MOST wanted: navigating the MAPK-OIS-SASP-tumor microenvironment axis in primary pediatric low-grade glioma and preclinical models.

Understanding the molecular and cellular mechanisms driving pediatric low-grade glioma (pLGG)-the most prevalent brain tumor in children-is essential for the identification and evaluation of novel effective treatments. This review explores the intricate relationship between the mitogen-activated protein kinase (MAPK) pathway, oncogene-induced senescence (OIS), the senescence-associated secretory phenotype (SASP), and the tumor microenvironment (TME), integrating these elements into a unified framework termed the MAPK/OIS/SASP/TME (MOST) axis. This integrated approach seeks to deepen our understanding of pLGG and improve therapeutic interventions by examining the MOST axis' critical influence on tumor biology and response to treatment. In this review, we assess the axis' capacity to integrate various biological processes, highlighting new targets for pLGG treatment, and the need for characterized in vitro and in vivo preclinical models recapitulating pLGG's complexity to test targets. The review underscores the need for a comprehensive strategy in pLGG research, positioning the MOST axis as a pivotal approach in understanding pLGG. This comprehensive framework will open promising avenues for patient care and guide future research towards inventive treatment options.

Rebound growth of BRAF mutant pediatric glioma cells after MAPKi withdrawal is associated with MAPK reactivation and secretion of microglia-recruiting cytokines.

INTRODUCTION: Patients with pediatric low-grade gliomas (pLGGs), the most common primary brain tumors in children, can often benefit from MAPK inhibitor (MAPKi) treatment. However, rapid tumor regrowth, also referred to as rebound growth, may occur once treatment is stopped, constituting a significant clinical challenge. METHODS: Four patient-derived pediatric glioma models were investigated to model rebound growth in vitro based on viable cell counts in response to MAPKi treatment and withdrawal. A multi-omics dataset (RNA sequencing and LC-MS/MS based phospho-/proteomics) was generated to investigate possible rebound-driving mechanisms. Following in vitro validation, putative rebound-driving mechanisms were validated in vivo using the BT-40 orthotopic xenograft model. RESULTS: Of the tested models, only a BRAFV600E-driven model (BT-40, with additional CDKN2A/Bdel) showed rebound growth upon MAPKi withdrawal. Using this model, we identified a rapid reactivation of the MAPK pathway upon MAPKi withdrawal in vitro, also confirmed in vivo. Furthermore, transient overactivation of key MAPK molecules at transcriptional (e.g. FOS) and phosphorylation (e.g. pMEK) levels, was observed in vitro. Additionally, we detected increased expression and secretion of cytokines (CCL2, CX3CL1, CXCL10 and CCL7) upon MAPKi treatment, maintained during early withdrawal. While increased cytokine expression did not have tumor cell intrinsic effects, presence of these cytokines in conditioned media led to increased attraction of microglia cells in vitro. CONCLUSION: Taken together, these data indicate rapid MAPK reactivation upon MAPKi withdrawal as a tumor cell intrinsic rebound-driving mechanism. Furthermore, increased secretion of microglia-recruiting cytokines may play a role in treatment response and rebound growth upon withdrawal, warranting further evaluation.

Generation of patient-derived pediatric pilocytic astrocytoma in-vitro models using SV40 large T: evaluation of a modeling workflow.

PURPOSE: Although pediatric low-grade gliomas (pLGG) are the most common pediatric brain tumors, patient-derived cell lines reflecting pLGG biology in culture are scarce. This also applies to the most common pLGG subtype pilocytic astrocytoma (PA). Conventional cell culture approaches adapted from higher-grade tumors fail in PA due to oncogene-induced senescence (OIS) driving tumor cells into arrest. Here, we describe a PA modeling workflow using the Simian Virus large T antigen (SV40-TAg) to circumvent OIS. METHODS: 18 pLGG tissue samples (17 (94%) histological and/or molecular diagnosis PA) were mechanically dissociated. Tumor cell positive-selection using A2B5 was perfomed in 8/18 (44%) cases. All primary cell suspensions were seeded in Neural Stem Cell Medium (NSM) and Astrocyte Basal Medium (ABM). Resulting short-term cultures were infected with SV40-TAg lentivirus. Detection of tumor specific alterations (BRAF-duplication and BRAF V600E-mutation) by digital droplet PCR (ddPCR) at defined time points allowed for determination of tumor cell fraction (TCF) and evaluation of the workflow. DNA-methylation profiling and gene-panel sequencing were used for molecular profiling of primary samples. RESULTS: Primary cell suspensions had a mean TCF of 55% (+/- 23% (SD)). No sample in NSM (0/18) and ten samples in ABM (10/18) were successfully transduced. Three of these ten (30%) converted into long-term pLGG cell lines (TCF 100%), while TCF declined to 0% (outgrowth of microenvironmental cells) in 7/10 (70%) cultures. Young patient age was associated with successful model establishment. CONCLUSION: A subset of primary PA cultures can be converted into long-term cell lines using SV40-TAg depending on sample intrinsic (patient age) and extrinsic workflow-related (e.g. type of medium, successful transduction) parameters. Careful monitoring of sample-intrinsic and extrinsic factors optimizes the process.

MAPK inhibitor sensitivity scores predict sensitivity driven by the immune infiltration in pediatric low-grade gliomas.

Pediatric low-grade gliomas (pLGG) show heterogeneous responses to MAPK inhibitors (MAPKi) in clinical trials. Thus, more complex stratification biomarkers are needed to identify patients likely to benefit from MAPKi therapy. Here, we identify MAPK-related genes enriched in MAPKi-sensitive cell lines using the GDSC dataset and apply them to calculate class-specific MAPKi sensitivity scores (MSSs) via single-sample gene set enrichment analysis. The MSSs discriminate MAPKi-sensitive and non-sensitive cells in the GDSC dataset and significantly correlate with response to MAPKi in an independent PDX dataset. The MSSs discern gliomas with varying MAPK alterations and are higher in pLGG compared to other pediatric CNS tumors. Heterogenous MSSs within pLGGs with the same MAPK alteration identify proportions of potentially sensitive patients. The MEKi MSS predicts treatment response in a small set of pLGG patients treated with trametinib. High MSSs correlate with a higher immune cell infiltration, with high expression in the microglia compartment in single-cell RNA sequencing data, while low MSSs correlate with low immune infiltration and increased neuronal score. The MSSs represent predictive tools for the stratification of pLGG patients and should be prospectively validated in clinical trials. Our data supports a role for microglia in the response to MAPKi.

Key Pharmacokinetic Parameters of 74 Pediatric Anticancer Drugs Providing Assistance in Preclinical Studies.

Novel drug treatments for pediatric patients with cancer are urgently needed. Success of drug development in pediatric oncology has been promising, but many drugs still fail in translation from preclinical to clinical phases. To increase the translational potential, several improvements have been implemented, including the use of clinically achievable concentrations in the drug testing phase. Although pharmacokinetic (PK) parameters of numerous investigated drugs are published, a comprehensive PK overview of the most common drugs in pediatric oncology could guide preclinical trial design and improve the translatability into clinical trials. A review of the literature was conducted for PK parameters of 74 anticancer drugs, from the drug sensitivity profiling library of the INdividualized Therapy FOr Relapsed Malignancies in Childhood (INFORM) registry. PK data in the pediatric population were reported and complemented by adult parameters when no pediatric data were available. In addition, blood-brain barrier (BBB)-penetration assessment of drugs was provided by using the BBB score. Maximum plasma concentration was available for 73 (97%), area under the plasma concentration-time curve for 69 (92%), plasma protein binding for 66 (88%), plasma half-life for 57 (76%), time to maximum concentration for 54 (72%), clearance for 52 (69%), volume of distribution for 37 (49%), lowest plasma concentration reached by the drug before the next dose administration for 21 (28%), and steady-state concentration for 4 (5%) of drugs. Pediatric PK data were available for 48 (65%) drugs. We provide a comprehensive review of PK data for 74 drugs studied in pediatric oncology. This data set can serve as a reference to design experiments more closely mimicking drug PK conditions in patients, and may thereby increase the probability of successful clinical translation.

The white matter is a pro-differentiative niche for glioblastoma.

Glioblastomas are hierarchically organised tumours driven by glioma stem cells that retain partial differentiation potential. Glioma stem cells are maintained in specialised microenvironments, but whether, or how, they undergo lineage progression outside of these niches remains unclear. Here we identify the white matter as a differentiative niche for glioblastomas with oligodendrocyte lineage competency. Tumour cells in contact with white matter acquire pre-oligodendrocyte fate, resulting in decreased proliferation and invasion. Differentiation is a response to white matter injury, which is caused by tumour infiltration itself in a tumoursuppressive feedback loop. Mechanistically, tumour cell differentiation is driven by selective white matter upregulation of SOX10, a master regulator of normal oligodendrogenesis. SOX10 overexpression or treatment with myelination-promoting agents that upregulate endogenous SOX10, mimic this response, leading to niche-independent pre-oligodendrocyte differentiation and tumour suppression in vivo. Thus, glioblastoma recapitulates an injury response and exploiting this latent programme may offer treatment opportunities for a subset of patients.