Quantitative Imaging of Tumor-Associated Macrophages and Their Response to Therapy Using 64Cu-Labeled Macrin

Hye-Yeong Kim1,2, Ran Li1, Thomas S C Ng1

  • 1Center for Systems Biology , Massachusetts General Hospital Research Institute , Boston , Massachusetts 02114 , United States.

ACS Nano
|December 4, 2018
PubMed

Insights

We developed Macrin, a novel nanoparticle for PET imaging, to quantify tumor-associated macrophages (TAMs). This imaging tool accurately tracks macrophage levels and distribution, aiding in nanomedicine treatment decisions.

Area of Science:

  • Biomedical Imaging
  • Nanotechnology
  • Cancer Biology

Background:

  • Tumor-associated macrophages (TAMs) significantly impact cancer progression and drug response.
  • Quantifying TAMs non-invasively remains a challenge for treatment optimization.
  • Current methods lack translational relevance for dynamic TAM assessment.

Purpose of the Study:

  • To develop a quantitative positron emission tomography (PET) imaging agent for macrophages in tumors.
  • To assess the utility of this agent in monitoring TAMs during therapy.
  • To correlate TAM imaging with nanotherapeutic delivery and efficacy.

Main Methods:

  • Development of a 64Cu-labeled polyglucose nanoparticle (Macrin) for PET imaging.
  • Combination of PET with in vivo confocal microscopy and ex vivo imaging.
  • Validation in orthotopic mouse models of lung and breast carcinoma.

Main Results:

  • Macrin demonstrated >90% selectivity for macrophage uptake.
  • Macrin uptake strongly correlated with macrophage content in healthy tissues and tumors.
  • Imaging revealed significant, therapy-induced increases in TAMs (180-650%).
  • TAM-rich tumors showed >700% higher accumulation of therapeutic nanoparticles.
  • Macrin imaging correlated with improved nanotherapy delivery and efficacy.

Conclusions:

  • Macrin provides a selective and translational method for quantifying TAMs via PET imaging.
  • Macrin imaging can monitor therapeutic responses and predict nanomedicine accumulation.
  • This approach may guide patient selection for nanomedicine clinical trials.

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