A comparative analysis of EGFR-targeting antibodies for gold nanoparticle CT imaging of lung cancer.

Abstract

Computed tomography (CT) is the standard imaging test used for the screening and assessment of suspected lung cancer, but distinguishing malignant from benign nodules by CT is an ongoing challenge. Consequently, a large number of avoidable invasive procedures are performed on patients with benign nodules in order to exclude malignancy. Improving cancer discrimination by non-invasive imaging could reduce the need for invasive diagnostics. In this work we focus on developing a gold nanoparticle contrast agent that targets the epidermal growth factor receptor (EGFR), which is expressed on the cell surface of most lung adenocarcinomas. Three different contrast agents were compared for their tumor targeting effectiveness: non-targeted nanoparticles, nanoparticles conjugated with full-sized anti-EGFR antibodies (cetuximab), and nanoparticles conjugated with a single-domain llama-derived anti-EGFR antibody, which is smaller than the cetuximab, but has a lower binding affinity. Nanoparticle targeting effectiveness was evaluated in vitro by EGFR-binding assays and in cell culture with A431 cells, which highly express EGFR. In vivo CT imaging performance was evaluated in both C57BL/6 mice and in nude mice with A431 subcutaneous tumors. The cetuximab nanoparticles had a significantly shorter blood residence time than either the non-targeted or the single-domain antibody nanoparticles. All of the nanoparticle contrast agents demonstrated tumor accumulation; however, the cetuximab-targeted group had significantly higher tumor gold accumulation than the other two groups, which were statistically indistinguishable from one another. In this study we found that the relative binding affinity of the targeting ligands had more of an effect on tumor accumulation than the circulation half life of the nanoparticles. This study provides useful insight into targeted nanoparticle design and demonstrates that nanoparticle contrast agents can be used to detect tumor receptor overexpression. Combining receptor status data with traditional imaging characteristics has the potential for better differentiation of malignant lung tumors from benign lesions.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1371/journal.pone.0206950

Publication Info

Ashton, Jeffrey R, Elizabeth B Gottlin, Edward F Patz, Jennifer L West and Cristian T Badea (2018). A comparative analysis of EGFR-targeting antibodies for gold nanoparticle CT imaging of lung cancer. PloS one, 13(11). p. e0206950. 10.1371/journal.pone.0206950 Retrieved from https://hdl.handle.net/10161/18055.

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Scholars@Duke

Ashton

Jeffrey Ashton

Clinical Associate in the Department of Radiology
Patz

Edward F. Patz

James and Alice Chen Distinguished Professor of Radiology

There are numerous ongoing clinical studies primarily focused on the early detection of cancer.

The basic science investigations in our laboratory concentration on three fundamental translational areas,

1) Development of molecular imaging probes - We have used several different approaches to develop novel imaging probes that characterize and phenotype tumors.

2) Discovery of novel lung cancer biomarkers - We explored the use of proteomics, autoantibodies, and genomics to discover blood and tissue biomarkers for early cancer detection and phenotyping of cancer.

3) Host response to cancer - We study the native immune response to tumors as this may provide cues to relevant diagnostic and therapeutic targets. Most recently we have focused on intratumoral lymphocytes and their specific tumor antigens.

 

Badea

Cristian Tudorel Badea

Professor in Radiology

  • Our lab's research focus lies primarily in developing novel quantitative imaging systems, reconstruction algorithms and analysis methods.  My major expertise is in preclinical CT.
  • Currently, we are particularly interested in developing novel strategies for spectral CT imaging using nanoparticle-based contrast agents for theranostics (i.e. therapy and diagnostics).
  • We are also engaged in developing new approaches for multidimensional CT image reconstruction suitable to address difficult undersampling cases in cardiac and spectral CT (dual energy and photon counting) using compressed sensing and/or deep learning.



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