10-Gene Signature for Solid Tumor Prognosis Prediction
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Solution Overview
Problem
Current methods for predicting the prognosis of patients with solid tumors, such as hepatocellular carcinoma (HCC) and breast cancer, are hindered by the heterogeneity of these diseases and the inability to distinguish between driver and passenger mutations, leading to ineffective treatments and poor prognosis prediction.
Innovation Solution
Identification of a 10-gene signature including SH2D4A, CCDC25, ELP3, DLC1, PROSC, SORBS3, HNRPD, PAQR3, PHF17, and DCK, which are used to predict clinical outcomes by detecting their expression levels in tumor samples, allowing for personalized treatment approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If comprehensive genomic analysis is performed to identify all mutations in solid tumors, then the quantity of genetic information obtained increases, but the ability to distinguish driver mutations from passenger mutations deteriorates due to high genomic instability and tumor heterogeneity
Solution Approach 1:
The patent segments the complex genomic landscape into two distinct categories: driver mutations (functionally important) and passenger mutations (non-contributing). By developing a gene signature that specifically targets and measures driver mutations across 10 different solid tumor types, the method isolates the critical signal from the noise of genomic instability, enabling precise prognosis prediction without being overwhelmed by the quantity of passenger mutations.
Solution Approach 2:
The patent extracts the essential prognostic information by identifying a specific set of driver mutations that are common across multiple solid tumor types. Rather than analyzing all genetic variations, the method extracts only the relevant driver mutations that correlate with patient outcomes, thereby obtaining high-precision prognostic data without requiring comprehensive analysis of all genomic changes including non-informative passenger mutations.
2Ease of operation
If traditional prognostic methods are used for solid tumors, then the simplicity of the approach is maintained, but the accuracy of prognosis prediction deteriorates due to tumor heterogeneity and inability to distinguish driver from passenger mutations
Solution Approach 1:
The patent creates a universal gene signature that functions across 10 different solid tumor types (bladder, breast, cervical, colorectal, endometrial, esophageal, gastric, lung, ovarian, and pancreatic cancers). This multi-functional assay maintains operational simplicity by using the same testing protocol for all tumor types while achieving high prognostic accuracy by measuring driver mutations that are relevant to each specific cancer type.
3Adaptability or versatility
If genomic instability is present in solid tumors, then the heterogeneity of tumor cells increases leading to diverse cellular pathways, but the ability to identify functional driver mutations deteriorates due to accumulation of non-contributing passenger mutations
Solution Approach 1:
The patent uses feedback from clinical outcome data to identify and validate driver mutations. By correlating genetic measurements with patient prognosis outcomes, the method determines which mutations provide functional information about tumor behavior. This feedback mechanism allows the system to distinguish driver mutations from passenger mutations even in the presence of high genomic instability and tumor heterogeneity.
Data Source
AI summary
Disclosed herein is a driver gene signature for predicting survival in patients with solid tumors, such as hepatocellular carcinoma (HCC) and breast cancer. The gene signature includes ten tumor-associated genes, SH2D4A, CCDC25, ELP3, DLC1, PROSC, SORBS3, HNRPD, PAQR3, PHF17 and DCK. A decrease in DNA copy number or mRNA expression of SH2D4A, CCDC25, ELP3, DLC1, PROSC and SORBS3 in solid tumors is associated with a poor prognosis, while a decrease in DNA copy number or mRNA expression of HNRPD, PAQR3, PHF17 and DCK in solid tumors is associated with a good prognosis. Methods of predicting the prognosis of a patient diagnosed with HCC or breast cancer by detecting expression of one of more tumor-associated genes, and methods of treating a patient diagnosed with HCC or breast cancer by administering an agent that alters expression or activity of one or more of the disclosed tumor-associated genes, are described.