Cell-Free DNA Lesion Mapping Through Synthetic Fluid Hypotheses
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Solution Overview
Problem
Existing methods struggle to accurately associate cell-free DNA (cfDNA) with specific lesion sites in patients, particularly in cases with multiple lesion sites, and lack the ability to monitor DNA shedding patterns for responsive or non-responsive tissues, leading to challenges in treatment strategies and reliance on imperfect imaging-based RECIST criteria.
Innovation Solution
A computer-implemented method constructs synthetic fluid hypotheses (SFs) from cfDNA genetic markers, compares these hypotheses to genomic profiles, and selects a subset based on goodness of fit to determine cell, tissue, or lesion representations, enabling precise tracking of treatment responses and reducing the need for invasive biopsies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If imaging-based RECIST criteria are used to monitor lesion response, then treatment assessment can be performed, but the method lacks precision in detecting minuscule DNA shedding and cannot accurately associate cfDNA with specific lesion sites
Solution Approach 1:
The patent segments the mixed cfDNA signal by constructing synthetic fluid hypotheses that represent different lesion contributions. Each hypothesis models the cfDNA profile as a combination of lesion-specific DNA signatures, allowing the system to deconvolute and attribute DNA fragments to specific lesion sites rather than treating the cfDNA as a homogeneous mixture.
Solution Approach 2:
The patent introduces computational models and synthetic fluid hypotheses as intermediaries between the raw cfDNA data and lesion-specific interpretations. These hypotheses act as mediators that translate the mixed cfDNA signal into lesion-attributed information by comparing observed cfDNA profiles against predicted profiles from multiple lesion hypotheses.
2Productivity
If traditional biopsy methods are used to assess tumor genetics, then detailed lesion information can be obtained, but the methods are invasive and cannot provide real-time monitoring
Solution Approach 1:
The patent creates computational copies of lesion DNA profiles through synthetic fluid hypotheses. Instead of physically sampling tissue through invasive biopsy, the system generates in silico representations of what the cfDNA profile should look like if it originated from specific lesions, allowing non-invasive comparison and attribution of DNA fragments to their source lesions.
Solution Approach 2:
The patent performs preliminary computational analysis by constructing synthetic fluid hypotheses before interpreting the actual cfDNA results. The system pre-establishes expected DNA profiles for each lesion based on known genomic characteristics, enabling real-time attribution of cfDNA fragments to specific lesions as the sequencing data becomes available.
3Quantity of substance
If cfDNA analysis is performed without lesion-specific attribution, then the overall tumor burden can be assessed, but the ability to identify responsive and non-responsive lesions is lost
Solution Approach 1:
The patent applies local quality by assigning specific genomic characteristics to different lesion sites. Each lesion is modeled with its own DNA signature based on its unique mutational profile, allowing the system to distinguish between DNA fragments originating from different lesions even though they are mixed in the same cfDNA sample.
Solution Approach 2:
The patent introduces dynamics by allowing the contribution of each lesion to the overall cfDNA profile to change over time. The synthetic fluid hypotheses can be updated to reflect changing lesion burdens, enabling the system to detect which lesions are responding to treatment (decreasing DNA shedding) versus those that are progressing (increasing or stable DNA shedding).
Data Source
AI summary
A computer-implemented method includes to determine a cell, tissue or a lesion representation in cell-free DNA comprises inputting, to a processor, cell-free DNA (cfDNA) genomic profiles from one or more fluid biopsy samples from a patient and one or more genomic profiles from one or more cells, tissues or lesions from the patient; constructing, by the processor, a plurality of synthetic fluid hypotheses (SFs); comparing, by the processor, each of the plurality of SFs to the cfDNA genomic profiles to determine goodness of fit, of each of the plurality of SFs; selecting, by the processor, a subset of the plurality of SFs, wherein each SF of the subset of SFs has a minimum distance in goodness of fit compared to the cfDNA genomic profile; and outputting, by the processor, based on the subset of SFs, a cell, tissue or a lesion representation in the cfDNA of the patient.