In vivo quantification of tumor receptor binding potential with dual-reporter molecular imaging

Kenneth M Tichauer1, Kimberley S Samkoe, Kristian J Sexton

  • 1Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA. Kenneth.Tichauer@dartmouth.edu

Abstract

Insights

A new dual-reporter method non-invasively quantifies tumor receptor binding potential. This advance offers a faster alternative to biopsies for guiding cancer treatment decisions.

Area of Science:

  • Oncology
  • Biomedical Imaging
  • Molecular Biology

Background:

  • Receptor availability is crucial for cancer management.
  • Current clinical methods rely on invasive tissue biopsies.
  • There is a need for non-invasive methods to assess receptor binding potential.

Purpose of the Study:

  • To present a novel dual-reporter methodology for quantifying tumor receptor binding potential in vivo.
  • To validate this methodology against established ex vivo and in vitro measures.
  • To establish a clinically relevant, non-invasive approach for cancer assessment.

Main Methods:

  • A fluorescence imaging-based adaptation of the dual-reporter methodology was developed.
  • The method was tested in four mouse tumor lines with varying epidermal growth factor receptor (EGFR) expression levels.
  • In vivo measurements were compared with ex vivo and in vitro binding potential data.

Main Results:

  • A strong correlation (r=0.99, p<0.01) was found between in vivo and ex vivo EGFR binding potential across all tumor lines.
  • High correlation (r=0.99, p<0.01) was also observed between in vivo and in vitro measures for tumors with lower EGFR expression.
  • The results demonstrate the accuracy and reliability of the in vivo methodology.

Conclusions:

  • The presented dual-reporter methodology provides a fast and robust measure of tumor receptor density.
  • This approach has significant implications for improving cancer intervention, evaluation, and monitoring.
  • The methodology is scalable for clinical use with imaging modalities like SPECT.

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