Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

Pleomorphic xanthoastrocytoma (PXA) in its classic manifestation exhibits distinct morphological features and is assigned to CNS WHO grade 2 or grade 3. Distinction from glioblastoma variants and lower grade glial and glioneuronal tumors is a common diagnostic challenge. We compared a morphologically defined set of PXA (histPXA) with an independent set, defined by DNA methylation analysis (mcPXA). HistPXA encompassed 144 tumors all subjected to DNA methylation array analysis. Sixty-two histPXA matched to the methylation class mcPXA. These were combined with the cases that showed the mcPXA signature but had received a histopathological diagnosis other than PXA. This cohort constituted a set of 220 mcPXA. Molecular and clinical parameters were analyzed in these groups. Morphological parameters were analyzed in a subset of tumors with FFPE tissue available. HistPXA revealed considerable heterogeneity in regard to methylation classes, with methylation classes glioblastoma and ganglioglioma being the most frequent mismatches. Similarly, the mcPXA cohort contained tumors of diverse histological diagnoses, with glioblastoma constituting the most frequent mismatch. Subsequent analyses demonstrated the presence of canonical pTERT mutations to be associated with unfavorable prognosis among mcPXA. Based on these data, we consider the tumor type PXA to be histologically more varied than previously assumed. Histological approach to diagnosis will predominantly identify cases with the established archetypical morphology. DNA methylation analysis includes additional tumors in the tumor class PXA that share similar DNA methylation profile but lack the typical morphology of a PXA. DNA methylation analysis also assist in separating other tumor types with morphologic overlap to PXA. Our data suggest the presence of canonical pTERT mutations as a robust indicator for poor prognosis in methylation class PXA.

Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly malignant neoplasms posing diagnostic challenge due to a lack of defining molecular markers. CNS neuroblastoma with forkhead box R2 (FOXR2) activation (CNS_NBL) emerged as a distinct pediatric brain tumor entity from a pool previously diagnosed as primitive neuroectodermal tumors of the central nervous system (CNS-PNETs). Current standard of identifying CNS_NBL relies on molecular analysis. We set out to establish immunohistochemical markers allowing safely distinguishing CNS_NBL from morphological mimics. To this aim we analyzed a series of 84 brain tumors institutionally diagnosed as CNS-PNET. As expected, epigenetic analysis revealed different methylation groups corresponding to the (1) CNS-NBL (24%), (2) glioblastoma IDH wild-type subclass H3.3 G34 (26%), (3) glioblastoma IDH wild-type subclass MYCN (21%) and (4) ependymoma with RELA_C11orf95 fusion (29%) entities. Transcriptome analysis of this series revealed a set of differentially expressed genes distinguishing CNS_NBL from its mimics. Based on RNA-sequencing data we established SOX10 and ANKRD55 expression as genes discriminating CNS_NBL from other tumors exhibiting CNS-PNET. Immunohistochemical detection of combined expression of SOX10 and ANKRD55 clearly identifies CNS_NBL discriminating them to other hemispheric CNS neoplasms harboring "PNET-like" microscopic appearance. Owing the rarity of CNS_NBL, a confirmation of the elaborated diagnostic IHC algorithm will be necessary in prospective patient series.

Ewing sarcoma (EwS) is characterized by EWSR1-ETS fusion transcription factors converting polymorphic GGAA microsatellites (mSats) into potent neo-enhancers. Although the paucity of additional mutations makes EwS a genuine model to study principles of cooperation between dominant fusion oncogenes and neo-enhancers, this is impeded by the limited number of well-characterized models. Here we present the Ewing Sarcoma Cell Line Atlas (ESCLA), comprising whole-genome, DNA methylation, transcriptome, proteome, and chromatin immunoprecipitation sequencing (ChIP-seq) data of 18 cell lines with inducible EWSR1-ETS knockdown. The ESCLA shows hundreds of EWSR1-ETS-targets, the nature of EWSR1-ETS-preferred GGAA mSats, and putative indirect modes of EWSR1-ETS-mediated gene regulation, converging in the duality of a specific but plastic EwS signature. We identify heterogeneously regulated EWSR1-ETS-targets as potential prognostic EwS biomarkers. Our freely available ESCLA (http://r2platform.com/escla/) is a rich resource for EwS research and highlights the power of comprehensive datasets to unravel principles of heterogeneous gene regulation by chimeric transcription factors.

Cerebral cavernous malformations are clusters of aberrant vessels that can lead to severe neurological complications. Pathogenic loss-of-function variants in the CCM1, CCM2, or CCM3 gene are associated with the autosomal dominant form of the disease. While interpretation of variants in protein-coding regions of the genes is relatively straightforward, functional analyses are often required to evaluate the impact of non-coding variants. Because of multiple alternatively spliced transcripts and different transcription start points, interpretation of variants in the 5' untranslated and upstream regions of CCM1 is particularly challenging. Here, we identified a novel deletion of the non-coding exon 1 of CCM1 in a proband with multiple CCMs which was initially classified as a variant of unknown clinical significance. Using CRISPR/Cas9 genome editing in human iPSCs, we show that the deletion leads to loss of CCM1 protein and deregulation of KLF2, THBS1, NOS3, and HEY2 expression in iPSC-derived endothelial cells. Based on these results, the variant could be reclassified as likely pathogenic. Taken together, variants in regulatory regions need to be considered in genetic CCM analyses. Our study also demonstrates that modeling variants of unknown clinical significance in an iPSC-based system can help to come to a final diagnosis.

Analysis of mutational signatures can reveal underlying molecular mechanisms of the processes that have imprinted the somatic mutations found in cancer genomes. Here, we analyze single base substitutions and small insertions and deletions in pediatric cancers encompassing 785 whole-genome sequenced tumors from 27 molecularly defined cancer subtypes. We identified only a small number of mutational signatures active in pediatric cancers, compared with previously analyzed adult cancers. Further, we report a significant difference in the proportion of pediatric tumors showing homologous recombination repair defect signatures compared with previous analyses. In pediatric leukemias, we identified an indel signature, not previously reported, characterized by long insertions in nonrepeat regions, affecting mainly intronic and intergenic regions, but also exons of known cancer genes. We provide a systematic overview of COSMIC v.3 mutational signatures active across pediatric cancers, which is highly relevant for understanding tumor biology and enabling future research in defining biomarkers of treatment response.

Accurate identification of brain tumor molecular subgroups is increasingly important. We aimed to establish the most accurate and reproducible ependymoma subgroup biomarker detection techniques, across 147 cases from International Society of Pediatric Oncology (SIOP) Ependymoma II trial participants, enrolled in the pan-European "Biomarkers of Ependymoma in Children and Adolescents (BIOMECA)" study.

Glioneuronal tumors are a heterogenous group of CNS neoplasms that can be challenging to accurately diagnose. Molecular methods are highly useful in classifying these tumors-distinguishing precise classes from their histological mimics and identifying previously unrecognized types of tumors. Using an unsupervised visualization approach of DNA methylation data, we identified a novel group of tumors (n = 20) that formed a cluster separate from all established CNS tumor types. Molecular analyses revealed ATRX alterations (in 16/16 cases by DNA sequencing and/or immunohistochemistry) as well as potentially targetable gene fusions involving receptor tyrosine-kinases (RTK; mostly NTRK1-3) in all of these tumors (16/16; 100%). In addition, copy number profiling showed homozygous deletions of CDKN2A/B in 55% of cases. Histological and immunohistochemical investigations revealed glioneuronal tumors with isomorphic, round and often condensed nuclei, perinuclear clearing, high mitotic activity and microvascular proliferation. Tumors were mainly located supratentorially (84%) and occurred in patients with a median age of 19 years. Survival data were limited (n = 18) but point towards a more aggressive biology as compared to other glioneuronal tumors (median progression-free survival 12.5 months). Given their molecular characteristics in addition to anaplastic features, we suggest the term glioneuronal tumor with ATRX alteration, kinase fusion and anaplastic features (GTAKA) to describe these tumors. In summary, our findings highlight a novel type of glioneuronal tumor driven by different RTK fusions accompanied by recurrent alterations in ATRX and homozygous deletions of CDKN2A/B. Targeted approaches such as NTRK inhibition might represent a therapeutic option for patients suffering from these tumors.

Actinomycin D is a potent cytotoxic drug against pediatric (and other) tumors that is thought to barely cross the blood-brain barrier. To evaluate its potential applicability for the treatment of patients with central nervous system (CNS) tumors, we established a cerebral microdialysis model in freely moving mice and investigated its CNS disposition by quantifying actinomycin D in cerebral microdialysate, brain tissue homogenate, and plasma. For this purpose, we developed and validated an ultraperformance liquid chromatography-tandem mass spectrometry assay suitable for ultra-sensitive quantification of actinomycin D in the pertinent biological matrices in micro-samples of only 20 µL, with a lower limit of quantification of 0.05 ng/mL. In parallel, we confirmed actinomycin D as a substrate of P-glycoprotein (P-gp) in in vitro experiments. Two hours after intravenous administration of 0.5 mg/kg, actinomycin D reached total brain tissue concentrations of 4.1 ± 0.7 ng/g corresponding to a brain-to-plasma ratio of 0.18 ± 0.03, while it was not detectable in intracerebral microdialysate. This tissue concentration exceeds the concentrations of actinomycin D that have been shown to be effective in in vitro experiments. Elimination of the drug from brain tissue was substantially slower than from plasma, as shown in a brain-to-plasma ratio of approximately 0.53 after 22 h. Because actinomycin D reached potentially effective concentrations in brain tissue in our experiments, the drug should be further investigated as a therapeutic agent in potentially susceptible CNS malignancies, such as ependymoma.

More than 60% of supratentorial ependymomas harbor a ZFTA-RELA (ZRfus) gene fusion (formerly C11orf95-RELA). To study the biology of ZRfus, we developed an autochthonous mouse tumor model using in utero electroporation (IUE) of the embryonic mouse brain. Integrative epigenomic and transcriptomic mapping was performed on IUE-driven ZRfus tumors by CUT&RUN, chromatin immunoprecipitation sequencing, assay for transposase-accessible chromatin sequencing, and RNA sequencing and compared with human ZRfus-driven ependymoma. In addition to direct canonical NFκB pathway activation, ZRfus dictates a neoplastic transcriptional program and binds to thousands of unique sites across the genome that are enriched with PLAGL family transcription factor (TF) motifs. ZRfus activates gene expression programs through recruitment of transcriptional coactivators (Brd4, Ep300, Cbp, Pol2) that are amenable to pharmacologic inhibition. Downstream ZRfus target genes converge on developmental programs marked by PLAGL TF proteins, and activate neoplastic programs enriched in Mapk, focal adhesion, and gene imprinting networks. SIGNIFICANCE: Ependymomas are aggressive brain tumors. Although drivers of supratentorial ependymoma (ZFTA- and YAP1-associated gene fusions) have been discovered, their functions remain unclear. Our study investigates the biology of ZFTA-RELA-driven ependymoma, specifically mechanisms of transcriptional deregulation and direct downstream gene networks that may be leveraged for potential therapeutic testing.This article is highlighted in the In This Issue feature, p. 2113.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has rarely been used in the field of therapeutic drug monitoring, partly because of the complexity of the ionization processes between the compounds to be quantified and the many MALDI matrices available. The development of a viable MALDI-MS method that meets regulatory guidelines for bioanalytical method validation requires prior knowledge of the suitability of (i) the MALDI matrix with the analyte class and properties for ionization, (ii) the crystallization properties of the MALDI matrix with automation features, and (iii) the MS instrumentation used to achieve sensitive and specific measurements in order to determine low pharmacological drug concentrations in biological matrices. In the present hybrid article/white paper, we review the developments required for the establishment of MALDI-MS assays for the quantification of drugs in tissues and plasma, illustrated with concrete results for the different steps. We summarize the necessary parameters that need to be controlled for the successful development of fully validated MALDI-MS methods according to regulatory authorities, as well as currently unsolved problems and promising ways to address them. Finally, we propose an expert opinion on future perspectives and needs in order to establish MALDI-MS as a universal method for therapeutic drug monitoring.

In 2012, an international consensus paper reported that medulloblastoma comprises four molecular subgroups (WNT, SHH, Group 3, and Group 4), each associated with distinct genomic features and clinical behavior. Independently, multiple recent reports have defined further intra-subgroup heterogeneity in the form of biologically and clinically relevant subtypes. However, owing to differences in patient cohorts and analytical methods, estimates of subtype number and definition have been inconsistent, especially within Group 3 and Group 4. Herein, we aimed to reconcile the definition of Group 3/Group 4 MB subtypes through the analysis of a series of 1501 medulloblastomas with DNA-methylation profiling data, including 852 with matched transcriptome data. Using multiple complementary bioinformatic approaches, we compared the concordance of subtype calls between published cohorts and analytical methods, including assessments of class-definition confidence and reproducibility. While the lowest complexity solutions continued to support the original consensus subgroups of Group 3 and Group 4, our analysis most strongly supported a definition comprising eight robust Group 3/Group 4 subtypes (types I-VIII). Subtype II was consistently identified across all component studies, while all others were supported by multiple class-definition methods. Regardless of analytical technique, increasing cohort size did not further increase the number of identified Group 3/Group 4 subtypes. Summarizing the molecular and clinico-pathological features of these eight subtypes indicated enrichment of specific driver gene alterations and cytogenetic events amongst subtypes, and identified highly disparate survival outcomes, further supporting their biological and clinical relevance. Collectively, this study provides continued support for consensus Groups 3 and 4 while enabling robust derivation of, and categorical accounting for, the extensive intertumoral heterogeneity within Groups 3 and 4, revealed by recent high-resolution subclassification approaches. Furthermore, these findings provide a basis for application of emerging methods (e.g., proteomics/single-cell approaches) which may additionally inform medulloblastoma subclassification. Outputs from this study will help shape definition of the next generation of medulloblastoma clinical protocols and facilitate the application of enhanced molecularly guided risk stratification to improve outcomes and quality of life for patients and their families.

Pituicytoma (PITUI), granular cell tumor (GCT), and spindle cell oncocytoma (SCO) are rare tumors of the posterior pituitary. Histologically, they may be challenging to distinguish and have been proposed to represent a histological spectrum of a single entity. We performed targeted next-generation sequencing, DNA methylation profiling, and copy number analysis on 47 tumors (14 PITUI; 12 GCT; 21 SCO) to investigate molecular features and explore possibilities of clinically meaningful tumor subclassification. We detected two main epigenomic subgroups by unsupervised clustering of DNA methylation data, though the overall methylation differences were subtle. The largest group (n = 23) contained most PITUIs and a subset of SCOs and was enriched for pathogenic mutations within genes in the MAPK/PI3K pathways (12/17 [71%] of sequenced tumors: FGFR1 (3), HRAS (3), BRAF (2), NF1 (2), CBL (1), MAP2K2 (1), PTEN (1)) and two with accompanying TERT promoter mutation. The second group (n = 16) contained most GCTs and a subset of SCOs, all of which mostly lacked identifiable genetic drivers. Outcome analysis demonstrated that the presence of chromosomal imbalances was significantly associated with reduced progression-free survival especially within the combined PITUI and SCO group (p = 0.031). In summary, we observed only subtle DNA methylation differences between posterior pituitary tumors, indicating that these tumors may be best classified as subtypes of a single entity. Nevertheless, our data indicate differences in mutation patterns and clinical outcome. For a clinically meaningful subclassification, we propose a combined histo-molecular approach into three subtypes: one subtype is defined by granular cell histology, scarcity of identifiable oncogenic mutations, and favorable outcome. The other two subtypes have either SCO or PITUI histology but are segregated by chromosomal copy number profile into a favorable group (no copy number changes) and a less favorable group (copy number imbalances present). Both of the latter groups have recurrent MAPK/PI3K genetic alterations that represent potential therapeutic targets.

Forkhead box R2 (FOXR2) is a forkhead transcription factor located on the X chromosome whose expression is normally restricted to the testis. In this study, we performed a pan-cancer analysis of FOXR2 activation across more than 10,000 adult and pediatric cancer samples and found FOXR2 to be aberrantly upregulated in 70% of all cancer types and 8% of all individual tumors. The majority of tumors (78%) aberrantly expressed FOXR2 through a previously undescribed epigenetic mechanism that involves hypomethylation of a novel promoter, which was functionally validated as necessary for FOXR2 expression and proliferation in FOXR2-expressing cancer cells. FOXR2 promoted tumor growth across multiple cancer lineages and co-opted ETS family transcription circuits across cancers. Taken together, this study identifies FOXR2 as a potent and ubiquitous oncogene that is epigenetically activated across the majority of human cancers. The identification of hijacking of ETS transcription circuits by FOXR2 extends the mechanisms known to active ETS transcription factors and highlights how transcription factor families cooperate to enhance tumorigenesis.

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

Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients' outcome. We aimed at establishing a uniform molecular classification using DNA methylation profiling. Nine molecular subgroups were identified in a large cohort of 500 tumors, 3 in each anatomical compartment of the CNS, spine, posterior fossa, supratentorial. Two supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification and thus might serve as a basis for the next World Health Organization classification of CNS tumors.

Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.

In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required.

Medulloblastomas are the most common malignant brain tumours in children. Identifying and understanding the genetic events that drive these tumours is critical for the development of more effective diagnostic, prognostic and therapeutic strategies. Recently, our group and others described distinct molecular subtypes of medulloblastoma on the basis of transcriptional and copy number profiles. Here we use whole-exome hybrid capture and deep sequencing to identify somatic mutations across the coding regions of 92 primary medulloblastoma/normal pairs. Overall, medulloblastomas have low mutation rates consistent with other paediatric tumours, with a median of 0.35 non-silent mutations per megabase. We identified twelve genes mutated at statistically significant frequencies, including previously known mutated genes in medulloblastoma such as CTNNB1, PTCH1, MLL2, SMARCA4 and TP53. Recurrent somatic mutations were newly identified in an RNA helicase gene, DDX3X, often concurrent with CTNNB1 mutations, and in the nuclear co-repressor (N-CoR) complex genes GPS2, BCOR and LDB1. We show that mutant DDX3X potentiates transactivation of a TCF promoter and enhances cell viability in combination with mutant, but not wild-type, β-catenin. Together, our study reveals the alteration of WNT, hedgehog, histone methyltransferase and now N-CoR pathways across medulloblastomas and within specific subtypes of this disease, and nominates the RNA helicase DDX3X as a component of pathogenic β-catenin signalling in medulloblastoma.

In neuroblastoma, the most common solid tumor of childhood, excellent prognosis is associated with extensive Schwann cell (SC) content and high-level expression of the neurotrophin receptor, NTRK1/TrkA, which is known to mediate neuroblastoma cell differentiation. We hypothesized that both stromal composition and neuroblastic differentiation are based on bidirectional neuroblastoma-SC interaction. Reanalysis of microarray data from human SY5Y neuroblastoma cells stably transfected with either NTRK1 or NTRK2 revealed upregulation of the mRNA for the SC growth factor, NRG1, in NTRK1-positive cells. Media conditioned by NTRK1-expressing neuroblastoma cells induced SC proliferation and migration, while antibody-based NRG1 neutralization significantly decreased these effects. Vice versa, NRG1-stimulated SC secreted the NTRK1-specific ligand, NGF. SC-conditioned medium activated the NTRK1 receptor in a neuroblastoma cell culture model conditionally expressing NTRK1 and induced differentiation markers in NTRK1-expressing cells. NTRK1 induction in neuroblastoma xenografts mixed with primary SC also significantly reduced tumor growth in vivo. We propose a model for NTRK1-mediated and NRG1-dependent attraction of adjacent SC, which in turn induce neuroblastic differentiation by secretion of the NTRK1-specific ligand, NGF. These findings have implications for understanding the mature and less malignant neuroblastoma phenotype associated with NTRK1 expression, and could assist the development of new therapeutic strategies for neuroblastoma differentiation.