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Data from Characterization of Peptides Targeting Metastatic Tumor Cells as Probes for Cancer Detection and Vehicles for Therapy Delivery

Authors :
Mikhail G. Kolonin
Ali Azhdarinia
Sukhen C. Ghosh
Solmaz AghaAmiri
Alexes C. Daquinag
Shraddha Subramanian
Publication Year :
2023
Publisher :
American Association for Cancer Research (AACR), 2023.

Abstract

Metastasis is the leading cause of cancer-related deaths, and metastatic cancers remain largely incurable due to chemoresistance. Biomarkers of metastatic cells are lacking, and probes that could be used to detect and target metastases would be highly valuable. Here we hypothesize that metastatic cancer cells express cell-surface receptors that can be harnessed for identification of molecules homing to metastases. Screening a combinatorial library in a mouse mammary tumor model of spontaneous metastasis identified cyclic peptides with tropism for cancer cells disseminated to the lungs. Two lead peptides, CLRHSSKIC and CRAGVGRGC, bound murine and human cells derived from breast carcinoma and melanoma in culture and were selective for metastatic cells in vivo. In mice, peptide CRAGVGRGC radiolabeled with 67Ga for biodistribution analysis demonstrated selective probe homing to lung metastases. Moreover, systemic administration of 68Ga-labeled CRAGVGRGC enabled noninvasive imaging of lung metastases in mice by PET. A CRAGVGRGC-derived peptide induced apoptosis upon cell internalization in vitro and suppressed metastatic burden in vivo. Colocalization of CLRHSSKIC and CRAGVGRGC with N-cadherin+/E-cadherin− cells indicated that both peptides are selective for cancer cells that have undergone the epithelial-to-mesenchymal transition. We conclude that CRAGVGRGC is useful as a probe to facilitate the development of imaging modalities and therapies targeting metastases.Significance:This study identifies new molecules that bind metastatic cells and demonstrates their application as noninvasive imaging probes and vehicles for cytotoxic therapy delivery in preclinical cancer models.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....a740360615d15addbb9e6309e6aa3955
Full Text :
https://doi.org/10.1158/0008-5472.c.6513490