Abstract 2416: Personalized minimal residual disease detection using tumor-derived structural variants in cell-free DNA
Authors: McDonald Bradon R. , Dennison Kirsten L. , Schussman Amanda L. , McGregor Stephanie M. , Pockaj Barbara A. , Murtaza Muhammed 
Published: March 2024

Abstract
We demonstrate development and technical validation of a novel approach for analysis of plasma circulating-tumor DNA (ctDNA) for residual disease detection and monitoring that offers improvements over existing technologies. ctDNA in blood can be leveraged to monitor treatment response, detect cancer recurrence, and inform treatment decisions for cancer patients. However, accurate detection and quantification of ctDNA is technically challenging due to low concentration of plasma DNA, especially in the adjuvant setting. Most current assays rely on interrogating multiple patient-specific point mutations to achieve exquisite limits of detection but struggle with polymerase errors, sequencing artifacts and limited conversion of input DNA. To overcome these challenges, we developed an approach called Structural Variant Enrichment and Normalization (SVEN) that targets patient-specific genomic rearrangements to achieve sensitive detection and quantification of plasma ctDNA. This method leverages intrinsic features of tumor-derived structural variants (SVs) to improve enrichment and confidence of detection at low tumor fraction through multiplexed PCR and amplicon-based next generation sequencing. To evaluate the assay, we performed whole genome sequencing of DNA from two breast cancer cell lines, as well as matched FFPE tumor and buffy coat samples from 21 early-stage breast cancer patients. We identified consensus SVs, and designed multiplexed SV-specific primers for each subject. We then conducted PCR amplification of cell-free DNA followed by sequencing, amplicon identification, and quantification. Analytical validation was conducted using 284 replicates of cell line dilutions from 1% to 0.003%, and 5 to 40 ng of input. We developed multiplexed assays targeting 25 and 32 SVs across two cell lines, and 3 - 43 SVs across breast cancer patients (median 23 per patient). 91% and 42% of PCR targets were successfully validated for cell lines and FFPE samples respectively. Cell line dilutions demonstrated a sensitivity of 89% at 0.003% tumor fraction with 10 ng input or more. Tumor fractions measured using SVEN agreed with known values (R2 = 0.989). Application of validated panels to patient plasma samples and tumor/germline dilutions is ongoing. SVEN is a novel approach for tumor-guided ctDNA detection and quantification with potential relevance for residual disease detection. On-going studies are investigating the performance of this approach for ctDNA detection in patients with early and locally advanced breast cancer treated with neoadjuvant therapy.
Citation Format: Bradon R. McDonald, Kirsten L. Dennison, Amanda L. Schussman, Stephanie M. McGregor, Barbara A. Pockaj, Muhammed Murtaza. Personalized minimal residual disease detection using tumor-derived structural variants in cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2416.