Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features.

Pituitary neuroendocrine tumors (PitNETs) are classified according to cell lineage, which requires immunohistochemistry for adenohypophyseal hormones and the transcription factors (TFs) PIT1, SF1, and TPIT. According to the current WHO 2022 classification, PitNETs with co-expression of multiple TFs are termed "plurihormonal". Previously, PIT1/SF1 co-expression was prevailingly reported in PitNETs, which otherwise correspond to the somatotroph lineage. However, little is known about such tumors and the WHO classification has not recognized their significance. We compiled an in-house case series of 100 tumors, previously diagnosed as somatotroph PitNETs. Following TF staining, histopathological features associated with PIT1/SF1 co-expression were assessed. Integration of in-house and publicly available sample data allowed for a meta-analysis of SF1-associated clinicopathological and molecular features across a total of 270 somatotroph PitNETs. The majority (74%, 52/70) of our densely granulated somatotroph PitNETs (DGST) unequivocally co-expressed PIT1 and SF1 (DGST-PIT1/SF1). None (0%, 0/30) of our sparsely granulated somatotroph PitNETs (SGST) stained positive for SF1 (SGST-PIT1). Among DGST, PIT1/SF1 co-expression was significantly associated with scarce FSH/LH expression and fewer fibrous bodies compared to DGST-PIT1. Integrated molecular analyses including publicly available samples confirmed that DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 represent distinct tumor subtypes. Clinicopathological meta-analyses indicated that DGST-PIT1 respond more favorably towards treatment with somatostatin analogs compared to DGST-PIT1/SF1, while both these subtypes show an overall less aggressive clinical course than SGST-PIT1. In this study, we spotlight that DGST with co-expression of PIT1 and SF1 represent a common, yet underrecognized, distinct PitNET subtype. Our study questions the rationale of generally classifying such tumors as "plurihormonal", and calls for a refinement of the WHO classification. We propose the term "somatogonadotroph PitNET".

Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma.

The longitudinal transition of phenotypes is pivotal in glioblastoma treatment resistance and DNA methylation emerged as an important tool for classifying glioblastoma phenotypes. We aimed to characterize DNA methylation subclass heterogeneity during progression and assess its clinical impact. Matched tissues from 47 glioblastoma patients were subjected to DNA methylation profiling, including CpG-site alterations, tissue and serum deconvolution, mass spectrometry, and immunoassay. Effects of clinical characteristics on temporal changes and outcomes were studied. Among 47 patients, 8 (17.0%) had non-matching classifications at recurrence. In the remaining 39 cases, 28.2% showed dominant DNA methylation subclass transitions, with 72.7% being a mesenchymal subclass. In general, glioblastomas with a subclass transition showed upregulated metabolic processes. Newly diagnosed glioblastomas with mesenchymal transition displayed increased stem cell-like states and decreased immune components at diagnosis and exhibited elevated immune signatures and cytokine levels in serum. In contrast, tissue of recurrent glioblastomas with mesenchymal transition showed increased immune components but decreased stem cell-like states. Survival analyses revealed comparable outcomes for patients with and without subclass transitions. This study demonstrates a temporal heterogeneity of DNA methylation subclasses in 28.2% of glioblastomas, not impacting patient survival. Changes in cell state composition associated with subclass transition may be crucial for recurrent glioblastoma targeted therapies.

Transcriptomic and epigenetic dissection of spinal ependymoma (SP-EPN) identifies clinically relevant subtypes enriched for tumors with and without NF2 mutation.

Ependymomas encompass multiple clinically relevant tumor types based on localization and molecular profiles. Tumors of the methylation class "spinal ependymoma" (SP-EPN) represent the most common intramedullary neoplasms in children and adults. However, their developmental origin is ill-defined, molecular data are scarce, and the potential heterogeneity within SP-EPN remains unexplored. The only known recurrent genetic events in SP-EPN are loss of chromosome 22q and NF2 mutations, but neither types and frequency of these alterations nor their clinical relevance have been described in a large, epigenetically defined series. Transcriptomic (n = 72), epigenetic (n = 225), genetic (n = 134), and clinical data (n = 112) were integrated for a detailed molecular overview on SP-EPN. Additionally, we mapped SP-EPN transcriptomes to developmental atlases of the developing and adult spinal cord to uncover potential developmental origins of these tumors. The integration of transcriptomic ependymoma data with single-cell atlases of the spinal cord revealed that SP-EPN display the highest similarities to mature adult ependymal cells. Unsupervised hierarchical clustering of transcriptomic data together with integrated analysis of methylation profiles identified two molecular SP-EPN subtypes. Subtype A tumors primarily carried previously known germline or sporadic NF2 mutations together with 22q loss (bi-allelic NF2 loss), resulting in decreased NF2 expression. Furthermore, they more often presented as multilocular disease and demonstrated a significantly reduced progression-free survival as compared to SP-EP subtype B. In contrast, subtype B predominantly contained samples without NF2 mutation detected in sequencing together with 22q loss (monoallelic NF2 loss). These tumors showed regular NF2 expression but more extensive global copy number alterations. Based on integrated molecular profiling of a large multi-center cohort, we identified two distinct SP-EPN subtypes with important implications for genetic counseling, patient surveillance, and drug development priorities.

Robust classification using average correlations as features (ACF).

MOTIVATION: In single-cell transcriptomics and other omics technologies, large fractions of missing values commonly occur. Researchers often either consider only those features that were measured for each instance of their dataset, thereby accepting severe loss of information, or use imputation which can lead to erroneous results. Pairwise metrics allow for imputation-free classification with minimal loss of data. RESULTS: Using pairwise correlations as metric, state-of-the-art approaches to classification would include the K-nearest-neighbor- (KNN) and distribution-based-classification-classifier. Our novel method, termed average correlations as features (ACF), significantly outperforms those approaches by training tunable machine learning models on inter-class and intra-class correlations. Our approach is characterized in simulation studies and its classification performance is demonstrated on real-world datasets from single-cell RNA sequencing and bottom-up proteomics. Furthermore, we demonstrate that variants of our method offer superior flexibility and performance over KNN classifiers and can be used in conjunction with other machine learning methods. In summary, ACF is a flexible method that enables missing value tolerant classification with minimal loss of data.

Direct 3D Sampling of the Embryonic Mouse Head: Layer-wise Nanosecond Infrared Laser (NIRL) Ablation from Scalp to Cortex for Spatially Resolved Proteomics.

Common workflows in bottom-up proteomics require homogenization of tissue samples to gain access to the biomolecules within the cells. The homogenized tissue samples often contain many different cell types, thereby representing an average of the natural proteome composition, and rare cell types are not sufficiently represented. To overcome this problem, small-volume sampling and spatial resolution are needed to maintain a better representation of the sample composition and their proteome signatures. Using nanosecond infrared laser ablation, the region of interest can be targeted in a three-dimensional (3D) fashion, whereby the spatial information is maintained during the simultaneous process of sampling and homogenization. In this study, we ablated 40 µm thick consecutive layers directly from the scalp through the cortex of embryonic mouse heads and analyzed them by subsequent bottom-up proteomics. Extra- and intracranial ablated layers showed distinct proteome profiles comprising expected cell-specific proteins. Additionally, known cortex markers like SOX2, KI67, NESTIN, and MAP2 showed a layer-specific spatial protein abundance distribution. We propose potential new marker proteins for cortex layers, such as MTA1 and NMRAL1. The obtained data confirm that the new 3D tissue sampling and homogenization method is well suited for investigating the spatial proteome signature of tissue samples in a layerwise manner. Characterization of the proteome composition of embryonic skin and bone structures, meninges, and cortex lamination in situ enables a better understanding of molecular mechanisms of development during embryogenesis and disease pathogenesis.

Spatial molecular profiling of a central nervous system low-grade diffusely infiltrative tumour with INI1 deficiency featuring a high-grade atypical teratoid/rhabdoid tumour component.

We performed spatial epigenetic and transcriptomic analyses of a highly unusual low-grade diffusely infiltrative tumour with INI1 deficiency (CNS LGDIT-INI1), which harboured a high-grade component corresponding to an atypical teratoid/rhabdoid tumour (AT/RT). Methylation profiles of both low-grade and high-grade components yielded high similarity with AT/RTs of the MYC subgroup, whereas RNA expression analyses revealed increased translational activity and MYC pathway activation in the high-grade component. Close follow-up of patients harbouring CNS LGDIT-INI1 is warranted.

Low-grade diffusely infiltrative tumour (LGDIT), SMARCB1-mutant: A clinical and histopathological distinct entity showing epigenetic similarity with ATRT-MYC.

Low-grade diffusely infiltrative tumour (LGDIT), SMARCB1-mutant, is a histopathological distinct low-grade lesion encountered in older children and young adults that shows epigenetic similarity with ATRT-MYC and has the potential for malignant progression.

Molecular characterization of histopathological ependymoma variants.

According to the WHO classification, ependymal tumors are classified as subependymomas, myxopapillary ependymomas, classic ependymomas, anaplastic ependymomas, and RELA-fusion-positive ependymomas (RELA-EPN). Among classic ependymomas, the WHO defines rare histological variants, i.e., the clear cell, papillary, and tanycytic ependymoma. In parallel, global DNA methylation patterns distinguish nine molecular groups, some of which tightly overlap with histopathological subgroups. However, the match of the aforementioned histological variants to DNA methylation classes remains unclear. We analyzed histomorphology, clinical parameters, and global DNA methylation of tumors with the initial histological diagnoses of tanycytic (n = 12), clear cell (n = 14), or papillary ependymoma (n = 19). Forty percent of these tumors did not match to the epigenetic profile of ependymomas, using a previously published DNA methylation-based classifier for brain tumors. Instead, they were classified as low-grade glioma (n = 3), plexus tumor (n = 2), CNS high-grade neuroepithelial tumor with MN1 alteration (n = 2), papillary tumor of the pineal region (n = 2), neurocytoma (n = 1), or did not match to any known brain tumor methylation class (n = 8). Overall, integrated diagnosis had to be changed in 35.6% of cases as compared to the initial diagnosis. Among the tumors molecularly classified as ependymoma (27/45 cases), tanycytic ependymomas were mostly located in the spine (5/7 cases) and matched to spinal or myxopapillary ependymoma. 6/8 clear cell ependymomas were found supratentorially and fell into the methylation class of RELA-EPN. Papillary ependymomas with a positive ependymoma match (12/19 cases) showed either a "papillary" (n = 5), a "trabecular" (n = 1), or a "pseudo-papillary" (n = 6) growth pattern. The papillary growth pattern was strongly associated with the methylation class B of posterior fossa ependymoma (PFB, 5/5 cases) and tumors displayed DNA methylation sites that were significantly different when compared to PFB ependymomas without papillary growth. Tumors with pseudo-papillary histology matched to the methylation class of myxopapillary ependymoma (4/6 cases), whereas the trabecular case was anatomically and molecularly a spinal ependymoma. Our results show that the diagnosis of histological ependymoma variants is challenging and epigenetic profiles may improve diagnostic accuracy of these cases. Whereas clear cell and papillary ependymomas display correlations between localization, histology, and methylation, tanycytic ependymoma does not represent a molecularly distinct subgroup.

TCF4 (E2-2) harbors tumor suppressive functions in SHH medulloblastoma.

The TCF4 gene encodes for the basic helix-loop-helix transcription factor 4 (TCF4), which plays an important role in the development of the central nervous system (CNS). Haploinsufficiency of TCF4 was found to cause Pitt-Hopkins syndrome (PTHS), a severe neurodevelopmental disorder. Recently, the screening of a large cohort of medulloblastoma (MB), a highly aggressive embryonal brain tumor, revealed almost 20% of adult patients with MB of the Sonic hedgehog (SHH) subtype carrying somatic TCF4 mutations. Interestingly, many of these mutations have previously been detected as germline mutations in patients with PTHS. We show here that overexpression of wild-type TCF4 in vitro significantly suppresses cell proliferation in MB cells, whereas mutant TCF4 proteins do not to the same extent. Furthermore, RNA sequencing revealed significant upregulation of multiple well-known tumor suppressors upon expression of wild-type TCF4. In vivo, a prenatal knockout of Tcf4 in mice caused a significant increase in apoptosis accompanied by a decreased proliferation and failed migration of cerebellar granule neuron precursor cells (CGNP), which are thought to be the cells of origin for SHH MB. In contrast, postnatal in vitro and in vivo knockouts of Tcf4 with and without an additional constitutive activation of the SHH pathway led to significantly increased proliferation of CGNP or MB cells. Finally, publicly available data from human MB show that relatively low expression levels of TCF4 significantly correlate with a worse clinical outcome. These results not only point to time-specific roles of Tcf4 during cerebellar development but also suggest a functional linkage between TCF4 mutations and the formation of SHH MB, proposing that TCF4 acts as a tumor suppressor during postnatal stages of cerebellar development.

Medulloblastoma: experimental models and reality.

Medulloblastoma is the most frequent malignant brain tumor in childhood, but it may also affect infants, adolescents, and young adults. Recent advances in the understanding of the disease have shed light on molecular and clinical heterogeneity, which is now reflected in the updated WHO classification of brain tumors. At the same time, it is well accepted that preclinical research and clinical trials have to be subgroup-specific. Hence, valid models have to be generated specifically for every medulloblastoma subgroup to properly mimic molecular fingerprints, clinical features, and responsiveness to targeted therapies. This review summarizes the availability of experimental medulloblastoma models with a particular focus on how well these models reflect the actual disease subgroup. We further describe technical advantages and disadvantages of the models and finally point out how some models have successfully been used to introduce new drugs and why some medulloblastoma subgroups are extraordinary difficult to model.

Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.

Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (nMPE = 84, nSP-EPN = 55). Self-supervised and weakly-supervised neural networks were used for classification. We employed attention analysis and supervised machine-learning methods for the discovery and quantification of morphological features and their correlation to the diagnoses of experienced neuropathologists. Our best performing model predicted the DNA methylation class with 98% test accuracy and used self-supervised learning to outperform pretrained encoder-networks (86% test accuracy). In contrast, the diagnoses of neuropathologists matched the DNA methylation class in only 83% of cases. Domain-adaptation techniques improved model generalization to an external validation cohort by up to 22%. Statistically significant morphological features were identified per molecular type and quantitatively correlated to human diagnoses. The approach was extended to recently defined subtypes of myxopapillary ependymomas (MPE-(A/B), 80% test accuracy). In summary, we demonstrated the accurate prediction of the DNA methylation class of spinal cord ependymomas (SP-EPN, MPE(-A/B)) using hematoxylin and eosin stained whole-slide images. Our approach may prospectively serve as a supplementary resource for integrated diagnostics and may even help to establish a standardized, high-quality level of histology-based diagnostics across institutions-in particular in low-income countries, where expensive DNA-methylation analyses may not be readily available.

Integrated proteomics spotlight the proteasome as a therapeutic vulnerability in embryonal tumors with multilayered rosettes.

BACKGROUND: Embryonal tumors with multilayered rosettes (ETMR) are rare malignant embryonal brain tumors. The prognosis of ETMR is poor and novel therapeutic approaches are desperately needed. Comprehension of ETMR tumor biology is currently based on only few previous molecular studies, which mainly focused on the analyses of nucleic acids. In this study, we explored integrated ETMR proteomics. METHODS: Using mass spectrometry, proteome data were acquired from 16 ETMR and the ETMR cell line BT183. Proteome data were integrated with case-matched global DNA methylation data, publicly available transcriptome data, and proteome data of further embryonal and pediatric brain tumors. RESULTS: Proteome-based cluster analyses grouped ETMR samples according to histomorphology, separating neuropil-rich tumors with neuronal signatures from primitive tumors with signatures relating to stemness and chromosome organization. Integrated proteomics showcased that ETMR and BT183 cells harbor proteasome regulatory proteins in abundance, implicating their strong dependency on the proteasome machinery to safeguard proteostasis. Indeed, in vitro assays using BT183 highlighted that ETMR tumor cells are highly vulnerable toward treatment with the CNS penetrant proteasome inhibitor Marizomib. CONCLUSIONS: In summary, histomorphology stipulates the proteome signatures of ETMR, and proteasome regulatory proteins are pervasively abundant in these tumors. As validated in vitro, proteasome inhibition poses a promising therapeutic option in ETMR.

A prognostic neural epigenetic signature in high-grade glioma.

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.

Mouse models of pediatric high-grade gliomas with MYCN amplification reveal intratumoral heterogeneity and lineage signatures.

Pediatric high-grade gliomas of the subclass MYCN (HGG-MYCN) are highly aggressive tumors frequently carrying MYCN amplifications, TP53 mutations, or both alterations. Due to their rarity, such tumors have only recently been identified as a distinct entity, and biological as well as clinical characteristics have not been addressed specifically. To gain insights into tumorigenesis and molecular profiles of these tumors, and to ultimately suggest alternative treatment options, we generated a genetically engineered mouse model by breeding hGFAP-cre::Trp53Fl/Fl::lsl-MYCN mice. All mice developed aggressive forebrain tumors early in their lifetime that mimic human HGG-MYCN regarding histology, DNA methylation, and gene expression. Single-cell RNA sequencing revealed a high intratumoral heterogeneity with neuronal and oligodendroglial lineage signatures. High-throughput drug screening using both mouse and human tumor cells finally indicated high efficacy of Doxorubicin, Irinotecan, and Etoposide as possible therapy options that children with HGG-MYCN might benefit from.

HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values.

Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Proteome datasets display high technical variability and frequent missing values. Sophisticated strategies for batch effect reduction are lacking or rely on error-prone data imputation. Here we introduce HarmonizR, a data harmonization tool with appropriate missing value handling. The method exploits the structure of available data and matrix dissection for minimal data loss, without data imputation. This strategy implements two common batch effect reduction methods-ComBat and limma (removeBatchEffect()). The HarmonizR strategy, evaluated on four exemplarily analyzed datasets with up to 23 batches, demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches. Compared to data imputation methods, HarmonizR was more efficient and performed superior regarding the detection of significant proteins. HarmonizR is an efficient tool for missing data tolerant experimental variance reduction and is easily adjustable for individual dataset properties and user preferences.

Comprehensive profiling of myxopapillary ependymomas identifies a distinct molecular subtype with relapsing disease.

BACKGROUND: Myxopapillary ependymoma (MPE) is a heterogeneous disease regarding histopathology and outcome. The underlying molecular biology is poorly understood, and markers that reliably predict the patients' clinical course are unknown. METHODS: We assembled a cohort of 185 tumors classified as MPE based on DNA methylation. Methylation patterns, copy number profiles, and MGMT promoter methylation were analyzed for all tumors, 106 tumors were evaluated histomorphologically, and RNA sequencing was performed for 37 cases. Based on methylation profiling, we defined two subtypes MPE-A and MPE-B, and explored associations with epidemiological, clinical, pathological, and molecular characteristics of these tumors. RESULTS: MPE-A occurred at a median age of 27 years and were enriched with tumors demonstrating papillary morphology and MGMT promoter hypermethylation. Half of these tumors could not be totally resected, and 85% relapsed within 10 years. Copy number alterations were more common in MPE-A. RNA sequencing revealed an enrichment for extracellular matrix and immune system-related signatures in MPE-A. MPE-B occurred at a median age of 45 years and included many tumors with a histological diagnosis of WHO grade II and tanycytic morphology. Patients within this subtype had a significantly better outcome with a relapse rate of 33% in 10 years (P = 3.4e-06). CONCLUSIONS: We unraveled the morphological and clinical heterogeneity of MPE by identifying two molecularly distinct subtypes. These subtypes significantly differed in progression-free survival and will likely need different protocols for surveillance and treatment.

Co-activation of Sonic hedgehog and Wnt signaling in murine retinal precursor cells drives ocular lesions with features of intraocular medulloepithelioma.

Intraocular medulloepithelioma (IO-MEPL) is a rare embryonal ocular neoplasm, prevalently occurring in children. IO-MEPLs share histomorphological features with CNS embryonal tumors with multilayered rosettes (ETMRs), referred to as intracranial medulloepitheliomas. While Sonic hedgehog (SHH) and WNT signaling pathways are crucial for ETMR pathogenesis, the impact of these pathways on human IO-MEPL development is unclear. Gene expression analyses of human embryonal tumor samples revealed similar gene expression patterns and significant overrepresentation of SHH and WNT target genes in both IO-MEPL and ETMR. In order to unravel the function of Shh and Wnt signaling for IO-MEPL pathogenesis in vivo, both pathways were activated in retinal precursor cells in a time point specific manner. Shh and Wnt co-activation in early Sox2- or Rax-expressing precursor cells resulted in infiltrative ocular lesions that displayed extraretinal expansion. Histomorphological, immunohistochemical, and molecular features showed a strong concordance with human IO-MEPL. We demonstrate a relevant role of WNT and SHH signaling in IO-MEPL and report the first mouse model to generate tumor-like lesions with features of IO-MEPL. The presented data may be fundamental for comprehending IO-MEPL initiation and developing targeted therapeutic approaches.