Targeted SNP Genotyping for Cancer Molecular Phenotype Prediction
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Solution Overview
Problem
Current methods fail to effectively predict and characterize cancer molecular phenotypes based on germline genomic analysis, particularly in understanding the interaction between germline genetics and cancer incidence, and there is a need for improved diagnosis and targeted treatment strategies.
Innovation Solution
Methods and systems for diagnosing and treating cancer by detecting specific genetic abnormalities, such as TMPRSS2-ERG fusion proteins, single nucleotide variations in FOXA1 UTRs, and reduced expression or deletions of TMPRSS2 and CDKN1B, using genotyping to identify associated single nucleotide polymorphisms (SNPs) in tumor DNA or RNA.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If germline genomic analysis is used to predict cancer molecular phenotypes, then diagnostic accuracy and treatment personalization are improved, but the complexity of genetic testing and data interpretation increases
Solution Approach 1:
The patent segments the complex germline genomic analysis into specific, targeted SNP genotyping assays for known cancer-associated loci. Instead of analyzing the entire genome, the invention focuses on specific polymorphisms (e.g., rs111620024, rs12500426, rs7679673) associated with particular cancer types and molecular phenotypes, thereby reducing testing complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The invention changes the parameter of genetic analysis from comprehensive genomic sequencing to targeted SNP genotyping. This parameter change simplifies the technical approach by focusing on specific genetic markers rather than analyzing all genetic variations, making the test more manageable while still providing actionable predictive information about cancer risk and molecular phenotype.
2Measurement precision
If comprehensive genetic screening for multiple SNPs is performed, then cancer risk prediction accuracy is improved, but the cost and time required for testing increases
Solution Approach 1:
The patent applies preliminary action by pre-identifying and validating specific SNP markers associated with cancer risk before clinical implementation. The invention uses prior research to determine which SNPs (e.g., rs111620024, rs12500426, rs7679673) are most predictive of cancer molecular phenotypes, allowing clinical tests to focus only on these pre-selected markers rather than screening all possible genetic variations.
Solution Approach 2:
The invention applies partial action by performing genotyping on a selected subset of SNPs rather than conducting comprehensive genomic analysis. The patent identifies specific polymorphisms that provide sufficient predictive power for cancer risk assessment, achieving effective risk prediction without the need to analyze every possible genetic marker.
3Reliability
If targeted cancer therapies are administered based on genetic profiling, then treatment efficacy is improved, but the complexity of treatment selection and monitoring increases
Solution Approach 1:
The patent applies local quality by matching specific genetic profiles with specific treatment approaches. Instead of using a one-size-fits-all treatment model, the invention identifies particular SNP combinations (e.g., rs111620024, rs12500426, rs7679673) that predict response to particular cancer therapies, allowing treatment selection to be tailored to the patient's specific genetic characteristics.
Solution Approach 2:
The invention incorporates feedback by using genetic test results to guide treatment selection and monitoring. The SNP genotyping data provides feedback about the patient's cancer risk and molecular phenotype, which then informs the choice of targeted therapy and subsequent treatment adjustments based on treatment response and genetic markers.
Data Source
AI summary
Disclosed herein are methods and systems for characterization, diagnosis, and treatment of cancer. Aspects of the present disclosure are directed to methods for prediction and identification of various molecular features of cancer using analysis of germline genetic information (e.g., polymorphisms). Certain aspects pertain to identification of one or more genetic abnormalities (e.g., mutations, translocations, etc.) of a cancer in a subject following genotyping the subject as having one or more polymorphisms associated with the one or more genetic abnormalities. Also disclosed are methods for diagnosis and characterization of cancer, as well as methods for treatment of cancer having particular genetic abnormalities associated with one or more polymorphisms.