Author(s):
Lourenço, Diana ; Lopes, Raquel ; Pestana, Carolina ; Queirós, Ana C. ; João, Cristina ; Carneiro, Emilie Arnault
Date: 2022
Persistent ID: http://hdl.handle.net/10362/145856
Origin: Repositório Institucional da UNL
Subject(s): 3D models; bone marrow microenvironment; ex vivo models; hematologic cancer; multiple myeloma; personalized therapy; primary cell culture; Catalysis; Molecular Biology; Spectroscopy; Computer Science Applications; Physical and Theoretical Chemistry; Organic Chemistry; Inorganic Chemistry; SDG 3 - Good Health and Well-being
Description
Funding Information: This work was supported by Fundação para a Ciência e a Tecnologia (FCT), research grant number PTDC/MED-ONC/1215/2021/PT. Funding Information: The authors thank FCT and the Champalimaud Foundation for funding. Publisher Copyright: © 2022 by the authors.
Despite the wide variety of existing therapies, multiple myeloma (MM) remains a disease with dismal prognosis. Choosing the right treatment for each patient remains one of the major challenges. A new approach being explored is the use of ex vivo models for personalized medicine. Two-dimensional culture or animal models often fail to predict clinical outcomes. Three-dimensional ex vivo models using patients’ bone marrow (BM) cells may better reproduce the complexity and heterogeneity of the BM microenvironment. Here, we review the strengths and limitations of currently existing patient-derived ex vivo three-dimensional MM models. We analyze their biochemical and biophysical properties, molecular and cellular characteristics, as well as their potential for drug testing and identification of disease biomarkers. Furthermore, we discuss the remaining challenges and give some insight on how to achieve a more biomimetic and accurate MM BM model. Overall, there is still a need for standardized culture methods and refined readout techniques. Including both myeloma and other cells of the BM microenvironment in a simple and reproducible three-dimensional scaffold is the key to faithfully mapping and examining the relationship between these players in MM. This will allow a patient-personalized profile, providing a powerful tool for clinical and research applications.
