Molecular Imaging of Galectin-1 Expression as a Biomarker of Papillary Thyroid Cancer by Using Peptide-Functionalized Imaging Probes.
Deborah FanfoneDimitri StanickiDenis NonclercqMarc PortLuce Vander ElstLaurent SophieRobert N MullerSven SaussezCarmen BurteaPublished in: Biology (2020)
Thyroid cancers are the most frequent endocrine cancers and their incidence is increasing worldwide. Thyroid nodules occur in over 19-68% of the population, but only 7-15% of them are diagnosed as malignant. Diagnosis relies on a fine needle aspiration biopsy, which is often inconclusive and about 90% of thyroidectomies are performed for benign lesions. Galectin-1 has been proposed as a confident biomarker for the discrimination of malignant from benign nodules. We previously identified by phage display two peptides (P1 and P7) targeting galectin-1, with the goal of developing imaging probes for non-invasive diagnosis of thyroid cancer. The peptides were coupled to ultra-small superparamagnetic particles of iron oxide (USPIO) or to a near-infrared dye (CF770) for non-invasive detection of galectin-1 expression in a mouse model of papillary thyroid cancer (PTC, as the most frequent one) by magnetic resonance imaging and fluorescence lifetime imaging. The imaging probes functionalized with the two peptides presented comparable image enhancement characteristics. However, those coupled to P7 were more favorable, and showed decreased retention by the liver and spleen (known for their galectin-1 expression) and high sensitivity (75%) and specificity (100%) of PTC detection, which confirm the aptitude of this peptide to discriminate human malignant from benign nodules (80% sensitivity, 100% specificity) previously observed by immunohistochemistry.
Keyphrases
- high resolution
- papillary thyroid
- poor prognosis
- magnetic resonance imaging
- fine needle aspiration
- small molecule
- fluorescence imaging
- mouse model
- iron oxide
- single molecule
- ultrasound guided
- living cells
- lymph node metastasis
- endothelial cells
- computed tomography
- pseudomonas aeruginosa
- cystic fibrosis
- deep learning
- quantum dots
- risk factors
- long non coding rna
- magnetic resonance
- drug delivery
- cancer therapy
- squamous cell
- sensitive detection