Patient-Specific Genomic Reference Graph for ctDNA Mutation Detection
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Solution Overview
Problem
Current liquid biopsies for cancer detection have limitations, as they rely on mapping sequence reads to a reference genome, leading to potential misinterpretation of mutations without considering the patient's healthy genotype, and fail to efficiently identify de novo mutations in circulating tumor DNA (ctDNA).
Innovation Solution
The use of a patient-specific genomic reference graph, such as a directed acyclic graph (DAG), that includes the patient's non-tumor genotype and known tumor-associated mutations, allows for efficient mapping of sequence reads from cell-free plasma DNA, identifying both known and de novo mutations in ctDNA, thereby providing a comprehensive report of tumor-related mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sequence reads are mapped to a reference genome, then the analysis process is simplified, but mutations may be misinterpreted without considering the patient's healthy genotype
Solution Approach 1:
The system performs preliminary sequencing of the patient's non-tumor (healthy) tissue to establish a personalized baseline genotype before analyzing tumor samples. This pre-established reference allows for accurate differentiation between germline variants and true somatic mutations, preventing misinterpretation while maintaining analytical simplicity.
Solution Approach 2:
The invention introduces an intermediary computational layer that compares sequence reads against both the reference genome and the patient-specific healthy genotype. This intermediary processing step reconciles the simplicity of reference-based mapping with the precision needed for accurate mutation identification by filtering out healthy genotype variants.
2Ease of operation
If traditional reference genome mapping is used, then the method is straightforward, but de novo mutations in ctDNA cannot be efficiently identified
Solution Approach 1:
The system performs preliminary sequencing of the patient's non-tumor tissue to capture their unique healthy genotype, including any personal polymorphisms. This pre-acquired information serves as a baseline that enables efficient identification of de novo mutations in ctDNA by highlighting variants absent from the healthy baseline.
Solution Approach 2:
The invention applies local quality customization by creating a patient-specific reference that incorporates their unique healthy genotype characteristics. Rather than using a universal reference genome, the system tailors the reference to each patient's specific genetic background, improving sensitivity for detecting their personal de novo mutations while maintaining straightforward mapping procedures.
3Productivity
If a standard reference genome is used for all patients, then the analysis is computationally efficient, but patient-specific genetic variations are not accounted for
Solution Approach 1:
The system performs a one-time preliminary sequencing of each patient's non-tumor tissue to establish their personalized genotype profile. This pre-computed patient-specific reference is then reused across multiple analyses, maintaining computational efficiency while accommodating individual genetic variations. The preliminary action eliminates the need for repeated customization while preserving patient-specific accuracy.
Solution Approach 2:
The invention creates a universal yet adaptable solution by developing a framework that works for all patients while being customizable to individual genotypes. The patient-specific reference graph serves multiple functions: it acts as a personalized baseline for mutation detection, a filter for common polymorphisms, and a template for subsequent ctDNA analyses, thereby achieving both efficiency and adaptability.
Data Source
AI summary
The invention provides oncogenomic methods for detecting tumors by identifying circulating tumor DNA. A patient-specific reference directed acyclic graph (DAG) represents known human genomic sequences and non-tumor DNA from the patient as well as known tumor-associated mutations. Sequence reads from cell-free plasma DNA from the patient are mapped to the patient-specific genomic reference graph. Any of the known tumor-associated mutations found in the reads and any de novo mutations found in the reads are reported as the patient’s tumor mutation burden.