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Development of a Multi-target Protein Biomarker Assay for Circulating Tumor Cells.

Diya Li1, Ceming Wang1, Yingjia Ni2

  • 1Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA.

Methods in Molecular Biology (Clifton, N.J.)
|January 30, 2022
PubMed
Summary

A novel liquid biopsy platform using carbon nanotubes (CNTs) offers sensitive detection of circulating tumor cells for early breast cancer metastasis and relapse detection. This noninvasive tool shows promise as an alternative to current imaging and biopsy methods.

Keywords:
Breast cancerCarbon nanotubeCirculating tumor cellElectrophoresisMetastasisSandwich assay

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Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Cancer Diagnostics

Background:

  • Early detection of metastatic breast cancer and its relapse is crucial for patient outcomes.
  • Current methods like MRI and tissue biopsies can be invasive, costly, and may have limitations in sensitivity.

Purpose of the Study:

  • To develop and validate a highly sensitive and selective liquid biopsy platform for early detection of metastatic breast cancer.
  • To quantify protein biomarker expressions from circulating tumor cells (CTCs) in blood.

Main Methods:

  • Utilized a carbon nanotube-switch (CNT-switch) liquid biopsy platform.
  • Employed off-chip micro-size membrane filtration for CTC isolation and enrichment (>99%).
  • Conducted on-chip detection of protein biomarkers using CNTs assembled via electrophoresis and dielectrophoresis (DEP) for signal generation.

Main Results:

  • Achieved high sensitivity and selectivity in detecting protein biomarkers from CTCs.
  • Demonstrated detection of biomarkers from as few as 5 spiked breast cancer cells in 7.5 ml of blood.
  • The CNT-switch mechanism amplifies signals through antibody-antigen-antibody complex formation.

Conclusions:

  • The CNT-switch liquid biopsy platform shows significant potential for early and noninvasive detection of metastatic breast cancer and its relapse.
  • This technology offers a promising, cost-effective alternative to traditional diagnostic methods.
  • Further validation is needed, but the platform demonstrates feasibility for clinical application.