Somatic Variant Clonality Estimation From Targeted NGS Panels
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Solution Overview
Problem
Conventional techniques for determining clonal fractions in tumor cells, such as whole exome sequencing (WES) and whole genome sequencing (WGS), are time and resource-intensive, while smaller data sets from targeted, panel-based next-generation sequencing (NGS) often fail to provide sufficient information for understanding tumor evolution and informing therapeutic strategies.
Innovation Solution
Methods and systems for determining clonal fractions using targeted, panel-based NGS by selecting a proxy sample tumor fraction based on the highest determined tumor fraction value from somatic alterations, leveraging sequence read data to accurately predict clonality, even when relying on panel-based NGS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole exome sequencing (WES) or whole genome sequencing (WGS) is used to determine clonal fractions, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent extracts and focuses only on the specific genomic regions relevant to cancer driver events using targeted panel-based NGS, rather than sequencing the entire exome or genome. This extraction approach maintains sufficient measurement precision for clonal fraction determination while dramatically reducing time and resource consumption by limiting sequencing to predetermined gene panels.
Solution Approach 2:
The patent employs a simplified, cost-effective computational model that uses basic statistical relationships between variant allele frequencies and tumor fractions, replacing complex, resource-intensive WES/WGS analysis pipelines. This approach provides adequate precision for clinical decision-making at a fraction of the time and computational resource cost.
2Productivity
If targeted, panel-based NGS is used to profile samples, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies local quality by concentrating sequencing depth and coverage on specific predetermined genomic regions (cancer driver gene panels) rather than distributing resources across the entire genome. This focused approach ensures sufficient measurement precision for detecting and characterizing driver events while maintaining high productivity through targeted enrichment.
Solution Approach 2:
The patent changes the parameter of genomic coverage from comprehensive (WES/WGS) to targeted (panel-based), and compensates by adjusting the analysis methodology to use statistical models that can accurately infer clonal fractions from the reduced but focused dataset. This parameter change maintains sufficient precision for clinical applications while dramatically improving productivity.
3Ease of operation
If conventional analysis techniques are applied to panel-based NGS data, then ease of operation is maintained, but loss of information occurs
Solution Approach 1:
The patent performs preliminary action by pre-defining panels of cancer driver genes and predetermined genomic regions before sequencing. This allows the use of simplified analysis techniques that focus specifically on these predetermined targets, maintaining ease of operation in clinical settings while preserving critical information about tumor evolution and resistance mechanisms that would be present in comprehensive sequencing.
Solution Approach 2:
The patent introduces an intermediary computational model that bridges panel-based NGS data and clonal fraction determination. This intermediary analysis layer translates the limited panel data into meaningful biological insights about tumor evolution and resistance, preventing information loss while maintaining the operational simplicity of targeted sequencing in clinical workflows.
Data Source
AI summary
Methods and systems for determining a clonal fraction are described. The methods may comprise, for example, receiving sequence read data associated with the sample from a subject; determining at least one somatic alteration based on the sequence read data; determining a tumor fraction for each somatic alteration of the at least one somatic alteration based on the sequence read data, such that a plurality of tumor fractions is obtained; determining a sample tumor fraction, the sample tumor fraction corresponding to a highest tumor fraction from the tumor fraction for each somatic alteration of the at least one somatic alterations; and determining a clonal fraction of a somatic alteration of the at least one somatic alteration based on the sample tumor fraction and the corresponding tumor fraction of the somatic alteration.