Ependymoma group-specific blood-brain barrier differences uncovered by a multi-omics approach

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Sundheimer, Julia Benzel, Aniello Federico, Stefanie Volz, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7491341/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract A significant obstacle in treating brain tumors is the limited drug penetration across the blood-brain barrier (BBB), characterized by an interplay of endothelial tight junctions and efflux pumps. Brain tumors can alter BBB characteristics; however, there is limited understanding in ependymoma (EPN), the third most common pediatric brain tumor. To this end, we characterized EPN tumor (n = 364) and healthy brain tissues (n = 225) at RNA level and identified a distinct EPN group-specific BBB transcriptional pattern. Analyses of a validation single-cell (n = 8) and publicly available datasets from Aubin and Gojo could further specify a novel BBB signature expressed in an endothelial subpopulation. Drugs that were effective against EPN in vitro were further evaluated for BBB penetration in our subtype-specific patient-derived xenograft (PDX) models. Idasanutlin reached low brain-to-plasma ratios in both tumor and surrounding brain tissue, while the P-glycoprotein (PGP) substrates temsirolimus and etoposide accumulated slightly more in zinc finger translocation associated (ZFTA)-fusion positive EPN than in PFA tumors and adjacent brain, consistent with slightly lower PGP levels in ZFTA compared to PFA PDX but not patient tumors. Despite these differences, all tested drugs remained below their effective in vitro levels. In summary, multi-omics analyses of BBB characteristics improve the understanding of drug penetrance and may potentially guide treatment choices in the context of molecular EPN groups within upcoming clinical trials. Biological sciences/Cancer Biological sciences/Neuroscience Health sciences/Oncology Blood-brain barrier tight junctions efflux pumps transporter receptor ependymoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The physiological blood-brain barrier (BBB) is a non-fenestrated monolayer of tightly sealed endothelial cells forming the neurovascular unit together with astrocytes, pericytes, and other supporting cells. Tight junctions form complexes between those cells, including proteins such as occludin (OCLN), claudin family members, e.g. claudin-3 (CLDN3), claudin-5 (CLDN5), junctional adhesion molecules, and a number of cytoplasmic accessory proteins such as zonula occludens 1 (ZO1/TJP1), 2 (ZO2), and 3 (ZO3). Together, they restrict the paracellular transport especially of large, hydrophilic compounds exceeding 800 Da 1,2 . In contrast, lipophilic drugs may passively diffuse through the cell membrane and cross the brain endothelium 1 . More specific BBB transfer mechanisms include solute carrier-mediated, receptor-mediated, and ion transport 1 . The maximum achievable concentration of many therapeutic compounds is further affected by ATP-binding cassette transporters actively pumping drugs back into the blood stream. The most important efflux pumps are P-glycoprotein (PGP, also known as ABCB1 transporter) and breast cancer resistance protein (BCRP, also known as ABCG2 transporter) 3 , 4 . Furthermore, the expression of BBB marker genes differs between different brain regions in human and mice with the greatest difference in BBB tightness between cerebellum and cortex and changes with age 5 , 6 . Pathological processes, such as brain tumors may compromise the physiological BBB integrity. Brain tumors are among the leading causes of cancer-related morbidity and mortality, especially in children 7 . Therapeutic opportunities are limited, in part due to the restrictive characteristics of the BBB, which impede drug delivery to tumor cells. The development of effective therapies for intracranial tumors necessitates the integrative understanding of both tumor-specific vulnerabilities and BBB characteristics to achieve drug delivery and efficacy. Around 95% of drugs, that were preclinically found to be effective against brain tumor cells, cannot cross the BBB and therefore fail in clinical trials 8 , 9 . In brain cancers, the BBB has been mainly studied in glioblastoma (GBM); here, high endothelial proliferation has been associated with widespread tumor infiltration and concomitant loss of tight junction expression in the tumor endothelium. In contrast, efflux pump expression remained stable or was even increased 10 – 12 . However, tumor entity-specific BBB alterations remain poorly understood for most other brain tumors. Nevertheless, BBB understanding is of particular interest in ependymoma (EPN), a group of difficult-to-treat and mostly chemoresistant brain tumors 13 . EPN is molecularly subdivided into 10 distinct groups localized in the cortex, cerebellum, or spine 13 . Intracranial EPN comprise posterior fossa group A (PFA) and B (PFB), as well as sub-EPN (PF-SE). In the supratentorial region sub-EPN (ST-SE), the zinc finger translocation associated (ZFTA) fusion-driven, and yes-associated protein 1 (YAP1) fusion-driven groups occur. The PFA and ZFTA groups exhibit the worst outcome despite the standard of care involving resection and irradiation 13 , 14 . In other pediatric brain tumors, the clinical importance of disease-specific BBB characteristics was previously shown to influence outcomes. In the molecular WNT subgroup of medulloblastoma (MB), a highly fenestrated vascular system contributed to a favorable outcome 15 , 16 . Correlative EPN studies such as BIOMECA have generated comprehensive molecular data for the identification of biomarkers, targetable alterations, and risk stratification 17 . Nevertheless, a methodology to translate molecular data into BBB characteristics is desirable. To this end, our study systematically characterized BBB features across molecular groups of EPN, using a multi-omics approach. First, we investigated group-specific BBB differences by analyzing tumor bulk transcriptome data. Next, we allocated the expression of key BBB markers at the single-cell level in varying extents to endothelial cell populations. We further validated selected BBB-associated factors on the protein level. To evaluate the translational relevance of EPN mouse models, we assessed BBB characteristics in patient-derived xenograft (PDX) mouse models of ZFTA and PFA and an in utero electroporation (IUE) mouse model of ZFTA. Finally, we demonstrated the functional impact of observed BBB differences on drug penetration to both tumor and adjacent normal brain regions within PDX models. By elucidating BBB penetrance and efflux mechanisms, this study offers a resource for molecularly informed BBB characterization to guide effective drug delivery and improve outcomes of EPN patients by optimized trial designs. Results BBB expression patterns reveal EPN group-specific alterations Initially, we curated two distinct BBB-associated gene lists based on a comprehensive literature review 1 , 4 , 18 – 23 encompassing either tight junction genes or critical transporters and receptors associated with the endothelial layer of the BBB (Tab. S1). On the basis of these lists, we analyzed Affymetrix RNA bulk gene expression datasets on the six human intracranial EPN groups (n = 364) 13 and healthy brain tissues from various brain regions (e.g. cortex, cerebellum (n = 225) 24 ). T-distributed stochastic neighbor embedding (tSNE) analyses based on these lists revealed clustering according to individual molecular EPN groups and healthy tissues (Fig. 1 a,b). Unsupervised hierarchical clustering analyses of tight junction genes confirmed a clear separation of EPN from healthy brain tissues (Fig. 1 c) and showed that a distinct cluster, corresponding to ZFTA, was characterized by a specifically high expression of various tight junction genes, including CLDN5 and desmoplakin (DSP). For all other EPN groups, CLDN5 showed lower expression than in healthy brain tissue. The tight junction genes TJP1 and OCLN were increased across all EPN groups in contrast to healthy tissue (Fig. 1 d). To further investigate regulatory mechanisms, Ingenuity Pathway Analysis was used based on tight junction list and identified transcription regulators such as snail family transcriptional repressor 1 (SNAI1) as a key upstream negative regulator of tight junction gene expression. Notably, low expression of SNAI1 led to high cadherin 5 (CDH5) and CLDN3 but low CLDN11 in ZFTA and PFA (Fig. S1 a,b). Hierarchical clustering using the transporter gene list revealed two main clusters – one with healthy brain tissue and a second with EPN samples from primary and relapse patients. The healthy brain samples had an overall higher expression of transporters than EPN tumors. Except for ZFTA tumors, efflux pumps PGP and BCRP were significantly lower expressed in EPN compared to healthy tissue, with YAP1 tumors showing the lowest expression for PGP and BCRP (Fig. 1 e,f). The transferrin receptor (TFRC) receptor showed a significant downregulation in all EPN groups except for PF-SE and ZFTA compared to healthy brains (Fig. 1 e,f). Transporter-focused Ingenuity Pathway Analysis revealed the ligand-dependent nuclear receptor estrogen receptor 1 (ESR1) as a key upstream regulator (Fig. S1 c,d). ESR1 showed a decreased expression in ZFTA and PFA tumor compared to healthy brain tissue. Consistent with this finding, PGP, which is typically ESR1 activated, had a slightly lower expression in ZFTA and PFA. Further, the typically ESR1-inhibited transporter BCRP showed high expression in ZFTA aligning with reduced ESR1 levels. Despite low ESR1 levels, PFA tumors exhibited low expression levels of BCRP inconsistent with the regulatory findings suggesting a more pronounced regulation by alternative regulators such as activating transcription factor 4 (ATF4, (Fig. S1 c,d)). In summary, bulk transcriptomic analysis revealed markedly elevated expression of tight junction components across molecular EPN groups compared to healthy brain tissue. Expression patterns of efflux transporters showed no consistent patterns of deregulation. Developmental and regional BBB differences To explore potential factors underlying EPN group-specific BBB expression patterns, we examined associations with patient age and tumor location within the human brain. To quantify these differences, we generated two distinct scores – one neural network-based tight junction score (TJ NN) and one neural network-based transporter score (TP NN) – applied to bulk transcriptomic data from various pediatric and adult brain tumors 25 . Next, we used a custom model-building approach to perform feature selection, quantifying how strongly each gene contributes to the NN scores. Among tight junctions, OCLN and TJP1 were among the top five ranked genes in the TJ NN score (Fig. S2 a). Notably, the TP NN score was primarily driven by SLC47A2, mediating excretion of organic cations and many drugs, followed by SLCO1C1 and APOE, both known to be highly enriched in the BBB as organic anion and lipid transporter, respectively (Fig. S2 b). Interestingly, the efflux pump genes, PGP and BCRP, were ranked lower (9th and 12th, respectively), while TFRC was not among the top 20 features (Fig. S2 b). We then assessed TJ and TP NN scores for cortex and cerebellum from humans with well annotated ages and developmental stages from a published dataset 26 . The TJ NN score declined with increasing age, consistent with a more permeable BBB in older individuals (Fig. 2 a) 6 . In contrast, the TP NN score showed a marked postnatal increase, particularly in the cortex, and continued to rise with age (Fig. 2 c). However, neither TJ nor TP NN score in tumor samples showed pronounced age-dependent differences (Fig. 2 b,d). The higher TP NN score observed in PF-SE samples may partly reflect the fact that these specimens predominantly originate from older patients. Nevertheless, tumor samples already exhibited an elevated score in the young-/mid-age groups compared to healthy cerebellum, with no further increase in older age groups (Fig. 2 d). Next, the impact of anatomical brain regions on BBB-associated gene expression was further examined. TJ NN scores were lowest in the cerebellum compared to regions such as the parietal and temporal lobes (Fig. 2 e). Importantly, TJ NN scores were higher in tumors and nearly identical across all EPN groups independent of brain region (Fig. 2 f). For transporters, TP NN score was also lowest in cerebellum (Fig. 2 g) and ST-SE displayed overall lower relative scores than corresponding healthy regions while TP NN scores were comparable between PF EPN and matched normal tissue (Fig. 2 h). In summary, neither age nor the anatomical location had a strong influence on tumor-specific BBB characteristics. Single-cell transcriptomics reveals endothelial enrichment of BBB-associated genes Given the limitation of tumor bulk analysis in resolving the cellular origins of observed expression patterns, we expanded the investigation of tight junction and transporter gene expression to human single-cell datasets. Following a comprehensive screening for published single-cell data, we selected sets from Gojo 27 and Aubin 28 , as these cover the main pediatric EPN groups with a well-balanced amount of tumor, stroma, and endothelial cells. The “PanglaoDB_Endothelial_cells” gene signature, derived from the cell type marker database PanglaoDB ( https://panglaodb.se/ ), allowed the identification of endothelial cells within the non-malignant cell clusters from the Gojo dataset (Fig. 3 a). Among these, our defined BBB-specific tight junction and transporter gene sets further resolved this cluster into two distinct endothelial subpopulations. We annotated these subclusters as ‘endothelial cells_BBB’ – characterized by high expression of BBB-associated genes (yellow, Fig. 3 a, Fig. S3c) – and ‘endothelial cells’, representing a population with lower BBB gene expression (green, Fig. 3 a). In the Aubin dataset, both endothelial cell clusters were found but less prominent with only a few cells aligning within the general endothelial cluster (Fig. S3h,i). To further validate these findings, we generated a new single-nuclei RNA sequencing dataset comprising all intracranial pediatric-type molecular EPN groups, including samples from four ZFTA, one YAP1, and three PFA patients. Both ‘endothelial cells_BBB’ and general endothelial cells populations were clearly observed (Fig. 3 b). The BBB-associated genes were mainly expressed in ‘endothelial cells_BBB’, followed by general endothelial cells (Fig. 3 c, Fig S3a). Tumor, stroma, and immune cells exhibited only minimal expression of tight junction and transporter genes in all datasets (Fig. 3 c, Fig. S3d,i). Of note, the absolute number of cells within endothelial subpopulations for individual patients and proportions of subpopulations differed between molecular groups but also between datasets suggesting high heterogeneity between patients (Fig. 3 d, Fig. S3e). To evaluate whether endothelial phenotypes are conserved across brain tumor entities, we compared our endothelial subclusters with those identified in a recent GBM single-cell RNA-seq study 29 . Unlike GBM, the source of endothelial cells in EPN could not distinguished between tumor core and periphery. Nonetheless, the GBM-informed Pe1 (peripheral, BBB-enriched) and Co1 (core, angiogenesis-enriched) gene lists showed average expression patterns resembling with the ‘endothelial_BBB cluster’ across all three EPN datasets (Fig. S3b,f,j), consistent with their reported high tight junction gene expression in these two GBM cluster. Notably in our validation dataset, the GBM- informed Co2 (core, cytoskeletal-associated) gene list showed the highest correlation with the ‘endothelial_BBB cluster’ (Fig. S3b). In contrast, the Co1 GBM cluster, but not the more specific BBB Pe1 GBM cluster gene list merged with high average expression for the general EPN endothelial cell cluster in all three datasets. Other GBM clusters, Co3 and Pe2, with important roles described in immune cell recruitment, did not present a clear overlap with EPN endothelial cells (Fig. S3b,f,j), suggesting potential biological differences between these tumor entities. While this may reflect distinct endothelial phenotypes, technical variation between single-cell technologies cannot be entirely excluded. Next, we investigated individual gene expression levels of tight junctions and transporters in all the newly identified cluster across all EPN datasets. Heatmaps indicating the top 20 upregulated genes highlighted consistent high expression of CLDN5 in the endothelial_BBB cluster. These markers showed peak expression in endothelial_BBB cells, except for INSR , which was more prominent in the general endothelial cluster (Fig. 3 e, Fig. S3g,k). Notably, key contributors (e.g. HEG1) to the TJ NN score were among the most upregulated genes at single-cell level, a pattern not observed for transporter genes (Fig. 3 e, Fig. S2 , S3g,k). These findings confirm that core BBB gene expression is confined to a specific endothelial subpopulation across multiple EPN datasets and can be extracted from bulk transcriptome data. RNA expression of tight junction factors predicts protein abundance in EPN To evaluate the functional consequences of observed transcriptomic signatures on the protein level, we conducted mass spectrometry, supplemented by MACSima™ imaging cyclic staining (MICS) on human EPN samples. Of the defined tight junction gene set, 15 proteins were detected by mass spectrometry in all intracranial EPN, showing a higher correlation than for the global set (r = 0.51; Fig. 4 a). Most BBB factors, aligned well with the regression model, on the contrary DSP showed higher expression in the proteome. Group-specific analyses revealed similar trends (PFA: r = 0.43; ZFTA: r = 0.6; Fig. S4a,c), though only 14 and 11 tight junction proteins were detected in ZFTA and PFA, respectively. Notably, CLDN5 expression exhibited higher transcriptome levels specifically in ZFTA tumors (Fig. S4a), nevertheless CLDN5 was enriched on protein level in ZFTA, but undetectable in healthy and PFA (Fig. 4 c). Comparative analyses of tight junction proteins among ZFTA, PFA and healthy tissue, revealed similar levels across groups for TJP1 and OCLN. The expression of 35 of 85 transporters was confirmed on protein level, with a lower overall correlation (r = 0.17) in all intracranial EPN and r = 0.29 for PFA and ZFTA group-specifically (Fig. 4 b, Fig. S4b,d). Next, we assessed individual protein levels of transporters. We observed that PGP levels were highly elevated at protein level in both tumor groups, while remaining below detection threshold for healthy brain tissues. BCRP levels were significantly increased in ZFTA and decreased in PFA in contrast to healthy brain. Lastly, TFRC protein levels were significantly decreased in ZFTA (Fig. 4 c). TSNE clustering of both tight junction and transporter protein expression revealed still separate cluster across EPN groups, but less distinct than in transcriptomic data, and interpretation is limited by the small number of samples (Fig. S4e-f). To spatially confirm the endothelial localization of BBB-associated proteins, MICS was performed on patient EPN tumor tissue. First, we quantified the number of endothelial cells based on CD34 staining, revealing similar numbers for ZFTA and PFA patients, with higher variations between PFA patients (Fig. 4 d). MICS technology confirmed endothelial-cell specific expression of PGP and BCRP (Fig. 4 e) in tumor tissue stained with CD56 (cyan). The tight junction proteins OCLN and TJP1 are shown as representative examples (Fig. 4 f), demonstrating colocalization with the endothelial marker CD34 in both ZFTA and PFA patient tissue. Overall, tight junction proteins, predominantly expressed in endothelial cells, show stronger RNA–protein correlation than transporters, suggesting better functional predictivity from molecular tumor data. PDX models recapitulate key BBB features for preclinical research Having confirmed the predictive value of molecular transcriptome data on the functional protein level, we next investigated the conservation of BBB characteristics between patients and mouse models being of importance for preclinical studies. We included both PDX (ZFTA- BT165, VBT242; PFA– EPD210) and IUE (ZFTA: ZFTA-RELA) models that molecularly recapitulate EPN tumors. In PDX models, endothelial cells are expected to be host-derived, which we confirmed by CD31 transcripts being mostly attributed to the mouse reference (Fig. S5a). However, most other BBB genes showed smaller proportions with mouse origin. Interestingly, the efflux pumps BCRP and at least one of the murine-equivalent isoforms of PGP revealed high murine proportions. In contrast, TFRC showed almost no murine origin (Fig. S5a). Using newly generated RNA sequencing data from PDX and IUE mouse models combined with partly published cohort of patients 30 , tSNE clustering based on tight junction and transporter gene lists showed that mouse-specific counts separated patients from mouse models (Fig. 5 a). In contrast, human-specific counts placed both ZFTA and PFA PDX models closer to patients, at least dependent on transporter gene list (Fig. 5 b). As none of the models consistently clustered closely with patients, we focused on PDX models as these compromise both ZFTA and PFA. Tight junction protein levels in PDX models were validated by western blot analysis and revealed similar protein levels for TJP1 and OCLN in the ZFTA (VBT242) and PFA (EPD210) PDX tumors, consistent with previous observations in human tumors. In contrast, protein levels of both factors were lower in BT165 PDX tumor tissue, showing significance for OCLN suggesting patient-specific differences (Fig. 5 c,d). TJP1 expression in PFA tumors was significantly higher compared to healthy cortex and cerebellum, whereas in human samples, PFA tumors and healthy brain showed comparable levels (Fig. 4 c). CLDN5, which had only been detected by mass spectrometry in ZFTA tumors, displayed strong protein expression in all PDX tumor tissues relative to the corresponding healthy regions, with a significant difference observed between PFA tumors and controls (Fig. 5 c,d). Further analysis of extratumoral regions surrounding the PFA and ZFTA PDX models revealed distinct expression differences within the same brain region across models (Fig. 5 c,d). Specifically, TJP1 levels were elevated in ZFTA (BT165) cortex and cerebellum compared to both regions in the PFA model, prompting an investigation of non-tumor-bearing mice. Western blot analyses of healthy, tumor-naïve mice tissue revealed similar protein expression for TJP1 as in ZFTA model (Fig. S5d). In addition, these analyses confirmed a trend towards higher OCLN and CLDN5 levels in the cerebellum (Fig. S5d). As the latter was not detectable in normal cerebellar tissue of both tumor-bearing models, this suggests that alterations of the BBB may extend beyond the tumor itself, affecting the surrounding healthy brain tissue. In contrast to human EPN tumors, protein levels of the transporter PGP were slightly lower in ZFTA PDX tumors than in PFA, though not significant (Fig. 5 c,d). Protein levels of the receptor TFRC showed significant upregulation in VBT242 ZFTA PDX tumors, compared to other tumor tissues and healthy surrounding (Fig. 5 c,d). Histological H&E assessment demonstrated overall low tumor infiltration across all three PDX models (Fig. 5 e). For an initial assessment of BBB permeability, we studied contrast agent enrichment using gadolinium enhanced T1 overlaying T2-weighted MRI images in PDX und IUE models. Baseline (pre-contrast) T1 images were not available, thus, enhancement patterns are described qualitatively from post-contrast scans only. Both ZFTA models exhibited homogenous gadolinium enhancement throughout the tumor on post-contrast T1 images with more enrichment in the BT165 model (Fig. 5 f), whereas the IUE model showed only a scattered contrast agent enhancement (Fig. S5e). The PFA PDX model showed heterogenous enhancement with markedly hyperintense signal in tumor core on post-contrast T1 images (Fig. 5 f). Localization of tight junctions and transporters was studied by immunofluorescence. All analyzed BBB markers (TJP1, CLDN5, OCLN, PGP, and BCRP) were restricted to endothelial cells across all PDX and IUE models (Fig. 5 g, S5b-c,e). The only exception was CLDN5, which was also detected in tumor cells in BT165 ZFTA model, potentially influencing paracellular transport or indicating an alternative function of CLDN5 in tumor cells (Fig. 5 g). The most notable difference between PDX and IUE models was an increased diameter of endothelial vessels in the latter. Quantification of endothelial cells revealed more endothelial cells in IUE tumors than in PDX tumors (Fig S5f). Both ZFTA PDX models showed similar endothelial cell densities in tumor and adjacent healthy brain, whereas the PFA PDX model exhibited the lowest abundance of endothelial cells, potentially reflecting the variability observed in patients. Of note, the number of endothelial cells is not the only determinant of BBB permeability. Overall, these results highlight the suitability of PDX models to study BBB penetration, as they capture patient-specific variability, including additional genetic alterations or bystander effects, whereas IUE models are more defined and largely driven by a single gene fusion. Temsirolimus and etoposide penetration is higher in ZFTA tumor compared to healthy surrounding Functional validation of BBB characteristics is essential to understand which drug classes display altered penetration in EPN compared to healthy brain. As a first step, we validated tight junction function by observing the penetration of sodium fluorescein, a small fluorescent tracer typically used to study BBB penetration. Based on this assay, ZFTA tumor tissue permeability was lower compared to the surrounding brain (Fig. 6 a,b). Next, we investigated BBB penetration of the chemotherapeutic etoposide 31 and the targeted inhibitors idasanutlin and temsirolimus. The latter two drugs were selected based on their response in EPN cell lines (unpublished drug screening data showing activity in EPN cell lines). To quantify the plasma and brain concentrations of the tested drugs, we applied validated UPLC-MS/MS methods. Both etoposide and idasanutlin are lipophilic with a molecular weight of around 600 Da, suggesting low likelihood of paracellular transport. Since idasanutlin is not a known transporter substrate, and ZFTA patients exhibited higher expression of key tight junctions, idasanutlin permeability was only assessed in this model. Despite a moderate Gupta BBB score, idasanutlin showed a consistently low brain-to-plasma ratio of around 0.015 with no significant differences between ZFTA tumor tissue and the corresponding cortex and cerebellum (Fig. 6 c,d). Etoposide, in contrast, is a known substrate of PGP and other multidrug resistance transporters such as ABCC2 and ABCC6 32 . We therefore analyzed etoposide penetration in both PFA and ZFTA tumors, also taking tumor volume into account. Although detailed analysis of PGP expression revealed no significant differences, we saw a trend toward lower PGP levels in ZFTA tumor tissue, particularly in both ZFTA PDX mouse model (Fig. 4 e, Fig. 5 d). Strikingly, etoposide penetration was significantly higher in large ZFTA tumors, with a brain-to-plasma ratio approaching 0.2, compared to corresponding healthy brain regions as well as small ZFTA and PFA tumor (Fig. 6 e,f). Temsirolimus, a larger molecule, unlikely to undergo paracellular transport, is known as a PGP and transferrin substrate, suggesting a potential contribution of TFRC to its transport. Remarkably, temsirolimus concentrations and brain-to-plasma ratios were significantly higher in ZFTA compared to PFA tumors and healthy brain tissue (Fig. 6 g,h), despite only slightly lower PGP and comparable TFRC expression in the BT165 ZFTA model relative to PFA PDX models. This finding suggests that even subtle reductions in PGP expression may enhance temsirolimus penetration (Fig. 5 d). In summary, sodium fluorescein penetration was limited in the ZFTA group, indicating reduced paracellular permeability. This aligns with elevated expression of specific tight junction proteins in ZFTA patients (Fig. 1 c), although sodium fluorescein transport may also depend on compound-specific characteristics. Regarding drug permeability, idasanutlin showed no difference in accumulation between ZFTA tumor and adjacent healthy brain, whereas temsirolimus and etoposide (in large tumors) exhibited higher accumulation compared to healthy regions. Since both drugs are known PGP substrates, their enhanced permeability might be linked to reduced PGP expression in ZFTA PDX models. However, caution is warranted when extrapolating to patients, as the differential PGP expression observed between ZFTA and PFA models does not fully reflect the human expression patterns, which support consideration of PGP inhibitors in patients. Discussion One of the major hurdles in identifying effective drugs for pediatric neuro-oncology is the limited ability to predict their BBB penetration 33 . Knowledge of differences between the BBB in healthy and tumor tissues is, therefore, highly relevant. In this study, we generated a multi-omics dataset of EPN patients and mouse models, revealing EPN group-specific differences in BBB-associated genes compared to normal tissue. While the BBB is generally believed to be disrupted in brain tumors 10 , 11 , we observed increased tight junction expression across intracranial EPN, suggesting reduced paracellular permeability. In contrast, no consistent trend was observed for transporters and receptors, which instead exhibited group-specific alterations. Our established TJ and TP NN scores reflected known brain region-specific differences 34 , 35 , but also indicated region-independent changes in EPN. The age-associated decrease in BBB transporters was not mirrored for PGP and BCRP in the literature, as PGP levels increase from the perinatal to adult stage, whereas BCRP expression remains largely stable throughout development 36 – 39 . Since some tight junction and transporter genes are also implicated in oncogene regulation and apoptotic signaling 40 , 41 , we validated their primary localization in endothelial cells. Our single-nuclei dataset, together with re-analysis of published single-cell studies, confirmed their localization in endothelial cells and revealed a previously uncharacterized endothelial subcluster in EPN, called “endothelial_BBB cluster”. This cluster strongly correlated with two endothelial cell populations previously described in GBM, namely a peripheral cluster enriched for BBB markers (Pe1) and a core-associated angiogenic cluster (Co1) 29 . However, a comparison between tumor core and periphery in EPN remains limited due to lack of spatially resolved datasets. To address RNA-protein translation, we calculated RNA-protein correlations, which were consistent with findings in medulloblastoma 42 . Although comparisons across datasets are methodologically challenging 43 , we observed positive correlations for most tight junctions and transporters. Exemplary staining confirmed their endothelial localization, supporting the utility of RNA-based TJ NN and TP NN scores to estimate patient-specific BBB permeability. However, interpretation of transporter gene expression is substrate-specific, since drugs rely on distinct transporters and TP NN score includes both import and efflux transporters. We further compared BBB features across mouse models, which remain essential tools in preclinical drug testing. Both IUE and PDX models clustered apart from patient samples. A key distinction was the higher abundance of endothelial cells in IUE ZFTA models compared to ZFTA PDX models. This may reflect differences in age (PDX: 5–6 months; IUE: 2 month), tumor induction method, or genetic background (NSG for PDX/ CD1 for IUE) 39 , 44 , 45 . Age-associated BBB differences are conserved across humans and rodents, with protective functions declining with age 44 . Since electroporation for IUE induction occurs during embryonic barrier formation (E10–E15), it may also affect BBB physiology 4 , 46 . A more detailed analysis revealed elevated CLDN5 RNA and protein levels in ZFTA patients. In PDX models, CLDN5 was highly expressed in both PFA and ZFTA tumors relative to healthy brain but uniquely enriched in both tumor cells and endothelial cells in the ZFTA BT165 model. This suggests a role beyond endothelial tight junctions, consistent with findings in glioma cell lines where lower CLDN5 expression correlated with higher tumor grade 47 . Whether a similar link exists to EPN aggressiveness remains to be tested. Functionally, low endothelial CLDN5 expression has been linked with increased permeability for smaller molecules (< 800 Da) 48 , consistent with our finding of reduced sodium fluorescein penetration in ZFTA tumors. Other tight junctions, including OCLN and TJP1, were upregulated in ZFTA and PFA patient samples compared to healthy brain, but in mouse models this pattern was only confirmed for TJP1 in VBT242 (ZFTA) and EPD210 (PFA). SNAI1, a known repressor of tight junctions, was expressed in EPN patients, in line with literature linking it to BBB disruption and epithelial to mesenchymal cell transition 49 , 50 . MRI-based assessment further supported our molecular findings. Gadolinium-enhanced T1 MRI revealed variability between ZFTA and PFA PDX models. However, interpretation was limited by missing baseline T1 images. Patient MRI data also showed variability, but nonetheless, recent advances in radiomic signatures already enable image-based differentiation of supratentorial EPN from other brain tumor entities, and future approaches may further refine EPN subtype 51 . While T1-weighted imaging indicated BBB disruption, dynamic-contrast enhanced MRI offers higher sensitivity 52 . Indeed, a recent study including posterior fossa EPN patients reported higher BBB penetration compared to medulloblastoma 53 , although direct comparison between ZFTA and PFA remains unresolved. Drug penetration analysis highlighted further differences. For many drugs, the mechanism of BBB transport remains unclear, although most are substrates of PGP and/or BCRP 54 . Idasanutlin, predicted to have moderate BBB penetration using the Gupta score, revealed consistently low brain-to-plasma ratios (0.02) in ZFTA tumor and healthy brain. This contrasts with higher ratios (0.3) reported in CD1 mice 55 , likely reflecting strain differences. Etoposide, predicted to have low penetration based on the Gupta score, exhibited brain-to-plasma ratios around 0.1 with higher accumulation in large ZFTA tumors compared to PFA or healthy tissue. This observation aligns well with slightly reduced PGP protein levels in ZFTA PDX tumors, although etoposide is also a substrate for other efflux pumps (ABCC1/2/3/6) 32,56 . Patient RNA data revealed higher ABCC6 expression in PFA, aligning with reduced etoposide penetration in this group. Temsirolimus, predicted to have low BBB penetration based on the Gupta score, is a PGP substrate. Despite only modestly lower PGP expression in ZFTA tumors, temsirolimus accumulated in ZFTA but not in PFA tumors. Importantly, PGP expression does not always correlate with functionality as already shown for other cancer entities 57 . Temsirolimus binds to transferrin, but it is unknown whether transferrin can still engage its receptor (TFRC) 58 , 59 . TFRC protein levels were comparable in BT165 ZFTA, PFA, and healthy tissue, but were significantly reduced in ZFTA patients suggesting a limited role of TFRC in temsirolimus transport. To enhance temsirolimus delivery and better understand the contribution of PGP and TFRC to its tumor penetration, combination strategies with PGP or TFRC inhibitors could be considered. In conclusion, molecular EPN groups harbor different BBB characteristics independent of brain region. Paracellular transport is limited, particularly in ZFTA, while drug penetration depends strongly on transporter activity. Idasanutlin showed similarly low penetration in tumor and healthy brain, consistent with its suggested transcellular transport pathway. In contrast, temsirolimus and etoposide displayed higher penetration in ZFTA tumors, presumably due to a reduced activity of slightly lower expressed PGP or involvement of other transporters. Overall, our data demonstrate that EPN maintain a more intact BBB than is typically observed in other brain tumors, with group-specific features that may inform therapy selection. Method details Human tissue processing All experiments in this study involving human tissue or data were conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the Medical Faculty of Heidelberg University (S-531/2020, S-502/2013; S-795/2020). Written informed consent was obtained from all participants or their legal guardians prior to inclusion in the study. Global BBB gene expression in EPN patients and healthy controls (Table S3) was analyzed in Affymetrix dataset from a combined EPN tumor cohort with integration of normal brain tissues available on R2 platform ( http://r2.amc.nl ) 13 , 24 . Bulk RNA Affymetrix datasets 13 , 60 , 61 were analyzed using the paired samples Wilcoxon test in the statistical software R. RNA isolation Tissue homogenates were processed to isolate RNA by following the manufacture´s protocol of the Maxwell® RSC simply RNA Tissue Kit (Promega, USA). Total RNA sequencing libraries were generated with TruSeq RNA library Prep kit (Illumina, USA) followed by RNA sequencing using NextSeq550/Novaseq S4 with PE 100 (Illumina, USA) in the Genomics Core Facility of the DKFZ (Heidelberg, Germany). TJ and TP Score Calculation The TJ and TP scores were calculated from log2 expression values of all tight junctions and transporters from manually curated gene list (Table S1 ) by training a neural network with an 80% training set of a comprehensive dataset (n = 2646). This dataset includes samples from different brain tumors including adults and children covering various brain regions. The score was applied to EPN patients, healthy controls and healthy controls from different developmental stages as indicated in Table S3 13,24,26 . Spider plots were created with the radarchart()function in R package fmsb. Single-cell and single nuclei sequencing Sample processing and nuclei isolation Nuclei were isolated from snap frozen tumor samples (n = 8) as previously described 62 , 63 . Briefly, tissue samples were fragmented in “CHAPS, with salts and Tris” (CST) buffer on ice. Cell debris was removed by filtering through 40 µm cell strainers (Greiner, Germany). After centrifugation, nuclei were counted using the Luna Automated cell counter (Logos Biosystems, South Korea) and approximately 9000 nuclei per sample were loaded onto the Chromium single-cell 3′ chip (10X Genomics, USA). 10X single-nuclei library preparation and sequencing The Chromium Next GEM Single-Cell 3ʹ Reagent Kits v3.1 (10X Genomics, USA) was used to prepare single-nuclei gene expression libraries according to the manufacturer´s instructions. RNA reverse transcription was performed in separate GEMs (Gel Bead-In-Emulsion) and on separate nuclei. Next, amplified and size-selected enriched cDNA formed the gene expression libraries and were analyzed for quality and quantity using the Qubit dsDNA HS Assay Kit (ThermoFisher Scientific, USA) and TapeStation (Agilent, USA). Equimolar pooled libraries (multiplexes) were sequenced on a NovaSeq 6000 (Illumina, USA) sequencer using S4 flow cell with paired-end reads, according to the manufacturer’s instructions, targeting approximately 50,000 reads per nucleus. Data processing and downstream analyses After pre-processing of raw sequencing data (FASTQ format) with the Cell Ranger pipeline (v6.1.1, 10X Genomics), reads were aligned to the GRCh38 human genome reference to identify expressed genes. Gene expression levels were assessed based on Unique Molecular Identifiers (UMIs) or individual transcript molecules. All downstream analyses described below were applied to this new validation dataset and publicly available single-cell and single-nuclei RNAseq datasets 27 – 29 for cross-validation as indicated in Table S3. Expression count matrices were generated by using the Seurat package (v3.23) within R (v4.0.1). To ensure high-quality data for downstream analysis, filtering steps were applied to each dataset, as described previously 64 . Data were normalized and scaled using the SCTransform function in Seurat, followed by principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for visualization and clustering purposes. These methods allowed the identification of distinct cellular clusters representing various cell types present in each sample. To limit sample-specific technical variations, Harmony (v1.0; 65 ) was employed for integration. Cell type annotations were determined by analyzing gene markers for each cluster using Seurat’s FindAllMarkers function. The endothelial_BBB cell population was identified through the expression of canonical endothelial markers and the presence of tight junction and transporter gene signatures, as described in this study and corroborated by findings from Xie et al. 29 . Gene expression signatures were scored using Seurat`s “AddModuleScore” function, generating module scores per cell that were compared across clusters and EPN groups. Data visualizations were generated using the Seurat and SCpubr packages (v2.0.2; 66 ). Mass spectrometry Peptides of lysed EPN tissue samples (overview in Table S3) were separated by an Easy NanoLC 1200 system with various C18 analytical columns (Acclaim PepMap RSLC, self-packed Reprosil-Pur, nanoEase BEH C18) coupled to Orbitrap Fusion or Q-Exactive HF instruments (Thermo Fisher Scientific). MS1 scans were acquired at 60,000 resolution (m/z 350–1500), with MS2 scans in DDA mode at 15,000 resolution using stepped collision energy. Raw data were analyzed with MaxQuant (v1.5.1.2) against the human Uniprot database using trypsin/P digestion, carbamidomethylation (C) as fixed, and oxidation (M)/acetylation (N-term) as variable modifications. Label-free quantification (LFQ) (provided in Table S2 ), iBAQ, and matching between runs were enabled. Data processing was done in Excel, Perseus (v1.6.1.3), and R (v3.5.1). RNA-Protein correlation Proteome data was pre-processed by normalizing and checking for batch effects. Median protein and RNA transcript intensities of matched samples were used to analyze RNA-to-protein correlation and a mean linear regression model was calculated over all genes. Residuals were calculated by predicting transcriptome intensities from proteome intensities using the above fitted general linear model and finally subtracting observed transcriptome intensities from predicted intensities for every gene and every sample individually. Differential correlated genes were identified by using the lmFit() function of the limma R package. We filtered for significance based on a log-fold change (logFC) greater than or equal to 2 and an adjusted p-value (adj.Pval) less than 0.05 Barplot. MACSima™ imaging cyclic staining (MICS) Human EPN tumor samples were cut in 4 µm tissue slices using a cryotome (Leica CM1950). Tissue slides were fixed in 4% PFA for 10 min and antibody staining was performed as previously described 67 . Our antibody panel was adapted from Scheuermann et al 67 with the following antibodies (Table 1 ) and correlation analysis was performed using the MACS iQ View software (version 1.3.1, Miltenyi Biotec, Germany). Table 1 Antibodies added to the established MACSima ™ panel. Antibody Clone Order number Dilution Fluorochrome ABCG2 (BCRP) REA909 130-115-328 1:50 PE CD338 (BCRP) 5D3 12-8888-42 1:50 PE Mdr1 (PGP) D11 sc-55510 1:50 FITC OCLN E5 sc-133256 1:50 FITC TJP1 NBP2-99094 NBP2-99094F 1:50 FITC ZO1 (TJP1) 1A12 33-9111 1:50 FITC H&E and immunofluorescence staining The tissue was sectioned with the Leica CM1950 with a thickness of 10 µm for H&E staining and immunofluorescence staining. For H&E staining, tissue slides were fixed for 30 min in 4% PFA, washed in water and stained in haematoxylin (2:45 min). After washing rinsing 1 min with water, dipping in 100% ethanol and washing again 4 min with water, slides were incubated 30 sec in 95% ethanol prior to staining with eosine for 30 sec. Then tissue slides were dehydrated in ascending ethanol solutions (70%, 95%,100%) and cleared with xylene for 2 min. Tissue was mounted with Eukitt (Orsatec, Germany). For immunofluorescence, sections were fixed in 4% PFA for 10 min and washed 10 min with PBS. Antigen retrieval was achieved by boiling 30 min in citrate buffer (10 mM sodium citrate tribasic dihydrate and 0.05% Tween20 in ddH2O; pH = 6.0), followed by 20 min cooling in this buffer. Cells were permeabilized with PBS with 0.1% Triton-X and blocked in 3% BSA in PBS for 1 h at RT. Primary antibodies were incubated at 4°C overnight, in the dilutions indicated in Table 2 . After 3 washing steps for 10 min, the secondary fluorophore-coupled antibodies were added in 3% BSA in PBS for 1 h at RT prior to mounting in DAPI-fluoromount. Only TJP1 and CD31 co-staining was performed without antigen retrieval step. Imaging was performed at confocal SP8 microscope (Leica, Germany) Table 2 Overview of used antibodies in Western Blot and Immunofluorescence Antibodies Species Manufacturer Catalog # IF dilution Western blot dilution Anti- Occludin rabbit Abcam ab216327 1:200 1:1000 Anti-BCRP rat Enzo ALX-801-036-C100 1:30 Anti-CD31 rabbit Abcam ab28364 1:30 Anti-CD31 rat Santa Cruz sc-18916 1:50 Anti-Claudin5 mouse Invitrogen 4C3C2 1:125 1:500 Anti-PGP mouse Santa Cruz sc-13131 1:20 Anti-ZO1/TJ1 rabbit Invitrogen 61-7300 1:35 1:666 Animal studies All animal experiments were conducted in accordance with legal and ethical regulations and approved by the responsible council (Germany, Regierungspräsidium Karlsruhe: G-164/17, G-187/18, G-227/19, G-228/19, G-255/19, G-76/20, G-58/22 and G-91/20). The study was performed according to GV-SOLAS and ARRIVE guidelines. Mice were obtained from in-house breeding of the DKFZ (Heidelberg, Germany), housed in IVC caging in the Center for Preclinical Research of the DKFZ (Heidelberg, Germany) and monitored daily for the presence of tumor-related symptoms. Cohort sizes were chosen to minimize the number of animals required to get statistically significant results. Patient-derived xenograft mouse models Patient-derived tumor cells were orthotopically injected in 6–8 week oldimmunocompromised mice (NOD-SCID gamma mice (NSG)). Before injections, experimental animals received a painkiller (Metamizol, s.c., 200 mg/kg or 5 mg/kg s.c. Carprofen). Mice were sedated with 1.5–2.5 Vol% isoflurane and eye ointment avoided dehydration. After a negative reflex test, mice were placed on a heating mat with stereotactic fixation. The head was disinfected, a 5 mm incision was performed, and a whole was drilled in the scull at the right position using coordinates from bregma (front suture intersection) and lambda (ZFTA cells in cortex and PFA cells in cerebellum). Tumor cells were intracranially injected with a hamilton needle. After a short waiting time and prior to retracting the needle, the wound was closed with tissue adhesive histoacryl (Braun, Germany). After surgery mice received painkiller via drinking water for three days. In utero electroporation (IUE) mouse models DNA fusion gene constructs were transfected into CD1 mice at E13.5 as described in Zheng et al 68 . After birth, mice developed tumors within days to weeks depending on the exact model. Tumor growth was measured via bioluminescence imaging or MRI techniques. RNA-Sequencing data processing and downstream analysis A combined genome reference was constructed with human reference genome GRCh38 and mouse reference GRCm38 using STAR (version 2.7.10b). Raw sequencing reads were aligned to the combined reference with STAR. Count matrices were generated subsequently with featureCounts (version 2.0.6) and a combined annotation file. Counts were then separated back into human counts and mouse counts by gene annotation. This dataset was combined with an independent human patient cohort from the INFORM database 30 and an IUE mouse model cohort as summarized in Table S3. Species specific counts were further processed using DESeq2 (version 1.46.0) in R (version 4.4.3). Size factors were estimated to normalize for sequencing depth and differences in library size across samples. Normalized counts were further transformed using variance stabilization transformation (vst) to assure constant variance along the range of mean values. Visualization of normalized and raw counts was performed using R packages ggplot2 (version 3.5.2) and pheatmap (version 1.0.13). After normalization, datasets were merged and subsetted by the respective marker genes. t-distributed Stochastic Neighbor Embedding (tSNE) was subsequently applied for dimensionality reduction via Rtsne package (version 0.17). Bioluminescence Imaging Bioluminescence measurements were performed to monitor tumor size developments in mouse models after tumor cell labelling with luciferase. Mice were sedated with 1.5-2 Vol% isoflurane, luciferin solution (15 mg/ml) was injected accordingly to the body weight and photons per seconds were measured. Magnet Resonance Imaging (MRI) MRI was carried out at the small animal imaging core facility at the DKFZ using a BioSpec 3 Tesla (Bruker, Germany) with ParaVision software 360 V1.1. For the imaging, mice were anesthetized with 3.5% sevoflurane in air. For lesion detection T2 weighted imaging were performed using a T2_TurboRARE sequence: TE = 48 ms, TR = 3350 ms, FOV 20x20 mm, slice thickness 1 mm, averages = 3, Scan Time 3m21s, echo spacing 12 ms, rare factor 8, slices 20, image size 192x192. Tumor volume was measured using a T1-FLASH sequence with contrast agent, 80–100 µl ProHance (Bracco Imaging, Germany) i.p.): TE = 3ms, TR = 500ms, FOV 20x20 mm, slice thickness 1 mm, slices 20, Flip angle 70, averages = 3, resolution = 0,104 mm. Tumor volume was determined by manual segmentation using Bruker ParaVision software 6.0.1. The regions of interest (ROI) were visualized and manually labelled using a the RadiAnt DICOM Viewer Software. Ultramicroscopy In order to label endothelial cells, Lectin-TexasRed (12 mg/kg body weight, Vector Laboratories; USA) was injected intravenously into mice prior to sedation. Cardiac perfusion was performed with sodium fluorescein (Sigma, Germany). Next, mice were perfused with 4% PFA and brains were fixed in 4% PFA for 24 h at RT, afterwards the brain was transferred in ascending butanol solutions (30%, 50%, 70%, 80%, 96%, 100%, 100%), each for 24 h at RT 69 . After refractive index matching and lipid removal in benzyl alcohol benzyl benzoate (BABB) solution, tissue was transferred in ethyl cinnamate for imaging in lightsheet 7 (Zeiss, Germany) with 20x objective. Mouse Treatment Study EPN PDX mouse models with a tumor bioluminescence signal of at least 3x10^6 photons/seconds were treated daily for three consecutive days with idasanutlin (Hycultec, Germany, 150mg/kg, i.p.) and temsirolimus (Alsachim, France, 150 mg/kg, i.p.). 4 h after the last treatment, mice were euthanized by increasing CO2 concentrations. Etoposide (Biozol, Germany, 20 mg/kg), i.p. was given daily for three consecutive days combined with carboplatin (Biozol, Germany, 50 mg/kg, i.v.) on the last day. At the last time point, both drugs were administered simultanously and mice were euthanized 0.5-1 h afterwards. Post mortem, blood was taken by cardiac puncture and brain tissue was collected for pharmacokinetic measurements after PBS perfusion separated into cortex, cerebellum und tumor. Mouse brain tissue homogenization and drug extraction 1 mg of brain tissue was dissolved in 10 µL of water/acetonitrile (H 2 O/ACN, 95/5) solution supplemented with 30 glass beads (0.75–1 mm; Carl Roth GmbH, Germany). Tissue samples were homogenized using the Bead Ruptor 4 homogenizer (Omni International Inc, USA) for at least 30 seconds and stored at -20°C. Development of UPLC-MS/MS methods for drugs quantification Stock solution of idasanutlin (Hycultec, Germany) was prepared in 1:1 H2O/ACN, and calibration/quality control solutions were prepared in 1:1 H2O/ACN + 0.1% FA. The stock solution of internal standard idasanutlin-d3-1 (Hycultec, Germany) was dissolved in ethanol and working solution was dissolved in 1:1 H2O/ACN. After plasma protein precipitation with acetonitrile, the samples were subjected to chromatographic separation on an ACQUITY UPLC BEH C18 column (Waters, USA) with an acetonitrile gradient. For UPLC-MS/MS quantification, a triple-stage quadrupole mass spectrometer (Waters Xevo TQ-S with Z-spray electrospray ionization (ESI) source) with an Acquity Classic UPLC® (Waters, Milford, MA, USA) was used. Idasanutlin calibration range was determined between 0.3–1000 ng/mL and 3–10,000 ng/g for mouse plasma and brain homogenate, respectively. Stock solutions, calibration solutions and quality control samples of temsirolimus (Alsachim, France) and its internal standard temsirolimus-13C3,2H7 (Alsachim, France) were prepared in 1:1 H2O/ACN. After whole blood protein precipitation with acetonitrile, the samples were subjected to chromatographic separation on an ACQUITY UPLC BEH C18 column (Waters, USA) with an acetonitrile gradient. For UPLC-MS/MS quantification the same device as described for idasanutlin was used. Temsirolimus calibration range was determined between 10–10,000 ng/mL and 100–100,000 ng/g in mouse whole blood and brain homogenate, respectively. Stock solutions of etoposide (Med Chem Express, USA) and its internal standard etoposide-d3 (Santa Cruz Biotechnology, USA) were diluted in 1:1 H2O/ACN. Calibration/quality control solutions, and working solution of the internal standard were prepared in 95/5 H 2 O/ACN + 0.1% FA. After plasma protein precipitation with acetonitrile, the samples were subjected to chromatographic separation on an ACQUITY UPLC BEH C18 column (Waters, USA) with an acetonitrile gradient in the presence of 5 mM ammonium acetate. For UPLC-MS/MS quantification, a triple-stage quadrupole mass spectrometer (Waters Xevo TQ-XS with Z-spray electrospray ionization (ESI) source) with an Acquity Classic UPLC® (Waters, Milford, MA, USA) was used. Etoposide calibration range was determined between 3 − 1,000ng/mL and 30 − 10,000 ng/g in plasma and brain homogenate, respectively. Western Blot Mouse tissues were homogenized in cold PBS by using a tissue homogenizer for 30 sec. After centrifugation of 10 min at 4°C 300 g, the pellet was dissolved in 150 µl RIPA buffer containing the protease inhibitor cocktail cOmplete (Merck, Germany) and incubated for 1 h on ice. After lysis, cell homogenate was centrifuged 10 min at 4°C 16200 g. Proteins in supernatant were separated and blotted as described in Okonechnikov et al . 70 . Membranes were blocked for 1 h with 5% BSA except for TJP1 antibody, for which 5% milk in Tris buffered saline with Tween20 (TBS-T). They were then incubated overnight at 4°C with the primary antibody (Table 2 ), followed by 1 h incubation in secondary horseradish peroxidase-conjugated antibodies (cell signaling, dilution 1:2500). Sodium fluorescein and endothelial cell quantification For sodium fluorescein intensity quantification, imagej was used to substract lectin staining and remaining intensity in all z-stack images was quantified. Endothelial cell quantification from human/mouse immunofluorescence images were performed with cellprofiler 4.2.6 using identify primary, secondary, tertiary objects followed by measureImageAreaOccupied. Declarations Acknowledgement The authors thank Natalie Stumpf, Lukas Schmitt, Paula Ertel, Norman Mack and Benjamin Schwalm for excellent technical assistance. We thank Samuel Walther for his help in the development of the analytical method for etoposide quantification. In addition, we thank the small animal imaging, light microscopy and sequencing core facility of DKFZ for their great support. Funding Funding was provided by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 404521405, SFB 1389 - UNITE Glioblastoma, Work Package C01 Authors` contributions Julia Sundheimer : Investigation, methodology, formal analysis, writing – review and editing. Julia Benzel : Investigation, methodology, formal analysis, writing – review and editing. Aniello Federico: Investigation, formal analysis writing – review and editing. Stefanie Volz Investigation, formal analysis. Szymon Kmiecik Investigation, formal analysis. Jürgen Burhenne: Investigation, formal analysis. Gzona Bajraktari-Sylejmani Investigation, formal analysis. Sophia Scheuermann: Investigation, formal analysis, writing – review & editing. Anke King Investigation, formal analysis. Jeroen Krijgsveld : Resources, writing – review & editing, supervision Christian Seitz Resources, writing – review & editing, supervision Marcel Kool Resources, writing – review & editing, supervision Maximilian Knoll: Investigation, formal analysis. Britta Statz: Investigation, formal analysis. Tuyu Zheng: Investigation, formal analysis. Stefan Pfister : Resources, funding acquisition, writing – review & editing, supervision. Kristian Pajtler : Conceptualization, supervision, resources, writing – review and editing, project administration. Kendra Maass : Conceptualization, investigation, methodology, formal analysis, supervision, resources, writing – review and editing, project administration. Competing interests The authors declare no conflict of interest. Data Availability Human RNA array data are publicly available (GEO: GSE64415 and GEO: GSE50161, GSE50385, GSE21687, GSE3526). All other data are available from the corresponding author upon request. 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14:20:01","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":228004,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/711ad2dcde73d3b247271d3d.html"},{"id":100426909,"identity":"0a030716-2701-447d-9f59-d37065efc267","added_by":"auto","created_at":"2026-01-16 14:20:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":996260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBBB-associated\u003c/strong\u003e \u003cstrong\u003egene expression analysis in EPN groups and healthy brain tissue controls.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e T-distributed stochastic neighbor embedding (tSNE) plot based on the expression of relevant tight junction and \u003cstrong\u003eb\u003c/strong\u003e transporter and receptor gene sets. \u003cstrong\u003ec\u003c/strong\u003e Heatmap showing unsupervised hierarchical clustering of tight junction \u003cstrong\u003ed\u003c/strong\u003e Boxplot for expression levels of selected tight junction factors: TJP1, OCLN, and CLDN5, and \u003cstrong\u003ee\u003c/strong\u003e efflux pumps PGP, BCRP, and the receptor TFRC. Data shown as median, 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e quartile and whiskers extend to smallest and largest values within 1.5 times the distance between the quartiles. Data points outside this range are plotted individually as outliers. \u003cstrong\u003ef\u003c/strong\u003e Heatmap showing unsupervised hierarchical clustering of transporter/receptor gene expression. Welch`s ANOVA test performed to calculate significance. Ns P \u0026gt; 0.05; *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001; ****P \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/03d28dca2c4237ef8f27cf23.png"},{"id":100426895,"identity":"946140df-fd87-4d5a-8dcf-7d2a6550b86f","added_by":"auto","created_at":"2026-01-16 14:20:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":448989,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTJ/TP NN scores differ between developmental stages, age, and anatomical brain region.\u003c/strong\u003e \u003cstrong\u003ea \u003c/strong\u003eTJ score in healthy cortex and cerebellum at different development stages. \u003cstrong\u003eb \u003c/strong\u003eTJ score in different EPN tumor tissues located in cortex or cerebellum at different development stages. \u003cstrong\u003e\u0026nbsp;c\u003c/strong\u003e TP NN score in healthy cortex and cerebellum at different development stages \u003cstrong\u003ed\u003c/strong\u003e TP score in different EPN tumor tissues located in cortex or cerebellum at different development stages. Data shown as median, 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e quartile and whiskers extend to smallest and largest values within 1.5 times the distance between the quartiles. Data points outside this range are plotted individually as outliers. \u003cstrong\u003ee\u003c/strong\u003e Spider plots highlighting TJ NN score for healthy and \u003cstrong\u003ef\u003c/strong\u003e tumor samples. \u003cstrong\u003eg\u003c/strong\u003e TP NN scores for healthy brain regions (green) and \u003cstrong\u003eh\u003c/strong\u003e EPN tumor tissue (red).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/7769e02c0bca21005fc90920.png"},{"id":100546446,"identity":"b52e3120-2796-4879-9453-e3b768895c93","added_by":"auto","created_at":"2026-01-19 08:08:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":503774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell analysis allocates main BBB-associated gene expression to endothelial cell clusters\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) with expression intensity of the Pangloa endothelial cell signature (left)­­ and with annotated cell types of the Gojo dataset (right). \u003cstrong\u003eb\u003c/strong\u003e Pangloa endothelial cell signature (left)­­ and annotated cell types (right) of the validation single-nuclei dataset. \u003cstrong\u003eC\u003c/strong\u003e Violin plot of tight junction and transporter signature expression across all clusters (validation dataset). \u003cstrong\u003eD\u003c/strong\u003e Bar plots illustrating ­­­­proportion of indicated endothelial cells in PFA, YAP1 and ZFTA molecular EPN groups. \u003cstrong\u003eE\u003c/strong\u003e Heatmap of the most differentially expressed tight junction and transporter genes in indicated single-nuclei clusters.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/c55c69844c57821eb64807df.png"},{"id":100426848,"identity":"d823363d-c1fb-49bd-b073-ac6f8941fbb5","added_by":"auto","created_at":"2026-01-16 14:19:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1088389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNA-protein correlation and subcellular protein localization of tight junctions and transporters.\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e a\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Linear regression model showing RNA to protein correlation of intracranial tumors for tight junctions, and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e transporter proteins. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Mass spectrometry-based protein levels of the tight junction factors OCLN, TJP1, the transporters PGP, BCRP and the receptor TFRC in ZFTA, PFA and healthy brain samples. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ed \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eQuantification of endothelial cells based on CD34 in MACSima™ imaging cyclic staining (MICS). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e MICS of a ZFTA patient tissue showing tumor cells (CD56, cyan) endothelial cells (CD34, pink), the transporters BCRP (green) and PGP (red). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e MICS from representative ZFTA and PFA patient tissue, showing tumor cells staining positive for CD56 (cyan) and endothelial cells staining positive for CD34 (pink), along with the tight junctions OCLN (red) and TJP1 (green). Plot on the right demonstrates correlation of these three markers., significance only indicated if significant *P \u0026lt; 0.05; ****P \u0026lt; 0.000­­­1\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/1150a0bdaaf3b219faca6603.png"},{"id":100426870,"identity":"91e47487-6f1a-42ba-8b17-742777cb0172","added_by":"auto","created_at":"2026-01-16 14:20:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":528634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTight junctions are highly expressed in endothelial cells of different mouse models.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e TSNE of RNA sequencing data from patients, IUE, and PDX models using mouse-specific counts for tight junction and transporter gene lists. \u003cstrong\u003eb\u003c/strong\u003e TSNE of human-specific counts for PDX models. \u003cstrong\u003ec \u003c/strong\u003eRepresentative western blots for TJP1, OCLN, CLDN5, PGP, TFRC, and actin (loading control) in tumor bearing PDX mice and corresponding extra-tumoral brain regions. The original blots are shown in supplementary Figure S6 \u003cstrong\u003ed\u003c/strong\u003e Quantification of western blots. Repeated-measure`s one-way ANOVA (within models) and ordinary one-way ANOVA (between tumors) were performed. Only significant differences are indicated as *P \u0026lt; 0.05; **P \u0026lt; 0.0­­­1, ***P \u0026lt; 0.00­­­1. \u003cstrong\u003ee\u003c/strong\u003e H\u0026amp;E staining showing tumor expansion in different PDX models. \u003cstrong\u003ef \u003c/strong\u003eT2-weighted brain images (grayscale) overlaid with post-contrast T1-weighted images (color-coded, contrast agent enhancement intensity from low (purple) to high (orange)). \u003cstrong\u003eg\u003c/strong\u003e Immunofluorescence images using CD31 as endothelial marker co-stained with CLDN5, TJP1 or OCLN. Line plots below each image illustrate co-localization of CD31 with respective tight junction markers, confirming endothelial-specific expression. Images are representative and consistent across replicates.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/6c161007f99d6fd4855f9342.png"},{"id":106809194,"identity":"582af4c8-741b-4bf9-a5bd-79dcc0897bdc","added_by":"auto","created_at":"2026-04-13 16:08:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4787411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/c7f9721b-a203-4f29-af08-d7b388a3c573.pdf"},{"id":100426945,"identity":"0766627c-0931-49e7-a22a-045205036942","added_by":"auto","created_at":"2026-01-16 14:20:06","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":50084,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytablesfinalrev.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/e8ec358fc0d6aa171499c3f0.xlsx"},{"id":100546380,"identity":"be9f3186-bb86-4b7b-976a-83e42ac5d4ae","added_by":"auto","created_at":"2026-01-19 08:07:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5098079,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiguresfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7491341/v1/7f91f499220c0fa29ad08f4d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ependymoma group-specific blood-brain barrier differences uncovered by a multi-omics approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe physiological blood-brain barrier (BBB) is a non-fenestrated monolayer of tightly sealed endothelial cells forming the neurovascular unit together with astrocytes, pericytes, and other supporting cells. Tight junctions form complexes between those cells, including proteins such as occludin (OCLN), claudin family members, e.g. claudin-3 (CLDN3), claudin-5 (CLDN5), junctional adhesion molecules, and a number of cytoplasmic accessory proteins such as zonula occludens 1 (ZO1/TJP1), 2 (ZO2), and 3 (ZO3). Together, they restrict the paracellular transport especially of large, hydrophilic compounds exceeding 800 Da \u003csup\u003e1,2\u003c/sup\u003e. In contrast, lipophilic drugs may passively diffuse through the cell membrane and cross the brain endothelium \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. More specific BBB transfer mechanisms include solute carrier-mediated, receptor-mediated, and ion transport \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The maximum achievable concentration of many therapeutic compounds is further affected by ATP-binding cassette transporters actively pumping drugs back into the blood stream. The most important efflux pumps are P-glycoprotein (PGP, also known as ABCB1 transporter) and breast cancer resistance protein (BCRP, also known as ABCG2 transporter) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Furthermore, the expression of BBB marker genes differs between different brain regions in human and mice with the greatest difference in BBB tightness between cerebellum and cortex and changes with age \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003ePathological processes, such as brain tumors may compromise the physiological BBB integrity. Brain tumors are among the leading causes of cancer-related morbidity and mortality, especially in children \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therapeutic opportunities are limited, in part due to the restrictive characteristics of the BBB, which impede drug delivery to tumor cells. The development of effective therapies for intracranial tumors necessitates the integrative understanding of both tumor-specific vulnerabilities and BBB characteristics to achieve drug delivery and efficacy. Around 95% of drugs, that were preclinically found to be effective against brain tumor cells, cannot cross the BBB and therefore fail in clinical trials \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn brain cancers, the BBB has been mainly studied in glioblastoma (GBM); here, high endothelial proliferation has been associated with widespread tumor infiltration and concomitant loss of tight junction expression in the tumor endothelium. In contrast, efflux pump expression remained stable or was even increased \u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, tumor entity-specific BBB alterations remain poorly understood for most other brain tumors. Nevertheless, BBB understanding is of particular interest in ependymoma (EPN), a group of difficult-to-treat and mostly chemoresistant brain tumors \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEPN is molecularly subdivided into 10 distinct groups localized in the cortex, cerebellum, or spine \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Intracranial EPN comprise posterior fossa group A (PFA) and B (PFB), as well as sub-EPN (PF-SE). In the supratentorial region sub-EPN (ST-SE), the zinc finger translocation associated (ZFTA) fusion-driven, and yes-associated protein 1 (YAP1) fusion-driven groups occur. The PFA and ZFTA groups exhibit the worst outcome despite the standard of care involving resection and irradiation \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In other pediatric brain tumors, the clinical importance of disease-specific BBB characteristics was previously shown to influence outcomes. In the molecular WNT subgroup of medulloblastoma (MB), a highly fenestrated vascular system contributed to a favorable outcome \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCorrelative EPN studies such as BIOMECA have generated comprehensive molecular data for the identification of biomarkers, targetable alterations, and risk stratification\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Nevertheless, a methodology to translate molecular data into BBB characteristics is desirable.\u003c/p\u003e \u003cp\u003eTo this end, our study systematically characterized BBB features across molecular groups of EPN, using a multi-omics approach. First, we investigated group-specific BBB differences by analyzing tumor bulk transcriptome data. Next, we allocated the expression of key BBB markers at the single-cell level in varying extents to endothelial cell populations. We further validated selected BBB-associated factors on the protein level. To evaluate the translational relevance of EPN mouse models, we assessed BBB characteristics in patient-derived xenograft (PDX) mouse models of ZFTA and PFA and an \u003cem\u003ein utero\u003c/em\u003e electroporation (IUE) mouse model of ZFTA. Finally, we demonstrated the functional impact of observed BBB differences on drug penetration to both tumor and adjacent normal brain regions within PDX models. By elucidating BBB penetrance and efflux mechanisms, this study offers a resource for molecularly informed BBB characterization to guide effective drug delivery and improve outcomes of EPN patients by optimized trial designs.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBBB expression patterns reveal EPN group-specific alterations\u003c/h2\u003e \u003cp\u003eInitially, we curated two distinct BBB-associated gene lists based on a comprehensive literature review\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e encompassing either tight junction genes or critical transporters and receptors associated with the endothelial layer of the BBB (Tab. S1). On the basis of these lists, we analyzed Affymetrix RNA bulk gene expression datasets on the six human intracranial EPN groups (n\u0026thinsp;=\u0026thinsp;364)\u003csup\u003e13\u003c/sup\u003e and healthy brain tissues from various brain regions (e.g. cortex, cerebellum (n\u0026thinsp;=\u0026thinsp;225)\u003csup\u003e24\u003c/sup\u003e). T-distributed stochastic neighbor embedding (tSNE) analyses based on these lists revealed clustering according to individual molecular EPN groups and healthy tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea,b). Unsupervised hierarchical clustering analyses of tight junction genes confirmed a clear separation of EPN from healthy brain tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and showed that a distinct cluster, corresponding to ZFTA, was characterized by a specifically high expression of various tight junction genes, including CLDN5 and desmoplakin (DSP). For all other EPN groups, CLDN5 showed lower expression than in healthy brain tissue. The tight junction genes TJP1 and OCLN were increased across all EPN groups in contrast to healthy tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). To further investigate regulatory mechanisms, Ingenuity Pathway Analysis was used based on tight junction list and identified transcription regulators such as snail family transcriptional repressor 1 (SNAI1) as a key upstream negative regulator of tight junction gene expression. Notably, low expression of SNAI1 led to high cadherin 5 (CDH5) and CLDN3 but low CLDN11 in ZFTA and PFA (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea,b).\u003c/p\u003e \u003cp\u003eHierarchical clustering using the transporter gene list revealed two main clusters \u0026ndash; one with healthy brain tissue and a second with EPN samples from primary and relapse patients. The healthy brain samples had an overall higher expression of transporters than EPN tumors. Except for ZFTA tumors, efflux pumps PGP and BCRP were significantly lower expressed in EPN compared to healthy tissue, with YAP1 tumors showing the lowest expression for PGP and BCRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee,f). The transferrin receptor (TFRC) receptor showed a significant downregulation in all EPN groups except for PF-SE and ZFTA compared to healthy brains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee,f). Transporter-focused Ingenuity Pathway Analysis revealed the ligand-dependent nuclear receptor estrogen receptor 1 (ESR1) as a key upstream regulator (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec,d). ESR1 showed a decreased expression in ZFTA and PFA tumor compared to healthy brain tissue. Consistent with this finding, PGP, which is typically ESR1 activated, had a slightly lower expression in ZFTA and PFA. Further, the typically ESR1-inhibited transporter BCRP showed high expression in ZFTA aligning with reduced ESR1 levels. Despite low ESR1 levels, PFA tumors exhibited low expression levels of BCRP inconsistent with the regulatory findings suggesting a more pronounced regulation by alternative regulators such as activating transcription factor 4 (ATF4, (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec,d)).\u003c/p\u003e \u003cp\u003eIn summary, bulk transcriptomic analysis revealed markedly elevated expression of tight junction components across molecular EPN groups compared to healthy brain tissue. Expression patterns of efflux transporters showed no consistent patterns of deregulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDevelopmental and regional BBB differences\u003c/h3\u003e\n\u003cp\u003eTo explore potential factors underlying EPN group-specific BBB expression patterns, we examined associations with patient age and tumor location within the human brain. To quantify these differences, we generated two distinct scores \u0026ndash; one neural network-based tight junction score (TJ NN) and one neural network-based transporter score (TP NN) \u0026ndash; applied to bulk transcriptomic data from various pediatric and adult brain tumors \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Next, we used a custom model-building approach to perform feature selection, quantifying how strongly each gene contributes to the NN scores. Among tight junctions, OCLN and TJP1 were among the top five ranked genes in the TJ NN score (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea). Notably, the TP NN score was primarily driven by SLC47A2, mediating excretion of organic cations and many drugs, followed by SLCO1C1 and APOE, both known to be highly enriched in the BBB as organic anion and lipid transporter, respectively (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). Interestingly, the efflux pump genes, PGP and BCRP, were ranked lower (9th and 12th, respectively), while TFRC was not among the top 20 features (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). We then assessed TJ and TP NN scores for cortex and cerebellum from humans with well annotated ages and developmental stages from a published dataset \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The TJ NN score declined with increasing age, consistent with a more permeable BBB in older individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In contrast, the TP NN score showed a marked postnatal increase, particularly in the cortex, and continued to rise with age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). However, neither TJ nor TP NN score in tumor samples showed pronounced age-dependent differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb,d). The higher TP NN score observed in PF-SE samples may partly reflect the fact that these specimens predominantly originate from older patients. Nevertheless, tumor samples already exhibited an elevated score in the young-/mid-age groups compared to healthy cerebellum, with no further increase in older age groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eNext, the impact of anatomical brain regions on BBB-associated gene expression was further examined. TJ NN scores were lowest in the cerebellum compared to regions such as the parietal and temporal lobes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Importantly, TJ NN scores were higher in tumors and nearly identical across all EPN groups independent of brain region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). For transporters, TP NN score was also lowest in cerebellum (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg) and ST-SE displayed overall lower relative scores than corresponding healthy regions while TP NN scores were comparable between PF EPN and matched normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). In summary, neither age nor the anatomical location had a strong influence on tumor-specific BBB characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSingle-cell transcriptomics reveals endothelial enrichment of BBB-associated genes\u003c/h3\u003e\n\u003cp\u003eGiven the limitation of tumor bulk analysis in resolving the cellular origins of observed expression patterns, we expanded the investigation of tight junction and transporter gene expression to human single-cell datasets. Following a comprehensive screening for published single-cell data, we selected sets from Gojo \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and Aubin \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, as these cover the main pediatric EPN groups with a well-balanced amount of tumor, stroma, and endothelial cells. The \u0026ldquo;PanglaoDB_Endothelial_cells\u0026rdquo; gene signature, derived from the cell type marker database PanglaoDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://panglaodb.se/\u003c/span\u003e\u003cspan address=\"https://panglaodb.se/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), allowed the identification of endothelial cells within the non-malignant cell clusters from the Gojo dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Among these, our defined BBB-specific tight junction and transporter gene sets further resolved this cluster into two distinct endothelial subpopulations. We annotated these subclusters as \u0026lsquo;endothelial cells_BBB\u0026rsquo; \u0026ndash; characterized by high expression of BBB-associated genes (yellow, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Fig. S3c) \u0026ndash; and \u0026lsquo;endothelial cells\u0026rsquo;, representing a population with lower BBB gene expression (green, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In the Aubin dataset, both endothelial cell clusters were found but less prominent with only a few cells aligning within the general endothelial cluster (Fig. S3h,i). To further validate these findings, we generated a new single-nuclei RNA sequencing dataset comprising all intracranial pediatric-type molecular EPN groups, including samples from four ZFTA, one YAP1, and three PFA patients. Both \u0026lsquo;endothelial cells_BBB\u0026rsquo; and general endothelial cells populations were clearly observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The BBB-associated genes were mainly expressed in \u0026lsquo;endothelial cells_BBB\u0026rsquo;, followed by general endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Fig S3a). Tumor, stroma, and immune cells exhibited only minimal expression of tight junction and transporter genes in all datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Fig. S3d,i). Of note, the absolute number of cells within endothelial subpopulations for individual patients and proportions of subpopulations differed between molecular groups but also between datasets suggesting high heterogeneity between patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, Fig. S3e).\u003c/p\u003e \u003cp\u003eTo evaluate whether endothelial phenotypes are conserved across brain tumor entities, we compared our endothelial subclusters with those identified in a recent GBM single-cell RNA-seq study \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Unlike GBM, the source of endothelial cells in EPN could not distinguished between tumor core and periphery. Nonetheless, the GBM-informed Pe1 (peripheral, BBB-enriched) and Co1 (core, angiogenesis-enriched) gene lists showed average expression patterns resembling with the \u0026lsquo;endothelial_BBB cluster\u0026rsquo; across all three EPN datasets (Fig. S3b,f,j), consistent with their reported high tight junction gene expression in these two GBM cluster. Notably in our validation dataset, the GBM- informed Co2 (core, cytoskeletal-associated) gene list showed the highest correlation with the \u0026lsquo;endothelial_BBB cluster\u0026rsquo; (Fig. S3b).\u003c/p\u003e \u003cp\u003eIn contrast, the Co1 GBM cluster, but not the more specific BBB Pe1 GBM cluster gene list merged with high average expression for the general EPN endothelial cell cluster in all three datasets. Other GBM clusters, Co3 and Pe2, with important roles described in immune cell recruitment, did not present a clear overlap with EPN endothelial cells (Fig. S3b,f,j), suggesting potential biological differences between these tumor entities. While this may reflect distinct endothelial phenotypes, technical variation between single-cell technologies cannot be entirely excluded. Next, we investigated individual gene expression levels of tight junctions and transporters in all the newly identified cluster across all EPN datasets. Heatmaps indicating the top 20 upregulated genes highlighted consistent high expression of \u003cem\u003eCLDN5\u003c/em\u003e in the endothelial_BBB cluster. These markers showed peak expression in endothelial_BBB cells, except for \u003cem\u003eINSR\u003c/em\u003e, which was more prominent in the general endothelial cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, Fig. S3g,k). Notably, key contributors (e.g. HEG1) to the TJ NN score were among the most upregulated genes at single-cell level, a pattern not observed for transporter genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, S3g,k). These findings confirm that core BBB gene expression is confined to a specific endothelial subpopulation across multiple EPN datasets and can be extracted from bulk transcriptome data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRNA expression of tight junction factors predicts protein abundance in EPN\u003c/h3\u003e\n\u003cp\u003eTo evaluate the functional consequences of observed transcriptomic signatures on the protein level, we conducted mass spectrometry, supplemented by MACSima\u0026trade; imaging cyclic staining (MICS) on human EPN samples. Of the defined tight junction gene set, 15 proteins were detected by mass spectrometry in all intracranial EPN, showing a higher correlation than for the global set (r\u0026thinsp;=\u0026thinsp;0.51; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Most BBB factors, aligned well with the regression model, on the contrary DSP showed higher expression in the proteome. Group-specific analyses revealed similar trends (PFA: r\u0026thinsp;=\u0026thinsp;0.43; ZFTA: r\u0026thinsp;=\u0026thinsp;0.6; Fig. S4a,c), though only 14 and 11 tight junction proteins were detected in ZFTA and PFA, respectively. Notably, CLDN5 expression exhibited higher transcriptome levels specifically in ZFTA tumors (Fig. S4a), nevertheless CLDN5 was enriched on protein level in ZFTA, but undetectable in healthy and PFA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Comparative analyses of tight junction proteins among ZFTA, PFA and healthy tissue, revealed similar levels across groups for TJP1 and OCLN. The expression of 35 of 85 transporters was confirmed on protein level, with a lower overall correlation (r\u0026thinsp;=\u0026thinsp;0.17) in all intracranial EPN and r\u0026thinsp;=\u0026thinsp;0.29 for PFA and ZFTA group-specifically (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, Fig. S4b,d). Next, we assessed individual protein levels of transporters. We observed that PGP levels were highly elevated at protein level in both tumor groups, while remaining below detection threshold for healthy brain tissues. BCRP levels were significantly increased in ZFTA and decreased in PFA in contrast to healthy brain. Lastly, TFRC protein levels were significantly decreased in ZFTA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). TSNE clustering of both tight junction and transporter protein expression revealed still separate cluster across EPN groups, but less distinct than in transcriptomic data, and interpretation is limited by the small number of samples (Fig. S4e-f).\u003c/p\u003e \u003cp\u003e To spatially confirm the endothelial localization of BBB-associated proteins, MICS was performed on patient EPN tumor tissue. First, we quantified the number of endothelial cells based on CD34 staining, revealing similar numbers for ZFTA and PFA patients, with higher variations between PFA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). MICS technology confirmed endothelial-cell specific expression of PGP and BCRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) in tumor tissue stained with CD56 (cyan). The tight junction proteins OCLN and TJP1 are shown as representative examples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), demonstrating colocalization with the endothelial marker CD34 in both ZFTA and PFA patient tissue.\u003c/p\u003e \u003cp\u003eOverall, tight junction proteins, predominantly expressed in endothelial cells, show stronger RNA\u0026ndash;protein correlation than transporters, suggesting better functional predictivity from molecular tumor data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePDX models recapitulate key BBB features for preclinical research\u003c/h3\u003e\n\u003cp\u003eHaving confirmed the predictive value of molecular transcriptome data on the functional protein level, we next investigated the conservation of BBB characteristics between patients and mouse models being of importance for preclinical studies. We included both PDX (ZFTA- BT165, VBT242; PFA\u0026ndash; EPD210) and IUE (ZFTA: ZFTA-RELA) models that molecularly recapitulate EPN tumors. In PDX models, endothelial cells are expected to be host-derived, which we confirmed by CD31 transcripts being mostly attributed to the mouse reference (Fig. S5a). However, most other BBB genes showed smaller proportions with mouse origin. Interestingly, the efflux pumps BCRP and at least one of the murine-equivalent isoforms of PGP revealed high murine proportions. In contrast, TFRC showed almost no murine origin (Fig. S5a). Using newly generated RNA sequencing data from PDX and IUE mouse models combined with partly published cohort of patients \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, tSNE clustering based on tight junction and transporter gene lists showed that mouse-specific counts separated patients from mouse models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In contrast, human-specific counts placed both ZFTA and PFA PDX models closer to patients, at least dependent on transporter gene list (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). As none of the models consistently clustered closely with patients, we focused on PDX models as these compromise both ZFTA and PFA. Tight junction protein levels in PDX models were validated by western blot analysis and revealed similar protein levels for TJP1 and OCLN in the ZFTA (VBT242) and PFA (EPD210) PDX tumors, consistent with previous observations in human tumors. In contrast, protein levels of both factors were lower in BT165 PDX tumor tissue, showing significance for OCLN suggesting patient-specific differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,d). TJP1 expression in PFA tumors was significantly higher compared to healthy cortex and cerebellum, whereas in human samples, PFA tumors and healthy brain showed comparable levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eCLDN5, which had only been detected by mass spectrometry in ZFTA tumors, displayed strong protein expression in all PDX tumor tissues relative to the corresponding healthy regions, with a significant difference observed between PFA tumors and controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,d).\u003c/p\u003e \u003cp\u003eFurther analysis of extratumoral regions surrounding the PFA and ZFTA PDX models revealed distinct expression differences within the same brain region across models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,d). Specifically, TJP1 levels were elevated in ZFTA (BT165) cortex and cerebellum compared to both regions in the PFA model, prompting an investigation of non-tumor-bearing mice. Western blot analyses of healthy, tumor-na\u0026iuml;ve mice tissue revealed similar protein expression for TJP1 as in ZFTA model (Fig. S5d). In addition, these analyses confirmed a trend towards higher OCLN and CLDN5 levels in the cerebellum (Fig. S5d). As the latter was not detectable in normal cerebellar tissue of both tumor-bearing models, this suggests that alterations of the BBB may extend beyond the tumor itself, affecting the surrounding healthy brain tissue. In contrast to human EPN tumors, protein levels of the transporter PGP were slightly lower in ZFTA PDX tumors than in PFA, though not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,d). Protein levels of the receptor TFRC showed significant upregulation in VBT242 ZFTA PDX tumors, compared to other tumor tissues and healthy surrounding (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,d). Histological H\u0026amp;E assessment demonstrated overall low tumor infiltration across all three PDX models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). For an initial assessment of BBB permeability, we studied contrast agent enrichment using gadolinium enhanced T1 overlaying T2-weighted MRI images in PDX und IUE models. Baseline (pre-contrast) T1 images were not available, thus, enhancement patterns are described qualitatively from post-contrast scans only. Both ZFTA models exhibited homogenous gadolinium enhancement throughout the tumor on post-contrast T1 images with more enrichment in the BT165 model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef), whereas the IUE model showed only a scattered contrast agent enhancement (Fig. S5e). The PFA PDX model showed heterogenous enhancement with markedly hyperintense signal in tumor core on post-contrast T1 images (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Localization of tight junctions and transporters was studied by immunofluorescence. All analyzed BBB markers (TJP1, CLDN5, OCLN, PGP, and BCRP) were restricted to endothelial cells across all PDX and IUE models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg, S5b-c,e). The only exception was CLDN5, which was also detected in tumor cells in BT165 ZFTA model, potentially influencing paracellular transport or indicating an alternative function of CLDN5 in tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). The most notable difference between PDX and IUE models was an increased diameter of endothelial vessels in the latter. Quantification of endothelial cells revealed more endothelial cells in IUE tumors than in PDX tumors (Fig S5f). Both ZFTA PDX models showed similar endothelial cell densities in tumor and adjacent healthy brain, whereas the PFA PDX model exhibited the lowest abundance of endothelial cells, potentially reflecting the variability observed in patients. Of note, the number of endothelial cells is not the only determinant of BBB permeability.\u003c/p\u003e \u003cp\u003eOverall, these results highlight the suitability of PDX models to study BBB penetration, as they capture patient-specific variability, including additional genetic alterations or bystander effects, whereas IUE models are more defined and largely driven by a single gene fusion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTemsirolimus and etoposide penetration is higher in ZFTA tumor compared to healthy surrounding\u003c/h2\u003e \u003cp\u003eFunctional validation of BBB characteristics is essential to understand which drug classes display altered penetration in EPN compared to healthy brain. As a first step, we validated tight junction function by observing the penetration of sodium fluorescein, a small fluorescent tracer typically used to study BBB penetration. Based on this assay, ZFTA tumor tissue permeability was lower compared to the surrounding brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b).\u003c/p\u003e \u003cp\u003eNext, we investigated BBB penetration of the chemotherapeutic etoposide \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and the targeted inhibitors idasanutlin and temsirolimus. The latter two drugs were selected based on their response in EPN cell lines (unpublished drug screening data showing activity in EPN cell lines). To quantify the plasma and brain concentrations of the tested drugs, we applied validated UPLC-MS/MS methods. Both etoposide and idasanutlin are lipophilic with a molecular weight of around 600 Da, suggesting low likelihood of paracellular transport. Since idasanutlin is not a known transporter substrate, and ZFTA patients exhibited higher expression of key tight junctions, idasanutlin permeability was only assessed in this model. Despite a moderate Gupta BBB score, idasanutlin showed a consistently low brain-to-plasma ratio of around 0.015 with no significant differences between ZFTA tumor tissue and the corresponding cortex and cerebellum (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d).\u003c/p\u003e \u003cp\u003eEtoposide, in contrast, is a known substrate of PGP and other multidrug resistance transporters such as ABCC2 and ABCC6 \u003csup\u003e32\u003c/sup\u003e. We therefore analyzed etoposide penetration in both PFA and ZFTA tumors, also taking tumor volume into account. Although detailed analysis of PGP expression revealed no significant differences, we saw a trend toward lower PGP levels in ZFTA tumor tissue, particularly in both ZFTA PDX mouse model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Strikingly, etoposide penetration was significantly higher in large ZFTA tumors, with a brain-to-plasma ratio approaching 0.2, compared to corresponding healthy brain regions as well as small ZFTA and PFA tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee,f).\u003c/p\u003e \u003cp\u003eTemsirolimus, a larger molecule, unlikely to undergo paracellular transport, is known as a PGP and transferrin substrate, suggesting a potential contribution of TFRC to its transport. Remarkably, temsirolimus concentrations and brain-to-plasma ratios were significantly higher in ZFTA compared to PFA tumors and healthy brain tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg,h), despite only slightly lower PGP and comparable TFRC expression in the BT165 ZFTA model relative to PFA PDX models. This finding suggests that even subtle reductions in PGP expression may enhance temsirolimus penetration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). In summary, sodium fluorescein penetration was limited in the ZFTA group, indicating reduced paracellular permeability. This aligns with elevated expression of specific tight junction proteins in ZFTA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), although sodium fluorescein transport may also depend on compound-specific characteristics. Regarding drug permeability, idasanutlin showed no difference in accumulation between ZFTA tumor and adjacent healthy brain, whereas temsirolimus and etoposide (in large tumors) exhibited higher accumulation compared to healthy regions. Since both drugs are known PGP substrates, their enhanced permeability might be linked to reduced PGP expression in ZFTA PDX models. However, caution is warranted when extrapolating to patients, as the differential PGP expression observed between ZFTA and PFA models does not fully reflect the human expression patterns, which support consideration of PGP inhibitors in patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOne of the major hurdles in identifying effective drugs for pediatric neuro-oncology is the limited ability to predict their BBB penetration \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Knowledge of differences between the BBB in healthy and tumor tissues is, therefore, highly relevant. In this study, we generated a multi-omics dataset of EPN patients and mouse models, revealing EPN group-specific differences in BBB-associated genes compared to normal tissue. While the BBB is generally believed to be disrupted in brain tumors \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, we observed increased tight junction expression across intracranial EPN, suggesting reduced paracellular permeability. In contrast, no consistent trend was observed for transporters and receptors, which instead exhibited group-specific alterations.\u003c/p\u003e \u003cp\u003eOur established TJ and TP NN scores reflected known brain region-specific differences \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, but also indicated region-independent changes in EPN. The age-associated decrease in BBB transporters was not mirrored for PGP and BCRP in the literature, as PGP levels increase from the perinatal to adult stage, whereas BCRP expression remains largely stable throughout development \u003csup\u003e\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Since some tight junction and transporter genes are also implicated in oncogene regulation and apoptotic signaling \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, we validated their primary localization in endothelial cells. Our single-nuclei dataset, together with re-analysis of published single-cell studies, confirmed their localization in endothelial cells and revealed a previously uncharacterized endothelial subcluster in EPN, called \u0026ldquo;endothelial_BBB cluster\u0026rdquo;. This cluster strongly correlated with two endothelial cell populations previously described in GBM, namely a peripheral cluster enriched for BBB markers (Pe1) and a core-associated angiogenic cluster (Co1) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, a comparison between tumor core and periphery in EPN remains limited due to lack of spatially resolved datasets.\u003c/p\u003e \u003cp\u003eTo address RNA-protein translation, we calculated RNA-protein correlations, which were consistent with findings in medulloblastoma \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Although comparisons across datasets are methodologically challenging \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, we observed positive correlations for most tight junctions and transporters. Exemplary staining confirmed their endothelial localization, supporting the utility of RNA-based TJ NN and TP NN scores to estimate patient-specific BBB permeability. However, interpretation of transporter gene expression is substrate-specific, since drugs rely on distinct transporters and TP NN score includes both import and efflux transporters.\u003c/p\u003e \u003cp\u003eWe further compared BBB features across mouse models, which remain essential tools in preclinical drug testing. Both IUE and PDX models clustered apart from patient samples. A key distinction was the higher abundance of endothelial cells in IUE ZFTA models compared to ZFTA PDX models. This may reflect differences in age (PDX: 5\u0026ndash;6 months; IUE: 2 month), tumor induction method, or genetic background (NSG for PDX/ CD1 for IUE) \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Age-associated BBB differences are conserved across humans and rodents, with protective functions declining with age \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Since electroporation for IUE induction occurs during embryonic barrier formation (E10\u0026ndash;E15), it may also affect BBB physiology \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA more detailed analysis revealed elevated CLDN5 RNA and protein levels in ZFTA patients. In PDX models, CLDN5 was highly expressed in both PFA and ZFTA tumors relative to healthy brain but uniquely enriched in both tumor cells and endothelial cells in the ZFTA BT165 model. This suggests a role beyond endothelial tight junctions, consistent with findings in glioma cell lines where lower CLDN5 expression correlated with higher tumor grade \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Whether a similar link exists to EPN aggressiveness remains to be tested. Functionally, low endothelial CLDN5 expression has been linked with increased permeability for smaller molecules (\u0026lt;\u0026thinsp;800 Da) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, consistent with our finding of reduced sodium fluorescein penetration in ZFTA tumors.\u003c/p\u003e \u003cp\u003eOther tight junctions, including OCLN and TJP1, were upregulated in ZFTA and PFA patient samples compared to healthy brain, but in mouse models this pattern was only confirmed for TJP1 in VBT242 (ZFTA) and EPD210 (PFA). SNAI1, a known repressor of tight junctions, was expressed in EPN patients, in line with literature linking it to BBB disruption and epithelial to mesenchymal cell transition \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMRI-based assessment further supported our molecular findings. Gadolinium-enhanced T1 MRI revealed variability between ZFTA and PFA PDX models. However, interpretation was limited by missing baseline T1 images. Patient MRI data also showed variability, but nonetheless, recent advances in radiomic signatures already enable image-based differentiation of supratentorial EPN from other brain tumor entities, and future approaches may further refine EPN subtype \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. While T1-weighted imaging indicated BBB disruption, dynamic-contrast enhanced MRI offers higher sensitivity \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Indeed, a recent study including posterior fossa EPN patients reported higher BBB penetration compared to medulloblastoma \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, although direct comparison between ZFTA and PFA remains unresolved.\u003c/p\u003e \u003cp\u003eDrug penetration analysis highlighted further differences. For many drugs, the mechanism of BBB transport remains unclear, although most are substrates of PGP and/or BCRP \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIdasanutlin, predicted to have moderate BBB penetration using the Gupta score, revealed consistently low brain-to-plasma ratios (0.02) in ZFTA tumor and healthy brain. This contrasts with higher ratios (0.3) reported in CD1 mice \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, likely reflecting strain differences. Etoposide, predicted to have low penetration based on the Gupta score, exhibited brain-to-plasma ratios around 0.1 with higher accumulation in large ZFTA tumors compared to PFA or healthy tissue. This observation aligns well with slightly reduced PGP protein levels in ZFTA PDX tumors, although etoposide is also a substrate for other efflux pumps (ABCC1/2/3/6) \u003csup\u003e32,56\u003c/sup\u003e. Patient RNA data revealed higher ABCC6 expression in PFA, aligning with reduced etoposide penetration in this group.\u003c/p\u003e \u003cp\u003eTemsirolimus, predicted to have low BBB penetration based on the Gupta score, is a PGP substrate. Despite only modestly lower PGP expression in ZFTA tumors, temsirolimus accumulated in ZFTA but not in PFA tumors. Importantly, PGP expression does not always correlate with functionality as already shown for other cancer entities \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Temsirolimus binds to transferrin, but it is unknown whether transferrin can still engage its receptor (TFRC) \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. TFRC protein levels were comparable in BT165 ZFTA, PFA, and healthy tissue, but were significantly reduced in ZFTA patients suggesting a limited role of TFRC in temsirolimus transport.\u003c/p\u003e \u003cp\u003eTo enhance temsirolimus delivery and better understand the contribution of PGP and TFRC to its tumor penetration, combination strategies with PGP or TFRC inhibitors could be considered.\u003c/p\u003e \u003cp\u003eIn conclusion, molecular EPN groups harbor different BBB characteristics independent of brain region. Paracellular transport is limited, particularly in ZFTA, while drug penetration depends strongly on transporter activity. Idasanutlin showed similarly low penetration in tumor and healthy brain, consistent with its suggested transcellular transport pathway. In contrast, temsirolimus and etoposide displayed higher penetration in ZFTA tumors, presumably due to a reduced activity of slightly lower expressed PGP or involvement of other transporters. Overall, our data demonstrate that EPN maintain a more intact BBB than is typically observed in other brain tumors, with group-specific features that may inform therapy selection.\u003c/p\u003e"},{"header":"Method details","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHuman tissue processing\u003c/h2\u003e \u003cp\u003e All experiments in this study involving human tissue or data were conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the Medical Faculty of Heidelberg University (S-531/2020, S-502/2013; S-795/2020). Written informed consent was obtained from all participants or their legal guardians prior to inclusion in the study. Global BBB gene expression in EPN patients and healthy controls (Table S3) was analyzed in Affymetrix dataset from a combined EPN tumor cohort with integration of normal brain tissues available on R2 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://r2.amc.nl\u003c/span\u003e\u003cspan address=\"http://r2.amc.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Bulk RNA Affymetrix datasets \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e were analyzed using the paired samples Wilcoxon test in the statistical software R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation\u003c/h2\u003e \u003cp\u003eTissue homogenates were processed to isolate RNA by following the manufacture\u0026acute;s protocol of the Maxwell\u0026reg; RSC simply RNA Tissue Kit (Promega, USA). Total RNA sequencing libraries were generated with TruSeq RNA library Prep kit (Illumina, USA) followed by RNA sequencing using NextSeq550/Novaseq S4 with PE 100 (Illumina, USA) in the Genomics Core Facility of the DKFZ (Heidelberg, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTJ and TP Score Calculation\u003c/h2\u003e \u003cp\u003eThe TJ and TP scores were calculated from log2 expression values of all tight junctions and transporters from manually curated gene list (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) by training a neural network with an 80% training set of a comprehensive dataset (n\u0026thinsp;=\u0026thinsp;2646). This dataset includes samples from different brain tumors including adults and children covering various brain regions. The score was applied to EPN patients, healthy controls and healthy controls from different developmental stages as indicated in Table S3 \u003csup\u003e13,24,26\u003c/sup\u003e. Spider plots were created with the radarchart()function in R package fmsb.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell and single nuclei sequencing\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eSample processing and nuclei isolation\u003c/h2\u003e \u003cp\u003eNuclei were isolated from snap frozen tumor samples (n\u0026thinsp;=\u0026thinsp;8) as previously described \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Briefly, tissue samples were fragmented in \u0026ldquo;CHAPS, with salts and Tris\u0026rdquo; (CST) buffer on ice. Cell debris was removed by filtering through 40 \u0026micro;m cell strainers (Greiner, Germany). After centrifugation, nuclei were counted using the Luna Automated cell counter (Logos Biosystems, South Korea) and approximately 9000 nuclei per sample were loaded onto the Chromium single-cell 3\u0026prime; chip (10X Genomics, USA).\u003c/p\u003e \u003cp\u003e \u003cb\u003e10X single-nuclei library preparation and sequencing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Chromium Next GEM Single-Cell 3ʹ Reagent Kits v3.1 (10X Genomics, USA) was used to prepare single-nuclei gene expression libraries according to the manufacturer\u0026acute;s instructions. RNA reverse transcription was performed in separate GEMs (Gel Bead-In-Emulsion) and on separate nuclei. Next, amplified and size-selected enriched cDNA formed the gene expression libraries and were analyzed for quality and quantity using the Qubit dsDNA HS Assay Kit (ThermoFisher Scientific, USA) and TapeStation (Agilent, USA). Equimolar pooled libraries (multiplexes) were sequenced on a NovaSeq 6000 (Illumina, USA) sequencer using S4 flow cell with paired-end reads, according to the manufacturer\u0026rsquo;s instructions, targeting approximately 50,000 reads per nucleus.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData processing and downstream analyses\u003c/h2\u003e \u003cp\u003eAfter pre-processing of raw sequencing data (FASTQ format) with the Cell Ranger pipeline (v6.1.1, 10X Genomics), reads were aligned to the GRCh38 human genome reference to identify expressed genes. Gene expression levels were assessed based on Unique Molecular Identifiers (UMIs) or individual transcript molecules. All downstream analyses described below were applied to this new validation dataset and publicly available single-cell and single-nuclei RNAseq datasets \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e for cross-validation as indicated in Table S3. Expression count matrices were generated by using the Seurat package (v3.23) within R (v4.0.1). To ensure high-quality data for downstream analysis, filtering steps were applied to each dataset, as described previously \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Data were normalized and scaled using the SCTransform function in Seurat, followed by principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for visualization and clustering purposes. These methods allowed the identification of distinct cellular clusters representing various cell types present in each sample. To limit sample-specific technical variations, Harmony (v1.0;\u003csup\u003e65\u003c/sup\u003e) was employed for integration. Cell type annotations were determined by analyzing gene markers for each cluster using Seurat\u0026rsquo;s FindAllMarkers function. The endothelial_BBB cell population was identified through the expression of canonical endothelial markers and the presence of tight junction and transporter gene signatures, as described in this study and corroborated by findings from Xie et al. \u003csup\u003e29\u003c/sup\u003e. Gene expression signatures were scored using Seurat`s \u0026ldquo;AddModuleScore\u0026rdquo; function, generating module scores per cell that were compared across clusters and EPN groups. Data visualizations were generated using the Seurat and SCpubr packages (v2.0.2;\u003csup\u003e66\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMass spectrometry\u003c/h2\u003e \u003cp\u003ePeptides of lysed EPN tissue samples (overview in Table S3) were separated by an Easy NanoLC 1200 system with various C18 analytical columns (Acclaim PepMap RSLC, self-packed Reprosil-Pur, nanoEase BEH C18) coupled to Orbitrap Fusion or Q-Exactive HF instruments (Thermo Fisher Scientific). MS1 scans were acquired at 60,000 resolution (m/z 350\u0026ndash;1500), with MS2 scans in DDA mode at 15,000 resolution using stepped collision energy.\u003c/p\u003e \u003cp\u003eRaw data were analyzed with MaxQuant (v1.5.1.2) against the human Uniprot database using trypsin/P digestion, carbamidomethylation (C) as fixed, and oxidation (M)/acetylation (N-term) as variable modifications. Label-free quantification (LFQ) (provided in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), iBAQ, and matching between runs were enabled. Data processing was done in Excel, Perseus (v1.6.1.3), and R (v3.5.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRNA-Protein correlation\u003c/h2\u003e \u003cp\u003eProteome data was pre-processed by normalizing and checking for batch effects. Median protein and RNA transcript intensities of matched samples were used to analyze RNA-to-protein correlation and a mean linear regression model was calculated over all genes. Residuals were calculated by predicting transcriptome intensities from proteome intensities using the above fitted general linear model and finally subtracting observed transcriptome intensities from predicted intensities for every gene and every sample individually. Differential correlated genes were identified by using the lmFit() function of the limma R package. We filtered for significance based on a log-fold change (logFC) greater than or equal to 2 and an adjusted p-value (adj.Pval) less than 0.05 Barplot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMACSima\u0026trade; imaging cyclic staining (MICS)\u003c/h2\u003e \u003cp\u003eHuman EPN tumor samples were cut in 4 \u0026micro;m tissue slices using a cryotome (Leica CM1950). Tissue slides were fixed in 4% PFA for 10 min and antibody staining was performed as previously described \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Our antibody panel was adapted from Scheuermann et al \u003csup\u003e67\u003c/sup\u003e with the following antibodies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and correlation analysis was performed using the MACS iQ View software (version 1.3.1, Miltenyi Biotec, Germany).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAntibodies added to the established MACSima\u003csup\u003e\u0026trade;\u003c/sup\u003e panel.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrder number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDilution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFluorochrome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABCG2 (BCRP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREA909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130-115-328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD338 (BCRP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5D3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12-8888-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMdr1 (PGP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esc-55510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFITC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esc-133256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFITC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTJP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNBP2-99094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNBP2-99094F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFITC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZO1 (TJP1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33-9111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFITC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eH\u0026amp;E and immunofluorescence staining\u003c/h2\u003e \u003cp\u003eThe tissue was sectioned with the Leica CM1950 with a thickness of 10 \u0026micro;m for H\u0026amp;E staining and immunofluorescence staining. For H\u0026amp;E staining, tissue slides were fixed for 30 min in 4% PFA, washed in water and stained in haematoxylin (2:45 min). After washing rinsing 1 min with water, dipping in 100% ethanol and washing again 4 min with water, slides were incubated 30 sec in 95% ethanol prior to staining with eosine for 30 sec. Then tissue slides were dehydrated in ascending ethanol solutions (70%, 95%,100%) and cleared with xylene for 2 min. Tissue was mounted with Eukitt (Orsatec, Germany). For immunofluorescence, sections were fixed in 4% PFA for 10 min and washed 10 min with PBS. Antigen retrieval was achieved by boiling 30 min in citrate buffer (10 mM sodium citrate tribasic dihydrate and 0.05% Tween20 in ddH2O; pH\u0026thinsp;=\u0026thinsp;6.0), followed by 20 min cooling in this buffer. Cells were permeabilized with PBS with 0.1% Triton-X and blocked in 3% BSA in PBS for 1 h at RT. Primary antibodies were incubated at 4\u0026deg;C overnight, in the dilutions indicated in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After 3 washing steps for 10 min, the secondary fluorophore-coupled antibodies were added in 3% BSA in PBS for 1 h at RT prior to mounting in DAPI-fluoromount. Only TJP1 and CD31 co-staining was performed without antigen retrieval step. Imaging was performed at confocal SP8 microscope (Leica, Germany)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of used antibodies in Western Blot and Immunofluorescence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibodies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManufacturer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCatalog #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIF dilution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWestern blot dilution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti- Occludin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erabbit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eab216327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1:1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-BCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnzo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALX-801-036-C100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-CD31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erabbit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eab28364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-CD31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSanta Cruz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esc-18916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Claudin5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInvitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4C3C2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1:500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-PGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSanta Cruz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esc-13131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-ZO1/TJ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erabbit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInvitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61-7300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1:666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eAnimal studies\u003c/h2\u003e \u003cp\u003e All animal experiments were conducted in accordance with legal and ethical regulations and approved by the responsible council (Germany, Regierungspr\u0026auml;sidium Karlsruhe: G-164/17, G-187/18, G-227/19, G-228/19, G-255/19, G-76/20, G-58/22 and G-91/20). The study was performed according to GV-SOLAS and ARRIVE guidelines. Mice were obtained from in-house breeding of the DKFZ (Heidelberg, Germany), housed in IVC caging in the Center for Preclinical Research of the DKFZ (Heidelberg, Germany) and monitored daily for the presence of tumor-related symptoms. Cohort sizes were chosen to minimize the number of animals required to get statistically significant results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePatient-derived xenograft mouse models\u003c/h2\u003e \u003cp\u003ePatient-derived tumor cells were orthotopically injected in 6\u0026ndash;8 week oldimmunocompromised mice (NOD-SCID gamma mice (NSG)). Before injections, experimental animals received a painkiller (Metamizol, s.c., 200 mg/kg or 5 mg/kg s.c. Carprofen). Mice were sedated with 1.5\u0026ndash;2.5 Vol% isoflurane and eye ointment avoided dehydration. After a negative reflex test, mice were placed on a heating mat with stereotactic fixation. The head was disinfected, a 5 mm incision was performed, and a whole was drilled in the scull at the right position using coordinates from bregma (front suture intersection) and lambda (ZFTA cells in cortex and PFA cells in cerebellum). Tumor cells were intracranially injected with a hamilton needle. After a short waiting time and prior to retracting the needle, the wound was closed with tissue adhesive histoacryl (Braun, Germany). After surgery mice received painkiller via drinking water for three days.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn utero\u003c/b\u003e \u003cb\u003eelectroporation (IUE) mouse models\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDNA fusion gene constructs were transfected into CD1 mice at E13.5 as described in Zheng et al \u003csup\u003e68\u003c/sup\u003e. After birth, mice developed tumors within days to weeks depending on the exact model. Tumor growth was measured via bioluminescence imaging or MRI techniques.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRNA-Sequencing data processing and downstream analysis\u003c/h2\u003e \u003cp\u003eA combined genome reference was constructed with human reference genome GRCh38 and mouse reference GRCm38 using STAR (version 2.7.10b). Raw sequencing reads were aligned to the combined reference with STAR. Count matrices were generated subsequently with featureCounts (version 2.0.6) and a combined annotation file. Counts were then separated back into human counts and mouse counts by gene annotation. This dataset was combined with an independent human patient cohort from the INFORM database \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and an IUE mouse model cohort as summarized in Table S3.\u003c/p\u003e \u003cp\u003eSpecies specific counts were further processed using DESeq2 (version 1.46.0) in R (version 4.4.3). Size factors were estimated to normalize for sequencing depth and\u003c/p\u003e \u003cp\u003edifferences in library size across samples. Normalized counts were further transformed using variance stabilization transformation (vst) to assure constant variance along the range of mean values. Visualization of normalized and raw counts was performed using R packages ggplot2 (version 3.5.2) and pheatmap (version 1.0.13). After normalization, datasets were merged and subsetted by the respective marker genes. t-distributed Stochastic Neighbor Embedding (tSNE) was subsequently applied for dimensionality reduction via Rtsne package (version 0.17).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eBioluminescence Imaging\u003c/h2\u003e \u003cp\u003eBioluminescence measurements were performed to monitor tumor size developments in mouse models after tumor cell labelling with luciferase. Mice were sedated with 1.5-2 Vol% isoflurane, luciferin solution (15 mg/ml) was injected accordingly to the body weight and photons per seconds were measured.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMagnet Resonance Imaging (MRI)\u003c/h2\u003e \u003cp\u003eMRI was carried out at the small animal imaging core facility at the DKFZ using a BioSpec 3 Tesla (Bruker, Germany) with ParaVision software 360 V1.1. For the imaging, mice were anesthetized with 3.5% sevoflurane in air. For lesion detection T2 weighted imaging were performed using a T2_TurboRARE sequence: TE\u0026thinsp;=\u0026thinsp;48 ms, TR\u0026thinsp;=\u0026thinsp;3350 ms, FOV 20x20 mm, slice thickness 1 mm, averages\u0026thinsp;=\u0026thinsp;3, Scan Time 3m21s, echo spacing 12 ms, rare factor 8, slices 20, image size 192x192. Tumor volume was measured using a T1-FLASH sequence with contrast agent, 80\u0026ndash;100 \u0026micro;l ProHance (Bracco Imaging, Germany) i.p.): TE\u0026thinsp;=\u0026thinsp;3ms, TR\u0026thinsp;=\u0026thinsp;500ms, FOV 20x20 mm, slice thickness 1 mm, slices 20, Flip angle 70, averages\u0026thinsp;=\u0026thinsp;3, resolution\u0026thinsp;=\u0026thinsp;0,104 mm. Tumor volume was determined by manual segmentation using Bruker ParaVision software 6.0.1. The regions of interest (ROI) were visualized and manually labelled using a the RadiAnt DICOM Viewer Software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eUltramicroscopy\u003c/h2\u003e \u003cp\u003eIn order to label endothelial cells, Lectin-TexasRed (12 mg/kg body weight, Vector Laboratories; USA) was injected intravenously into mice prior to sedation. Cardiac perfusion was performed with sodium fluorescein (Sigma, Germany). Next, mice were perfused with 4% PFA and brains were fixed in 4% PFA for 24 h at RT, afterwards the brain was transferred in ascending butanol solutions (30%, 50%, 70%, 80%, 96%, 100%, 100%), each for 24 h at RT \u003csup\u003e69\u003c/sup\u003e. After refractive index matching and lipid removal in benzyl alcohol benzyl benzoate (BABB) solution, tissue was transferred in ethyl cinnamate for imaging in lightsheet 7 (Zeiss, Germany) with 20x objective.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eMouse Treatment Study\u003c/h2\u003e \u003cp\u003eEPN PDX mouse models with a tumor bioluminescence signal of at least 3x10^6 photons/seconds were treated daily for three consecutive days with idasanutlin (Hycultec, Germany, 150mg/kg, i.p.) and temsirolimus (Alsachim, France, 150 mg/kg, i.p.). 4 h after the last treatment, mice were euthanized by increasing CO2 concentrations. Etoposide (Biozol, Germany, 20 mg/kg), i.p. was given daily for three consecutive days combined with carboplatin (Biozol, Germany, 50 mg/kg, i.v.) on the last day. At the last time point, both drugs were administered simultanously and mice were euthanized 0.5-1 h afterwards. Post mortem, blood was taken by cardiac puncture and brain tissue was collected for pharmacokinetic measurements after PBS perfusion separated into cortex, cerebellum und tumor.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMouse brain tissue homogenization and drug extraction\u003c/h2\u003e \u003cp\u003e1 mg of brain tissue was dissolved in 10 \u0026micro;L of water/acetonitrile (H\u003csub\u003e2\u003c/sub\u003eO/ACN, 95/5) solution supplemented with 30 glass beads (0.75\u0026ndash;1 mm; Carl Roth GmbH, Germany). Tissue samples were homogenized using the Bead Ruptor 4 homogenizer (Omni International Inc, USA) for at least 30 seconds and stored at -20\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of UPLC-MS/MS methods for drugs quantification\u003c/h2\u003e \u003cp\u003eStock solution of idasanutlin (Hycultec, Germany) was prepared in 1:1 H2O/ACN, and calibration/quality control solutions were prepared in 1:1 H2O/ACN\u0026thinsp;+\u0026thinsp;0.1% FA. The stock solution of internal standard idasanutlin-d3-1 (Hycultec, Germany) was dissolved in ethanol and working solution was dissolved in 1:1 H2O/ACN. After plasma protein precipitation with acetonitrile, the samples were subjected to chromatographic separation on an ACQUITY UPLC BEH C18 column (Waters, USA) with an acetonitrile gradient. For UPLC-MS/MS quantification, a triple-stage quadrupole mass spectrometer (Waters Xevo TQ-S with Z-spray electrospray ionization (ESI) source) with an Acquity Classic UPLC\u0026reg; (Waters, Milford, MA, USA) was used. Idasanutlin calibration range was determined between 0.3\u0026ndash;1000 ng/mL and 3\u0026ndash;10,000 ng/g for mouse plasma and brain homogenate, respectively.\u003c/p\u003e \u003cp\u003eStock solutions, calibration solutions and quality control samples of temsirolimus (Alsachim, France) and its internal standard temsirolimus-13C3,2H7 (Alsachim, France) were prepared in 1:1 H2O/ACN. After whole blood protein precipitation with acetonitrile, the samples were subjected to chromatographic separation on an ACQUITY UPLC BEH C18 column (Waters, USA) with an acetonitrile gradient. For UPLC-MS/MS quantification the same device as described for idasanutlin was used. Temsirolimus calibration range was determined between 10\u0026ndash;10,000 ng/mL and 100\u0026ndash;100,000 ng/g in mouse whole blood and brain homogenate, respectively.\u003c/p\u003e \u003cp\u003eStock solutions of etoposide (Med Chem Express, USA) and its internal standard etoposide-d3 (Santa Cruz Biotechnology, USA) were diluted in 1:1 H2O/ACN. Calibration/quality control solutions, and working solution of the internal standard were prepared in 95/5 H\u003csub\u003e2\u003c/sub\u003eO/ACN\u0026thinsp;+\u0026thinsp;0.1% FA. After plasma protein precipitation with acetonitrile, the samples were subjected to chromatographic separation on an ACQUITY UPLC BEH C18 column (Waters, USA) with an acetonitrile gradient in the presence of 5 mM ammonium acetate. For UPLC-MS/MS quantification, a triple-stage quadrupole mass spectrometer (Waters Xevo TQ-XS with Z-spray electrospray ionization (ESI) source) with an Acquity Classic UPLC\u0026reg; (Waters, Milford, MA, USA) was used. Etoposide calibration range was determined between 3\u0026thinsp;\u0026minus;\u0026thinsp;1,000ng/mL and 30\u0026thinsp;\u0026minus;\u0026thinsp;10,000 ng/g in plasma and brain homogenate, respectively.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWestern Blot\u003c/h3\u003e\n\u003cp\u003eMouse tissues were homogenized in cold PBS by using a tissue homogenizer for 30 sec. After centrifugation of 10 min at 4\u0026deg;C 300 g, the pellet was dissolved in 150 \u0026micro;l RIPA buffer containing the protease inhibitor cocktail cOmplete (Merck, Germany) and incubated for 1 h on ice. After lysis, cell homogenate was centrifuged 10 min at 4\u0026deg;C 16200 g. Proteins in supernatant were separated and blotted as described in Okonechnikov \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e70\u003c/sup\u003e. Membranes were blocked for 1 h with 5% BSA except for TJP1 antibody, for which 5% milk in Tris buffered saline with Tween20 (TBS-T). They were then incubated overnight at 4\u0026deg;C with the primary antibody (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), followed by 1 h incubation in secondary horseradish peroxidase-conjugated antibodies (cell signaling, dilution 1:2500).\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eSodium fluorescein and endothelial cell quantification\u003c/h2\u003e \u003cp\u003eFor sodium fluorescein intensity quantification, imagej was used to substract lectin staining and remaining intensity in all z-stack images was quantified.\u003c/p\u003e \u003cp\u003eEndothelial cell quantification from human/mouse immunofluorescence images were performed with cellprofiler 4.2.6 using identify primary, secondary, tertiary objects followed by measureImageAreaOccupied.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Natalie Stumpf, Lukas Schmitt, Paula Ertel, Norman Mack and Benjamin Schwalm for excellent technical assistance. We thank Samuel Walther for his help in the development of the analytical method for etoposide quantification. In addition, we thank the small animal imaging, light microscopy and sequencing core facility of DKFZ for their great support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 404521405, SFB 1389 - UNITE Glioblastoma, Work Package C01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors` contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJulia Sundheimer\u003c/strong\u003e: Investigation, methodology, formal analysis, writing – review and editing. \u003cstrong\u003eJulia Benzel\u003c/strong\u003e: Investigation, methodology, formal analysis, writing – review and editing.\u0026nbsp;\u003cstrong\u003eAniello Federico:\u0026nbsp;\u003c/strong\u003eInvestigation, formal analysis writing – review and editing. \u003cstrong\u003eStefanie Volz\u003c/strong\u003e Investigation, formal analysis. \u003cstrong\u003eSzymon Kmiecik\u003c/strong\u003e Investigation, formal analysis. \u003cstrong\u003eJürgen Burhenne:\u0026nbsp;\u003c/strong\u003eInvestigation, formal analysis.\u0026nbsp;\u003cstrong\u003eGzona Bajraktari-Sylejmani\u003c/strong\u003e Investigation, formal analysis. \u003cstrong\u003eSophia Scheuermann:\u0026nbsp;\u003c/strong\u003eInvestigation, formal analysis, writing – review \u0026amp; editing. \u003cstrong\u003eAnke King\u0026nbsp;\u003c/strong\u003eInvestigation, formal analysis. \u003cstrong\u003eJeroen Krijgsveld\u003c/strong\u003e: Resources, writing – review \u0026amp; editing, supervision \u003cstrong\u003eChristian Seitz\u0026nbsp;\u003c/strong\u003eResources, writing – review \u0026amp; editing, supervision\u003cstrong\u003e\u0026nbsp;Marcel Kool\u0026nbsp;\u003c/strong\u003eResources, writing – review \u0026amp; editing, supervision\u003cstrong\u003eMaximilian Knoll:\u0026nbsp;\u003c/strong\u003eInvestigation, formal analysis.\u0026nbsp;\u003cstrong\u003eBritta Statz:\u0026nbsp;\u003c/strong\u003eInvestigation, formal analysis.\u0026nbsp;\u003cstrong\u003eTuyu Zheng:\u0026nbsp;\u003c/strong\u003eInvestigation, formal analysis.\u003cstrong\u003e\u0026nbsp;Stefan Pfister\u003c/strong\u003e: Resources, funding acquisition, writing – review \u0026amp; editing, supervision.\u003cstrong\u003e\u0026nbsp;Kristian Pajtler\u003c/strong\u003e: Conceptualization, supervision, resources, writing – review and editing, project administration. \u003cstrong\u003eKendra Maass\u003c/strong\u003e: Conceptualization, investigation, methodology, formal analysis, supervision, resources, writing – review and editing, project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman RNA array data are publicly available (GEO: GSE64415 and GEO: GSE50161, GSE50385, GSE21687, GSE3526). All other data are available from the corresponding author upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode for single cell analysis is available here: https://github.com/afederico-sci/scRNAseq_BBB_analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments in this study involving human tissue or data approved by the ethics committee of the Medical Faculty of Heidelberg University (S-531/2020, S-502/2013; S-795/2020). Animal experiments were performed in accordance with national and European guidelines for the care and use of laboratory animals (European regulations 2010/63/EU) under the German license number: G-164/17, G-187/18, G-227/19, G-228/19, G-255/19, G-76/20, G-58/22 and G-91/20 approved by the responsible regional council (Regierungspräsidium Karlsruhe, Germany) and the internal reference number DKFZ374.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGawdi, R., Shumway, K. R. \u0026amp; Emmady, P. D. in \u003cem\u003eStatPearls\u003c/em\u003e (StatPearls Publishing Copyright \u0026copy; 2022, StatPearls Publishing LLC., (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbott, N. J., R\u0026ouml;nnb\u0026auml;ck, L. \u0026amp; Hansson, E. Astrocyte-endothelial interactions at the blood-brain barrier. \u003cem\u003eNat. Rev. 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Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 2300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41467-023-38044-0\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41467-023-38044-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blood-brain barrier, tight junctions, efflux pumps, transporter, receptor, ependymoma","lastPublishedDoi":"10.21203/rs.3.rs-7491341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7491341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA significant obstacle in treating brain tumors is the limited drug penetration across the blood-brain barrier (BBB), characterized by an interplay of endothelial tight junctions and efflux pumps. Brain tumors can alter BBB characteristics; however, there is limited understanding in ependymoma (EPN), the third most common pediatric brain tumor. To this end, we characterized EPN tumor (n\u0026thinsp;=\u0026thinsp;364) and healthy brain tissues (n\u0026thinsp;=\u0026thinsp;225) at RNA level and identified a distinct EPN group-specific BBB transcriptional pattern. Analyses of a validation single-cell (n\u0026thinsp;=\u0026thinsp;8) and publicly available datasets from Aubin and Gojo could further specify a novel BBB signature expressed in an endothelial subpopulation. Drugs that were effective against EPN \u003cem\u003ein vitro\u003c/em\u003e were further evaluated for BBB penetration in our subtype-specific patient-derived xenograft (PDX) models. Idasanutlin reached low brain-to-plasma ratios in both tumor and surrounding brain tissue, while the P-glycoprotein (PGP) substrates temsirolimus and etoposide accumulated slightly more in zinc finger translocation associated (ZFTA)-fusion positive EPN than in PFA tumors and adjacent brain, consistent with slightly lower PGP levels in ZFTA compared to PFA PDX but not patient tumors. Despite these differences, all tested drugs remained below their effective \u003cem\u003ein vitro\u003c/em\u003e levels. In summary, multi-omics analyses of BBB characteristics improve the understanding of drug penetrance and may potentially guide treatment choices in the context of molecular EPN groups within upcoming clinical trials.\u003c/p\u003e","manuscriptTitle":"Ependymoma group-specific blood-brain barrier differences uncovered by a multi-omics approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 14:15:40","doi":"10.21203/rs.3.rs-7491341/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-24T05:51:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T15:11:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161980830288246005432202597083623800588","date":"2026-01-16T13:25:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-13T17:05:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61641032241141421448790019940016837919","date":"2026-01-13T16:16:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T15:58:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T04:53:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-15T14:15:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-15T14:08:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41fd1f9f-70ef-40ac-97c1-2abe22684c9f","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61115022,"name":"Biological sciences/Cancer"},{"id":61115023,"name":"Biological sciences/Neuroscience"},{"id":61115024,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-13T16:04:20+00:00","versionOfRecord":{"articleIdentity":"rs-7491341","link":"https://doi.org/10.1038/s41598-026-47499-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-10 15:58:20","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2026-01-16 14:15:40","video":"","vorDoi":"10.1038/s41598-026-47499-2","vorDoiUrl":"https://doi.org/10.1038/s41598-026-47499-2","workflowStages":[]},"version":"v1","identity":"rs-7491341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7491341","identity":"rs-7491341","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}