Abstract 3448: A novel liquid biopsy lung cancer detection method using a coverage and fragment end motif machine learning analysis

Abstract

Abstract Lung cancer remains the leading cause of cancer death in the United States. One reason for this high mortality is that >40% of cases are diagnosed at a late stage, where the 5-year survival rate is low. Liquid biopsy molecular testing has transformed lung cancer care through the identification of driver mutations when tissue is inaccessible. It also has promise as a tool in early detection and diagnosis, thereby reducing mortality. The existing paradigm for lung cancer screening and diagnosis includes low dose CT (LDCT) scans and tissue biopsy. LDCT scans yield a visualization of lung abnormalities. However, many of these abnormalities are difficult to characterize and next steps are often unclear to physicians. One option is to continue to monitor the patient, which may risk a delay in cancer treatment. Up to 15% of all individuals who are put on a monitoring regime will ultimately be diagnosed with lung cancer. A second option is to biopsy the nodule, which risks unnecessary invasive procedures and expenses to patients. 42-62% of indeterminate nodules detected by LDCT are found to be benign after biopsy. Furthermore, 15% of transthoracic needle biopsies result in complications. Genece Health aims to decrease uncertainty in cancer diagnosis and improve patient outcomes through a minimally invasive liquid biopsy assay that detects the presence of lung cancer. The technology utilizes a proprietary algorithm that leverages machine learning models to detect lung cancer signals in low-pass whole genome sequencing (LP-WGS) of patient cfDNA isolated from plasma. To establish assay performance, 50 clinical lung cancer samples from a commercial biobank, and 100 presumed normal samples were collected in Streck cfDNA BCT Devices. Following cfDNA extraction from plasma, LP-WGS was performed. Sequencing data was then processed to generate fragment end-motif and size (FEMS) and fragment coverage data that was input into Genece Health’s proprietary machine learning classifier to generate a final cancer prediction score. In addition, a limit of detection was determined using three late-stage lung cancer samples, with tumor fractions estimated using whole exome sequencing and ichorCNA scores. Our results show >80% specificity, >90% sensitivity, and a limit of detection of <1% tumor fraction. Through this testing, we demonstrate the assay’s ability to detect lung cancer across various lung cancer stages, including early stage disease, and histologies. Genece has developed a highly sensitive and specific lung cancer detection liquid biopsy assay. We present this assay as a cost-effective approach to discriminate between benign and malignant nodules identified by LDCT. In addition, this assay has the potential to be applied across the cancer care continuum. For example, future work will involve characterizing the assay’s ability to be applied in early cancer screening. Citation Format: Bryan Leatham, Michael Salmans, Molly Smith, Mengchi Wang, Andrew Carson, Kristin Fathe, Byung In Lee. A novel liquid biopsy lung cancer detection method using a coverage and fragment end motif machine learning analysis [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 3448.