MR-Detect: Genome-wide Mutation Integration for Ultra-Sensitive Detection of ctDNA

Background & Unmet Need

• Circulating tumor DNA (ctDNA) in plasma cell-free DNA (cfDNA) enables non-invasive cancer detection through routine blood draws
• ctDNA levels correlate with tumor burden and reflect responses to treatment, serving as a valuable biomarker for early detection and disease monitoring
• While late-stage cancers show tumor fractions (TFs) up to 20% in cfDNA, early-stage disease exhibits dramatically lower TFs
• A conventional blood draw (5mL) yields only 2,000-5,000 genomic equivalents of cfDNA, limiting sequencing depth
• The combination of low TF and limited genomic equivalents creates a fundamental barrier to early cancer detection through conventional mutation sequencing
• Unmet Need: Development of ultra-sensitive methods capable of detecting plasma tumor fraction <0.1% to enable early cancer detection

Technology Overview

• The Technology: MRDetect, a tumor-informed method for ultra-sensitive ctDNA detection using genome-wide mutational integration and an advanced error suppression framework
• MRDetect is available via non-exclusive license
• MRDetect uses average depth whole genome sequencing (WGS) to pool information across multiple sites in the genome, increasing the effective ceiling of sequencing depth
• Read-level error suppression frameworks address noise associated with lower quality sequencing metrics and read depth in single nucleotide variants (SNVs) and copy number variants (CNVs)
• PoC Data: MRDetect enables ctDNA detection in fractions as low as 10-5 with a modest sequencing depth (35x), with assay-level specificity of 95%
• PoC data has been generated in clinical plasma samples from patients with lung adenocarcinoma, colorectal cancer, and metastatic melanoma

Technology Applications

• Early detection of cancer in high-risk populations
• Detection of minimal residual disease following cancer treatment
• Patient stratification for adjuvants to reduce unnecessary treatments
• Treatment selection based on patient-specific molecular features and response monitoring

Technology Advantages

• Simple WGS workflow does not require custom panel creation or molecular barcodes and can work with limited input material
• Enables dynamic treatment optimization through ongoing monitoring of patient response and outcomes

Figure: Illustration of genome-wide SNV integration for inference of plasma TF, and analytical validation scheme.