Principal Investigator

Tza-Huei Jeff
Wang
Awardee Organization

Johns Hopkins University
United States

Fiscal Year
2024
Activity Code
R33
Early Stage Investigator Grants (ESI)
Not Applicable
Project End Date

A low-cost, multiplexed digital high resolution melt platform for DNA methylation-based detection and identification of cancers in liquid biopsies

This year alone, over 600,000 people in the U.S. will die from cancer, with each patient losing an average of 15.6 years of life. However, upwards of 25% of these deaths could likely be avoided if these cancers were detected at earlier stages. One particularly attractive approach for cancer diagnostics is the use of circulating cell-free DNA (cfDNA) from so-called “liquid-biopsies” of patient-derived serum/plasma as these samples are often enriched in genetic material from tissues, including tumors, located throughout the body. Nonetheless, tumor specific alterations, such as mutations and aberrant DNA methylation, are typically only present at extraordinarily low copy numbers (< 10 copies/ml) and fractional concentrations (< 0.1%) within a large background of healthy-tissue DNA. This issue in particular has proven problematic for current technologies and has thus far precluded development of a cfDNA diagnostic method that is simple, low-cost and, most importantly, able to detect cancer at stages sufficiently early to improve patient outcomes. In the present project, we aim to develop REM-DREAMing: a low-cost, highly-multiplexed digital methylation analysis platform that provides highly-sensitive and parallelized assessment of cfDNA methylation patterns to enable detection of rare tumor DNA, even from early-stage cancers. At the core of the REM-DREAMing platform is a unique, locus-specific DNA methylation assay, called DREAMing (Discrimination of Rare EpiAlleles by Melt), that has been successfully developed by our lab to provide detection and absolute quantification of cancer-specific DNA methylation even at extremely low fractions (<< 0.1%). Recently, we successfully incorporated the DREAMing assay into a massively-parallel digital microfluidic array to enable detection of a single copy of aberrantly-methylated DNA in a background of 2 million unmethylated alleles. Here, we propose to dramatically enhance the microfluidic DREAMing approach by significantly expanding its digitization power and incorporating novel, methylation-agnostic probes with a unique ratiometric fluorescence multiplexing scheme to achieve simultaneous digital assessment of a panel of 50 “cancer-detecting” and “cancer-identifying” methylation biomarkers, enabling liquid-biopsy-based detection and identification of early-stage cancers at a cost of only a few dollars per sample. To achieve this goal, we plan to accomplish the following aims: (1) Develop dual, 27-plex DREAMing assay panels targeting a panel of 50 pan-cancer-detecting and cancer-identifying methylation biomarkers. (2) Design, fabricate and validate a dual 400k-well, 4-color fluorescence-decoding dHRM platform to perform parallelized REM-DREAMing for simultaneous detection and identification of 50 methylation biomarkers. and (3) Assess and benchmark the ability of the REM-DREAMing platform to detect and identify six different cancer types from liquid biopsies.

Publications

  • Traylor A, Lee PW, Hsieh K, Wang TH. Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration. Analytica chimica acta. 2024 Apr 8;1297:342371. Epub 2024 Feb 17. PMID: 38438240
  • Zhao Y, O'Keefe CM, Hsieh K, Cope L, Joyce SC, Pisanic TR, Herman JG, Wang TH. Multiplex Digital Methylation-Specific PCR for Noninvasive Screening of Lung Cancer. Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2023 Jun;10(16):e2206518. Epub 2023 Apr 11. PMID: 37039321
  • O'Keefe CM, Zhao Y, Cope LM, Ho CM, Fader AN, Stone R, Ferris JS, Beavis A, Levinson K, Wethington S, Wang TL, Pisanic TR, Shih IM, Wang TH. Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer. Clinical and translational medicine. 2024 Aug;14(8):e1778. PMID: 39083293