# Optimization and Validation of Single-Molecule Kinetic Fingerprinting Technology for Rapid, Ultra-Specific Detection of Cancer Mutations

> **NIH NIH R33** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $388,440

## Abstract

PROJECT SUMMARY
The ultimate vision of this proposal is to develop a technology platform for the direct, rapid, and robust
detection of cancer-associated DNA mutations for diagnostics and research. DNA mutations have been known
to be fundamental to cancer development for decades and specific fragments of mutated DNA have been
sought as non-invasive biomarkers for cancer in blood, stool and other samples. However, a major barrier to
progress has been the lack of highly sensitive, specific, and quantitative methods for detecting DNA mutation
when mutant DNA fragments are present as rare alleles in a high background of wild-type DNA. We propose
here an approach to DNA mutation detection and quantification that is conceptually simple, yet takes
advantage of sophisticated and elegant advances in single molecule imaging science. The approach is based
on using total internal reflection microscopy to detect the binding and release of a fluorescently-tagged probe
to immobilized target DNA molecules on the surface of a glass slide. Differences in nucleotide sequence even
at a single nucleotide, give rise to differences in the free energy of hybridization, and this affects the kinetics of
binding and release of a probe in a manner that can be detected by single-molecule microscopy. We have
already performed proof-of-concept and established feasibility of the approach. In this project, we aim to
perform optimizations that will increase sensitivity and increase throughput, and to perform validation with
clinical samples from lung cancer patients and controls.

## Key facts

- **NIH application ID:** 10000971
- **Project number:** 5R33CA229023-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** MUNEESH TEWARI
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $388,440
- **Award type:** 5
- **Project period:** 2018-09-13 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000971

## Citation

> US National Institutes of Health, RePORTER application 10000971, Optimization and Validation of Single-Molecule Kinetic Fingerprinting Technology for Rapid, Ultra-Specific Detection of Cancer Mutations (5R33CA229023-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10000971. Licensed CC0.

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