Abstract 4796: Improving cancer screening performance for women with dense breast tissue
Authors: Molly Smith, Mengchi Wang, Andrew Carson, Byung In Lee, Michael Salmans, Bryan Leatham
Journal: Cancer Research (2024)

Abstract Breast cancer is the most common cancer in women in the United States. To reduce mortality, screening by mammography in females is recommended by the United States Preventive Services Task Force, successfully reducing mortality by 15 - 32% in women aged 40 - 69 years. This is partially due to the high compliance rate (60 - 80%) among the candidate population. Mammography increases early-stage disease detection, which is correlated with better outcomes. While mammography has been largely successful, this screening method has limitations. Overall, mammography has false-negative and false-positive rates estimated at 12 - 20% and 7 - 12%, respectively. Consequently, annual mammograms result in 50 - 60% of women having a false-positive result within a 10-year span. Additionally, high breast density, found in approximately 50% of females over the age of 40, increases susceptibility to false positive and negative results. For this reason, in 2023 the FDA required that dense breast tissue status, an independent risk factor for cancer, be disclosed to mammogram recipients. The subsequent diagnostic pathway for women with dense breast tissue is not well defined for the care provider. Alternative imaging options have performance issues or are less accessible. Here we describe a liquid biopsy test to complement mammography in women with dense breast tissue to help resolve ambiguity in results. Blood from early- and late-stage breast cancer patients was prospectively collected in Streck cfDNA BCT Devices (n=100) or retrospectively acquired (n=100); and blood from presumed normal samples was collected (n=200). cfDNA was extracted and converted into libraries for low-pass whole genome sequencing (LP-WGS). Sequenced reads were analyzed to generate fragment end-motif and size (FEMS) and fragment coverage dataframes for classification using Genece Health’s proprietary analysis pipeline and machine-learning (ML) algorithm. 150 breast cancer samples distributed across stages and 150 presumed normal samples were selected to create a training cohort. Performance was tested using a 5-fold cross validation strategy. The best performance was observed using a convolution neural network (CNN) ML model with &amp;gt;80% sensitivity when specificity was set at 85%. The remaining 50 breast cancer samples and 50 presumed normal samples were used as an external test set. The performance in this cohort was comparable to the training cohort, with both specificity and sensitivity &amp;gt;80%. Genece presents an early proof of concept breast cancer screening liquid biopsy assay that uses LP-WGS fragmentomics and ML to detect cancer from a single blood sample. Continuing development with additional cohorts and optimization will yield even greater performance. The Genece liquid biopsy test has the potential to be paired with mammography, especially in women with dense breast tissue, to improve screening sensitivity, specificity, and overall outcomes. Citation Format: Molly Smith, Mengchi Wang, Andrew Carson, Byung In Lee, Michael Salmans, Bryan Leatham. Improving cancer screening performance for women with dense breast tissue [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 4796.