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1.
Methods Mol Biol ; 2777: 99-122, 2024.
Article in English | MEDLINE | ID: mdl-38478339

ABSTRACT

Cancer stem-like cells (CSC) are a major contributing factor to chemoresistance, tumor recurrence, and poor survival outcomes in patients across cancer types. Signaling from non-tumor cells in the tumor microenvironment (TME) enriches for and supports CSC. This complex cell-cell signaling in the heterogeneous TME presents a challenge for patient survival; however, it also presents an opportunity to develop new targeted therapies that can inhibit survival of CSC. In this chapter, we report a multicellular tumoroid model which can be used to investigate the interactions between cancer cells and non-tumor cells in the TME to better understand the contribution of various cell types to cancer cell phenotypes, as well as the underlying mechanisms involved. The following methods allow for each cell type to be distinguished using FACS and studied individually. Gene expression can be analyzed for cancer cells, as well as the other non-tumor cells using qPCR following sorting. The response to chemotherapeutic agents and expression of stem markers can be determined for cancer cells using flow cytometry, excluding the other cell types to get an accurate view of the cancer cells. Furthermore, the viability of non-tumor cells can be analyzed as well to determine if there are cytotoxic effects of the drugs on non-tumor cells. Thus, the multicellular tumoroid model will reveal the interactions between the CSC and non-tumor cells in the heterogenous TME, resulting in discoveries in the fields of cancer biology, novel targeted therapies, and personalized drug screening for precision medicine.


Subject(s)
Antineoplastic Agents , Neoplasms , Humans , Tumor Microenvironment , Neoplasms/genetics , Neoplasms/pathology , Signal Transduction , Antineoplastic Agents/pharmacology , Cell Communication , Neoplastic Stem Cells/pathology
2.
Methods Mol Biol ; 2777: 191-204, 2024.
Article in English | MEDLINE | ID: mdl-38478345

ABSTRACT

Nanoparticle drug delivery has been promoted as an effective mode of delivering antineoplastic therapeutics. However, most nanoparticle designs fail to consider the multifaceted tumor microenvironment (TME) that produce pro-tumoral niches, which are often resistant to chemo- and targeted therapies. In order to target the chemoresistant cancer stem-like cells (CSCs) and their supportive TME, in this chapter we describe a nanoparticle-based targeted co-delivery that addresses the paracrine interactions between CSC and non-cancerous mesenchymal stem cells (MSCs) in the TME. Carcinoma-activated MSCs have been shown to increase the chemoresistance and metastasis of CSC. Yet their contributions to protect the CSC TME have not yet been systematically investigated in the design of nanoparticles for drug delivery. Therefore, we describe the fabrication of degradable poly(lactic-co-glycolic acid) (PLGA) nanoparticles (120-200 nm), generated with an electrospraying process that encapsulates both a conventional chemotherapeutic, paclitaxel, and a targeted tyrosine kinase inhibitor, sunitinib, to limit MSC interactions with CSC. In the 3D hetero-spheroid model that comprises both CSCs and MSCs, the delivery of sunitinib as a free drug disrupted the MSC-protected CSC stemness and migration. Therefore, this chapter describes the co-delivery of paclitaxel and sunitinib via PLGA nanoparticles as a potential targeted therapy strategy for targeting CSCs. Overall, nanoparticles can provide an effective delivery platform for targeting CSCs and their TME together. Forthcoming studies can corroborate similar combined therapies with nanoparticles to improve the killing of CSC and chemoresistant cancer cells, thereby improving treatment efficiency.


Subject(s)
Antineoplastic Agents , Nanoparticles , Neoplasms , Polylactic Acid-Polyglycolic Acid Copolymer , Polyglycolic Acid , Glycols , Sunitinib/pharmacology , Lactic Acid , Antineoplastic Agents/pharmacology , Paclitaxel/pharmacology , Cell Line, Tumor , Drug Carriers , Neoplasms/drug therapy
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