Abstract PO-042: Nanoparticles imaging for cancer metastasis diagnosis

Abstract

Abstract The main cause of cancer-related mortality is metastasis; thus, its prediction can critically affect the survival rate. Metastases are currently predicted by lymph-node status, tumor size, histopathology and genetic testing, however, all these are not infallible and getting the results may require weeks. The identification new potential prognostic factors will be an important source of risk information for the practicing oncologist, potentially leading to enhanced patient care through the proactive optimization of treatment strategies. We propose to use fluorescent nanoparticles for metastasis detection. Nowadays nanoparticles are widely used for targeted drug delivery, while particles with specific coatings are encapsulated by cancer cells via endocytosis. We have used low-cost carboxylate-modified fluorescent 200 nm particles to achieve the adhesion and encapsulation efficiency of breast cancer cells with high (MDA-MB-231) and low (MCF7) metastatic potential. Using high-content fluorescence imaging microscope (ImageXpress Micro XL), we have discovered that during short time (up to 1h) highly metastatic cells are able to adhere and encapsulate sufficiently more (p<0.05) nanoparticles than lowly metastatic cells. We have created automatic image analysis algorithms to find quantitative colocalization (Pearson’s and Overlap coefficients) of fluorescent nanoparticles with imaged cells. Migration and invasion of cancer cells is a critical step in metastases formation. The cytoskeleton machinery mechanisms (i.e. actin network), utilized by metastatic cells for invasion process have been found similar to the involvement of actin cytoskeleton in endocytosis process. From the other side, for migration process cells use adhesive structures (developed also by nano-particles adhesion) to probe their surroundings and adapt their mechanical properties. We have previously characterized the invasive and migrative abilities of breast cancer cells, as well as patient-derived tumor cells, by indentation and migration assays. We have achieved quantitative and significantly different results for various cancer cells; however, the data-acquisition of such kind assays requires 4-72hs. The obtained results of encapsulation and adhesion efficiency may lead to developing novel clinical tool for metastasis prediction. The proposed here method is very simple, it does not require neither expensive materials and equipment nor cell manipulations (e.g. serum starvation, staining, expansion), it is potentially suitable for a variety of cells. Rapid (up to 1h), quantitative, patient-specific determination of the metastatic likelihood from biopsy/surgery sample, will directly affect the choice of treatment protocols for cancer patients and eventually increase their life-expectancy. Citation Format: Yulia Merkher, Elizaveta Kontareva, Anna Melekhova, Sergey Leonov. Nanoparticles imaging for cancer metastasis diagnosis [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-042.