121-Gene Expression Signature for Hyperprogressive Disease Prediction
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Solution Overview
Problem
Existing immunotherapies targeting PD-1, such as anti-PD-1 therapy, fail to predict hyperprogressive disease (HPD) in a significant subset of patients, leading to accelerated tumor growth, and current diagnostic criteria for HPD are inconsistent and limited.
Innovation Solution
Development of a 121-gene expression signature that predicts HPD through whole-exome sequencing and RNA sequencing of tumor samples before and after anti-PD-1 therapy, identifying specific biomarkers and mutation clusters associated with HPD, using a classifier system to assess the likelihood of HPD development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-PD-1 immunotherapy is administered to patients, then therapeutic response is achieved in 20-30% of patients, but the majority of patients do not respond and a significant subset experiences hyperprogressive disease
Solution Approach 1:
The patent segments the complex diagnostic challenge into a specific 121-gene expression signature that can be measured and analyzed separately. This gene signature breaks down the complex biological response to anti-PD-1 therapy into discrete, measurable molecular components that can be evaluated independently to predict HPD risk.
Solution Approach 2:
The patent introduces a classifier system as an intermediary between the gene expression data and the HPD prediction. This classifier processes the 121-gene expression signature and generates a predictive output, serving as a mediator that translates complex molecular data into clinically actionable information about HPD risk.
2Measurement precision
If current diagnostic criteria for HPD are used, then identification of HPD patients is attempted, but the criteria are inconsistent and limited
Solution Approach 1:
The patent changes the measurement parameters from clinical observation-based criteria to molecular biology-based gene expression measurements. By measuring the expression levels of 121 specific genes and analyzing their patterns, the system achieves more precise and objective HPD prediction compared to subjective clinical criteria.
Solution Approach 2:
The patent replaces the mechanical/clinical assessment system with a molecular biology-based system. Instead of relying on physical examination and clinical judgment to detect HPD, the system uses gene expression profiling and computational analysis to automatically predict HPD risk with higher precision.
3Loss of information
If whole-exome sequencing and RNA sequencing are performed to identify HPD mechanisms, then comprehensive genomic and immune features are identified, but the complexity of analysis increases
Solution Approach 1:
The patent extracts the essential predictive information from comprehensive sequencing data by focusing on a specific 121-gene signature. Rather than analyzing all genomic and transcriptomic data, the system identifies and extracts the subset of genes that are most relevant for predicting HPD, simplifying the analysis while retaining critical information.
Solution Approach 2:
The patent performs preliminary bioinformatics analysis to identify the 121-gene signature from comprehensive sequencing data before clinical application. This preliminary action of data processing and gene selection is done once to establish the signature, which can then be applied to patient samples without requiring full whole-exome sequencing for each case.
Data Source
AI summary
A method of determining a patient's HPD status comprising the steps of a) examining a patient tumor sample for the expression level of HPD-diagnostic biomarkers, and b) determining whether the signature of the biomarkers is similar to that of a HPD positive signature is disclosed.