Preclinical Models of Low-Grade Gliomas

SIMPLE SUMMARY: Preclinical models are essential for the advancement of our understanding of glioma biology and the development of novel therapeutics. Much of our progress in the treatment of low-grade glioma has been hampered by our limited ability to develop ideal preclinical models. This has proven to be a formidable task given the complex factors one must account for, such as genetic background, intratumoral heterogeneity, intact blood–brain barrier, and the tumor microenvironment. As new knowledge is acquired regarding low-grade glioma, preclinical models must be refined and adjusted to reflect the actual biology of human glioma as closely as possible. In this review, we delve into in vitro and in vivo models of low-grade glioma with particular attention to illuminating the multifaceted task of developing the most optimal models. ABSTRACT: Diffuse infiltrating low-grade glioma (LGG) is classified as WHO grade 2 astrocytoma with isocitrate dehydrogenase (IDH) mutation and oligodendroglioma with IDH1 mutation and 1p/19q codeletion. Despite their better prognosis compared with glioblastoma, LGGs invariably recur, leading to disability and premature death. There is an unmet need to discover new therapeutics for LGG, which necessitates preclinical models that closely resemble the human disease. Basic scientific efforts in the field of neuro-oncology are mostly focused on high-grade glioma, due to the ease of maintaining rapidly growing cell cultures and highly reproducible murine tumors. Development of preclinical models of LGG, on the other hand, has been difficult due to the slow-growing nature of these tumors as well as challenges involved in recapitulating the widespread genomic and epigenomic effects of IDH mutation. The most recent WHO classification of CNS tumors emphasizes the importance of the role of IDH mutation in the classification of gliomas, yet there are relatively few IDH-mutant preclinical models available. Here, we review the in vitro and in vivo preclinical models of LGG and discuss the mechanistic challenges involved in generating such models and potential strategies to overcome these hurdles.