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Detecting diverse nucleic acid biomarkers of cancer with solid-state nanopores


Year of Award:
2020
Status:
Active
Award Type:
R33
Project Number:
CA246448
RFA Number:
RFA-CA-19-020
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
HALL, ADAM ROGER
Institution:
WAKE FOREST UNIVERSITY HEALTH SCIENCES

Project Summary Nucleic acid biomarkers have tremendous potential value to patient health, but by their nature are often difficult to probe. Solid-state (SS-) nanopores are uniquely positioned to contribute to a solution for this challenge. The platform enables individual molecules to be probed as they translocate electrically through a synthetic, nanometer-scale aperture. The intrinsic sensitivity, compact nature, and electronic output make the system an attractive candidate for use as a translational diagnostic device. The central goal of this R33 project is to use SS-nanopore technology to assess nucleic acid biomarkers with applications to cancer through a highly selective assay developed in our lab. With our novel approach, target nucleic acid fragments can be detected and quantified only when bound to a protein chaperone, yielding a binary output. The measurement is rapid, sensitive, and yields an unambiguous electrical signal for analysis. We propose to apply this general measurement scheme in ways that enable the assessment of two broadly significant families of nucleic acid biomarkers. In Aim 1, we will assess epigenetic modifications and single base lesions. Modified bases regulate a variety of cellular functions but are challenging to probe with conventional technology. We will first use our approach to study global abundance of radiation-induced base modifications in model human cancer cell lines and then develop a strategy for determining the gene-specific location of modifications. In Aim 2, we will probe nucleic acid sequence motifs, focusing on microRNA. The link between microRNAs and cell function/malfunction is well established, but such short motifs can be challenging to study. We will first optimize our measurement for probing specific sequences, and then apply it to the assessment of lung cancer-relevant microRNA in de-identified patient blood samples.