Programmable Multiplexed Detection of Cell-free DNA Mutation for Liquid Biopsy

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection being critical for improving patient outcomes. Current diagnostic methods, including imaging and tissue biopsy, are often invasive, costly, and require specialized expertise. Liquid biopsy offers a promising alternative for noninvasive lung cancer screening by detecting circulating tumor biomarkers in blood. The detection of cell-free DNA (cfDNA) mutations enables early identification of lung cancer and assessment of treatment response. Especially, as highly effective targeted therapies become more accessible, there is a clear need for affordable and accurate cancer mutation diagnostics. Digital droplet polymerase chain reaction (ddPCR) and next-generation sequencing have been widely used to detect and quantify cfDNA mutations due to their high sensitivity, accuracy, and robustness. However, they remain limited by the complexity, high cost, and time-intensive workflow. Here, we propose to develop and validate a simple, rapid, and affordable Programmable Enzyme-Assisted Mutation Sequencing (PEAM-Seq) diagnostic platform for the quantitative detection of multiple cfDNA mutations in blood samples, enabling noninvasive, accurate, and early detection of lung cancer. In our preliminary study, we developed and validated a PCR-based PEAM-Seq Version 1 (PEAM-Seq V1.0) assay to quantitatively detect cfDNA mutations with single-nucleotide resolution. To simplify the assay, we will further develop and optimize a simple, rapid, and sensitive PEAM-Seq V2.0 assay by combining isothermal amplification, enzymatic cleavage, and CRISPR quantitative detection. To enable point-of-care diagnostics, we will incorporate the PEAM-Seq V2.0 assay into microfluidics to develop a simple and integrated PEAM-Seq chip. To eliminate the need for costly and bulky equipment, we will build a portable, smartphone-based platform to pair with the disposable PEAM-Seq chip. We will assess the clinical utility of our PEAM-Seq diagnostic platform by analyzing blood samples from lung cancer patients to validate its effectiveness in cancer early detection. While lung cancer detection serves as the initial proof of principle, this project will ultimately provide a transformative platform technology for rapid, cost-effective, and accessible diagnostics of a wide range of cancers. Project Number: 1R33CA309727-01 | Fiscal Year: 2026 | NIH Institute/Center: National Cancer Institute (NCI) | Principal Investigator: Changchun Liu | Institution: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT, FARMINGTON, CT | Award Amount: $414,585 | Activity Code: R33 | Study Section: Special Emphasis Panel[ZRG1 BTC-N (55)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11312147