Representative DNA Sequencing for Tumor Heterogeneity
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Solution Overview
Problem
Current diagnostic oncology methods are limited by under-sampling of tumors, leading to biased results due to the small size of biopsy samples, which fail to represent the entire tumor mass, resulting in incomplete detection of genetic variants and inaccurate assessment of clonal and subclonal mutations.
Innovation Solution
The Representative Sequencing (Rep-Seq) method involves homogenizing all residual tumor material to create a well-mixed solution for next-generation sequencing, allowing for unbiased sampling of the entire tumor mass, thereby detecting a broader range of genetic variants and improving the accuracy of clonal and subclonal mutation identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If small biopsy samples are used for sequencing, then sequencing cost and complexity are reduced, but measurement precision and reliability of genetic variant detection deteriorate due to under-sampling of tumor heterogeneity
Solution Approach 1:
The tumor sample is segmented into multiple spatial regions that are sequenced separately. This allows comprehensive coverage of tumor heterogeneity while maintaining manageable sequencing complexity for each individual region, resolving the contradiction between sample size and detection accuracy
Solution Approach 2:
The approach transitions from analyzing a single small biopsy sample to analyzing multiple spatial regions across the tumor. This dimensional expansion from one sample to multiple regions enables comprehensive genetic variant detection while distributing the sequencing workload across manageable units
2Measurement precision
If multiple biopsy samples from different tumor regions are sequenced, then detection of genetic variants improves, but device complexity and analysis burden increase
Solution Approach 1:
The patent extracts and sequences only the most informative genetic variants from each tumor region rather than analyzing entire genomes. This selective approach maintains high detection accuracy while significantly reducing sequencing and analysis complexity
Solution Approach 2:
The method performs sequencing on multiple tumor regions beyond what a single biopsy would provide, but focuses analysis on key genetic variants rather than exhaustive genome-wide analysis. This partial action approach achieves comprehensive variant detection while controlling analytical burden
3Ease of operation
If a single biopsy sample is used, then sampling procedure is simple, but reliability of clonal and subclonal mutation assessment deteriorates due to inability to represent entire tumor mass
Solution Approach 1:
The tumor is segmented into multiple sampling regions, each providing information about different clonal populations. This segmentation maintains operational simplicity by using standard biopsy procedures while dramatically improving reliability through comprehensive spatial coverage
Solution Approach 2:
Data from multiple biopsy samples across different tumor regions are merged and integrated to create a comprehensive view of clonal and subclonal mutations. This merging approach maintains the simplicity of individual sampling procedures while achieving high reliability through combined analysis
Data Source
AI summary
Disclosed herein is a method of deriving a plurality of genetic variants from a homogenized input sample. Also disclosed herein are methods of identifying a plurality of genetic variants in a sample comprising: homogenizing one or more input samples to provide a homogenized sample; preparing genomic material isolated from the homogenized input sample for sequencing; and identifying the plurality of genetic variants within sequencing data derived after sequencing the prepared genomic material.