Abstract 6510: REM-DREAMing: Low-cost digital microfluidic analysis of DNA methylation heterogeneity for enhanced, liquid biopsy-based detection of early-stage lung cancer (2023)

Abstract We report the results of a study exploring the ability to exploit molecular heterogeneity in DNA methylation for improving the performance of liquid biopsy-based screening for early-stage (I &amp; II) non-small cell lung cancer (NSCLC). Annual low-dose CT (LDCT) screening is currently recommended for adults aged 50 or older who are at high-risk of developing lung cancer. While this approach has resulted in improvements in survival, the false positive rate of lung nodules detection by LDCT remains over 95%, leading to unnecessary invasive follow-up procedures and further points to the need for new, complementary methods to improve diagnostics performance and reduce patient risk. DNA methylation biomarkers have demonstrated considerable potential as tumor-specific biomarkers for blood-based detection of early-stage NSCLC. Nonetheless, cell-free DNA (cfDNA) assessment techniques, such as methylation-specific PCR (MSP) or bisulfite sequencing, have limited sensitivity to assess epigenetic heterogeneity of rare epiallelic variants in a cost-effective manner. Here we reported a new platform, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which provides a simple, low-cost solution for multiplexed assessment of loci-specific DNA methylation heterogeneity at single molecule sensitivity. The microfluidic nanoarray contains four independent but identical 10,040 nanowell modules. Methylation biomarkers are differentiated by a ratiometric fluorescence scheme and the assessment of individual epiallele species of each locus are achieved through digitization in the nanoarray and precise high-resolution melt (HRM) analysis. In this study, we explore the potential utility of REM-DREAMing as a complementary assay for improving LDCT screening of NSCLC by testing a cohort of 48 clinical samples (28 cancer and 20 control samples) of low-volume liquid biopsy specimens from patients with CT-scan indeterminant pulmonary nodules. A machine learning algorithm incorporating logistic regression models with leave-one-out cross validation was developed to identify a proper methylation density threshold of each biomarker in the panel. Evaluation of the receiver operation characteristic (ROC) curve yielded an area under the curve (AUC) of 0.97 (95% CI, 0.94-1) with 93% sensitivity at 95% specificity for the REM-DREAMing assay, compared with 93% sensitivity at 62% specificity achieved using a traditional, MSP-based approach. These results suggest that the assessment of intermolecular epigenetic heterogeneity can provide superior clinical performance for cfDNA methylation and noninvasive detection of early-stage NSCLC, in particular. Our results warrant further investigation in a larger sample cohort to validate its utility for improving routine screening of NSCLC in high-risk populations. Citation Format: Yang Zhao, Christine M. O’Keefe, James G. Herman, Thomas R. Pisanic, Tza-Huei Wang. REM-DREAMing: Low-cost digital microfluidic analysis of DNA methylation heterogeneity for enhanced, liquid biopsy-based detection of early-stage lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6510.