LGG-17. Preventing recurrence: targeting molecular mechanisms driving tumor growth rebound after MAPKi withdrawal in pediatric low-grade glioma

Pediatric low-grade gliomas, a diverse group of WHO grade 1 and 2 glial or glioneural tumors, comprise the most common category of primary brain tumors in children. The majority of these tumors are driven by alterations in the MAPK pathway, making them in principle susceptible to MAPKi therapy. While patients often benefit from MAPKi during treatment, tumor rebound may occur once treatment is stopped, constituting a significant clinical challenge. BT-40, patient-derived cells with molecular features of pleomorphic xanthoastrocytoma (BRAF(V600E), CDKN2Adel), were used to model the rebound growth in vitro, based on viable cell counts in response to treatment and withdrawal of the clinically relevant BRAF(V600E) specific inhibitor dabrafenib. Standard-of-care chemotherapy (vincristine and carboplatin) was used as a reference. MAPK pathway reactivation upon withdrawal was assessed by WB and qPCR analysis. Based on the observed cell-regrowth and MAPK-reactivation pattern, key-timepoints during withdrawal were identified, which are currently being further analyzed through RNAseq and phospho-/proteomics. BT-40 cells started to proliferate again two days after dabrafenib withdrawal, and earliest five days after chemotherapy withdrawal. MAPK pathway activity, based on Mek and Erk phosphorylation, reached baseline levels three hours after dabrafenib withdrawal, this was associated with 2.5-fold increased c-Fos gene expression two hours after withdrawal. The earlier cell regrowth after dabrafenib withdrawal compared to chemotherapy withdrawal matches clinical observations, making the model suitable to study the rebound. The observed MAPK overactivation suggests the growth rebound might not only be caused by a fast reactivation of the pathway but also by other mechanisms, e.g. accumulation of upstream activators due to loss of negative feedback or parallel pathways. To investigate this, key-timepoints during treatment withdrawal will be analyzed using a multi-omics approach. Based on these findings, possible rebound-driving mechanisms will be identified and further validated using BT-40 and additional PXA models in vitro and in vivo.