3D Tumor Models and Their Use for the Testing of Immunotherapies

Abstract

Over the past decade, immunotherapy has become a powerful and evident tool in the fight against cancers. Notably, the rise of checkpoint blockade using monoclonal antibodies (anti-CTLA4, anti-PD1) to avoid interaction between inhibitory molecules allowed the betterment of patient care. Indeed, immunotherapies led to increased overall survival in forms of cutaneous melanoma or lung cancer. However, the percentage of patients responding varies from 20 to 40% depending on the type of cancer and on the expression of the target molecules by the tumor. This is due to the tumor microenvironment which allows the acquisition of resistance mechanisms to immunotherapies by tumor cells. These are closely linked to the architecture and cellular composition of the tumor microenvironment. This one acts on different parameters such as the immune cells infiltrate its composition and therefore, favors the recruitment of immunosuppressive cells as well as the tumor expression of checkpoint inhibitors such as Programmed Death Ligand-1 (PD-L1). Therefore, the analysis and modeling of the complexity of the microenvironment is an important parameter to consider, not only in the search for new therapies but also for the identification and stratification of patients likely to respond to immunotherapy. This is why the use of 3D culture models, reflecting the architecture and cellular composition of a tumor, is essential in immuno-oncology studies. Nowadays, there are several 3-D culture methods such as spheroids and organoids, which are applicable to immuno-oncology. In this review we evaluate 3D culture models as tools for the development of treatments in the field of immuno-oncology.

Keywords: 3D culture; immune infiltrate; immunotherapy; organoid; patient derived organoids; spheroid; tumor microenvironment; tumor on a chip.

Figure 1

Representation of 3D culture models according to their complexity. 3D culture models are depicted as a range from spheroids derived from a single cell line to a very complex model derived from patient tissue or tumor upgraded with a microfluidic chip. 2D culture and tumor biopsy are used as complexity references. 3D cultures can be separated between cell line derived and patient derived models. Patient derived 3D models require either tissue mincing or both tissue mincing and enzymatic digestion prior to the culture. Noteworthy, Bioprinting can be used to generate most models that require multiple cell type-dependent structures, and can be applied directly on microfluidic chips.