalized treatment strategies by determining a patient's likelihood of benefiting from radiation therapy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If personalized genomic analysis is implemented, then treatment precision is improved, but device complexity increases

Engineering Contradiction:

Improvetreatment precisionVSAvoiddevice complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex genomic analysis into discrete gene panels (e.g., 14-gene signature) that can be tested individually. This segmentation allows the complex problem of personalized treatment prediction to be broken down into manageable, testable components while maintaining overall precision.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes the parameters of gene expression levels for specific genes (MAGP, IRF1-AS1, CFTR, CYFIP1, IL18BP, KDM5A, RAB13) to create a simplified predictive model. By focusing on specific gene expression parameters rather than whole-genome analysis, the system achieves treatment precision with reduced complexity.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If gene expression profiling is performed, then treatment response prediction is improved, but loss of time increases

Engineering Contradiction:

Improvetreatment response predictionVSAvoidtime for genomic analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts only the most relevant gene expression data from the complex genomic profile, focusing on a specific 14-gene signature. This extraction approach maintains treatment response prediction accuracy while significantly reducing the time required for analysis by eliminating unnecessary genomic data processing.

Inventive Principle:

Principle #2Taking out (Extraction)

3Adaptability or versatility

If radiation therapy is administered to all patients, then treatment coverage is improved, but object-affected harmful factors increase

Engineering Contradiction:

Improvetreatment coverageVSAvoidside effects

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The patent applies local quality by tailoring radiation therapy recommendations to individual patients based on their specific gene expression profiles. Instead of uniform treatment, the system identifies which specific patients will benefit from radiation therapy, applying treatment selectively to those with favorable genomic markers while avoiding unnecessary exposure for others.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20220002807A1Supervised learning methods for the prediction of tumor radiosensitivity to preoperative radiochemotherapy

Publication Date: 2022.01.06 H LEE MOFFITT CANCER CENTER & RESEARCH INSTITUTE INC

AI summary

Disclosed is a gene expression panel that can predict radiation sensitivity (radiosensitivity) of a tumor in a subject. A method of predicting radiation sensitivity is provided that is based on cellular clonogenic survival after 2 Gy (SF2) for 48 cell lines. Gene expression is used as the basis of the prediction model. The radiosensitivity cell-based prediction model is validated using clinical patient data from rectal and esophagus cancer patients that received RT before surgery. The radiosensitivity genomic-based prediction model identifies patients with rectal cancer that may benefit from RT treatment by assigning higher values of SF2 to radio-resistant patients and lower values of SF2 to radio-sensitive patients.