Successes and challenges in modeling heterogeneous BRAF^V600E mutated central nervous system neoplasms

Central nervous system (CNS) neoplasms are difficult to treat due to their sensitive location. Over the past two decades, the availability of patient tumor materials facilitated large scale genomic and epigenomic profiling studies, which have resulted in detailed insights into the molecular underpinnings of CNS tumorigenesis. Based on results from these studies, CNS tumors have high molecular and cellular intra-tumoral and inter-tumoral heterogeneity. CNS cancer models have yet to reflect the broad diversity of CNS tumors and patients and the lack of such faithful cancer models represents a major bottleneck to urgently needed innovations in CNS cancer treatment. Pediatric cancer model development is lagging behind adult tumor model development, which is why we focus this review on CNS tumors mutated for BRAF^V600E which are more prevalent in the pediatric patient population. BRAF^V600E-mutated CNS tumors exhibit high inter-tumoral heterogeneity, encompassing clinically and histopathological diverse tumor types. Moreover, BRAF^V600E is the second most common alteration in pediatric low-grade CNS tumors, and low-grade tumors are notoriously difficult to recapitulate in vitro and in vivo. Although the mutation predominates in low-grade CNS tumors, when combined with other mutations, most commonly CDKN2A deletion, BRAF^V600E-mutated CNS tumors are prone to develop high-grade features, and therefore BRAF^V600E-mutated CNS are a paradigm for tumor progression. Here, we describe existing in vitro and in vivo models of BRAF^V600E-mutated CNS tumors, including patient-derived cell lines, patient-derived xenografts, syngeneic models, and genetically engineered mouse models, along with their advantages and shortcomings. We discuss which research gaps each model might be best suited to answer, and identify those areas in model development that need to be strengthened further. We highlight areas of potential research focus that will lead to the heightened predictive capacity of preclinical studies, allow for appropriate validation, and ultimately improve the success of "bench to bedside" translational research.