Cell-Free DNA Variant Classification Without White Blood Cell Sequencing

The patent extracts and removes non-tumor variants from the cfDNA sample by identifying and filtering out variants originating from clonal hematopoiesis or other non-cancerous sources, retaining only tumor-derived variants for analysis. This extraction approach improves specificity without requiring full WBC sequencing by focusing on distinguishing tumor-specific molecular signatures.

The patent introduces an intermediary computational layer that uses machine learning models and bioinformatic algorithms to differentiate tumor variants from non-tumor variants. This intermediary system processes the cfDNA data to identify patterns characteristic of tumor origin versus hematopoietic origin, resolving the contradiction by providing specificity enhancement through software rather than additional wet-lab sequencing steps.

The patent employs cost-effective computational methods and publicly available machine learning models to achieve variant differentiation. Instead of investing in expensive WBC sequencing infrastructure, the approach uses affordable bioinformatic pipelines that process cfDNA data to filter non-tumor variants, significantly reducing the quantity of financial resources required while maintaining high specificity.

The patent uses computational copies and simulations of WBC sequencing results by training machine learning models on reference datasets. Rather than performing actual WBC sequencing on each patient sample, the system creates virtual representations of expected non-tumor variant patterns from training data, enabling cost-effective filtering that replicates the benefits of WBC sequencing without the associated costs.

The patent replaces the mechanical/biological system of WBC sequencing with a computational/informatic system. Instead of physically separating and sequencing white blood cells, the invention uses machine learning algorithms and bioinformatic analysis to virtually differentiate tumor variants from non-tumor variants in plasma-only samples. This substitution maintains ease of operation while achieving measurement precision through advanced computational methods.

The patent changes the analytical parameters by applying machine learning models that evaluate multiple features of detected variants simultaneously (frequency, distribution patterns, molecular signatures) rather than relying on simple presence/absence thresholds. This parameter transformation enables accurate tumor versus non-tumor classification in plasma-only samples, maintaining both operational simplicity and measurement precision.