Potential Oncogenic Role of PRSS1 Identified by Whole Exome Sequencing in Glioma Primary Cell Lines

Potential Oncogenic Role of PRSS1 Identified by Whole Exome Sequencing in Glioma Primary Cell Lines | 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 Potential Oncogenic Role of PRSS1 Identified by Whole Exome Sequencing in Glioma Primary Cell Lines Alvaro Monago Sanchez, Natalia Iglesias Bueno, Josefa Carrion Navarro, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8979829/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Glioblastoma (GBM) is the most common and aggressive adult primary brain malignancy, with poor survival and marked resistance to therapy. In pediatric patients, diffuse midline gliomas (DMG), including diffuse intrinsic pontine gliomas (DIPG) with H3K27 alterations, are similarly lethal and completely refractory to therapy. These tumors present high inter and intratumoral heterogeneity, driven in part by glioma stemlike cells (GSCs), which compromises therapeutic responses and complicates model development. Whole-exome sequencing (WES) of patient derived GSCs can clarify subtype specific Single Nucleotide Variant (SNV)/Copy Number Variation (CNV) patterns and potentially expose novel vulnerabilities. Methods To identify recurrent molecular patterns and candidate therapeutic targets, we established a cohort of GSCs derived from eight patients, comprising six adult GBM and two pediatric DIPG, including H3 mutant and wild-type subtypes. WES of this cohort was performed to characterize copy number alterations and somatic point mutations. Results Adult-derived GBM IDH-Wild-Type GSCs recapitulated canonical genomic alterations including chromosome 7 duplication, chromosome 10 loss, EGFR amplification, and CDKN2A/B deletion. DIPG derived lines exhibited more heterogeneous genomic profiles, reflecting subtype-specific divergence. Importantly, all cell lines harbored a recurrent nonsense mutation in PRSS1 (p.Gly177*), truncating the catalytic domain. This mutation was also observed in primary GBM samples. Reduced PRSS1 expression correlated with poorer survival in GBM datasets, and we propose a mechanism whereby PRSS1 loss disrupts PAR2 signaling and promotes compensatory PAR1 activation, enhancing tumor progression. Conclusions Our study presents a comprehensive WES analysis of patient-derived GSCs, revealing key genomic alterations across adult and pediatric tumors. We identified a recurrent PRSS1 stop-gain mutation across all cells, suggesting a potential novel oncogenic role in gliomas of the PAR2 protease signaling axis, uncovering a common vulnerability and a potential therapeutic target in high-grade gliomas. Biological sciences/Cancer Biological sciences/Genetics Health sciences/Oncology Glioma Glioblastoma Diffuse intrinsic pontine gliomas WES PRSS1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Gliomas represent the most common primary malignancies of the central nervous system (CNS). Among adults, Glioblastoma (GBM) is the most common and aggressive form, with an incidence of 3.27 cases per 100,000 population [ 1 ]. Classified as an IDH-wild type grade IV tumor, GBM is characterized histopathologically by marked cellular pleomorphism, high mitotic activity, necrosis, and microvascular proliferation [ 2 ]. GBM patients have a poor prognosis with a five-year survival rate lower than 5% [ 3 ]. The current standard of care consists of maximal surgical resection with radiotherapy and concomitant temozolomide chemotherapy [ 4 ]. Notably, tumor-treating fields (TTFields) represent a recent advancement, extending median overall survival (mOS) from 14.6 to 20.9 months [ 5 ]. In pediatric patients, diffuse intrinsic pontine glioma (DIPG) represents the most common subtype of diffuse midline glioma (DMG), and the most common brainstem tumors, with an incidence of around 2 cases per 100,000 population. These tumors commonly arise from the brainstem or pontine region and can be further classified by the H3F3A K27M mutation in histone 3, with around 75% of H3-mutant cases [ 6 , 7 ]. Prognosis is also dismal, with a five-year survival of around 1% and a mOS of just 9 months [ 2 ]. These tumors are also characterized by having a relatively intact blood brain barrier, which further limits drug penetration [ 8 , 9 ]. Therapeutical options are limited to radiotherapy, as surgery is not viable and traditional chemotherapeutic regimens are inefficacy [ 10 ]. Despite decades of investigation into novel modalities including targeted agents, immunotherapies, and advanced radiotherapeutic techniques, the vast majority have not yielded significant improvements in patient outcomes[ 11 , 12 ]. A major barrier to effective treatment of gliomas is their marked intra- and intertumoral heterogeneity driven in part by the presence of glioma stem-like cancer stem cells (GSCs). These GSCs are a dynamic and heterogeneous population characterized by clonal evolution, self-renewal capacity, and the ability to initiate and sustain tumor growth [ 13 ]. Importantly, GSCs can recapitulate the histological and molecular features of the original tumor in experimental models [ 14 ], underscoring their role in resistance to therapy and tumor recurrence. Their identification, isolation, and molecular characterization are thus essential for elucidating tumoral biology and for the development of effective, personalized therapeutic strategies [ 15 ]. Consequently, managing these malignancies remains a critical and unmet clinical challenge, underscoring an urgent need for continued translational research and refinement of precision oncology approaches [ 16 ]. The detection of patient-specific mutations, particularly those conferring therapeutic vulnerability, should become a cornerstone of clinical diagnostics and decision-making [ 17 ]. Intratumoral genetic and phenotypic heterogeneity is a defining hallmark of many cancers, with special relevance in the latest World Health Organization (WHO) CNS tumor classification, with GBM and DIPG representing one of the most extreme examples. Whole exome sequencing (WES) offers a powerful means of dissecting tumor evolution and identifying both common and rare driver mutations [ 2 ]. Its application to GSCs enables the study of tumor-initiating populations at a molecular level and has the potential to inform precision medicine approaches [ 18 ]. Furthermore, WES allows comparative studies across spatially distinct tumor regions and across patient subgroups. To explore the genetic landscape of GSCs and uncover potential unexplored therapeutic targets, we performed WES on a panel of eight patient-derived GSC lines representing both adult and pediatric gliomas. The adult cohort included six GBM lines: GBM18, GBM27, GBM38, GBM123, GBM128B, and GBM128D. Notably, GBM128B and GBM128D were derived from distinct tumor regions of the same patient, offering a unique opportunity to explore intratumoral heterogeneity. The pediatric cohort consisted of two DIPG lines: DIPG-Mut, harboring the canonical H3K27M mutation, and VUMC-DIPG10 (termed DIPG-WT from here on), which is wild-type for this mutation. These models capture key clinical and molecular subtypes, allowing for comparative analyses across age groups, tumor types and spatial contexts. Our analysis confirmed preservation of canonical CNV alterations of GBM, as well as a clear CNV/SNV divergence between GB128B and GBM128D, highlighting intratumoral heterogeneity. Most importantly, we detected an early stop codon mutation in PRSS1 present in all cell lines, suggesting a novel oncogenic role for this gene, which may play a role in glioma tumoral progression. Materials and Methods Human samples GBM and DIPG H3K27M mutant GSCs were originally isolated from surgical human GBM and DIPG specimens as previously described [ 19 ]. GBM GSCs were maintained in growth media (M21) containing DMEM/F-12 (Gibco, Grand Island, NY, USA) supplemented with: Non Essential Amino Acids (1% v/v; Gibco), HEPES (38 mM; Gibco), D-Glucose (Sigma-Aldrich, St. Louis, MI, USA), BSA-FV (0,01% v/v; Invitrogen, Carlsbad, CA, USA), Sodium Pyruvate (1 mM; Invitrogen), L-Glutamine (4 mM; Gibco), Antibiotic-Antimycotic (0.4% v/v; Invitrogen), N1 Supplement (1% v/v; Invitrogen), Hydrocortisone (0.3 µg/mL; Sigma-Aldrich), Tri-iodothyronine (0.03 µg/mL; Sigma-Aldrich), EGF (10 ng/mL; Sigma-Aldrich), bFGF (20 ng/mL; Sigma-Aldrich) and Heparin (2 µg/mL; Sigma-Aldrich) under standard tissue culture conditions (37ºC and 5% CO2). The human DIPG H3K27MWT cell line (VUMC-DIPG10) was kindly provided by Dr. Piotr Waranecki (VU University Medical Center, Amsterdam). DMG GSCs were maintained in growth media (TSM) containing Neurobasal (Gibco, USA) and DMEM/F12 (Gibco, USA) supplemented with Hepes (2,5 mM; Gibco, USA), Non Essential Amino Acids (1% v/v; Gibco, USA), Sodium Piruvate (1mM; Invitrogen, USA), L-Glutamine (2mM; Gibco, USA), Antibiotic-Antimycotic (1% v/v; Invitrogen, USA), B-27 Supplement (1% v/v; Gibco, USA),PDGF α + β (10 ng/mL; Irvine Scientific, Japan) EGF (10 ng/mL; Sigma-Aldrich, USA), bFGF (20 ng/mL; Sigma-Aldrich, USA) and Heparin (2 µg/mL; Sigma-Aldrich, USA) under standard tissue culture conditions of 5% CO 2 and 37ºC. Tissue samples from GBM patients were kindly provided by the HGM BioBank, integrated in RETICS, National Network Biobanks, funding by Instituto de Salud Carlos III, from Biobanco La Fe (PT17/0015/0043), Valencia, Spain, and HM Hospitales, Madrid, Spain. Samples were processed following the current procedures and frozen immediately after their reception. WES of GSCs Genomic DNA was extracted from GSCs and GBM tissue samples using the DNeasy Blood & Tissue kit (Qiagen) following the manufacturer’s instructions. DNA concentration was quantified by fluorimetry with a Qubit (Thermo Fisher Scientific Inc), and purity was assessed using a Nanodrop spectrophotometer (Thermo Fisher Scientific Inc). Exome sequencing was performed on GSCs by NimGenetics (Madrid, Spain). Libraries were prepared with the Twist Comprehensive Exome Panel (Twist Bioscience, USA) and quality controlled using a TapeStation (Agilent Technologies, USA). Paired-end sequencing (2X150 bp) was carried out on the NovaSeq 6000 platform (Illumina, Inc.). Sequencing analysis, quality control and data processing were performed with a NimGenetics custom bioinformatics pipeline. CNV and SNV Analysis CNVs and SNVs were annotated and classified using a NimGenetics custom pipeline. SNVs analysis integrated population allele frequencies from gnomAD, clinical assertions from ClinVar, and in-silico functional impact scores (SIFT, LRT, MutationTaster). Genomic coordinates were referenced to GRCh38. For TMB calculation, the total number of nonsynonymous SNVs per cell line was divided by the effective megabases (Mb) captured by the Twist panel to obtain the number of mutations per Mb of DNA. Pathway annotation analysis was performed by automatic category selection of genes in the Dr. Tom bioinformatic platform. Conventional PCR and Sanger Sequencing Selected variants identified by WES were validated by polymerase chain reaction (PCR). For KMT2C , a nested PCR was required to amplify the region containing the rs150073007 mutation. The first round of amplification was performed using the following pair of primers Fw: 5’-TGGAAGTTGAAGGCCCTGAA-3’, Rv: 5’-GACCGAGGTCTACCAGGAGA-3’. The resulting product was used as template for the second PCR with primers Fw: 5’- CATGCTGCATAATTACCCTT-3’, Rv: 5’- GACCGAGGTCTACCAGGAGA-3’. For the rs1240508430 mutation in PRSS1, conventional PCR was used with the following primers Fw: 5’- TCTCTTCCTGATCCTCACAGC-3’, Rv: 5´- GCATGGGAAGGGTTGGTCAA-3’. PCR products were purified and subjected to Sanger sequencing by the Genomics Service at Instituto de Investigaciones Biomédicas Albert Sols (IIBM, Madrid, Spain). Sequences were compared and aligned using the BLAST algorithm. Chromatograms were visualized with the FinchTV software (version 1.4.0). Hierarchical clustering on Principal Components Hierarchical clustering was performed to separate the patients into clusters using Ward’s linkage method. Euclidean distance was used as the dissimilarity metric. Prior to clustering, Principal Component Analysis (PCA) was applied. After generating the dendrogram, the optimal number of clusters was determined by analyzing the vertical distance between successive merges. A cut was made at a vertical distance threshold of 7 units on the y-axis of the dendrogram. All the analysis was implemented in Python, using the scipy.cluster.hierarchy module for clustering and matplotlib for visualization. GlioVis data accession and survival analysis mRNA expression and clinical data for the TCGA and CGGA GBM cohort were sourced from the GlioVis data portal [ 20 ]. The dataset included gene expression profiles generated on the RNA-seq platform. All analyses excluded IDH-mutant GBM tumors. Results Improving the Accuracy of Mutation Identification To increase the accuracy of pathogenic mutation identification and reduce false-positive variant calls inherent to exome sequencing, we applied a pre-filtering strategy based on a curated list of 435 genes (Supplementary Table 1) known to exhibit࿼࿼. Among these, we identified 116 genes across our dataset, with members of the mucin (e.g., MUC2 , MUC3A , MUC4 , MUC5AC , MUC16, MUC20, etc ) and HLA (e.g., HLA-C , HLA-DRB1 , HLA-DQB1 , HLA-DRB5 , HLA-DQA1 ) gene families, as well as ZNF717 , ranking among the 20 most frequently mutated loci prior to filtering (Supplementary Table 2). Following artifact filtering, we visualized mutation distribution using VarDecrypt’s oncoplots[ 22 ] and identified several recurrently mutated genes across all eight GSC lines. Most mutations were missense, however; KMT2C (also known as MLL3 ) presented a notable exception with a recurrent nonsense mutation (Y816*, rs150073007) observed in seven out of eight samples. While previous WES studies have reported this stop-gain variant in multiple cancer types, including [ 23 ]࿼, and laryngeal head and neck squamous cell carcinomas [ 24 ]࿼ GBM [ 25 ]࿼ validation by Sanger sequencing confirmed that this variant represents a sequencing artifact in our dataset (Figure S1 ). GSCs CNV alterations recapitulate GBM tumor genetic heterogeneity. Analysis of CNVs confirmed that adult GBM GSCs were consistent with the canonical chromosomal patterns of GBM (Fig. 1 A, 1 B, S2), with all lines presenting canonical duplications in chromosome 7and five out of six lines showed deletions in chromosome 10. Moreover, deletions in tumor suppressor genes CDKN2A / B and PTEN are the most frequently observed, together with the amplification of EGFR (Fig. 1 C). These alterations are associated to the EGFR/PTEN/Akt/mTOR pathway, key in the development of GBM[ 26 ]. Interestingly, the two different subclones isolated from the same patient (GBM128) differ in the loss of RB1 and PDGFRA , the second most frequently mutated tyrosine kinase receptor in GBM. This observation highlights the significant intratumoral heterogeneity of these tumors, where each biopsy can only represent a fraction of the tumoral mutational load. Pediatric derived GSCs differ significantly in CNV profile, but, interestingly, wild type cells presented the classical duplicated chromosome 7, with a section of chromosome 10 being deleted in H3 mutant cells (Fig. 1 A, 1 B). These differences also were reflected in key genes, with both lines not sharing any of the cell cycle and RTKs related genes; somewhat paradoxically, H3 mutant cells have duplications in the key CDKN2A/B genes (Fig. 1 C). These results are in line with the established classification of DMG patients based on the presence of the Histone H3F3A K27M mutation [ 2 ]. SNV clustering of GSC lines We next characterized SNVs in our cohort. A principal component analysis (PCA) based on the variant allele frequency (VAF) of the top 200 most mutated genes was used to classify the cell lines. PCA showed that DIPG-wt cells formed a distinct cluster, separate from other lines, whereas DIPG-Mut cells clustered with GBM27, a highly diffuse GBM line [ 19 ]. Despite considerable dispersion among the GBM lines, indicating significant genetic differences, the GBM128 subclones clustered together. (Fig. 2 A). Hierarchical clustering confirmed these results, showing a single cluster for the GBM128 subclones and a separate cluster for DIPG-wt cells (Fig. 2 B). General evaluation of SNV landscape across cell line cohort After unsupervised clustering, we decided to investigate SNVs in more detail. We started by evaluating the TMB in our cell lines. We observed that all cell lines presented values above 30 mutations per Mb of DNA, with almost all consistently around 34. GBM38 had the lowest value at 32, and the DIPG-WT cell line presented the highest value at almost 37 (Fig. 2 C). All these cell lines can therefore be characterized as high TMB [ 27 ], revealing the elevated mutational load of these tumors. We then compared the number of common and unique SNVs in adult and pediatric lines, identifying over 12,000 common SNVs. Interestingly GBM-derived lines presented over 90,000 unique mutations over 51,000 for pediatric cells. This difference may suggest a comparatively more homogeneous SNV landscape in pediatric tumors (Fig. 3 A). Ordering genes by mutation frequency identified several genes of interest among the top 20 most mutated genes across all cell lines, like POLR1C , involved in RNA polymerase I and III and the serine-protease genes PRSS1 and PRSS2 . Most alterations in these 20 genes correspond to intron variants or other variations that have no impact on the final protein, but PRSS1 and PRSS2 exhibited the most heterogenous mutational composition, while also presenting some of the highest numbers of mutations (Fig. 3 B). We then restricted our analysis to the top 20 genes reported in COSMIC based on mutation frequency [ 28 ] for GBM and DMG. In the adult gene set, our cohort was dominated by alterations in NF1, PDGFRA , NOTCH1 , RB1 , EGFR and TP53 , while IDH1/IDH2 presented the lowest number of mutations consistent to our IDH-WT cohort. Most mutations were again intronic, with TP53 and NOTCH1 displaying a more heterogeneous landscape (Fig. 3 C). In the case of DMG genes, TERT and PEDGFRA had the largest number of variants, with CDKN2A presenting almost no alterations, with a dominant presence of variants of the coding sequence. Other genes like MET , ACVR1 and TERT also displayed a lower proportion of intronic variants (Fig. 3 D). Genes with no mutations in our cohort are not shown, together with KMT2C , as this gene was filtered from the dataset as previously explained. Finally, we studied unique SNVs in all 8 cell lines. As expected, GBM128 subclones presented the smallest amount of unique variants, while all other 6 cell lines presented over 20,000 unique SNVs, with DIPG-WT presenting the highest number of unique SNVs (Fig. 3 E). Taken together, this data suggests that all gliomas share a mutational core, but present ample differences between patients and within different tumoral areas from the same patient. Elucidating protein altering mutations in GSCs After exploring mutations common to all cell lines, we decided to focus on the rare mutations with a higher probability of impacting the final protein. Specifically, we looked at frameshift and start or stop codon alterations. As such, we identified 34 frameshift alterations, as well as 12 mutations that are categorized as stop or start codon alterations which were common and identical to all cell lines (Fig. 4 A). These mutations occurred in a subset of 32 genes, most of which only presented a single of the selected alterations (Fig. 4 B). These altered genes are related to important cellular pathways, like senescence, MAPK and NOTCH signaling, as well as core metabolic and transcriptional functions, including RNApolymerase- activity, making them great candidates for tumoral drivers (Fig. 4 C). Given their predicted high functional impact, we focused specifically on early stop codon variants. From our 10 alterations (Table 1) we found an early stop codon in PRSS1, (p.Gly177*, rs1240508430), truncating the protein within the peptidase S1 catalytic domain, rendering the resulting protein deficient (Fig. 4 D). Its low occurrence frequency in population databases, such as gnomAD and ClinVar, suggests it is a rare potentially deleterious variant rather than a benign polymorphism. We then looked at in silico functional impact scores. This mutation was predicted to be deleterious by LRT, damaging by SIFT and as disease causing automatic by MutationTaster (labelled as A). Moreover, some previous reports link alterations in this protein to pathologies such as pancreatitis and even other cancers [ 29 , 30 ]. The fact that this alteration was identified in all our cell lines, combined with its predicted loss of catalytic function and damaging impact makes PRSS1 a compelling target. PRSS1 Deficiency may Promote Glioma Progression To verify the recurrent nonsense variant identified in silico , we performed Sanger sequencing on all eight lines, which confirmed the G > T substitution that introduces p.Gly177* (Fig. 5 A). We could also observe another G > T alteration in the following base, leading to a missense mutation, which was also correctly identified in our WES data (Supplementary Table 3). To confirm that this alteration was not a tissue culture artefact, we performed Sanger sequencing on 10 primary adult GBM samples (Supplementary Table 4). We confirmed the presence of this mutation in 10% of our patients, validating that the p.Gly177* mutation also can be found in primary tumoral tissues (Fig. 5 B & 5 C). Analysis of adult gliomas using the GlioVis platform [ 20 ] showed that Low PRSS1 expression was associated with shorter overall survival in IDH-WT GBM datasets. (log-rank P < 0.036; Fig. 5 D), These correlative findings should be interpreted as hypothesis-generating, as they may be influenced by unmeasured confounding and do not establish direct causality. PRSS1 encodes trypsin-1, a serine protease that selectively activates the protease-activated receptor-2 ( PAR2 ). Beyond its canonical digestive role, PAR2 has documented functions in the CNS where it modulates neuroinflammation and neuronal excitability [ 31 ]. In immune contexts, PAR2 signalling supports CD8⁺ T-cell recruitment, dendritic-cell activation and IFN induction [ 32 , 33 ]; therefore, loss of PRSS1/PAR2 activity could attenuate antitumor immunity. Conversely, several injury models show that diminished PAR2 signaling triggers compensatory up-regulation of PAR1. Consistent with this, we observed that PAR1 expression increases with histological grade in adult glioma datasets (Supplementary Fig. 3), with direct correlation to tumoral progression [ 34 ]. PAR1 is pro-tumorigenic in other cancers, promoting angiogenesis, migration and growth [ 35 ]. Taken together, we propose a plausible model in which the truncating p.Gly177* variant reduces PRSS1 activity,potentially dampening PAR2-mediated signaling and favoring relative PAR1 activity (Fig. 5 E). This framework may help explain associations with higher grade but requires functional validation. Discussion Gliomas represent some of the most lethal human cancers, with dismal prognosis despite SOC therapy and no significant clinical advances in the last decades. Genetic alterations are a hallmark of cancer and serve as biomarkers to characterize molecularly distinct tumor subtypes. The identification of these genotype specific features is essential to personalized cancer therapy, especially in tumors with no effective therapies. To this end, NGS technologies present an opportunity for identifying novel and rare alterations in these patients. WES is a powerful genomic tool that utilizes massively parallel sequencing to achieve high-throughput characterization of the nucleotide sequence across the entirety of an organism's exome or targeted coding regions [ 36 ]. This allows for the massive identification of mutations in the exome, where approximately 85% of disease-causing mutations occur [ 37 ]. The aggressive nature of GBM and DMG is frequently attributed to GSCs that confer resistance to conventional chemo- and radiotherapy. To explore the genetic basis of this phenotype, we analyzed potential disparities in GSC genetic variants[ 38 ], providing a comparative view of the exome of 8 GSC lines obtained from adult IDH-wild type GBM and pediatric DIPG patients. These cell lines conserved canonical GBM copy-number alterations (Chr7 gain, Chr10 loss, EGFR amplification, CDKN2A/PTEN loss), with notable examples of intertumoral and intratumoral variations, when comparing subclones from the same patient. These observations are consistent with prior large scale genomic studies and further solidify GSCs as powerful models in the context of gliomas [ 39 , 40 ]. Regarding SNV analysis, our gene filtering step allowed for a more focalized study of relevant alterations and allowed for the identification of a sequencing artefact mutation in KMT2C , which emphasizes the need for orthogonal validation to fully confirm key single point mutations, particularly in highly variable and noisy genomic regions. This discrepancy suggests that the prevalence of this mutation in other tumors, as reported in previous studies, may need to be reassessed [ 23 ]. This validation has also been recommended by other authors and consortiums, with focus in clinical practice [ 41 , 42 ]. TMB serves as a biomarker for the genomic divergence of a tumor, representing the burden of somatic sequence alterations accumulated relative to the germline. Our results are similar to previous genomic studies demonstrating considerable heterogeneity, with reported values ranging from 0 to 76 mutations per genome [ 43 ]. TMB generally correlates negatively with both response to immune checkpoint inhibitors and patient survival [ 44 ]. This fundamental lack of immune recognition provides a compelling explanation for the consistent lack of success in immunotherapy clinical trials for these tumor types [ 45 , 46 ]. When examining the point-mutation landscape, we identified over 12.000 shared variants among adult and pediatric cell lines, with adults having over 90.000 unique mutations in contrast to 51.000 of pediatric cells, which suggests that both glioma types conserve a common mutational core, but adult GBM presents a greater SNV heterogeneity. This finding is consistent with data from an analysis of over 8,000 tumor specimens, which reported that a specific tumor type shared between 10% and 13% of its mutations, reinforcing the concept of a conserved genetic foundation across related cancers [ 47 ]. After focusing on protein-altering mutations common to all 8 cell lines, we identified a stop gain alteration in PRSS1 (p.Gly177*), which truncated its peptidase catalytic domain in host cell lines, interestingly, we observed that low PRSS1 expression in the TCGA cohort correlated with poorer prognosis in GBM. This mutation was also identified in 10% of patients from an adult GBM cohort, confirming that it is not a tissue culture artefact. PRSS1 is the main extracellular activator of the PAR2 receptor, key in neural inflammation processes and in T cell activation [ 48 ]. The loss of this signaling axis may directly impact immune function in the tumoral microenvironment. Moreover, this could trigger a compensatory upregulation of the PAR1 pathway, promoting pro-tumoral consequences. This upregulation can be clearly observed when tumoral histological grade increases. Therefore, we propose a model by which PRSS1 loss promotes a PAR2 to PAR1 switch which decreases immunosurveillance and promotes protumoral signaling. Our findings should be interpreted in light of several limitations. First, patient-derived GSCs can acquire culture adaptation changes, as GSC establishment selects for stem-like features and proliferating cells, which promotes bias in in vitro propagation. This may explain why this PRSS1 mutation is overly represented in our GSC lines compared to patients. Moreover, this observation is further supported by independent clinical evidence, which identifies PRSS1 as one of the most mutated genes observed in tumoral tissue derived directly from 164 GBM patients [ 49 ]. Second, functional consequences were inferred in silico , and our expression survival analyses are unadjusted for clinical and molecular covariates; therefore, residual confounding factors may remain. Third, the sample size, particularly for pediatric lines, is limited, which constrains generalizability. These considerations support viewing our results as exploratory and hypothesis-generating. Targeting the PRSS1–PAR2 axis—either by restoring PRSS1 activity or inhibiting PAR1—represents a compelling therapeutic hypothesis, establishing PRSS1 as a vulnerability worthy of preclinical validation. Conclusions This WES analysis of eight patient-derived GSC lines recapitulates canonical CNV patterns and highlights a recurrent truncating PRSS1 variant (p.Gly177*) present across adult and pediatric lines, which was also confirmed in primary tumor samples. Public dataset analyses show associations between PRSS1 expression and outcome, suggesting a testable PRSS1–PAR axis model. Given the limited cohort and in silico nature of several inferences, these results should be considered preliminary and hypothesis-generating, delineating clear next steps for mechanistic and translational evaluation. Declarations Ethics declarations Ethical approval and consent to participate Ethical approval for the use of these samples was granted by the institutional review board of HM Hospitals (CEIm No: 23.06.2206-GHM). All patients provided informed written consent prior to enrollment, and all procedures were conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable Competing interests The authors declare no competing interests. Availability of data All WES files are publicly available at Sequence Read Archive (SRA) under accession code PRJNA1334426. Funding This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project "PI21/01353" and co-founded by the European Union to NGR and AAS. 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Very low mutation burden is a feature of inflamed recurrent glioblastomas responsive to cancer immunotherapy. Nat. Commun. Nat. Res. 12. https://doi.org/10.1038/s41467-020-20469-6 (2021). Van Den Ende, B. et al. Exploring the tumor microenvironment in diffuse intrinsic pontine glioma: immunological insights and therapeutic challenges. J. Immunother Cancer BMJ Publishing Group. https://doi.org/10.1136/jitc-2025-012009 (2025). Liu, Y., Zhou, F., Ali, H., Lathia, J. D. & Chen, P. Immunotherapy for glioblastoma: current state, challenges, and future perspectives. Cell. Mol. Immunol. Springer Nature; pp. 1354–75. (2024). https://doi.org/10.1038/s41423-024-01226-x Foote, M. B. et al. Analysis of Shared Variants between Cancer Biospecimens. Clin. Cancer Res. Am. Association Cancer Res. Inc . 31 , 376–386. https://doi.org/10.1158/1078-0432.CCR-24-1583 (2025). Liang, G. et al. Naive T cells sense the cysteine protease allergen papain through protease-activated receptor 2 and propel TH2 immunity. Journal of Allergy and Clinical Immunology 129 (Mosby Inc., 2012). https://doi.org/10.1016/j.jaci.2012.02.035 Demetriou, A. N. et al. Profiling the molecular and clinical landscape of glioblastoma utilizing the Oncology Research Information Exchange Network brain cancer database 6 (Oxford University Press, 2024). Neurooncol Adv https://doi.org/10.1093/noajnl/vdae046 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryDataOverview.docx TableS1ListofGenestoFilter.xlsx TableS2FilteredGenes.xlsx TableS3PRSS1Mutations.xlsx TableS4GBMPatientCohort.xlsx SupplementaryFigures.docx Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 May, 2026 Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 02 Mar, 2026 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. 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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-8979829","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615128135,"identity":"66975660-bc88-46a5-93f7-64f98e501786","order_by":0,"name":"Alvaro Monago Sanchez","email":"","orcid":"","institution":"Francisco de Vitoria University","correspondingAuthor":false,"prefix":"","firstName":"Alvaro","middleName":"Monago","lastName":"Sanchez","suffix":""},{"id":615128136,"identity":"61dd68c2-91d3-40cd-af17-bd4b83c20ea0","order_by":1,"name":"Natalia Iglesias Bueno","email":"","orcid":"","institution":"Francisco de Vitoria 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1","display":"","copyAsset":false,"role":"figure","size":846122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCopy Number Variation (CNV) Analysis in Glioma Stem Cells (GSCs). \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA)\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eGenome-wide schematic representation of CNV profiles across all chromosomes (columns) for each GSC line (rows). Deletions are indicated by descending red bars, while amplifications are shown as ascending green bars. B) Zoomed-in views of recurrently altered regions on chromosomes 7 (left) and 10 (right), commonly affected in adult GBM. C) CNV status of selected genes involved in key oncogenic pathways. DNA losses (red) and gains (green) are shown for genes associated with the cell cycle (highlighted in red), receptor tyrosine kinases (RTKs, green).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/fe2bded50ce26d8635253fee.png"},{"id":106095634,"identity":"4ee298d2-2952-414e-91d6-623708a56ae2","added_by":"auto","created_at":"2026-04-03 11:50:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSNV clustering of cell lines \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA) Two component PCA plot of our 8 cell lines. The VAF value of the top 200 most mutated genes were used for representation. Each dot is labeled to the corresponding cell line. (B) Hierarchical clustering of our 8 glioma cell lines. C) Tumoral burden (TMB) results of the 8 GSC lines. TMB values are reported as the number of mutations per Mb of DNA in the SNV analysis. Blue bars represent adult-derived GSCs, while red refers to pediatric lines\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/2ca50088a9014c20d208aac5.png"},{"id":106095590,"identity":"d515f601-bff3-4e23-bbb8-f55721a5ab96","added_by":"auto","created_at":"2026-04-03 11:49:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":200789,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnalysis of SNVs in cell line cohort: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA) Venn diagram of common (Brown) and unique SNVs between adult (Blue) and pediatric (Green) derived cell lines. B) Bar graph of top 20 most mutated genes across our cohort. Bar length represents the total number of unique mutations, with each colored segmented color signifying a different type of mutation. C) Number of mutations (bar length) and type (segment color) from the top 20 most mutated genes in COSMIC from adult patients (left) and pediatric cases (right). D) Unique SNVs identified in each cell line (columns), bar length represents the number of unique mutations, with color denoting mutation type.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/92c694bd178de3a183c57957.png"},{"id":106095543,"identity":"9fd2d6d0-6c49-4e4d-b05d-9c130ee999af","added_by":"auto","created_at":"2026-04-03 11:49:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":493042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIdentification of high impact mutations across cell line cohort: A\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Bar graph of common SNV alterations across our cohort. Each bar represents a different SNV subtype, and height corresponds to number of alterations. B) Bar graph of common SNVs with the biggest potential impact on the final protein, including start codon alterations (purple), frameshift variants (red), start loss variants (blue) and stop loss variants (yellow). C) KEGG pathway analysis of genes presenting high-impact SNVs from B). D) Schematic representation of PRSS1 gene, with the Gly177* mutation identified across all samples highlighted in red. The lost S1 peptidase domain is identified by a pale red shape.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/6bc8e285b89c486a7dfab4ef.png"},{"id":106095178,"identity":"94f40aea-6637-47ad-a9f2-861a48ba710a","added_by":"auto","created_at":"2026-04-03 11:46:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":416560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePRSS1 deficiency promotes glioma progression: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRepresentative Sanger sequencing chromatograms from GSCs (A) in adult (GBM18, left) and pediatric cells (DIPG-Mut, right); and primary tumors (B). The black box highlights the site of the Gly177* point mutation. C) Pie chart of PRSS1 mutant (red) patients. N=10 independent patients. D) Kaplan-Meier survival plot of TCGA GBM (IDH-WT) patients. E) Proposed mechanism of PRSS1 involvement in glioma progression. Upon PRSS1 mutation, the PAR2 signaling axis gets compromised, which causes a reduction in immune system activation and compensatory upregulation of PAR1 signaling, this in turn leads directly to tumoral growth.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/242bae8e78399de42b624afe.png"},{"id":106097590,"identity":"708c6e47-c7b8-42bb-93f9-4e6ef0c8f72c","added_by":"auto","created_at":"2026-04-03 11:59:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2900594,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/35a74c59-2f03-482e-b123-81ad64d63d9d.pdf"},{"id":106095179,"identity":"3f29c49a-470e-4f81-bd01-81a153233b3f","added_by":"auto","created_at":"2026-04-03 11:46:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15587,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataOverview.docx","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/552a1ead371484f8e695b399.docx"},{"id":106095613,"identity":"aa796580-d6e9-4b51-a315-8418d32194e4","added_by":"auto","created_at":"2026-04-03 11:50:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12841,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1ListofGenestoFilter.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/969aa27f1e499504c366b76e.xlsx"},{"id":106096127,"identity":"7486373e-39d9-4053-ab76-9801b4f7dc54","added_by":"auto","created_at":"2026-04-03 11:52:55","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10184,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2FilteredGenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/b79da10fa4974abab0db36aa.xlsx"},{"id":106095181,"identity":"04431b4e-b86c-4426-9d9a-5ff9d6d1676f","added_by":"auto","created_at":"2026-04-03 11:46:07","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18137875,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3PRSS1Mutations.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/d5b544c9c87a2ca05b5fc6a8.xlsx"},{"id":106095581,"identity":"b75a6bf6-e308-405d-ae2f-47c1b9977a21","added_by":"auto","created_at":"2026-04-03 11:49:46","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10604,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4GBMPatientCohort.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/b69a6950533969e9171f93ed.xlsx"},{"id":106095552,"identity":"b59d8ded-7ca6-4b04-80ef-5fc8113bf5c0","added_by":"auto","created_at":"2026-04-03 11:49:24","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":352766,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/60027475869431f7e79e3c47.docx"},{"id":106095183,"identity":"1b8f7f2d-cf01-4299-8b08-41faff6dd227","added_by":"auto","created_at":"2026-04-03 11:46:16","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":18914,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8979829/v1/db0d0d151889a5b81d599b56.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential Oncogenic Role of PRSS1 Identified by Whole Exome Sequencing in Glioma Primary Cell Lines","fulltext":[{"header":"Background","content":"\u003cp\u003eGliomas represent the most common primary malignancies of the central nervous system (CNS). Among adults, Glioblastoma (GBM) is the most common and aggressive form, with an incidence of 3.27 cases per 100,000 population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Classified as an IDH-wild type grade IV tumor, GBM is characterized histopathologically by marked cellular pleomorphism, high mitotic activity, necrosis, and microvascular proliferation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. GBM patients have a poor prognosis with a five-year survival rate lower than 5% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The current standard of care consists of maximal surgical resection with radiotherapy and concomitant temozolomide chemotherapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, tumor-treating fields (TTFields) represent a recent advancement, extending median overall survival (mOS) from 14.6 to 20.9 months [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn pediatric patients, diffuse intrinsic pontine glioma (DIPG) represents the most common subtype of diffuse midline glioma (DMG), and the most common brainstem tumors, with an incidence of around 2 cases per 100,000 population. These tumors commonly arise from the brainstem or pontine region and can be further classified by the \u003cem\u003eH3F3A K27M\u003c/em\u003e mutation in histone 3, with around 75% of H3-mutant cases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Prognosis is also dismal, with a five-year survival of around 1% and a mOS of just 9 months [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These tumors are also characterized by having a relatively intact blood brain barrier, which further limits drug penetration [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therapeutical options are limited to radiotherapy, as surgery is not viable and traditional chemotherapeutic regimens are inefficacy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite decades of investigation into novel modalities including targeted agents, immunotherapies, and advanced radiotherapeutic techniques, the vast majority have not yielded significant improvements in patient outcomes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA major barrier to effective treatment of gliomas is their marked intra- and intertumoral heterogeneity driven in part by the presence of glioma stem-like cancer stem cells (GSCs). These GSCs are a dynamic and heterogeneous population characterized by clonal evolution, self-renewal capacity, and the ability to initiate and sustain tumor growth [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Importantly, GSCs can recapitulate the histological and molecular features of the original tumor in experimental models [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], underscoring their role in resistance to therapy and tumor recurrence. Their identification, isolation, and molecular characterization are thus essential for elucidating tumoral biology and for the development of effective, personalized therapeutic strategies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, managing these malignancies remains a critical and unmet clinical challenge, underscoring an urgent need for continued translational research and refinement of precision oncology approaches [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The detection of patient-specific mutations, particularly those conferring therapeutic vulnerability, should become a cornerstone of clinical diagnostics and decision-making [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntratumoral genetic and phenotypic heterogeneity is a defining hallmark of many cancers, with special relevance in the latest World Health Organization (WHO) CNS tumor classification, with GBM and DIPG representing one of the most extreme examples. Whole exome sequencing (WES) offers a powerful means of dissecting tumor evolution and identifying both common and rare driver mutations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Its application to GSCs enables the study of tumor-initiating populations at a molecular level and has the potential to inform precision medicine approaches [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, WES allows comparative studies across spatially distinct tumor regions and across patient subgroups.\u003c/p\u003e \u003cp\u003eTo explore the genetic landscape of GSCs and uncover potential unexplored therapeutic targets, we performed WES on a panel of eight patient-derived GSC lines representing both adult and pediatric gliomas. The adult cohort included six GBM lines: GBM18, GBM27, GBM38, GBM123, GBM128B, and GBM128D. Notably, GBM128B and GBM128D were derived from distinct tumor regions of the same patient, offering a unique opportunity to explore intratumoral heterogeneity. The pediatric cohort consisted of two DIPG lines: DIPG-Mut, harboring the canonical H3K27M mutation, and VUMC-DIPG10 (termed DIPG-WT from here on), which is wild-type for this mutation. These models capture key clinical and molecular subtypes, allowing for comparative analyses across age groups, tumor types and spatial contexts.\u003c/p\u003e \u003cp\u003eOur analysis confirmed preservation of canonical CNV alterations of GBM, as well as a clear CNV/SNV divergence between GB128B and GBM128D, highlighting intratumoral heterogeneity. Most importantly, we detected an early stop codon mutation in \u003cem\u003ePRSS1\u003c/em\u003e present in all cell lines, suggesting a novel oncogenic role for this gene, which may play a role in glioma tumoral progression.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman samples\u003c/h2\u003e \u003cp\u003eGBM and DIPG H3K27M mutant GSCs were originally isolated from surgical human GBM and DIPG specimens as previously described [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. GBM GSCs were maintained in growth media (M21) containing DMEM/F-12 (Gibco, Grand Island, NY, USA) supplemented with: Non Essential Amino Acids (1% v/v; Gibco), HEPES (38 mM; Gibco), D-Glucose (Sigma-Aldrich, St. Louis, MI, USA), BSA-FV (0,01% v/v; Invitrogen, Carlsbad, CA, USA), Sodium Pyruvate (1 mM; Invitrogen), L-Glutamine (4 mM; Gibco), Antibiotic-Antimycotic (0.4% v/v; Invitrogen), N1 Supplement (1% v/v; Invitrogen), Hydrocortisone (0.3 \u0026micro;g/mL; Sigma-Aldrich), Tri-iodothyronine (0.03 \u0026micro;g/mL; Sigma-Aldrich), EGF (10 ng/mL; Sigma-Aldrich), bFGF (20 ng/mL; Sigma-Aldrich) and Heparin (2 \u0026micro;g/mL; Sigma-Aldrich) under standard tissue culture conditions (37\u0026ordm;C and 5% CO2).\u003c/p\u003e \u003cp\u003eThe human DIPG H3K27MWT cell line (VUMC-DIPG10) was kindly provided by Dr. Piotr Waranecki (VU University Medical Center, Amsterdam). DMG GSCs were maintained in growth media (TSM) containing Neurobasal (Gibco, USA) and DMEM/F12 (Gibco, USA) supplemented with Hepes (2,5 mM; Gibco, USA), Non Essential Amino Acids (1% v/v; Gibco, USA), Sodium Piruvate (1mM; Invitrogen, USA), L-Glutamine (2mM; Gibco, USA), Antibiotic-Antimycotic (1% v/v; Invitrogen, USA), B-27 Supplement (1% v/v; Gibco, USA),PDGF α\u0026thinsp;+\u0026thinsp;β (10 ng/mL; Irvine Scientific, Japan) EGF (10 ng/mL; Sigma-Aldrich, USA), bFGF (20 ng/mL; Sigma-Aldrich, USA) and Heparin (2 \u0026micro;g/mL; Sigma-Aldrich, USA) under standard tissue culture conditions of 5% CO\u003csub\u003e2\u003c/sub\u003e and 37\u0026ordm;C.\u003c/p\u003e \u003cp\u003eTissue samples from GBM patients were kindly provided by the HGM BioBank, integrated in RETICS, National Network Biobanks, funding by Instituto de Salud Carlos III, from Biobanco La Fe (PT17/0015/0043), Valencia, Spain, and HM Hospitales, Madrid, Spain. Samples were processed following the current procedures and frozen immediately after their reception.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWES of GSCs\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from GSCs and GBM tissue samples using the DNeasy Blood \u0026amp; Tissue kit (Qiagen) following the manufacturer\u0026rsquo;s instructions. DNA concentration was quantified by fluorimetry with a Qubit (Thermo Fisher Scientific Inc), and purity was assessed using a Nanodrop spectrophotometer (Thermo Fisher Scientific Inc).\u003c/p\u003e \u003cp\u003eExome sequencing was performed on GSCs by NimGenetics (Madrid, Spain). Libraries were prepared with the \u003cem\u003eTwist Comprehensive Exome Panel\u003c/em\u003e (Twist Bioscience, USA) and quality controlled using a \u003cem\u003eTapeStation\u003c/em\u003e (Agilent Technologies, USA). Paired-end sequencing (2X150 bp) was carried out on the NovaSeq 6000 platform (Illumina, Inc.). Sequencing analysis, quality control and data processing were performed with a NimGenetics custom bioinformatics pipeline.\u003c/p\u003e\n\u003ch3\u003eCNV and SNV Analysis\u003c/h3\u003e\n\u003cp\u003eCNVs and SNVs were annotated and classified using a NimGenetics custom pipeline. SNVs analysis integrated population allele frequencies from gnomAD, clinical assertions from ClinVar, and in-silico functional impact scores (SIFT, LRT, MutationTaster). Genomic coordinates were referenced to GRCh38. For TMB calculation, the total number of nonsynonymous SNVs per cell line was divided by the effective megabases (Mb) captured by the Twist panel to obtain the number of mutations per Mb of DNA. Pathway annotation analysis was performed by automatic category selection of genes in the Dr. Tom bioinformatic platform.\u003c/p\u003e\n\u003ch3\u003eConventional PCR and Sanger Sequencing\u003c/h3\u003e\n\u003cp\u003eSelected variants identified by WES were validated by polymerase chain reaction (PCR). For \u003cem\u003eKMT2C\u003c/em\u003e, a nested PCR was required to amplify the region containing the rs150073007 mutation. The first round of amplification was performed using the following pair of primers Fw: 5\u0026rsquo;-TGGAAGTTGAAGGCCCTGAA-3\u0026rsquo;, Rv: 5\u0026rsquo;-GACCGAGGTCTACCAGGAGA-3\u0026rsquo;. The resulting product was used as template for the second PCR with primers Fw: 5\u0026rsquo;- CATGCTGCATAATTACCCTT-3\u0026rsquo;, Rv: 5\u0026rsquo;- GACCGAGGTCTACCAGGAGA-3\u0026rsquo;.\u003c/p\u003e \u003cp\u003eFor the rs1240508430 mutation in PRSS1, conventional PCR was used with the following primers Fw: 5\u0026rsquo;- TCTCTTCCTGATCCTCACAGC-3\u0026rsquo;, Rv: 5\u0026acute;- GCATGGGAAGGGTTGGTCAA-3\u0026rsquo;.\u003c/p\u003e \u003cp\u003ePCR products were purified and subjected to Sanger sequencing by the Genomics Service at Instituto de Investigaciones Biom\u0026eacute;dicas Albert Sols (IIBM, Madrid, Spain). Sequences were compared and aligned using the BLAST algorithm. Chromatograms were visualized with the FinchTV software (version 1.4.0).\u003c/p\u003e\n\u003ch3\u003eHierarchical clustering on Principal Components\u003c/h3\u003e\n\u003cp\u003eHierarchical clustering was performed to separate the patients into clusters using Ward\u0026rsquo;s linkage method. Euclidean distance was used as the dissimilarity metric. Prior to clustering, Principal Component Analysis (PCA) was applied. After generating the dendrogram, the optimal number of clusters was determined by analyzing the vertical distance between successive merges. A cut was made at a vertical distance threshold of 7 units on the y-axis of the dendrogram. All the analysis was implemented in Python, using the scipy.cluster.hierarchy module for clustering and matplotlib for visualization.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGlioVis data accession and survival analysis\u003c/h2\u003e \u003cp\u003emRNA expression and clinical data for the TCGA and CGGA GBM cohort were sourced from the GlioVis data portal [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The dataset included gene expression profiles generated on the RNA-seq platform. All analyses excluded IDH-mutant GBM tumors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImproving the Accuracy of Mutation Identification\u003c/h2\u003e \u003cp\u003eTo increase the accuracy of pathogenic mutation identification and reduce false-positive variant calls inherent to exome sequencing, we applied a pre-filtering strategy based on a curated list of 435 genes (Supplementary Table\u0026nbsp;1) known to exhibit࿼࿼. Among these, we identified 116 genes across our dataset, with members of the mucin (e.g., \u003cem\u003eMUC2\u003c/em\u003e, \u003cem\u003eMUC3A\u003c/em\u003e, \u003cem\u003eMUC4\u003c/em\u003e, \u003cem\u003eMUC5AC\u003c/em\u003e, \u003cem\u003eMUC16, MUC20, etc\u003c/em\u003e) and HLA (e.g., \u003cem\u003eHLA-C\u003c/em\u003e, \u003cem\u003eHLA-DRB1\u003c/em\u003e, \u003cem\u003eHLA-DQB1\u003c/em\u003e, \u003cem\u003eHLA-DRB5\u003c/em\u003e, \u003cem\u003eHLA-DQA1\u003c/em\u003e) gene families, as well as \u003cem\u003eZNF717\u003c/em\u003e, ranking among the 20 most frequently mutated loci prior to filtering (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eFollowing artifact filtering, we visualized mutation distribution using VarDecrypt\u0026rsquo;s oncoplots[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and identified several recurrently mutated genes across all eight GSC lines. Most mutations were missense, however; \u003cem\u003eKMT2C\u003c/em\u003e (also known as \u003cem\u003eMLL3\u003c/em\u003e) presented a notable exception with a recurrent nonsense mutation (Y816*, rs150073007) observed in seven out of eight samples. While previous WES studies have reported this stop-gain variant in multiple cancer types, including [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]࿼, and laryngeal head and neck squamous cell carcinomas [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]࿼ GBM [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]࿼ validation by Sanger sequencing confirmed that this variant represents a sequencing artifact in our dataset (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGSCs CNV alterations recapitulate GBM tumor genetic heterogeneity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalysis of CNVs confirmed that adult GBM GSCs were consistent with the canonical chromosomal patterns of GBM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, S2), with all lines presenting canonical duplications in chromosome 7and five out of six lines showed deletions in chromosome 10. Moreover, deletions in tumor suppressor genes \u003cem\u003eCDKN2A\u003c/em\u003e/\u003cem\u003eB\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e are the most frequently observed, together with the amplification of \u003cem\u003eEGFR\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These alterations are associated to the \u003cem\u003eEGFR/PTEN/Akt/mTOR\u003c/em\u003e pathway, key in the development of GBM[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, the two different subclones isolated from the same patient (GBM128) differ in the loss of \u003cem\u003eRB1\u003c/em\u003e and \u003cem\u003ePDGFRA\u003c/em\u003e, the second most frequently mutated tyrosine kinase receptor in GBM. This observation highlights the significant intratumoral heterogeneity of these tumors, where each biopsy can only represent a fraction of the tumoral mutational load. Pediatric derived GSCs differ significantly in CNV profile, but, interestingly, wild type cells presented the classical duplicated chromosome 7, with a section of chromosome 10 being deleted in H3 mutant cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These differences also were reflected in key genes, with both lines not sharing any of the cell cycle and RTKs related genes; somewhat paradoxically, H3 mutant cells have duplications in the key \u003cem\u003eCDKN2A/B\u003c/em\u003e genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These results are in line with the established classification of DMG patients based on the presence of the Histone \u003cem\u003eH3F3A\u003c/em\u003e K27M mutation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSNV clustering of GSC lines\u003c/h2\u003e \u003cp\u003eWe next characterized SNVs in our cohort. A principal component analysis (PCA) based on the variant allele frequency (VAF) of the top 200 most mutated genes was used to classify the cell lines. PCA showed that DIPG-wt cells formed a distinct cluster, separate from other lines, whereas DIPG-Mut cells clustered with GBM27, a highly diffuse GBM line [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite considerable dispersion among the GBM lines, indicating significant genetic differences, the GBM128 subclones clustered together. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Hierarchical clustering confirmed these results, showing a single cluster for the GBM128 subclones and a separate cluster for DIPG-wt cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGeneral evaluation of SNV landscape across cell line cohort\u003c/h2\u003e \u003cp\u003eAfter unsupervised clustering, we decided to investigate SNVs in more detail. We started by evaluating the TMB in our cell lines. We observed that all cell lines presented values above 30 mutations per Mb of DNA, with almost all consistently around 34. GBM38 had the lowest value at 32, and the DIPG-WT cell line presented the highest value at almost 37 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). All these cell lines can therefore be characterized as high TMB [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], revealing the elevated mutational load of these tumors.\u003c/p\u003e \u003cp\u003eWe then compared the number of common and unique SNVs in adult and pediatric lines, identifying over 12,000 common SNVs. Interestingly GBM-derived lines presented over 90,000 unique mutations over 51,000 for pediatric cells. This difference may suggest a comparatively more homogeneous SNV landscape in pediatric tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOrdering genes by mutation frequency identified several genes of interest among the top 20 most mutated genes across all cell lines, like \u003cem\u003ePOLR1C\u003c/em\u003e, involved in RNA polymerase I and III and the serine-protease genes \u003cem\u003ePRSS1\u003c/em\u003e and \u003cem\u003ePRSS2\u003c/em\u003e. Most alterations in these 20 genes correspond to intron variants or other variations that have no impact on the final protein, but \u003cem\u003ePRSS1\u003c/em\u003e and \u003cem\u003ePRSS2\u003c/em\u003e exhibited the most heterogenous mutational composition, while also presenting some of the highest numbers of mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eWe then restricted our analysis to the top 20 genes reported in COSMIC based on mutation frequency [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] for GBM and DMG. In the adult gene set, our cohort was dominated by alterations in \u003cem\u003eNF1, PDGFRA\u003c/em\u003e, \u003cem\u003eNOTCH1\u003c/em\u003e, \u003cem\u003eRB1\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e, while \u003cem\u003eIDH1/IDH2\u003c/em\u003e presented the lowest number of mutations consistent to our IDH-WT cohort. Most mutations were again intronic, with \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eNOTCH1\u003c/em\u003e displaying a more heterogeneous landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In the case of DMG genes, \u003cem\u003eTERT\u003c/em\u003e and \u003cem\u003ePEDGFRA\u003c/em\u003e had the largest number of variants, with \u003cem\u003eCDKN2A\u003c/em\u003e presenting almost no alterations, with a dominant presence of variants of the coding sequence. Other genes like \u003cem\u003eMET\u003c/em\u003e, \u003cem\u003eACVR1\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e also displayed a lower proportion of intronic variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Genes with no mutations in our cohort are not shown, together with \u003cem\u003eKMT2C\u003c/em\u003e, as this gene was filtered from the dataset as previously explained.\u003c/p\u003e \u003cp\u003eFinally, we studied unique SNVs in all 8 cell lines. As expected, GBM128 subclones presented the smallest amount of unique variants, while all other 6 cell lines presented over 20,000 unique SNVs, with DIPG-WT presenting the highest number of unique SNVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eTaken together, this data suggests that all gliomas share a mutational core, but present ample differences between patients and within different tumoral areas from the same patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eElucidating protein altering mutations in GSCs\u003c/h2\u003e \u003cp\u003eAfter exploring mutations common to all cell lines, we decided to focus on the rare mutations with a higher probability of impacting the final protein. Specifically, we looked at frameshift and start or stop codon alterations.\u003c/p\u003e \u003cp\u003eAs such, we identified 34 frameshift alterations, as well as 12 mutations that are categorized as stop or start codon alterations which were common and identical to all cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These mutations occurred in a subset of 32 genes, most of which only presented a single of the selected alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These altered genes are related to important cellular pathways, like senescence, MAPK and NOTCH signaling, as well as core metabolic and transcriptional functions, including RNApolymerase- activity, making them great candidates for tumoral drivers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven their predicted high functional impact, we focused specifically on early stop codon variants. From our 10 alterations (Table\u0026nbsp;1) we found an early stop codon in PRSS1, (p.Gly177*, rs1240508430), truncating the protein within the peptidase S1 catalytic domain, rendering the resulting protein deficient (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Its low occurrence frequency in population databases, such as gnomAD and ClinVar, suggests it is a rare potentially deleterious variant rather than a benign polymorphism. We then looked at \u003cem\u003ein silico\u003c/em\u003e functional impact scores. This mutation was predicted to be deleterious by LRT, damaging by SIFT and as disease causing automatic by MutationTaster (labelled as A). Moreover, some previous reports link alterations in this protein to pathologies such as pancreatitis and even other cancers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The fact that this alteration was identified in all our cell lines, combined with its predicted loss of catalytic function and damaging impact makes \u003cem\u003ePRSS1\u003c/em\u003e a compelling target.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePRSS1 Deficiency may Promote Glioma Progression\u003c/h2\u003e \u003cp\u003eTo verify the recurrent nonsense variant identified \u003cem\u003ein silico\u003c/em\u003e, we performed Sanger sequencing on all eight lines, which confirmed the G\u0026thinsp;\u0026gt;\u0026thinsp;T substitution that introduces p.Gly177* (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We could also observe another G\u0026thinsp;\u0026gt;\u0026thinsp;T alteration in the following base, leading to a missense mutation, which was also correctly identified in our WES data (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo confirm that this alteration was not a tissue culture artefact, we performed Sanger sequencing on 10 primary adult GBM samples (Supplementary Table\u0026nbsp;4). We confirmed the presence of this mutation in 10% of our patients, validating that the p.Gly177* mutation also can be found in primary tumoral tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eAnalysis of adult gliomas using the GlioVis platform [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] showed that Low PRSS1 expression was associated with shorter overall survival in IDH-WT GBM datasets. (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.036; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), These correlative findings should be interpreted as hypothesis-generating, as they may be influenced by unmeasured confounding and do not establish direct causality.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePRSS1\u003c/em\u003e encodes trypsin-1, a serine protease that selectively activates the protease-activated receptor-2 (\u003cem\u003ePAR2\u003c/em\u003e). Beyond its canonical digestive role, \u003cem\u003ePAR2\u003c/em\u003e has documented functions in the CNS where it modulates neuroinflammation and neuronal excitability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In immune contexts, PAR2 signalling supports CD8⁺ T-cell recruitment, dendritic-cell activation and IFN induction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; therefore, loss of PRSS1/PAR2 activity could attenuate antitumor immunity.\u003c/p\u003e \u003cp\u003eConversely, several injury models show that diminished PAR2 signaling triggers compensatory up-regulation of PAR1. Consistent with this, we observed that PAR1 expression increases with histological grade in adult glioma datasets (Supplementary Fig.\u0026nbsp;3), with direct correlation to tumoral progression [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. PAR1 is pro-tumorigenic in other cancers, promoting angiogenesis, migration and growth [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaken together, we propose a plausible model in which the truncating p.Gly177* variant reduces PRSS1 activity,potentially dampening PAR2-mediated signaling and favoring relative PAR1 activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). This framework may help explain associations with higher grade but requires functional validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGliomas represent some of the most lethal human cancers, with dismal prognosis despite SOC therapy and no significant clinical advances in the last decades. Genetic alterations are a hallmark of cancer and serve as biomarkers to characterize molecularly distinct tumor subtypes. The identification of these genotype specific features is essential to personalized cancer therapy, especially in tumors with no effective therapies. To this end, NGS technologies present an opportunity for identifying novel and rare alterations in these patients. WES is a powerful genomic tool that utilizes massively parallel sequencing to achieve high-throughput characterization of the nucleotide sequence across the entirety of an organism's exome or targeted coding regions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This allows for the massive identification of mutations in the exome, where approximately 85% of disease-causing mutations occur [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The aggressive nature of GBM and DMG is frequently attributed to GSCs that confer resistance to conventional chemo- and radiotherapy. To explore the genetic basis of this phenotype, we analyzed potential disparities in GSC genetic variants[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], providing a comparative view of the exome of 8 GSC lines obtained from adult IDH-wild type GBM and pediatric DIPG patients.\u003c/p\u003e \u003cp\u003eThese cell lines conserved canonical GBM copy-number alterations (Chr7 gain, Chr10 loss, EGFR amplification, CDKN2A/PTEN loss), with notable examples of intertumoral and intratumoral variations, when comparing subclones from the same patient. These observations are consistent with prior large scale genomic studies and further solidify GSCs as powerful models in the context of gliomas [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding SNV analysis, our gene filtering step allowed for a more focalized study of relevant alterations and allowed for the identification of a sequencing artefact mutation in \u003cem\u003eKMT2C\u003c/em\u003e, which emphasizes the need for orthogonal validation to fully confirm key single point mutations, particularly in highly variable and noisy genomic regions. This discrepancy suggests that the prevalence of this mutation in other tumors, as reported in previous studies, may need to be reassessed [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This validation has also been recommended by other authors and consortiums, with focus in clinical practice [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTMB serves as a biomarker for the genomic divergence of a tumor, representing the burden of somatic sequence alterations accumulated relative to the germline. Our results are similar to previous genomic studies demonstrating considerable heterogeneity, with reported values ranging from 0 to 76 mutations per genome [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. TMB generally correlates negatively with both response to immune checkpoint inhibitors and patient survival [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This fundamental lack of immune recognition provides a compelling explanation for the consistent lack of success in immunotherapy clinical trials for these tumor types [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen examining the point-mutation landscape, we identified over 12.000 shared variants among adult and pediatric cell lines, with adults having over 90.000 unique mutations in contrast to 51.000 of pediatric cells, which suggests that both glioma types conserve a common mutational core, but adult GBM presents a greater SNV heterogeneity. This finding is consistent with data from an analysis of over 8,000 tumor specimens, which reported that a specific tumor type shared between 10% and 13% of its mutations, reinforcing the concept of a conserved genetic foundation across related cancers [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter focusing on protein-altering mutations common to all 8 cell lines, we identified a stop gain alteration in \u003cem\u003ePRSS1\u003c/em\u003e (p.Gly177*), which truncated its peptidase catalytic domain in host cell lines, interestingly, we observed that low PRSS1 expression in the TCGA cohort correlated with poorer prognosis in GBM. This mutation was also identified in 10% of patients from an adult GBM cohort, confirming that it is not a tissue culture artefact.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePRSS1\u003c/em\u003e is the main extracellular activator of the \u003cem\u003ePAR2\u003c/em\u003e receptor, key in neural inflammation processes and in T cell activation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The loss of this signaling axis may directly impact immune function in the tumoral microenvironment. Moreover, this could trigger a compensatory upregulation of the \u003cem\u003ePAR1\u003c/em\u003e pathway, promoting pro-tumoral consequences. This upregulation can be clearly observed when tumoral histological grade increases. Therefore, we propose a model by which \u003cem\u003ePRSS1\u003c/em\u003e loss promotes a \u003cem\u003ePAR2\u003c/em\u003e to \u003cem\u003ePAR1\u003c/em\u003e switch which decreases immunosurveillance and promotes protumoral signaling.\u003c/p\u003e \u003cp\u003eOur findings should be interpreted in light of several limitations. First, patient-derived GSCs can acquire culture adaptation changes, as GSC establishment selects for stem-like features and proliferating cells, which promotes bias in \u003cem\u003ein vitro\u003c/em\u003e propagation. This may explain why this \u003cem\u003ePRSS1\u003c/em\u003e mutation is overly represented in our GSC lines compared to patients. Moreover, this observation is further supported by independent clinical evidence, which identifies \u003cem\u003ePRSS1\u003c/em\u003e as one of the most mutated genes observed in tumoral tissue derived directly from 164 GBM patients [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, functional consequences were inferred \u003cem\u003ein silico\u003c/em\u003e, and our expression survival analyses are unadjusted for clinical and molecular covariates; therefore, residual confounding factors may remain. Third, the sample size, particularly for pediatric lines, is limited, which constrains generalizability. These considerations support viewing our results as exploratory and hypothesis-generating.\u003c/p\u003e \u003cp\u003eTargeting the PRSS1\u0026ndash;PAR2 axis\u0026mdash;either by restoring PRSS1 activity or inhibiting PAR1\u0026mdash;represents a compelling therapeutic hypothesis, establishing PRSS1 as a vulnerability worthy of preclinical validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis WES analysis of eight patient-derived GSC lines recapitulates canonical CNV patterns and highlights a recurrent truncating PRSS1 variant (p.Gly177*) present across adult and pediatric lines, which was also confirmed in primary tumor samples. Public dataset analyses show associations between \u003cem\u003ePRSS1\u003c/em\u003e expression and outcome, suggesting a testable PRSS1\u0026ndash;PAR axis model. Given the limited cohort and \u003cem\u003ein silico\u003c/em\u003e nature of several inferences, these results should be considered preliminary and hypothesis-generating, delineating clear next steps for mechanistic and translational evaluation.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the use of these samples was granted by the institutional review board of HM Hospitals (CEIm No: 23.06.2206-GHM). All patients provided informed written consent prior to enrollment, and all procedures were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll WES files are publicly available at Sequence Read Archive (SRA) under accession code PRJNA1334426.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been funded by Instituto de Salud Carlos III (ISCIII) through the project \u0026quot;PI21/01353\u0026quot; and co-founded by the European Union to NGR and AAS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNGR and AAS conceived the experiments. PCN generated the DIPG-mut cell line. AMS, LMM, CCR and SFM cultured cells and prepared DNA for sequencing. NIB, AMS, JP, CGJ and CQ performed data analysis. NGR and AMS wrote the manuscript. All authors reviewed the manuscript and participated in editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrice, M. et al. \u003cem\u003eCBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2017\u0026ndash;2021. Neuro Oncol\u003c/em\u003e Vol. 26, vi1\u0026ndash;85 (Oxford University Press, 2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/neuonc/noae145\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/noae145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis, D. N. et al. 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Analysis of Shared Variants between Cancer Biospecimens. \u003cem\u003eClin. Cancer Res. Am. Association Cancer Res. Inc\u003c/em\u003e. \u003cb\u003e31\u003c/b\u003e, 376\u0026ndash;386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1078-0432.CCR-24-1583\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-24-1583\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang, G. et al. \u003cem\u003eNaive T cells sense the cysteine protease allergen papain through protease-activated receptor 2 and propel TH2 immunity. Journal of Allergy and Clinical Immunology\u003c/em\u003e 129 (Mosby Inc., 2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jaci.2012.02.035\u003c/span\u003e\u003cspan address=\"10.1016/j.jaci.2012.02.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemetriou, A. N. et al. \u003cem\u003eProfiling the molecular and clinical landscape of glioblastoma utilizing the Oncology Research Information Exchange Network brain cancer database\u003c/em\u003e 6 (Oxford University Press, 2024). Neurooncol Adv\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/noajnl/vdae046\u003c/span\u003e\u003cspan address=\"10.1093/noajnl/vdae046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Glioma, Glioblastoma, Diffuse intrinsic pontine gliomas, WES, PRSS1","lastPublishedDoi":"10.21203/rs.3.rs-8979829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8979829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGlioblastoma (GBM) is the most common and aggressive adult primary brain malignancy, with poor survival and marked resistance to therapy. In pediatric patients, diffuse midline gliomas (DMG), including diffuse intrinsic pontine gliomas (DIPG) with H3K27 alterations, are similarly lethal and completely refractory to therapy. These tumors present high inter and intratumoral heterogeneity, driven in part by glioma stemlike cells (GSCs), which compromises therapeutic responses and complicates model development. Whole-exome sequencing (WES) of patient derived GSCs can clarify subtype specific Single Nucleotide Variant (SNV)/Copy Number Variation (CNV) patterns and potentially expose novel vulnerabilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo identify recurrent molecular patterns and candidate therapeutic targets, we established a cohort of GSCs derived from eight patients, comprising six adult GBM and two pediatric DIPG, including H3 mutant and wild-type subtypes. WES of this cohort was performed to characterize copy number alterations and somatic point mutations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAdult-derived GBM IDH-Wild-Type GSCs recapitulated canonical genomic alterations including chromosome 7 duplication, chromosome 10 loss, EGFR amplification, and CDKN2A/B deletion. DIPG derived lines exhibited more heterogeneous genomic profiles, reflecting subtype-specific divergence. Importantly, all cell lines harbored a recurrent nonsense mutation in PRSS1 (p.Gly177*), truncating the catalytic domain. This mutation was also observed in primary GBM samples. Reduced PRSS1 expression correlated with poorer survival in GBM datasets, and we propose a mechanism whereby PRSS1 loss disrupts PAR2 signaling and promotes compensatory PAR1 activation, enhancing tumor progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur study presents a comprehensive WES analysis of patient-derived GSCs, revealing key genomic alterations across adult and pediatric tumors. We identified a recurrent PRSS1 stop-gain mutation across all cells, suggesting a potential novel oncogenic role in gliomas of the PAR2 protease signaling axis, uncovering a common vulnerability and a potential therapeutic target in high-grade gliomas.\u003c/p\u003e","manuscriptTitle":"Potential Oncogenic Role of PRSS1 Identified by Whole Exome Sequencing in Glioma Primary Cell Lines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 13:02:47","doi":"10.21203/rs.3.rs-8979829/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"152662333692937794319657724252739185775","date":"2026-05-13T20:19:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T08:41:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76765197010946412010055576964726831998","date":"2026-04-06T20:29:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50068767242746542292208185579136690708","date":"2026-03-31T08:41:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41253700550491195498379830989637411820","date":"2026-03-30T17:42:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-30T16:46:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T07:53:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T21:30:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-02T13:28:29+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":"7dc0f32c-5461-491d-b4d6-69ab9dbda49d","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"152662333692937794319657724252739185775","date":"2026-05-13T20:19:34+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65452959,"name":"Biological sciences/Cancer"},{"id":65452960,"name":"Biological sciences/Genetics"},{"id":65452961,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-01T13:02:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 13:02:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8979829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8979829","identity":"rs-8979829","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}