Transcriptomic and Proteomic Spatial Profiling of Pediatric and Adult Diffuse Midline Glioma H3 K27-Altered, Reveals Region Specific Differences and Limited Overlap between mRNA and Protein

Transcriptomic and Proteomic Spatial Profiling of Pediatric and Adult Diffuse Midline Glioma H3 K27-Altered, Reveals Region Specific Differences and Limited Overlap between mRNA and Protein | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transcriptomic and Proteomic Spatial Profiling of Pediatric and Adult Diffuse Midline Glioma H3 K27-Altered, Reveals Region Specific Differences and Limited Overlap between mRNA and Protein Sudarshawn Damodharan, Jack M. Shireman, Elliot Xie, Emily Distler, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4139314/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Diffuse midline glioma, H3 K27 -altered (DMG-Alt) are highly aggressive malignancies of the central nervous system (CNS) that primarily affect the pediatric population. Large scale spatial transcriptomic studies have implicated that tumor microenvironmental landscape plays an important role in determining the phenotypic differences in tumor presentation and clinical course, however, data connecting overall transcriptomic changes to the protein level is lacking. The NanoString GeoMx™ Digital Spatial Profiler platform was used to determine the spatial transcriptomic and proteomic landscape in a cohort of both pediatric and adult H3 K27 -altered DMG biopsy samples. Three fluorescently labeled antibodies targeting immune cells (CD45), epithelial cells (PanCK), tumor cells ( H3 K27M ) and a nucleic acid stain (SYTO-13) were used to establish regions of interest (ROI) for genomic and proteomic analysis. We found genetic alterations within the tumor which can be delineated across patient age and spatial location. We show that the H3 K27M mutation itself has a profound impact on tumor cells transcriptomics and interestingly we found limited fidelity between overall transcriptome and proteome. Our data also validate the previously described OPC like precursor signature at the proteomic level and reveal a special shift in the signature based on the local TME composition. Biological sciences/Cancer Biological sciences/Immunology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Diffuse midline glioma H3 K27 -altered is a fatal central nervous system (CNS) malignancy that predominantly affects children of age 5–7 years. It is classified as a World Health Organization (WHO) grade 4 neoplasm with no curative treatments. The current standard approach to treatment is radiation therapy which is only palliative. The incidence rate for these tumors is difficult to accurately quantify due to the recent change in molecular classification as well as the rarity of cases. The most current incidence data collected by the World Health Organization (WHO) reports an incidence rate of 0.54 cases per million person years in adults, and a rate of 2.23 cases per million person years in people ≤ 20 years of age 1 , 2 . The prognosis of this neoplasm is extremely poor, especially in children with a 2-year survival rate of < 10% 1,2 . Due to the rarity of the tumors in adults and the many subclassifications of DMG’s, true prognosis in adults is still debated; however, it’s generally agreed that adults have a better, while still overall poor, clinical prognosis 3 – 5 . These neoplasms occur primarily in midline structures within the CNS, with the brainstem and thalamus being the most prevalent and equally fatal anatomical locations 6 – 9 . Due to the critical anatomical locations of DMGs, surgical resection or needle biopsies were traditionally not performed, limiting the amount of pathological and molecular data obtained to date. However, with the development of sophisticated surgical technology over the last decade, biopsy has become routine resulting in a better understanding of DMGs at a genomic and molecular level. These advanced molecular studies have led to further classification of DMGs into DMG H3K27-altered (DMG-Alt) based on specific histone mutations 10 , 11 the most significant being highly recurrent histone mutations ( H3F3A or HIST1H3B/C ) which are detected mostly in pediatric cases 7 , 12 , 13 . Liu et al provided the first look into the genomic landscape of DMG-Alt at a spatial level across varying age and anatomical location 14 . In doing so, oligodendroglial lineage of precursor tumor cells were identified to be the majority in all clinical and anatomical groups and mesenchymal precursor signatures were increased in older patients with DMG-Alt 14 . Our understanding of the molecular and genetic landscape of these malignancies continues to grow dramatically, but primarily at the mRNA and transcriptomic level 10 – 12 , 14 , downstream proteomic concordance of the transcriptomic landscape has yet to be evaluated. Although the mRNA landscape provides a good overview of the overall genomic alteration of a malignancy, it is the overall functional proteins that are responsible for the pathological behavior of the malignancy. Translation of mRNA into protein is a complex process with varying levels of translational fidelity between mRNA and protein 15 – 17 . Correlations between the differential expression of specific mRNAs and their corresponding proteins have been assessed in varying human disease processes with varying levels of concordance 18 – 23 . The fidelity between transcriptomic and proteomic landscape has never been explored in DMG-Alt. To address these gaps in the literature we initiated this study in which we utilized spatial multi-omics profiling to comprehensively evaluate the molecular landscape of a cohort of pediatric and adult DMG-Alt tumor samples. Methods Clinical Sample Processing: Tissue samples (n = 8) and clinical data was collected from previously archived formalin-fixed-paraffin-embedded tissue (FFPE) samples of both pediatric (n = 4) and adult (n = 4) patients with confirmed pathological diagnosis of DMG, H3 K27 -altered. IRB approval was obtained to utilize the archived pediatric and adult pathological samples for analysis at the University of Wisconsin-Madison (2022-0164-CP001). All patients analyzed within the study provided informed consent and all analyses were carried out according to UWSMPH/UW-Madison IRB direction and supervision. All samples were obtained from biopsies or resections done pre-treatment at the time of initial diagnosis. From each sample, three cores were obtained to have each patient sample represented as a triplicate to evaluate the broader tumor landscape and proteogenomic profiles. (Fig. 1 A & B, Table 1 , Sup Fig. 1). All analysiss conducted on human samples was performed in accordance with relevant guidelines and regulations. Table 1 Clinical characteristics of pediatric and adult patients Patient Age at Diagnosis (years) Sex Race Location Treatment OS (months) 1 6 Male Caucasian Right Thalamus XRT + TMZ, ONC201 14 2 11 Female Hispanic Right Thalamus XRT 9 3 14 Female Black Right Thalamus XRT 12 4 3 Female Black Pontine XRT 2 5 43 Male Caucasian Pontine XRT 6 6 46 Male Hispanic Left Thalamus None 1 7 48 Male Caucasian Pontine XRT + TMZ, ONC201 26 8 25 Male Black Right Thalamus XRT + bevacizumab 24 XRT – radiotherapy, OS – overall survival, TMZ – temozolomide Spatial Genomic Profiling: Transcriptomic and proteomic profiling was done on the above samples. The anatomical locations included the thalamus (n = 5) and pons (n = 3). To evaluate the intra-tumoral landscape and heterogeneity, the samples were stained with antibodies against the epithelial cell marker, PanCK; the leukocyte marker, CD45; mutant-tumor specific marker against H3 K27M; and the nuclear stain SYTO13. We performed spatial transcriptomic and proteomic characterization of all samples via in situ sequencing utilizing the Nanostring digital spatial profiling (DSP) system and Illumina sequencing platforms (Fig. 1 B & C). Across all samples, a total of 49 ROIs were marked for this experiment; divided between both populations of samples (25 for pediatric and 24 for adult). Each ROI accounted for approximately 500uM of total space with the differing cells encompassed within this. The ROI categories utilized for this experiment were H3 K27M Ab predominant, H3 K27 ab + CD45 and areas predominantly with unlabeled tumor cells (non-H3 K27M positive tumor cells). Each ROI was assessed for the composition of mutant neoplastic cells, immune cells and interactions with one another. ROI Collection and Filtering: A total of 94 ROIs were initially collected for analysis (Fig. 1 C). The subsequent filtering process, which incorporated criteria such as gene detection rates, total nuclei count, and the intrinsic relevance of each ROI type, resulted in 85 ROIs being selected for further analysis. To ensure a robust and meaningful deconvolution analysis, necessitating sufficient diversity among the ROIs, a more stringent filtering criterion based on gene detection rates was applied, ultimately retaining 49 ROIs for deconvolution analysis. Annotation of ROIs: Each ROI underwent several runs of manual annotation. This involved an in-depth examination of the corresponding-colored images and the screening of highly expressed protein markers characteristic of each region. Correlation Analysis between Protein and RNA: For the correlation analysis, proteins and RNA were matched according to their respective GeneIDs. A Pearson correlation coefficient was calculated for each pair to assess the degree of linear correlation between the protein and RNA expression levels in the ROIs (Fig. 1 D). Hypothesis testing to identify differences between average correlations in adult vs. pediatrics (and in mutated vs. non-mutated vs. TME) was conducted by transforming correlations using Fisher's Z-transformation and evaluating p-values via Students t-test (ANOVA). Deconvolution Analysis: Deconvolution of the spatial data was carried out using the SpatialDecon algorithm 24 . This method utilizes log-normal regression and integrates background modeling, thereby enhancing performance beyond that of traditional least-squares methods. SpatialDecon is adept at quantifying cell populations within ROIs and provides precise estimates of cell abundance. The reference matrix, crucial for this analysis, was sourced from a study focused on DMG single-cell RNA sequencing data 14 . The processing of this data was in strict accordance with the protocols and methodologies detailed in Liu et al., 2022 14 . For comparisons among the SpatialDecon identified regions a Wilcoxon ranked tested adjusted for multiple comparisons was utilized with adjusted p values reported. 0. Gene Regulatory Network Reconstruction and Cell-State Identification: To comprehensively characterize the transcriptional state of each ROI, we utilized SCENIC 25 , a computational approach for concurrent gene regulatory network reconstruction and cell-state identification. The SCENIC methodology involves three steps: initially, GRNboost2 identifies gene sets co-expressed with transcription factors. Subsequently, these gene sets are refined through cis-regulatory motif analysis using RcisTarget, leading to the formation of regulons by retaining modules with significant motif enrichment. The activity of each regulon in individual ROIs is quantified using the AUCell algorithm, yielding an AUC score that reflects the subnetwork activity. This score is then utilized to assess the characteristics of each ROI. Over-representation Analysis : Over-representation analysis (ORA) was done using webgestalt 26 – 28 with default parameters on functional database “gene ontology” or biological pathways “KEGG”. Enrichment statics are calculated using a hypergeometric test to evaluate the significance of enrichment and calculate a p-value which is then adjusted for multiple comparisons as well as a false discovery rate (FDR) 26 – 28 . For this study FDR is reported for all ORA comparisons made with significance being considered if FDR < 0.10. Results Patient Cohort and Clinical Characteristics: Patient characteristics along with treatment details are listed in Table 1 and Fig. 1 A along with experimental details and schematics (Fig. 1 B, C, & D). All patients in the study had mutations within the H3F3A gene which encodes histone H3.3 and typically correlates with a more severe phenotype in this tumor 5 . For the pediatric patients, the median age at diagnosis was 8.5 (range: 3–14 years); median overall survival (OS) was 9.2 months (range: 2–14 months). For the adult patients, the median age of diagnosis was 40.5 (range: 25–48); median overall survival (OS) was 14.2 months (range: 1–26 months) SCENIC profiling reveals spatially differential regulon enrichment: SCENIC was used to reconstruct gene regulatory networks and estimate cellular states for each ROI. The combined adult and pediatric samples were classified spatially by grouped ROIs into 3 categories: H3K27M mutated tumor cells (Mutated), Non H3K27M mutated tumor cells (Non-Mutated), and tumor cells + surrounding microenvironment (TME). These ROIs were confirmed using IHC staining on our tissue samples, allowing for anchoring and targeting of the resulting genomic and proteomic analysis done by the GEOMX system. Mutated ROIs contained only cells expressing H3K27M marks (green), while TME ROIs contained tumor cells (green), immune cells (pink), and endothelial cells (blue) (Fig. 2 A). ROIs containing cells with no staining (Non-Mutated) were hypothesized to contain mostly tumor cells that do not carry the H2K27m mutation which is seen in the tumor bulk according to recent reports 8 , 29 . To investigate this hypothesis, the top enriched genes/regulons identified using SCENIC were subject to over representation analysis (ORA) and compared to an equal number of enriched genes/regulons derived from a dataset of normal thalamic and pontine tissue ( http://www.brain-map.org/ ). This comparison demonstrated highly enriched signaling for cancer associated genes and tumor suppressors with enriched terms such as Transcriptional Regulation in Cancer and Pathways in Cancer (FDR < 0.05). Conversely, ORA analysis on the normal thalamic and pontine tissue showed typical enrichment for cell developmental processes and neuronal signatures with enrichment terms for regulation of synaptic signaling and mononuclear cell proliferation (FDR < 0.01) (Sup Fig. 2A & B). Across our spatial classifications, SCENIC identified significantly different regulons driven by ARID3C and MSX1 in the TME, TBP and ELK1 in Mutated, and NFATC2 and Stat6 in Non-mutated ROIs (Fig. 2 B & C). These regulons? were enriched for cytokine and interleukin signaling in the TME ROIs (FDR < 0.05), cancer induced senescence and TP53 gene regulation in the Mutated ROIs (FDR < 0.10), and TNF and WNT pathway signaling in Non-Mutated ROIs (FDR < 0.05) (Fig. 2 D). These results highlight that spatially distinct regions within the tumor rely on differential transcriptional programs especially with regards to invading immune cells and surrounding vasculature. Furthermore, the H3K27 mutation itself seems to impact the transcriptional landscape significantly with non-mutated tumor cells displaying more developmental like signaling pathways as opposed to mutated tumor cells displaying more canonical cancer signaling networks. OPC like cell signature varies across age and spatial location: Molecular and genomic classification of DMG-Alt remains in its infancy due to the extremely limited number of available patient samples compared to other glioma types. In a recently published seminal study 14 the authors describe a ubiquitous oligodendrocyte precursor cell (OPC) like cellular state across both pediatric and adult DMG-Alt. Other cell states identified in the tumor include Mesenchymal (Mes) like and Astrocytic (AC) like, as well as cycling cells. Interestingly, neuronal signatures are not seen within DMGs from either population. We validated this in our dataset as well where we only saw enrichment for neuronal signatures during ORA analysis of normal midbrain controls (Sup Fig. 2A & B). Utilizing their published datasets, we compared our samples across age and spatial location and were able to corroborate the baseline OPC like signatures seen in adults and pediatrics. In our data examining Mutated ROIs we found pediatric tumors enriching for OPC like (specifically OPC like 2) and microglial signatures while adult tumors enrich for Mes like and AC signatures (Fig. 3 A). Interestingly, when we examined TME ROIs we saw a shift in the observed signatures with adult tumors now showing increased OPC like signature enrichment as well as a stronger microglial signature ( peds vs adults: padj < 0.05 vs padj = 1.0) (Fig. 3 B). These genomic signatures were originally derived from RNA data. However, our dataset includes a subset of protein targets allowing for partial validation of the expression pattern for one of the component genes of the OPC like signature, EGFR (Sup Fig. 3) at the protein level. These results indicate that even though our study has a relatively limited sample size, it still captures and validates a previously published genomic classification across patient age using both RNA and protein expression data. Our results also highlight a spatially driven shift in these signatures when more complex cell compositions are examined. This could indicate a TME driven shift in the tumor cells' biology or simply be reflective of the more complex TME present along outer edges of tumor compared to the tumor cell dense cores. Correlation between RNA and Protein varies both spatially and across patient age: A large portion of the current genomics work assumes that increased mRNA abundance results in increased protein abundance. While this is often the case, it is well known that the amount of translated protein can vary widely from the amount of detected mRNA 30 – 32 . Leveraging the unique Nanostring GeoMx technology we detected both RNA and protein probes across our entire range of samples. This allowed for comparison of matched RNA to protein pairs to assess transcription to translation fidelity and characterize the extent to which it varies across our samples. We first visualized the correlation between mRNA and protein abundance (ranging from − 1 being 100% negative correlation to 1 being 100% positive correlation) across the adult and pediatric tumor samples. We found similar rates of correlation on average in adults (0.19 +/- 0.27 SD) and pediatrics (0.33 +/- 0.25 SD) (Sup Fig. 4A) but differences were observed among the top 10 gene pairs that are most (and least) correlated (Fig. 4 A, B, & C). Spatially, similar correlation results were observed with no statistical difference in correlation rates (mutated vs non-mutated vs TME: 0.28 +/- 0.31SD, 0.24 +/- 0.26SD, 0.29 +/- 0.27 SD; p > 0.05) (Sup Fig. 4B), however, differences among the specific gene pairs that were correlated vs. anticorrelated were seen (Fig. 4 D, E, &F). Across adult and pediatric samples positive correlation values between RNA and protein were similar for some genes (OLIG2, Vimentin, MAP2) but differential correlation was also seen in adults and pediatric samples. Spatially, similar proteins were observed positively correlated across regions that were also observed across ages (OLIG2, MBP, MAP2). In terms of negative correlation CTLA4 was a top negative correlation across both age and spatial ROIs, an interesting finding due to the relevance of CTLA4 treatments across many other cancer types 33 – 35 . Overall, these results show that not only are different transcriptional programs being activated in tumors across age and regions, but these transcriptional programs do not always faithfully lead to the corresponding proteins being translated. If translational fidelity is not considered, predictions biased towards RNA content, but not true protein translation, may result. Common genomic therapeutic targets show differing RNA to protein correlation: Much of the upcoming targeted and immunotherapeutic interventions that are being studied for DMG-Alt have come about based upon previously established transcriptomic information. However, downstream proteomic data of clinically relevant therapeutic targets have not been readily studied. To examine this in our dataset we found RNA-Protein pairs that represented promising or already tried therapeutic targets in this group of malignancy and quantified their RNA and protein levels across all samples. Expression of receptor tyrosine kinase (RTK) has been shown to be prevalent on these malignancies with on-going and previously completed studies examining EGFR as a relevant target both as a small molecule inhibitor and in combination with immunotherapeutic agents 36 – 38 . Interestingly, low concordance was observed for EGFR between RNA and protein, with protein levels higher than would have been predicted by mRNA abundance (Fig. 5 ). Checkpoint blockade has also been tested both as the monotherapy and combinatorial immunotherapy for this group of malignancy 39 – 41 , thus we examined PD1, PDL-1 and CTLA-4 expression correlation in our dataset. We observed low concordance between RNA and protein across all the above markers with CTLA-4 especially enriched for negative correlation when examined in our TME ROIs (see Fig. 4 E). For these molecules, the amount of RNA detected was more than the actual amount of protein detected which could lead to skewed optimistic prediction of outcome for therapeutic targeting in clinical settings. B7-H3 and BRAF have been well characterized across tumors and within our dataset we found close concordance for RNA and protein levels for BRAF but not B7-H3 with the latter producing more protein than detected RNA. To further explore more clinically relevant targets with our data we filtered GSEA enrichment results using Enrichr 42 for genes involved in pathways that resulted in molecular secretions. The results demonstrated the top enriched pathways belonging to IL6 and ATF2 signaling events with glucocorticoid receptor signaling and calcium signaling also being present, likely related to the very neuronally active locations of these tumors (Sup Fig. 5). Together, these results highlight the crucial point that extensive targeting solely based on transcriptomics may result in false positives as well as false negatives and integrating proteomic data may strengthen early-stage clinical drug screening. Discussion DMG-Alt continues to be a devastating disease with limited therapeutic options, however, in the last decade neurosurgical and neuro-oncological techniques have advanced considerably allowing for biopsy and sample collection from these tumors. This in turn has led to several novel clinical trials currently underway based on the genomic targets discovered from comprehensive transcriptomic studies. Our study aims to push the field forward by taking the next step in genomic screening by examining spatial aspects of the tumor architecture as well as proteomic profiles. Spatially, we found that tumor ROIs that contain portions of TME display distinct genetic signatures compared to regions with dense tumor cell populations. Using proteomics, we demonstrate that transcription to translation fidelity is not always maintained in both adult and pediatric DMG-Alt. Finally, we interrogated common genetic targets proposed both for these tumors as well as broadly used in other gliomas and reveal the shortfalls of predicting therapeutic targeting efficacy based on RNA data alone. Continued research in this field will be crucial in advancing both the molecular and clinical understanding of these rare and lethal tumors. A recent publication by Liu and colleagues demonstrated an increased tendency for pediatric DMG-Alt to be dominated by an OPC like cell type while adult DMG-Alt enriched for a mesenchymal cell phenotype 14 . In our cohort we saw similar patterns across adult and pediatric tumors, however when examining the spatial landscape, we observed a change in the signature in ROIs composed of both tumor and TME cells. Also of note, in both our cohorts of data there is little to no neuronal signature expressed within any sample set. This is interesting because these tumors arise in very neuronally dense and eloquent tissue but seem to not be as intermixed with their surroundings like glioblastomas (GBM), the most common primary CNS malignancy in adults, which are known to integrate into neuronal synapses and proliferate more in response to neuronal firing 43 – 46 . This may indicate that DMG-Alts interacts more with other brain resident cells such as microglia, astrocytes, or oligodendrocytes as opposed to neurons. It’s also worth noting that there was little to no outside immune cell infiltration into the tumor bulk itself which is consistent with reports of DMG-Alt being an immune desert 47 , 48 . Our study is the first to include spatial proteomic profiling and we demonstrate that while translational fidelity was measured at similar levels across adults and pediatric samples, as well as across the ROIs, there were subtle differences in the types of genes that were either faithfully or not faithfully translated from mRNA. These results have significant implications for cancer therapeutics in general which to date has relied mostly on screening mRNA due to difficulties in obtaining proteomic data. Our examination of common genomic targets discovered using mRNA alone shows that although this method can result in true proteomic hits there is also room for error including both false positive and false negative hits being prioritized (such as EGFR false negative vs. PD-1 false positive). Although there are some studies suggesting mRNA, specifically differentially expressed mRNA, may be a faithful readout of true protein synthesis 49 , this has yet to be widely examined in the context of brain malignancies, especially in the pediatric population. One of the limitations of our study is that the number of protein probes available is far less than the number of RNA probes, and so a genome-wide comparison of RNA to protein was not possible. We also did not have a large enough number of samples to include statistically significant sex comparisons. In addition, due to the technical limitations of the Nanostring platform regarding the number of antibodies that can be used for defining ROIs, we had a subset of cells that were unstained. Since all our samples were targeted needle biopsies aimed at obtaining the most pathological tumor tissue, we hypothesized that these were most likely tumor cells that lacked the H3K27m mutation. In validating this using ORA analysis, we discovered a unique genetic program within these cells compared to H3K27m tumor cells involving some immune signatures as well as cancer and developmental gene enrichment. Although we only visualize a limited number of immune cells in our samples, there were samples with strong enrichment for immune related signaling, especially when TME ROIs were considered. This may be a result of tumor cells hijacking immune signaling methods (seen in other gliomas 50 – 52 ) or could result from immune cells sitting mostly on the peripheral edges of the tumor (which would not be captured during biopsy) inducing inflammation. An in depth understanding of the immune response and its resulting inflammation in this context may prove critical as we try to advance therapies, particularly CAR-T cell based therapies which are currently undergoing clinical trials 53 – 55 . Although DMG-Alt remains a 100% lethal tumor with incredibly limited survival, novel research is starting to illuminate its genetic background and expose possible therapeutic vulnerabilities. Further studies across multiple institutions to increase sample size will be the key in validating and advancing effective therapies and ultimately making a difference in patient survival. Declarations Conflict of Interest: The authors declare no relevant conflicts of interest. Data Availability: All sequencing raw and processed data will be deposited into GEO and released publicly upon publication of the manuscript. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions: SD/JMS/EX/CK/MD wrote and edited the manuscript. SD/EX/JMS/ED constructed figures and tables. JMS/EX completed data analysis. CK/MD supervised the project and supplied funding. SD/JMS contributed equally to the manuscript and are co-first authors. Funding: This work was supported by the NIH K08NS092895 grant (MD). JMS is partly supported by NIH/NINDS T32 NS105602. References Louis, D. N. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncol. 23, 1231–1251 (2021). Central Nervous System Tumours . vol. 5 (International Agency for Research on Cancer, 2021). Dono, A., Takayasu, T., Ballester, L. Y. & Esquenazi, Y. Adult diffuse midline gliomas: Clinical, radiological, and genetic characteristics. J. Clin. Neurosci. 82, 1–8 (2020). Meyronet, D. et al. Characteristics of H3 K27M-mutant gliomas in adults. Neuro-Oncol. 19, 1127–1134 (2017). Lu, V. M., Alvi, M. A., McDonald, K. L. & Daniels, D. J. Impact of the H3K27M mutation on survival in pediatric high-grade glioma: a systematic review and meta-analysis. J. Neurosurg.: Pediatr. 23, 308–316 (2019). Mackay, A. et al. Integrated Molecular Meta-Analysis of 1,000 Pediatric High-Grade and Diffuse Intrinsic Pontine Glioma. Cancer Cell 32, 520–537.e5 (2017). Khuong-Quang, D.-A. et al. K27M mutation in histone H3.3 defines clinically and biologically distinct subgroups of pediatric diffuse intrinsic pontine gliomas. Acta Neuropathol. 124, 439–447 (2012). Jones, C. & Baker, S. J. Unique genetic and epigenetic mechanisms driving paediatric diffuse high-grade glioma. Nat. Rev. Cancer 14, 651–661 (2014). Wu, G. et al. Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas. Nat. Genet. 44, 251–253 (2012). Huang, T. et al. Detection of histone H3 K27M mutation and post-translational modifications in pediatric diffuse midline glioma via tissue immunohistochemistry informs diagnosis and clinical outcomes. Oncotarget 9, 37112–37124 (2018). Wang, J. et al. Epigenomic landscape and 3D genome structure in pediatric high-grade glioma. Sci Adv 7, eabg4126 (2021). Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012). Tune, B. G. et al. From Pediatric to Adult Brain Cancer: Exploring Histone H3 Mutations in Australian Brain Cancer Patients. Biomedicines 11, 2907 (2023). Liu, I. et al. The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location. Nat. Genet. 54, 1881–1894 (2022). Kapur, M. & Ackerman, S. L. mRNA Translation Gone Awry: Translation Fidelity and Neurological Disease. Trends Genet. 34, 218–231 (2018). Chung, C. et al. The fidelity of transcription in human cells. Proc. Natl. Acad. Sci. 120, e2210038120 (2023). Gamba, P. & Zenkin, N. Transcription fidelity and its roles in the cell. Curr. Opin. Microbiol. 42, 13–18 (2018). Asplund, A. et al. Expression profiling of microdissected cell populations selected from basal cells in normal epidermis and basal cell carcinoma. Br. J. Dermatol. 158, 527–538 (2008). Dhanasekaran, S. M. et al. Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822–826 (2001). Greenbaum, D., Colangelo, C., Williams, K. & Gerstein, M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117 (2003). Fu, N. et al. Comparison of Protein and mRNA Expression Evolution in Humans and Chimpanzees. PLoS ONE 2, e216 (2007). Shankavaram, U. T. et al. Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study. Mol. Cancer Ther. 6, 820–832 (2007). Gry, M. et al. Correlations between RNA and protein expression profiles in 23 human cell lines. BMC Genom. 10, 365 (2009). Danaher, P. et al. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nat. Commun. 13, 385 (2022). Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017). Wang, J., Vasaikar, S., Shi, Z., Greer, M. & Zhang, B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130–W137 (2017). Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res 47, W199–W205 (2019). Zhang, B., Kirov, S. & Snoddy, J. WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 33, W741–W748 (2005). Larson, J. D. et al. Histone H3.3 K27M Accelerates Spontaneous Brainstem Glioma and Drives Restricted Changes in Bivalent Gene Expression. Cancer Cell 35, 140–155.e7 (2019). Abreu, R. de S., Penalva, L. O., Marcotte, E. M. & Vogel, C. Global signatures of protein and mRNA expression levels. Mol. Biosyst. 5, 1512–1526 (2009). Sonneveld, S., Verhagen, B. M. P. & Tanenbaum, M. E. Heterogeneity in mRNA Translation. Trends Cell Biol. 30, 606–618 (2020). Edfors, F. et al. Gene-specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol. 12, 883 (2016). He, X. & Xu, C. Immune checkpoint signaling and cancer immunotherapy. Cell Res 30, 660–669 (2020). Sampson, J. H., Gunn, M. D., Fecci, P. E. & Ashley, D. M. Brain immunology and immunotherapy in brain tumours. Nat Rev Cancer 20, 12–25 (2020). Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 19, 133–150 (2019). Dong, X. et al. Anti-VEGF therapy improves EGFR-vIII-CAR-T cell delivery and efficacy in syngeneic glioblastoma models in mice. J. Immunother. Cancer 11, e005583 (2023). Wang, X. et al. EGFR is a master switch between immunosuppressive and immunoactive tumor microenvironment in inflammatory breast cancer. Sci. Adv. 8, eabn7983 (2022). Weller, M. et al. Rindopepimut with temozolomide for patients with newly diagnosed, EGFRvIII-expressing glioblastoma (ACT IV): a randomised, double-blind, international phase 3 trial. Lancet Oncol 18, 1373–1385 (2017). Cacciotti, C. et al. Immune checkpoint inhibition for pediatric patients with recurrent/refractory CNS tumors: a single institution experience. J. Neuro-Oncol. 149, 113–122 (2020). Ausejo-Mauleon, I. et al. TIM-3 blockade in diffuse intrinsic pontine glioma models promotes tumor regression and antitumor immune memory. Cancer Cell 41, 1911–1926.e8 (2023). Kline, C. et al. Reirradiation and PD-1 inhibition with nivolumab for the treatment of recurrent diffuse intrinsic pontine glioma: a single-institution experience. J. Neuro-Oncol. 140, 629–638 (2018). Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 14, 128 (2013). Venkatesh, H. S. et al. Electrical and synaptic integration of glioma into neural circuits. Nature 573, 1–7 (2019). Venkatesh, H. S. et al. Neuronal Activity Promotes Glioma Growth through Neuroligin-3 Secretion. Cell 161, 803–816 (2015). Monje, M. et al. Roadmap for the Emerging Field of Cancer Neuroscience. Cell 181, 219–222 (2020). Venkatesh, H. S. et al. Targeting neuronal activity-regulated neuroligin-3 dependency in high-grade glioma. Nature 549, 533–537 (2017). Lin, G. L. et al. Non-inflammatory tumor microenvironment of diffuse intrinsic pontine glioma. Acta Neuropathol. Commun. 6, 51 (2018). Lieberman, N. A. P. et al. Characterization of the immune microenvironment of diffuse intrinsic pontine glioma: implications for development of immunotherapy. Neuro-Oncol. 21, 83–94 (2019). Koussounadis, A., Langdon, S. P., Um, I. H., Harrison, D. J. & Smith, V. A. Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system. Sci. Rep. 5, 10775 (2015). Razavi, S.-M. et al. Immune Evasion Strategies of Glioblastoma. Frontiers Surg 3, 11 (2016). Gangoso, E. et al. Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion. Cell (2021) doi: 10.1016/j.cell.2021.03.023 . Weverwijk, A. van & Visser, K. E. de. Mechanisms driving the immunoregulatory function of cancer cells. Nat Rev Cancer 1–23 (2023) doi: 10.1038/s41568-022-00544-4 . Majzner, R. G. et al. GD2-CAR T cell therapy for H3K27M-mutated diffuse midline gliomas. Nature 603, 934–941 (2022). Vitanza, N. A. et al. Intraventricular B7-H3 CAR T cells for diffuse intrinsic pontine glioma: preliminary first-in-human bioactivity and safety. Cancer Discov. 13, 114–131 (2022). Thomas, B. C. et al. CAR T cell therapies for diffuse midline glioma. Trends Cancer 9, 791–804 (2023). Additional Declarations No competing interests reported. Supplementary Files DIPGSupMaterials.docx Supplemental Figure 1: Spatial staining of DIPG patient tissue: A & B) Raw staining and ROI selection images from the two GeoMx slides used in our experiment. Supplemental Figure 2: Over-representation analysis demonstrates cancer signatures in unlabeled cells compared to normal midline brain. A) Over-representation analysis against biological processes conducted on our unlabeled cells vs a set of midbrain control genes. B) Over-representation analysis against biological pathways conducted on our unlabeled cells vs a set of midbrain control genes. Supplemental Figure 3: EGFR expression validated at the protein level: A) Top 5 co-expression of proteins of EGFR (top) and EGFR protein expression level across all samples (bottom). B) EGFR protein expression visualized in pediatric (red) or adult samples (blue) in either TME ROIs (top) or Mutated ROIs (bottom). Supplemental Figure 4: RNA to protein correlation varies across age and ROI: A) RNA to protein correlation across all 3 ROIs contained in our sample populations. B) RNA to protein correlation across all adult (blue) and pediatric (red) samples. Supplemental Figure 5: Enrichment of Gene regulatory pathways involved in molecular secretion : A) RNA to protein correlation across all 3 ROIs contained in our sample populations. B) RNA to protein correlation across all adult (blue) and pediatric (red) samples. Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Aug, 2024 Reviews received at journal 09 Jun, 2024 Reviews received at journal 03 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 16 May, 2024 Reviewers invited by journal 16 May, 2024 Editor assigned by journal 05 Apr, 2024 Editor invited by journal 02 Apr, 2024 Submission checks completed at journal 02 Apr, 2024 First submitted to journal 20 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4139314","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286783722,"identity":"70ec16bb-b424-4915-a8f3-9e73f445c49c","order_by":0,"name":"Sudarshawn Damodharan","email":"","orcid":"","institution":"Ann \u0026 Robert Lurie Children's Hospital of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Sudarshawn","middleName":"","lastName":"Damodharan","suffix":""},{"id":286783723,"identity":"be58347c-8595-4e83-b715-9a2a1e49b6c9","order_by":1,"name":"Jack M. Shireman","email":"","orcid":"","institution":"University of Wisconsin School of Medicine \u0026 Public Health","correspondingAuthor":false,"prefix":"","firstName":"Jack","middleName":"M.","lastName":"Shireman","suffix":""},{"id":286783724,"identity":"87d5676d-3bc6-4f89-9d05-f971dbec2b86","order_by":2,"name":"Elliot Xie","email":"","orcid":"","institution":"University of Wisconsin School of Medicine \u0026 Public Health","correspondingAuthor":false,"prefix":"","firstName":"Elliot","middleName":"","lastName":"Xie","suffix":""},{"id":286783725,"identity":"e2df7325-b5ab-4459-9812-8295df5219e5","order_by":3,"name":"Emily Distler","email":"","orcid":"","institution":"University of Wisconsin School of Medicine \u0026 Public Health","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Distler","suffix":""},{"id":286783726,"identity":"079d5b27-e477-45a3-b6bc-828b0bc4004b","order_by":4,"name":"Christina Kendziorski","email":"","orcid":"","institution":"University of Wisconsin School of Medicine \u0026 Public Health","correspondingAuthor":false,"prefix":"","firstName":"Christina","middleName":"","lastName":"Kendziorski","suffix":""},{"id":286783727,"identity":"878c4409-7415-48df-9540-b7121d3d6d32","order_by":5,"name":"Mahua Dey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYDCCAwyMD9CEGBsQJHYtzAYka2GTwCqBUwvf8R6z6oKKw/n8/GfMPn6puJfYIJHc/OEHg43shgPYtUieOWN2e8aZw5YzZ+QYz5Y5UwzUktgm2cOQZoxLi8GNHLPbvG2HDQxu8BgzS7YlJDZIJ7YxMzAcTsSp5f4bs2Lef0At588AtfwDa2n+zMDwH7eWGzxmzLwNQC0HcowZPzaAtTRIA4MFpxbJM2nF0jzH0g0kZ6QVMzMcSzBuk38I9ItBsvFMHFr4jh/e+JmnxtqAn//wZsYfNQmy/TzHH3/4UWEn24dDCwMDByImmXkYGBzbIA7GpRwE2B/AmYw/GBjs8akdBaNgFIyCkQkACw5hHxe0Vs0AAAAASUVORK5CYII=","orcid":"","institution":"University of Wisconsin School of Medicine \u0026 Public Health","correspondingAuthor":true,"prefix":"","firstName":"Mahua","middleName":"","lastName":"Dey","suffix":""}],"badges":[],"createdAt":"2024-03-20 19:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4139314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4139314/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-73199-w","type":"published","date":"2024-09-30T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54192906,"identity":"a2cf9f90-aaba-4c4d-9959-5eaeba9561bd","added_by":"auto","created_at":"2024-04-05 21:14:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7306956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePatient cohort and experimental workflow: \u003c/em\u003e\u003cstrong\u003eA)\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eGraphic depicting the tumor location, patient age, and location of the tissue biopsy analyzed for the study. \u003cstrong\u003eB) \u003c/strong\u003eGraphic depicting the slide layout of the Nanostring GeoMx slide used for sequencing. \u003cstrong\u003eC) \u003c/strong\u003eCartoon illustrating the experimental workflow of the project and how the ROIs were defined. \u003cstrong\u003eD) \u003c/strong\u003eCartoon depicting the lack of translation fidelity in cancer compared to normal cells.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-4139314/v1/b472ebad8d6ecb05f0ac934a.png"},{"id":54192840,"identity":"95c0297d-fa3f-4d35-9a03-b295cbdab093","added_by":"auto","created_at":"2024-04-05 21:14:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4287512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSCENIC profiling reveals spatially differential regulon enrichment: \u003c/em\u003e\u003cstrong\u003eA) \u003c/strong\u003eIHC representations of the composition of the selected ROIs analyzed by SCNEIC. \u003cstrong\u003eB) \u003c/strong\u003eSCENIC regulon enrichment across our selected ROIs visualized using a heatmap which includes the set of significantly enriched terms for each ROI. \u003cstrong\u003eC) \u003c/strong\u003eRegulon Specificity Score (RSS) across the analyzed ROIs with the top genes highlighted in red. \u003cstrong\u003eD) \u003c/strong\u003eOver representation analysis of the genes contained within the SCENIC enrichment for each ROI analyzed. Statistical comparisons made within SCENIC using RSS score with FDR reported in the legend.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-4139314/v1/0b9121680c48a341cbb62216.png"},{"id":54192841,"identity":"93bd9910-8f79-445b-8a91-18f71f4a036b","added_by":"auto","created_at":"2024-04-05 21:14:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4600674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOPC like cell signature varies across age and spatial location: \u003c/em\u003e\u003cstrong\u003eA) \u003c/strong\u003eVisual representation of the Mutated ROI used for deconvolution analysis as well as a histogram of the results of analysis comparing the adult (red) and pediatric (blue) populations within the Mutated ROI. \u003cstrong\u003eB)\u003c/strong\u003e Visual representation of the TME ROI used for deconvolution analysis as well as a histogram of the results of analysis comparing the adult and pediatric populations within the TME ROI.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-4139314/v1/6dbfa7d1375197a2552ce759.png"},{"id":54192904,"identity":"2dc2953a-d5f2-4a88-85d1-8dca3e45f97a","added_by":"auto","created_at":"2024-04-05 21:14:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5866722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation between RNA and Protein varies both spatially and across patient age: \u003c/em\u003e\u003cstrong\u003eA) \u003c/strong\u003eHistogram representations of the top 10 positively correlated RNA/protein pairs across adults (blue) or pediatric samples (red). \u003cstrong\u003eB) \u003c/strong\u003eHistogram representations of the top 10 negatively correlated RNA/protein pairs across adults (blue) or pediatric samples (red). \u003cstrong\u003eC) \u003c/strong\u003eCartoon representation of translational fidelity using the top 3 positive or negatively correlated RNA/protein pairs in adult or pediatric samples. \u003cstrong\u003eD) \u003c/strong\u003eHistogram representations of the top 10 positively correlated RNA/protein pairs across all 3 ROIs contained in our sample population. \u003cstrong\u003eE) \u003c/strong\u003eHistogram representations of the top 10 negatively correlated RNA/protein pairs across all 3 ROIs contained in our sample population. \u003cstrong\u003eF) \u003c/strong\u003eCartoon representation of translational fidelity using top positive or negatively correlated RNA/protein pairs in cells from each ROI contained within our sample population.\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-4139314/v1/6ad3635f9ad13dd6be8a71d9.png"},{"id":54192903,"identity":"dd6244d4-39db-4392-9694-504a7ea66864","added_by":"auto","created_at":"2024-04-05 21:14:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":419816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCommon genomic therapeutic targets show differing RNA to protein correlation:\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eHistogram representation of expression of RNA (blue) and protein (red) levels across all samples for common genomic targets of DMG-Alt.\u003c/p\u003e","description":"","filename":"Slide5.png","url":"https://assets-eu.researchsquare.com/files/rs-4139314/v1/68cd512ac2125ebc3ec69246.png"},{"id":66096714,"identity":"00a29e1a-1df5-4d6a-b607-d23d1e0dc9a3","added_by":"auto","created_at":"2024-10-07 16:07:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22615752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4139314/v1/7fa02cf8-2465-4106-bba6-6cee84a1f1e2.pdf"},{"id":54192843,"identity":"a4b23da5-fe1d-46b3-9927-9316c673545a","added_by":"auto","created_at":"2024-04-05 21:14:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4039265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1: \u003c/strong\u003e\u003cem\u003eSpatial staining of DIPG patient tissue: \u003c/em\u003e\u003cstrong\u003eA \u0026amp; B)\u003c/strong\u003e Raw staining and ROI selection images from the two GeoMx slides used in our experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure 2: \u003c/strong\u003e\u003cem\u003eOver-representation analysis demonstrates cancer signatures in unlabeled cells compared to normal midline brain. \u003c/em\u003e\u003cstrong\u003eA) \u003c/strong\u003eOver-representation analysis against biological processes conducted on our unlabeled cells vs a set of midbrain control genes. \u003cstrong\u003eB) \u003c/strong\u003eOver-representation analysis against biological pathways conducted on our unlabeled cells vs a set of midbrain control genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure 3: \u003c/strong\u003e\u003cem\u003eEGFR expression validated at the protein level: \u003c/em\u003e\u003cstrong\u003eA) \u003c/strong\u003eTop 5 co-expression of proteins of EGFR (top) and EGFR protein expression level across all samples (bottom). \u003cstrong\u003eB) \u003c/strong\u003eEGFR protein expression visualized in pediatric (red) or adult samples (blue) in either TME ROIs (top) or Mutated ROIs (bottom).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure 4: \u003c/strong\u003e\u003cem\u003eRNA to protein correlation varies across age and ROI: \u003c/em\u003e\u003cstrong\u003eA) \u003c/strong\u003eRNA to protein correlation across all 3 ROIs contained in our sample populations. \u003cstrong\u003eB) \u003c/strong\u003eRNA to protein correlation across all adult (blue) and pediatric (red) samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure 5: \u003c/strong\u003e\u003cem\u003eEnrichment of\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eGene regulatory pathways involved in molecular secretion : \u003c/em\u003e\u003cstrong\u003eA) \u003c/strong\u003eRNA to protein correlation across all 3 ROIs contained in our sample populations. \u003cstrong\u003eB) \u003c/strong\u003eRNA to protein correlation across all adult (blue) and pediatric (red) samples.\u003c/p\u003e","description":"","filename":"DIPGSupMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4139314/v1/5f6281c9d6884216d21500b1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic and Proteomic Spatial Profiling of Pediatric and Adult Diffuse Midline Glioma H3 K27-Altered, Reveals Region Specific Differences and Limited Overlap between mRNA and Protein","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiffuse midline glioma \u003cem\u003eH3 K27\u003c/em\u003e-altered is a fatal central nervous system (CNS) malignancy that predominantly affects children of age 5\u0026ndash;7 years. It is classified as a World Health Organization (WHO) grade 4 neoplasm with no curative treatments. The current standard approach to treatment is radiation therapy which is only palliative. The incidence rate for these tumors is difficult to accurately quantify due to the recent change in molecular classification as well as the rarity of cases. The most current incidence data collected by the World Health Organization (WHO) reports an incidence rate of 0.54 cases per million person years in adults, and a rate of 2.23 cases per million person years in people\u0026thinsp;\u0026le;\u0026thinsp;20 years of age\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The prognosis of this neoplasm is extremely poor, especially in children with a 2-year survival rate of \u0026lt;\u0026thinsp;10%\u003csup\u003e1,2\u003c/sup\u003e. Due to the rarity of the tumors in adults and the many subclassifications of DMG\u0026rsquo;s, true prognosis in adults is still debated; however, it\u0026rsquo;s generally agreed that adults have a better, while still overall poor, clinical prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These neoplasms occur primarily in midline structures within the CNS, with the brainstem and thalamus being the most prevalent and equally fatal anatomical locations\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Due to the critical anatomical locations of DMGs, surgical resection or needle biopsies were traditionally not performed, limiting the amount of pathological and molecular data obtained to date. However, with the development of sophisticated surgical technology over the last decade, biopsy has become routine resulting in a better understanding of DMGs at a genomic and molecular level.\u003c/p\u003e \u003cp\u003eThese advanced molecular studies have led to further classification of DMGs into DMG H3K27-altered (DMG-Alt) based on specific histone mutations\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e the most significant being highly recurrent histone mutations (\u003cem\u003eH3F3A\u003c/em\u003e or \u003cem\u003eHIST1H3B/C\u003c/em\u003e) which are detected mostly in pediatric cases\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Liu et al provided the first look into the genomic landscape of DMG-Alt at a spatial level across varying age and anatomical location\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In doing so, oligodendroglial lineage of precursor tumor cells were identified to be the majority in all clinical and anatomical groups and mesenchymal precursor signatures were increased in older patients with DMG-Alt\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Our understanding of the molecular and genetic landscape of these malignancies continues to grow dramatically, but primarily at the mRNA and transcriptomic level\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, downstream proteomic concordance of the transcriptomic landscape has yet to be evaluated.\u003c/p\u003e \u003cp\u003eAlthough the mRNA landscape provides a good overview of the overall genomic alteration of a malignancy, it is the overall functional proteins that are responsible for the pathological behavior of the malignancy. Translation of mRNA into protein is a complex process with varying levels of translational fidelity between mRNA and protein\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Correlations between the differential expression of specific mRNAs and their corresponding proteins have been assessed in varying human disease processes with varying levels of concordance\u003csup\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. The fidelity between transcriptomic and proteomic landscape has never been explored in DMG-Alt.\u003c/p\u003e \u003cp\u003eTo address these gaps in the literature we initiated this study in which we utilized spatial multi-omics profiling to comprehensively evaluate the molecular landscape of a cohort of pediatric and adult DMG-Alt tumor samples.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical Sample Processing:\u003c/h2\u003e \u003cp\u003eTissue samples (n\u0026thinsp;=\u0026thinsp;8) and clinical data was collected from previously archived formalin-fixed-paraffin-embedded tissue (FFPE) samples of both pediatric (n\u0026thinsp;=\u0026thinsp;4) and adult (n\u0026thinsp;=\u0026thinsp;4) patients with confirmed pathological diagnosis of DMG, \u003cem\u003eH3 K27\u003c/em\u003e-altered. IRB approval was obtained to utilize the archived pediatric and adult pathological samples for analysis at the University of Wisconsin-Madison (2022-0164-CP001). All patients analyzed within the study provided informed consent and all analyses were carried out according to UWSMPH/UW-Madison IRB direction and supervision. All samples were obtained from biopsies or resections done pre-treatment at the time of initial diagnosis. From each sample, three cores were obtained to have each patient sample represented as a triplicate to evaluate the broader tumor landscape and proteogenomic profiles. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u0026amp; B, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Sup Fig.\u0026nbsp;1). All analysiss conducted on human samples was performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003e \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\u003eClinical characteristics of pediatric and adult patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge at Diagnosis (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOS (months)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRight Thalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXRT\u0026thinsp;+\u0026thinsp;TMZ, ONC201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRight Thalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRight Thalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePontine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePontine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeft Thalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePontine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXRT\u0026thinsp;+\u0026thinsp;TMZ, ONC201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRight Thalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXRT\u0026thinsp;+\u0026thinsp;bevacizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eXRT \u0026ndash; radiotherapy, OS \u0026ndash; overall survival, TMZ \u0026ndash; temozolomide\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Genomic Profiling:\u003c/h2\u003e \u003cp\u003eTranscriptomic and proteomic profiling was done on the above samples. The anatomical locations included the thalamus (n\u0026thinsp;=\u0026thinsp;5) and pons (n\u0026thinsp;=\u0026thinsp;3). To evaluate the intra-tumoral landscape and heterogeneity, the samples were stained with antibodies against the epithelial cell marker, PanCK; the leukocyte marker, CD45; mutant-tumor specific marker against H3 K27M; and the nuclear stain SYTO13. We performed spatial transcriptomic and proteomic characterization of all samples via \u003cem\u003ein situ\u003c/em\u003e sequencing utilizing the Nanostring digital spatial profiling (DSP) system and Illumina sequencing platforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u0026amp; C). Across all samples, a total of 49 ROIs were marked for this experiment; divided between both populations of samples (25 for pediatric and 24 for adult). Each ROI accounted for approximately 500uM of total space with the differing cells encompassed within this. The ROI categories utilized for this experiment were H3 K27M Ab predominant, H3 K27 ab\u0026thinsp;+\u0026thinsp;CD45 and areas predominantly with unlabeled tumor cells (non-H3 K27M positive tumor cells). Each ROI was assessed for the composition of mutant neoplastic cells, immune cells and interactions with one another.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eROI Collection and Filtering:\u003c/h2\u003e \u003cp\u003eA total of 94 ROIs were initially collected for analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The subsequent filtering process, which incorporated criteria such as gene detection rates, total nuclei count, and the intrinsic relevance of each ROI type, resulted in 85 ROIs being selected for further analysis. To ensure a robust and meaningful deconvolution analysis, necessitating sufficient diversity among the ROIs, a more stringent filtering criterion based on gene detection rates was applied, ultimately retaining 49 ROIs for deconvolution analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnnotation of ROIs:\u003c/h2\u003e \u003cp\u003eEach ROI underwent several runs of manual annotation. This involved an in-depth examination of the corresponding-colored images and the screening of highly expressed protein markers characteristic of each region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis between Protein and RNA:\u003c/h2\u003e \u003cp\u003eFor the correlation analysis, proteins and RNA were matched according to their respective GeneIDs. A Pearson correlation coefficient was calculated for each pair to assess the degree of linear correlation between the protein and RNA expression levels in the ROIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Hypothesis testing to identify differences between average correlations in adult vs. pediatrics (and in mutated vs. non-mutated vs. TME) was conducted by transforming correlations using Fisher's Z-transformation and evaluating p-values via Students t-test (ANOVA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDeconvolution Analysis:\u003c/h2\u003e \u003cp\u003eDeconvolution of the spatial data was carried out using the SpatialDecon algorithm\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This method utilizes log-normal regression and integrates background modeling, thereby enhancing performance beyond that of traditional least-squares methods. SpatialDecon is adept at quantifying cell populations within ROIs and provides precise estimates of cell abundance. The reference matrix, crucial for this analysis, was sourced from a study focused on DMG single-cell RNA sequencing data\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The processing of this data was in strict accordance with the protocols and methodologies detailed in Liu et al., 2022\u003csup\u003e14\u003c/sup\u003e. For comparisons among the SpatialDecon identified regions a Wilcoxon ranked tested adjusted for multiple comparisons was utilized with adjusted p values reported. 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGene Regulatory Network Reconstruction and Cell-State Identification:\u003c/h2\u003e \u003cp\u003eTo comprehensively characterize the transcriptional state of each ROI, we utilized SCENIC\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, a computational approach for concurrent gene regulatory network reconstruction and cell-state identification. The SCENIC methodology involves three steps: initially, GRNboost2 identifies gene sets co-expressed with transcription factors. Subsequently, these gene sets are refined through cis-regulatory motif analysis using RcisTarget, leading to the formation of regulons by retaining modules with significant motif enrichment. The activity of each regulon in individual ROIs is quantified using the AUCell algorithm, yielding an AUC score that reflects the subnetwork activity. This score is then utilized to assess the characteristics of each ROI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eOver-representation Analysis\u003c/em\u003e:\u003c/h2\u003e \u003cp\u003eOver-representation analysis (ORA) was done using webgestalt\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e with default parameters on functional database \u0026ldquo;gene ontology\u0026rdquo; or biological pathways \u0026ldquo;KEGG\u0026rdquo;. Enrichment statics are calculated using a hypergeometric test to evaluate the significance of enrichment and calculate a p-value which is then adjusted for multiple comparisons as well as a false discovery rate (FDR)\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. For this study FDR is reported for all ORA comparisons made with significance being considered if FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePatient Cohort and Clinical Characteristics:\u003c/h2\u003e \u003cp\u003ePatient characteristics along with treatment details are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA along with experimental details and schematics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C, \u0026amp; D). All patients in the study had mutations within the \u003cem\u003eH3F3A\u003c/em\u003e gene which encodes histone H3.3 and typically correlates with a more severe phenotype in this tumor\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. For the pediatric patients, the median age at diagnosis was 8.5 (range: 3\u0026ndash;14 years); median overall survival (OS) was 9.2 months (range: 2\u0026ndash;14 months). For the adult patients, the median age of diagnosis was 40.5 (range: 25\u0026ndash;48); median overall survival (OS) was 14.2 months (range: 1\u0026ndash;26 months)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSCENIC profiling reveals spatially differential regulon enrichment:\u003c/h2\u003e \u003cp\u003eSCENIC was used to reconstruct gene regulatory networks and estimate cellular states for each ROI. The combined adult and pediatric samples were classified spatially by grouped ROIs into 3 categories: H3K27M mutated tumor cells (Mutated), Non H3K27M mutated tumor cells (Non-Mutated), and tumor cells\u0026thinsp;+\u0026thinsp;surrounding microenvironment (TME). These ROIs were confirmed using IHC staining on our tissue samples, allowing for anchoring and targeting of the resulting genomic and proteomic analysis done by the GEOMX system. Mutated ROIs contained only cells expressing H3K27M marks (green), while TME ROIs contained tumor cells (green), immune cells (pink), and endothelial cells (blue) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). ROIs containing cells with no staining (Non-Mutated) were hypothesized to contain mostly tumor cells that do not carry the H2K27m mutation which is seen in the tumor bulk according to recent reports\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. To investigate this hypothesis, the top enriched genes/regulons identified using SCENIC were subject to over representation analysis (ORA) and compared to an equal number of enriched genes/regulons derived from a dataset of normal thalamic and pontine tissue (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.brain-map.org/\u003c/span\u003e\u003cspan address=\"http://www.brain-map.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This comparison demonstrated highly enriched signaling for cancer associated genes and tumor suppressors with enriched terms such as Transcriptional Regulation in Cancer and Pathways in Cancer (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, ORA analysis on the normal thalamic and pontine tissue showed typical enrichment for cell developmental processes and neuronal signatures with enrichment terms for regulation of synaptic signaling and mononuclear cell proliferation (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Sup Fig.\u0026nbsp;2A \u0026amp; B). Across our spatial classifications, SCENIC identified significantly different regulons driven by ARID3C and MSX1 in the TME, TBP and ELK1 in Mutated, and NFATC2 and Stat6 in Non-mutated ROIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB \u0026amp; C). These regulons? were enriched for cytokine and interleukin signaling in the TME ROIs (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), cancer induced senescence and TP53 gene regulation in the Mutated ROIs (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.10), and TNF and WNT pathway signaling in Non-Mutated ROIs (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These results highlight that spatially distinct regions within the tumor rely on differential transcriptional programs especially with regards to invading immune cells and surrounding vasculature. Furthermore, the H3K27 mutation itself seems to impact the transcriptional landscape significantly with non-mutated tumor cells displaying more developmental like signaling pathways as opposed to mutated tumor cells displaying more canonical cancer signaling networks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOPC like cell signature varies across age and spatial location:\u003c/h2\u003e \u003cp\u003eMolecular and genomic classification of DMG-Alt remains in its infancy due to the extremely limited number of available patient samples compared to other glioma types. In a recently published seminal study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e the authors describe a ubiquitous oligodendrocyte precursor cell (OPC) like cellular state across both pediatric and adult DMG-Alt. Other cell states identified in the tumor include Mesenchymal (Mes) like and Astrocytic (AC) like, as well as cycling cells. Interestingly, neuronal signatures are not seen within DMGs from either population. We validated this in our dataset as well where we only saw enrichment for neuronal signatures during ORA analysis of normal midbrain controls (Sup Fig.\u0026nbsp;2A \u0026amp; B). Utilizing their published datasets, we compared our samples across age and spatial location and were able to corroborate the baseline OPC like signatures seen in adults and pediatrics. In our data examining Mutated ROIs we found pediatric tumors enriching for OPC like (specifically OPC like 2) and microglial signatures while adult tumors enrich for Mes like and AC signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Interestingly, when we examined TME ROIs we saw a shift in the observed signatures with adult tumors now showing increased OPC like signature enrichment as well as a stronger microglial signature ( peds vs adults: padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs padj\u0026thinsp;=\u0026thinsp;1.0) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese genomic signatures were originally derived from RNA data. However, our dataset includes a subset of protein targets allowing for partial validation of the expression pattern for one of the component genes of the OPC like signature, EGFR (Sup Fig.\u0026nbsp;3) at the protein level. These results indicate that even though our study has a relatively limited sample size, it still captures and validates a previously published genomic classification across patient age using both RNA and protein expression data. Our results also highlight a spatially driven shift in these signatures when more complex cell compositions are examined. This could indicate a TME driven shift in the tumor cells' biology or simply be reflective of the more complex TME present along outer edges of tumor compared to the tumor cell dense cores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between RNA and Protein varies both spatially and across patient age:\u003c/h2\u003e \u003cp\u003eA large portion of the current genomics work assumes that increased mRNA abundance results in increased protein abundance. While this is often the case, it is well known that the amount of translated protein can vary widely from the amount of detected mRNA\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Leveraging the unique Nanostring GeoMx technology we detected both RNA and protein probes across our entire range of samples. This allowed for comparison of matched RNA to protein pairs to assess transcription to translation fidelity and characterize the extent to which it varies across our samples. We first visualized the correlation between mRNA and protein abundance (ranging from \u0026minus;\u0026thinsp;1 being 100% negative correlation to 1 being 100% positive correlation) across the adult and pediatric tumor samples. We found similar rates of correlation on average in adults (0.19 +/- 0.27 SD) and pediatrics (0.33 +/- 0.25 SD) (Sup Fig.\u0026nbsp;4A) but differences were observed among the top 10 gene pairs that are most (and least) correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, \u0026amp; C). Spatially, similar correlation results were observed with no statistical difference in correlation rates (mutated vs non-mutated vs TME: 0.28 +/- 0.31SD, 0.24 +/- 0.26SD, 0.29 +/- 0.27 SD; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Sup Fig.\u0026nbsp;4B), however, differences among the specific gene pairs that were correlated vs. anticorrelated were seen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, E, \u0026amp;F). Across adult and pediatric samples positive correlation values between RNA and protein were similar for some genes (OLIG2, Vimentin, MAP2) but differential correlation was also seen in adults and pediatric samples. Spatially, similar proteins were observed positively correlated across regions that were also observed across ages (OLIG2, MBP, MAP2). In terms of negative correlation CTLA4 was a top negative correlation across both age and spatial ROIs, an interesting finding due to the relevance of CTLA4 treatments across many other cancer types\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Overall, these results show that not only are different transcriptional programs being activated in tumors across age and regions, but these transcriptional programs do not always faithfully lead to the corresponding proteins being translated. If translational fidelity is not considered, predictions biased towards RNA content, but not true protein translation, may result.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCommon genomic therapeutic targets show differing RNA to protein correlation:\u003c/h2\u003e \u003cp\u003eMuch of the upcoming targeted and immunotherapeutic interventions that are being studied for DMG-Alt have come about based upon previously established transcriptomic information. However, downstream proteomic data of clinically relevant therapeutic targets have not been readily studied. To examine this in our dataset we found RNA-Protein pairs that represented promising or already tried therapeutic targets in this group of malignancy and quantified their RNA and protein levels across all samples. Expression of receptor tyrosine kinase (RTK) has been shown to be prevalent on these malignancies with on-going and previously completed studies examining EGFR as a relevant target both as a small molecule inhibitor and in combination with immunotherapeutic agents\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Interestingly, low concordance was observed for EGFR between RNA and protein, with protein levels higher than would have been predicted by mRNA abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Checkpoint blockade has also been tested both as the monotherapy and combinatorial immunotherapy for this group of malignancy\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, thus we examined PD1, PDL-1 and CTLA-4 expression correlation in our dataset. We observed low concordance between RNA and protein across all the above markers with CTLA-4 especially enriched for negative correlation when examined in our TME ROIs (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). For these molecules, the amount of RNA detected was more than the actual amount of protein detected which could lead to skewed optimistic prediction of outcome for therapeutic targeting in clinical settings. B7-H3 and BRAF have been well characterized across tumors and within our dataset we found close concordance for RNA and protein levels for BRAF but not B7-H3 with the latter producing more protein than detected RNA. To further explore more clinically relevant targets with our data we filtered GSEA enrichment results using Enrichr\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e for genes involved in pathways that resulted in molecular secretions. The results demonstrated the top enriched pathways belonging to IL6 and ATF2 signaling events with glucocorticoid receptor signaling and calcium signaling also being present, likely related to the very neuronally active locations of these tumors (Sup Fig.\u0026nbsp;5). Together, these results highlight the crucial point that extensive targeting solely based on transcriptomics may result in false positives as well as false negatives and integrating proteomic data may strengthen early-stage clinical drug screening.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDMG-Alt continues to be a devastating disease with limited therapeutic options, however, in the last decade neurosurgical and neuro-oncological techniques have advanced considerably allowing for biopsy and sample collection from these tumors. This in turn has led to several novel clinical trials currently underway based on the genomic targets discovered from comprehensive transcriptomic studies. Our study aims to push the field forward by taking the next step in genomic screening by examining spatial aspects of the tumor architecture as well as proteomic profiles. Spatially, we found that tumor ROIs that contain portions of TME display distinct genetic signatures compared to regions with dense tumor cell populations. Using proteomics, we demonstrate that transcription to translation fidelity is not always maintained in both adult and pediatric DMG-Alt. Finally, we interrogated common genetic targets proposed both for these tumors as well as broadly used in other gliomas and reveal the shortfalls of predicting therapeutic targeting efficacy based on RNA data alone. Continued research in this field will be crucial in advancing both the molecular and clinical understanding of these rare and lethal tumors.\u003c/p\u003e \u003cp\u003eA recent publication by Liu and colleagues demonstrated an increased tendency for pediatric DMG-Alt to be dominated by an OPC like cell type while adult DMG-Alt enriched for a mesenchymal cell phenotype\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In our cohort we saw similar patterns across adult and pediatric tumors, however when examining the spatial landscape, we observed a change in the signature in ROIs composed of both tumor and TME cells. Also of note, in both our cohorts of data there is little to no neuronal signature expressed within any sample set. This is interesting because these tumors arise in very neuronally dense and eloquent tissue but seem to not be as intermixed with their surroundings like glioblastomas (GBM), the most common primary CNS malignancy in adults, which are known to integrate into neuronal synapses and proliferate more in response to neuronal firing\u003csup\u003e\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This may indicate that DMG-Alts interacts more with other brain resident cells such as microglia, astrocytes, or oligodendrocytes as opposed to neurons. It\u0026rsquo;s also worth noting that there was little to no outside immune cell infiltration into the tumor bulk itself which is consistent with reports of DMG-Alt being an immune desert\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study is the first to include spatial proteomic profiling and we demonstrate that while translational fidelity was measured at similar levels across adults and pediatric samples, as well as across the ROIs, there were subtle differences in the types of genes that were either faithfully or not faithfully translated from mRNA. These results have significant implications for cancer therapeutics in general which to date has relied mostly on screening mRNA due to difficulties in obtaining proteomic data. Our examination of common genomic targets discovered using mRNA alone shows that although this method can result in true proteomic hits there is also room for error including both false positive and false negative hits being prioritized (such as EGFR false negative vs. PD-1 false positive). Although there are some studies suggesting mRNA, specifically differentially expressed mRNA, may be a faithful readout of true protein synthesis\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, this has yet to be widely examined in the context of brain malignancies, especially in the pediatric population.\u003c/p\u003e \u003cp\u003eOne of the limitations of our study is that the number of protein probes available is far less than the number of RNA probes, and so a genome-wide comparison of RNA to protein was not possible. We also did not have a large enough number of samples to include statistically significant sex comparisons. In addition, due to the technical limitations of the Nanostring platform regarding the number of antibodies that can be used for defining ROIs, we had a subset of cells that were unstained. Since all our samples were targeted needle biopsies aimed at obtaining the most pathological tumor tissue, we hypothesized that these were most likely tumor cells that lacked the H3K27m mutation. In validating this using ORA analysis, we discovered a unique genetic program within these cells compared to H3K27m tumor cells involving some immune signatures as well as cancer and developmental gene enrichment. Although we only visualize a limited number of immune cells in our samples, there were samples with strong enrichment for immune related signaling, especially when TME ROIs were considered. This may be a result of tumor cells hijacking immune signaling methods (seen in other gliomas\u003csup\u003e\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e) or could result from immune cells sitting mostly on the peripheral edges of the tumor (which would not be captured during biopsy) inducing inflammation. An in depth understanding of the immune response and its resulting inflammation in this context may prove critical as we try to advance therapies, particularly CAR-T cell based therapies which are currently undergoing clinical trials\u003csup\u003e\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough DMG-Alt remains a 100% lethal tumor with incredibly limited survival, novel research is starting to illuminate its genetic background and expose possible therapeutic vulnerabilities. Further studies across multiple institutions to increase sample size will be the key in validating and advancing effective therapies and ultimately making a difference in patient survival.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no relevant conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eAll sequencing raw and processed data will be deposited into GEO and released publicly upon publication of the manuscript. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eSD/JMS/EX/CK/MD wrote and edited the manuscript. SD/EX/JMS/ED constructed figures and tables. JMS/EX completed data analysis. CK/MD supervised the project and supplied funding. SD/JMS contributed equally to the manuscript and are co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the NIH K08NS092895 grant (MD). JMS is partly supported by NIH/NINDS T32 NS105602.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLouis, D. N. \u003cem\u003eet al.\u003c/em\u003e The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncol. 23, 1231\u0026ndash;1251 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eCentral Nervous System Tumours\u003c/em\u003e. vol. 5 (International Agency for Research on Cancer, 2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDono, A., Takayasu, T., Ballester, L. Y. \u0026amp; Esquenazi, Y. Adult diffuse midline gliomas: Clinical, radiological, and genetic characteristics. J. Clin. Neurosci. 82, 1\u0026ndash;8 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyronet, D. \u003cem\u003eet al.\u003c/em\u003e Characteristics of H3 K27M-mutant gliomas in adults. Neuro-Oncol. 19, 1127\u0026ndash;1134 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, V. M., Alvi, M. A., McDonald, K. L. \u0026amp; Daniels, D. J. Impact of the H3K27M mutation on survival in pediatric high-grade glioma: a systematic review and meta-analysis. J. Neurosurg.: Pediatr. 23, 308\u0026ndash;316 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackay, A. \u003cem\u003eet al.\u003c/em\u003e Integrated Molecular Meta-Analysis of 1,000 Pediatric High-Grade and Diffuse Intrinsic Pontine Glioma. Cancer Cell 32, 520\u0026ndash;537.e5 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhuong-Quang, D.-A. \u003cem\u003eet al.\u003c/em\u003e K27M mutation in histone H3.3 defines clinically and biologically distinct subgroups of pediatric diffuse intrinsic pontine gliomas. Acta Neuropathol. 124, 439\u0026ndash;447 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones, C. \u0026amp; Baker, S. J. Unique genetic and epigenetic mechanisms driving paediatric diffuse high-grade glioma. Nat. Rev. Cancer 14, 651\u0026ndash;661 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, G. \u003cem\u003eet al.\u003c/em\u003e Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas. Nat. Genet. 44, 251\u0026ndash;253 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, T. \u003cem\u003eet al.\u003c/em\u003e Detection of histone H3 K27M mutation and post-translational modifications in pediatric diffuse midline glioma via tissue immunohistochemistry informs diagnosis and clinical outcomes. Oncotarget 9, 37112\u0026ndash;37124 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J. \u003cem\u003eet al.\u003c/em\u003e Epigenomic landscape and 3D genome structure in pediatric high-grade glioma. Sci Adv 7, eabg4126 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartzentruber, J. \u003cem\u003eet al.\u003c/em\u003e Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226\u0026ndash;231 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTune, B. G. \u003cem\u003eet al.\u003c/em\u003e From Pediatric to Adult Brain Cancer: Exploring Histone H3 Mutations in Australian Brain Cancer Patients. Biomedicines 11, 2907 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, I. \u003cem\u003eet al.\u003c/em\u003e The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location. Nat. Genet. 54, 1881\u0026ndash;1894 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapur, M. \u0026amp; Ackerman, S. L. mRNA Translation Gone Awry: Translation Fidelity and Neurological Disease. Trends Genet. 34, 218\u0026ndash;231 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung, C. \u003cem\u003eet al.\u003c/em\u003e The fidelity of transcription in human cells. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e 120, e2210038120 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGamba, P. \u0026amp; Zenkin, N. Transcription fidelity and its roles in the cell. Curr. Opin. Microbiol. 42, 13\u0026ndash;18 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsplund, A. \u003cem\u003eet al.\u003c/em\u003e Expression profiling of microdissected cell populations selected from basal cells in normal epidermis and basal cell carcinoma. Br. J. Dermatol. 158, 527\u0026ndash;538 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhanasekaran, S. M. \u003cem\u003eet al.\u003c/em\u003e Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822\u0026ndash;826 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenbaum, D., Colangelo, C., Williams, K. \u0026amp; Gerstein, M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, N. \u003cem\u003eet al.\u003c/em\u003e Comparison of Protein and mRNA Expression Evolution in Humans and Chimpanzees. PLoS ONE 2, e216 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShankavaram, U. T. \u003cem\u003eet al.\u003c/em\u003e Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study. Mol. Cancer Ther. 6, 820\u0026ndash;832 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGry, M. \u003cem\u003eet al.\u003c/em\u003e Correlations between RNA and protein expression profiles in 23 human cell lines. BMC Genom. 10, 365 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanaher, P. \u003cem\u003eet al.\u003c/em\u003e Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nat. Commun. 13, 385 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAibar, S. \u003cem\u003eet al.\u003c/em\u003e SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083\u0026ndash;1086 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., Vasaikar, S., Shi, Z., Greer, M. \u0026amp; Zhang, B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130\u0026ndash;W137 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao, Y., Wang, J., Jaehnig, E. J., Shi, Z. \u0026amp; Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res 47, W199\u0026ndash;W205 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, B., Kirov, S. \u0026amp; Snoddy, J. WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 33, W741\u0026ndash;W748 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarson, J. D. \u003cem\u003eet al.\u003c/em\u003e Histone H3.3 K27M Accelerates Spontaneous Brainstem Glioma and Drives Restricted Changes in Bivalent Gene Expression. Cancer Cell 35, 140\u0026ndash;155.e7 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbreu, R. de S., Penalva, L. O., Marcotte, E. M. \u0026amp; Vogel, C. Global signatures of protein and mRNA expression levels. Mol. Biosyst. 5, 1512\u0026ndash;1526 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonneveld, S., Verhagen, B. M. P. \u0026amp; Tanenbaum, M. E. Heterogeneity in mRNA Translation. Trends Cell Biol. 30, 606\u0026ndash;618 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdfors, F. \u003cem\u003eet al.\u003c/em\u003e Gene-specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol. 12, 883 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, X. \u0026amp; Xu, C. Immune checkpoint signaling and cancer immunotherapy. Cell Res 30, 660\u0026ndash;669 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampson, J. H., Gunn, M. D., Fecci, P. E. \u0026amp; Ashley, D. M. Brain immunology and immunotherapy in brain tumours. Nat Rev Cancer 20, 12\u0026ndash;25 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHavel, J. J., Chowell, D. \u0026amp; Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 19, 133\u0026ndash;150 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, X. \u003cem\u003eet al.\u003c/em\u003e Anti-VEGF therapy improves EGFR-vIII-CAR-T cell delivery and efficacy in syngeneic glioblastoma models in mice. J. Immunother. Cancer 11, e005583 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X. \u003cem\u003eet al.\u003c/em\u003e EGFR is a master switch between immunosuppressive and immunoactive tumor microenvironment in inflammatory breast cancer. Sci. Adv. 8, eabn7983 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeller, M. \u003cem\u003eet al.\u003c/em\u003e Rindopepimut with temozolomide for patients with newly diagnosed, EGFRvIII-expressing glioblastoma (ACT IV): a randomised, double-blind, international phase 3 trial. Lancet Oncol 18, 1373\u0026ndash;1385 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacciotti, C. \u003cem\u003eet al.\u003c/em\u003e Immune checkpoint inhibition for pediatric patients with recurrent/refractory CNS tumors: a single institution experience. J. Neuro-Oncol. 149, 113\u0026ndash;122 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAusejo-Mauleon, I. \u003cem\u003eet al.\u003c/em\u003e TIM-3 blockade in diffuse intrinsic pontine glioma models promotes tumor regression and antitumor immune memory. Cancer Cell 41, 1911\u0026ndash;1926.e8 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKline, C. \u003cem\u003eet al.\u003c/em\u003e Reirradiation and PD-1 inhibition with nivolumab for the treatment of recurrent diffuse intrinsic pontine glioma: a single-institution experience. J. Neuro-Oncol. 140, 629\u0026ndash;638 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, E. Y. \u003cem\u003eet al.\u003c/em\u003e Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 14, 128 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, H. S. \u003cem\u003eet al.\u003c/em\u003e Electrical and synaptic integration of glioma into neural circuits. Nature 573, 1\u0026ndash;7 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, H. S. \u003cem\u003eet al.\u003c/em\u003e Neuronal Activity Promotes Glioma Growth through Neuroligin-3 Secretion. Cell 161, 803\u0026ndash;816 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonje, M. \u003cem\u003eet al.\u003c/em\u003e Roadmap for the Emerging Field of Cancer Neuroscience. Cell 181, 219\u0026ndash;222 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, H. S. \u003cem\u003eet al.\u003c/em\u003e Targeting neuronal activity-regulated neuroligin-3 dependency in high-grade glioma. Nature 549, 533\u0026ndash;537 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, G. L. \u003cem\u003eet al.\u003c/em\u003e Non-inflammatory tumor microenvironment of diffuse intrinsic pontine glioma. Acta Neuropathol. Commun. 6, 51 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLieberman, N. A. P. \u003cem\u003eet al.\u003c/em\u003e Characterization of the immune microenvironment of diffuse intrinsic pontine glioma: implications for development of immunotherapy. Neuro-Oncol. 21, 83\u0026ndash;94 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoussounadis, A., Langdon, S. P., Um, I. H., Harrison, D. J. \u0026amp; Smith, V. A. Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system. Sci. Rep. 5, 10775 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazavi, S.-M. \u003cem\u003eet al.\u003c/em\u003e Immune Evasion Strategies of Glioblastoma. Frontiers Surg 3, 11 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGangoso, E. \u003cem\u003eet al.\u003c/em\u003e Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion. Cell (2021) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2021.03.023\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2021.03.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeverwijk, A. van \u0026amp; Visser, K. E. de. Mechanisms driving the immunoregulatory function of cancer cells. Nat Rev Cancer 1\u0026ndash;23 (2023) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41568-022-00544-4\u003c/span\u003e\u003cspan address=\"10.1038/s41568-022-00544-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajzner, R. G. \u003cem\u003eet al.\u003c/em\u003e GD2-CAR T cell therapy for H3K27M-mutated diffuse midline gliomas. Nature 603, 934\u0026ndash;941 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitanza, N. A. \u003cem\u003eet al.\u003c/em\u003e Intraventricular B7-H3 CAR T cells for diffuse intrinsic pontine glioma: preliminary first-in-human bioactivity and safety. Cancer Discov. 13, 114\u0026ndash;131 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas, B. C. \u003cem\u003eet al.\u003c/em\u003e CAR T cell therapies for diffuse midline glioma. Trends Cancer 9, 791\u0026ndash;804 (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":"","lastPublishedDoi":"10.21203/rs.3.rs-4139314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4139314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiffuse midline glioma, \u003cem\u003eH3 K27\u003c/em\u003e-altered (DMG-Alt) are highly aggressive malignancies of the central nervous system (CNS) that primarily affect the pediatric population. Large scale spatial transcriptomic studies have implicated that tumor microenvironmental landscape plays an important role in determining the phenotypic differences in tumor presentation and clinical course, however, data connecting overall transcriptomic changes to the protein level is lacking.\u003c/p\u003e \u003cp\u003eThe NanoString GeoMx\u0026trade; Digital Spatial Profiler platform was used to determine the spatial transcriptomic and proteomic landscape in a cohort of both pediatric and adult \u003cem\u003eH3 K27\u003c/em\u003e-altered DMG biopsy samples. Three fluorescently labeled antibodies targeting immune cells (CD45), epithelial cells (PanCK), tumor cells (\u003cem\u003eH3 K27M\u003c/em\u003e) and a nucleic acid stain (SYTO-13) were used to establish regions of interest (ROI) for genomic and proteomic analysis.\u003c/p\u003e \u003cp\u003eWe found genetic alterations within the tumor which can be delineated across patient age and spatial location. We show that the H3 K27M mutation itself has a profound impact on tumor cells transcriptomics and interestingly we found limited fidelity between overall transcriptome and proteome. Our data also validate the previously described OPC like precursor signature at the proteomic level and reveal a special shift in the signature based on the local TME composition.\u003c/p\u003e","manuscriptTitle":"Transcriptomic and Proteomic Spatial Profiling of Pediatric and Adult Diffuse Midline Glioma H3 K27-Altered, Reveals Region Specific Differences and Limited Overlap between mRNA and Protein","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 21:14:29","doi":"10.21203/rs.3.rs-4139314/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-02T08:35:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-09T19:10:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-03T23:10:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319373334808414176944219613978774308594","date":"2024-05-29T04:12:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197572659851426902093206405824277821226","date":"2024-05-16T19:56:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-16T16:16:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-05T13:03:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-02T16:50:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-02T16:49:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-20T19:56:34+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":"9f815fed-22e8-427a-901b-9af8eaa0eb22","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30187948,"name":"Biological sciences/Cancer"},{"id":30187949,"name":"Biological sciences/Immunology"},{"id":30187950,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2024-10-07T15:59:07+00:00","versionOfRecord":{"articleIdentity":"rs-4139314","link":"https://doi.org/10.1038/s41598-024-73199-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-30 15:56:58","publishedOnDateReadable":"September 30th, 2024"},"versionCreatedAt":"2024-04-05 21:14:29","video":"","vorDoi":"10.1038/s41598-024-73199-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-73199-w","workflowStages":[]},"version":"v1","identity":"rs-4139314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4139314","identity":"rs-4139314","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}