Intracranial sarcomas can arise secondarily from primary brain tumors, including gliomas and meningiomas, either spontaneously or following radiotherapy. The current WHO classification recognizes sarcomatous transformation in several tumor entities; however, sarcomas arising from meningiomas remain poorly characterized and are regarded as a possible histological manifestation within the spectrum of anaplastic meningiomas. We analyzed nine matched meningioma–sarcoma pairs using integrated histopathological assessment and molecular profiling, including DNA methylation analysis, next-generation sequencing, copy number profiling, and proteomics. Although recurrent sarcomatous tumors were clonally related to their meningioma precursors—sharing identical NF2 alterations and overlapping chromosomal aberrations—they demonstrated pronounced divergence at the histological, immunophenotypic, and epigenetic levels. Importantly, sarcomatous transformation occurred in four cases without prior radiotherapy. Sarcomatous recurrences exhibited loss of meningothelial markers and acquired expression of cytokeratin and myogenic markers. DNA methylation profiling revealed a shift away from canonical meningioma signatures toward profiles resembling non-meningothelial mesenchymal tumors. Proteomic analysis showed consistent upregulation of SOX2 in sarcomatous tumors compared with their primary counterparts, suggesting acquisition of stem-like features during lineage divergence. Clinically, these tumors were associated with aggressive growth, early recurrence, and extracranial metastases, resembling malignant sarcomas more closely than anaplastic meningiomas. In addition, analysis of an institutional cohort of NF2-mutant intracranial tumors (n = 316) suggests that sarcomas with inactivating NF2 mutations may originate from meningiomas even in the absence of a clinically recognized precursor. Together, these findings suggest that sarcomatous transformation represents a rare evolutionary endpoint in NF2-mutant meningiomas, marked by clonal continuity but pronounced biological divergence. These results highlight limitations of morphology-based classification and emphasize the value of integrated molecular diagnostics in distinguishing these tumors from conventional high-grade meningiomas. Given their sarcoma-like behavior despite a meningioma ancestry, these tumors may not be adequately captured by current meningioma grading schemes.

Sellar region neurocytoma (SELN) is a rare neoplasm whose relationship to other neurocytomas within the central nervous system (CNS) has remained unclear. Prior reports have variably classified SELN as a variant of extraventricular neurocytoma (EVN), while immunohistochemical and ultrastructural studies have suggested a hypothalamic origin. Here, we performed unsupervised clustering of DNA methylation data across a large pan-cancer reference set and identified SELN (n = 20) as distinct from other neurocytomas and regional mimics, as well as clustering with neuroendocrine tumors from other organ sites. SELN exhibited a CIMP-like phenotype, TTF1 negativity (0/8), and AVP (vasopressin) promoter hypomethylation, implicating a magnocellular hypothalamic cell of origin. In evaluable cases, a neuronal/neuroendocrine immunophenotype was observed (synaptophysin 8/8, chromogranin A 5/5) with absent pituitary transcription factor expression (PIT1 and TPIT negative 0/4). DNA sequencing (n = 5) and RNA-based fusion profiling (n = 4) did not detect recurrent mutations or gene fusions, respectively. Patients often presented with visual disturbances or headaches and spanned pediatric and older age groups (median 42 years, range 12.5–75), with no sex predilection. Despite locally aggressive imaging features in some cases (cavernous sinus invasion, carotid encasement, hydrocephalus), disease-free survival (n = 13) was comparable to central neurocytoma, with no disease-related deaths during the limited follow-up. Together, these findings support SELN as a molecularly distinct hypermethylated neuroendocrine-like epitype and clinicopathologic entity.

Abstract Corticotroph pituitary neuroendocrine tumours (PitNETs)/adenomas are heterogeneous sellar neoplasms. Currently established histopathological classification approaches are often considered limited in fully capturing the clinical and biological complexity of these tumours. Thus far, a molecular-based classification has not been established in corticotroph PitNETs. We compile molecular data of 270 corticotroph PitNETs (111 internal, 159 external), encompassing epigenome, transcriptome, and proteome profiles. Comprehensive integrative analyses are performed to identify, validate and characterise definitive molecular subgroups. Corticotroph PitNETs separate into four robust and clinicopathologically distinct molecular subgroups, which are broadly distinguishable by microscopy using SSTR1, GATA3 and SSTR5 immunohistochemistry. An integrated stratification model incorporating these molecular subgroups demonstrates significant prognostic utility. Our findings support the establishment of a refined molecular-based corticotroph PitNET classification, the full clinical value of which will require validation in prospective studies. To facilitate future research, we provide an easy-to-use epigenomic classifier for corticotroph PitNETs.

INTRODUCTION:Radiation necrosis (RN) complicates neuro-oncological care, mimicking tumor recurrence and lacking high-level evidence for standardized management. METHODS:A European Association for Neuro-Oncology (EANO) expert panel utilized a three-round Delphi process to create a comprehensive expert opinion document based on the available current scientific evidence. A series of statements, derived from the published literature were created by the experts in each field. Consensus was defined as ≥ 80% agreement using a 5-point Likert scale. RESULTS:After three rounds among 20 experts that included adaptation of statements, the Delphi process reached a consensus (≥80% agreement) on 53 statements out of 57. RN occurs in 4% to 30% of patients, typically appearing 6 to 24 months after radiotherapy for primary (glial) or metastatic brain tumors. Experts identified perfusion MRI and amino acid PET as the most suitable imaging modalities for differentiation from tumor recurrence. While histopathology remains the gold standard, identifying viable tumor cells in irradiated gliomas is challenging due to overlapping cytological features with reactive glia. For symptomatic management, corticosteroids may be tried, and bevacizumab is recommended for corticosteroid-refractory cases, with evidence suggesting profound efficacy even at low doses. Surgery is considered effective for rapid symptom relief and definitive diagnosis in accessible lesions. Laser Interstitial Thermal Therapy (LITT) can be considered an additional treatment option for symptomatic RN. CONCLUSIONS:Despite the absence of Level 1 evidence, these Delphi-survey-formulated recommendations provide actionable guidance for clinical practice. There is a need for prospective randomized trials focusing on symptomatic RN.

Abstract Recent advances in neuro-oncology have highlighted the meninges as a key immunological hub at the interface between systemic immunity and the central nervous system (CNS). While meningiomas are primarily benign, ∼20% exhibit high-grade features including brain invasion, recurrence, and treatment resistance. Macrophages in the tumor microenvironment are of particular interest due to their high abundance and phenotypic plasticity. While many studies have investigated macrophage diversity in cancer, this study spatially resolves myeloid activation programs in their native tissue context, exploring correlates with clinical parameters including meningioma molecular subtypes and recurrence. We combine spatial transcriptomics (Xenium) with multiplexed ion beam imaging (MIBI), integrating gene expression and high-dimensional protein data at single-cell resolution. Our large multi-center retrospective cohort of 368 FFPE samples includes cases with detailed molecular and clinical annotations. To enable scalable spatial analysis, we sampled pathologist-annotated regions and imaged tissue-micro-arrays (TMAs). Myeloid activation programs were discovered using cNMF. Delaunay algorithms delineated niches in Xenium data, while CellCharter discovered tissue niches in MIBI data. Spatial protein and transcriptomic data from consecutive sections reveal spatially segregated biological processes. The myeloid compartment in meningioma makes up a significant part of the tumor bulk, especially in low-grade tumors. Protein-level analysis shows clear tissue zonation marked by specific metabolic marker expression such as MCT1 and differential infiltration by microglia-like macrophage subsets. Spatial transcriptomics revealed at least 5 nuanced transcriptional myeloid activation programs that differ across methylation classes of over 320 cases. These differential enrichments were accompanied by distinct infiltration patterns and cell-type co-localization suggesting interaction-driven activation of myeloid programs. Overall, tumor-associated macrophages (TAMs) exhibit context-dependent prognostic significance across cancer types, most evidence supports their association with poor prognosis. In meningiomas, dense TAM infiltration is a key feature of benign subtypes. This study provides spatial context to meningioma methylation classes and addresses critical questions regarding the microenvironmental cues that shape TAM diversity and whether specific TAM activation programs have prognostic value, potentially revealing novel therapeutic avenues. Citation Format: Domenico Calafato, Yiheng Tang, Gleb Rukhovich, Elyas Heidari, Leonille Schweizer, Michael Weller, Christine Haberler, Till Acker, Bhuvic Patel, Abigail Suwala, Felix Sahm, Felix Hartmann, Moritz Gerstung. Large-scale spatial molecular profiling uncovers distinct macrophage activation states across meningioma methylation classes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1206.

Automating pathology workflows with deep learning is increasingly feasible and clinically relevant. We present an AI-based method that identifies diagnostically relevant areas directly from H&E-stained slides, trained on 250 glioma cases using sparse, incomplete annotations. First, we show that attention-based multiple instance learning achieves accurate predictions despite noisy labels, easing the annotation burden. Second, the model highlights tumor regions with high cellularity or grade, offering reproducible guidance for tissue selection. In a prospective evaluation, AI-selected regions achieved a mean Dice score of 0.743 [±0.077], supporting integration into neuropathology workflows as reliable guidance for molecular diagnostics.

Molecular testing is essential for classifying central nervous system (CNS) tumors, with methylation profiling providing the highest diagnostic granularity. However, this requires more resources and time than conventional hematoxylin and eosin (H&E) histopathology, which is widely available globally. Here we propose Hetairos, an artificial intelligence algorithm that predicts 102 methylation-based CNS tumor subtypes from digital H&E slides. Built and validated on 9,606 patients and over 11,000 slides from 11 centers across four continents, Hetairos identified 50-70% of cases with high confidence, achieving an accuracy of 0.87 for its highest-rated predictions. Hetairos outperformed five board-certified neuropathologists in a direct histology-only comparison (0.68 versus 0.30). Prospective evaluation in routine diagnostics confirmed its performance, reducing turnaround time from 12 days (molecular testing) to 12 min. Hetairos supports diagnostic decision-making across the full spectrum of pediatric and adult CNS tumors by narrowing differential diagnoses and guiding efficient testing.

BACKGROUND:Replication repair deficiency is associated with increased risk of developing malignant gliomas. The aim of this study was to investigate primary mismatch repair deficient gliomas (PMMRDGs), a group of IDH-wildtype and H3-wildtype gliomas that is enriched among patients with CMMRD and Lynch syndrome. METHODS:We investigated how PMMRDGs differ from other gliomas with respect to DNA methylation profile, genomic alterations, histopathology, and clinical outcomes. RESULTS:PMMRDGs occur in pediatric, adolescents and the elderly, falling in two related methylation clusters and are characterized by a high frequency of replication repair deficiency. Histology showed multinucleated giant cells, and immunohistochemistry demonstrated loss of MMR protein expression. Survival analysis revealed long-term survival in patients with high mutational burden (>50 mut/Mb) and an intact chromosome 9p region, which was validated in an independent reference cohort. CONCLUSIONS:Overall, our findings indicate that PMMRDGs represent a distinct type of IDH-wildtype gliomas with potential for long-term survival likely driven by immune activation.

Abstract WHO grade 2 meningiomas exhibit highly heterogeneous clinical courses. While the Ki67 proliferation index is a standard biomarker, its prognostic utility remains limited by methodological inconsistency and potential time-dependent dynamics. We evaluated an automated, artifact-adjusted Ki67 assessment and its integration with molecular risk profiling. 98 WHO grade 2 meningiomas (WHO 2021) were analyzed using an automated QuPath-based pipeline with HistoART for artifact exclusion. Molecular risk was defined by methylation and copy number profiling to calculate the integrated molecular-morphologic risk score by Maas et al. We employed extended Cox models to account for proportional hazards violations. Automated Ki67 values were significantly lower than routine pathological estimates (median 2.91% vs. 10%; p < 0.001) and correlated modestly with integrated risk scores (ρ = 0.26, p = 0.009). We identified a biphasic risk pattern: within the first 38 postoperative months, an automated Ki67 > 3.62% was a strong independent predictor for local recurrence (HR 5.06, p < 0.001) and progression-free survival (HR 4.15, p = 0.002), remaining significant alongside subtotal resection and the integrated risk group. Beyond 38 months, prognostic impact attenuated. Ki67 and the integrated molecular risk score contributed independently in multivariable models, suggesting complementary biological dimensions. Automated, artifact-adjusted Ki67 quantification provides time-dependent, independent prognostic information in WHO grade 2 meningioma, complementary to molecular risk stratification. It may serve as a cost-effective surveillance marker—both as an adjunct to molecular profiling and as a standalone tool where molecular testing is unavailable.

Abstract Background Posterior fossa ependymoma can be classified into three distinct molecular types: PF-EPN-A, PF-EPN-B, and PF-EPN-SE. Characterization of PF-EPN-B has primarily focused on describing its molecular heterogeneity and identifying subtypes. PF-EPN-B is characterized by late relapses, without known risk markers to distinguish high- from low-risk cases. However, research focusing on identifying prognostic markers and characterizing this molecular type using high-resolution approaches such as single-nucleus RNA sequencing remains limited. Methods Using an updated cohort of 340 DNA methylation profiles, including 230 patients with available survival data and 16 single-nucleus RNA sequencing profiles, we performed an integrative analysis to identify prognostic markers and to discern the heterogeneity within PF-EPN-B at the single-cell level. Results Based on the DNA methylation data, we were able to identify the previously described subtypes 1-5, however, the clusters 1-3 exhibited less stable and less distinct features compared to clusters 4 and 5. Single-nucleus RNA sequencing of 82,000 cells revealed differences in immune infiltration and distinct immune cell-specific activated metaprograms across the subtypes. Training a linear support vector machine on the 10,000 most variable CpG sites allowed stratification into two risk groups with significantly different progression-free survival (median group 1: 56.6 months, median group 2: 165.0 months, p = 0.0026). Additionally, gain of chromosome 1q emerged as a potential negative prognostic marker for progression-free survival, with enrichment in subtype 1. Conclusion PF-EPN-B exhibits substantial heterogeneity in both copy number alteration profiles and gene expression patterns across its subtypes. The identification of chromosome 1q gain as a possible prognostic marker and differences in immune infiltration between the subtypes may enable improved risk stratification and support the development of more precise, patient-tailored therapeutic strategies.

BACKGROUND:Oligodendrogliomas, characterized by isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletion, often exhibit telomerase reverse transcriptase promoter (TERTp) mutations, which have been linked to telomere maintenance (TM) and tumor proliferation. Although there are a few reports on a TERTp-wildtype subset of these tumors in adolescents and young adults, the frequency, molecular characteristics, and prognostic implications of TERTp-wildtype status in oligodendrogliomas remain elusive. METHODS:We retrospectively analyzed 166 IDH-mutant and 1p/19q-codeleted oligodendroglioma cases through comprehensive histopathological review and molecular analyses, including Sanger sequencing, DNA methylation profiling, and whole-exome sequencing (WES). RESULTS:A TERTp-wildtype status was observed in 20/166 cases (12.0%) and was significantly associated with noticeably young age (age range: 14-27, P < .001), CNS WHO grade 2 (P = .003), and the absence of additional DNA copy number variations (CNVs) beyond the pathognomonic 1p/19q codeletion (P < .001). Epigenetic profiling demonstrated TERTp-wildtype tumors shaped a distinct subgroup at the utmost periphery of TERTp-mutant oligodendrogliomas. Methylation analysis of the upstream and proximal TERTp regions revealed that, in line with the absence of genetic alterations, epigenetic regulation does not favor TERT overexpression in TERTp-wildtype oligodendrogliomas. WES showed no TM-related gene alterations in TERTp-wildtype cases. Cox regression analysis confirmed TERTp-wildtype status as an independent prognostic factor for more favorable progression-free survival (PFS) (P = .009). CONCLUSIONS:In conclusion, "oligodendroglioma, IDH-mutant, 1p/19q-codeleted, and TERTp-wildtype" represent a distinct molecular subgroup associated with younger age and a better clinical course compared to CNS WHO grade 2 oligodendrogliomas.

Meningiomas are the most common primary intracranial neoplasms, with highly variable patient outcomes. While most meningiomas are benign, a significant subset recurs postoperatively, presenting substantial treatment challenges. BAP1 gene inactivation has been suggested as a marker for aggressive meningiomas, although its precise molecular and clinical roles remain poorly understood. To comprehensively investigate BAP1-altered meningiomas, we used six meningiomas with known BAP1 alterations as a discovery set. Genome-wide DNA methylation profiling of these samples, along 11,151 reference meningiomas, identified a distinct molecular cluster (n = 42) using unsupervised visualization approaches. These tumors were further characterized by DNA/RNA sequencing, histopathological examination, and a retrospective review of clinical data, compared to reference meningioma cohorts, providing a thorough characterization of this rare tumor subtype. Our integrative analysis revealed BAP1-altered meningiomas as a distinct CNS tumor subtype, characterized by recurrent loss of chromosome 3p21 and driven by various BAP1-inactivating alterations. Although rhabdoid morphology is present in some cases, it is not exclusive and should not be used as a grading criterion. Progression-free survival analysis showed a median of 21 months (95% CI: 12-NA), with a 2-year overall survival rate of 79% (95% CI: 60%-100%), highlighting the aggressive nature of these tumors. Gene expression profiling revealed upregulation of PRC target genes, dysregulated Polycomb signaling, and elevated expression in several cellular and growth factor pathways. BAP1-altered meningiomas represent a distinct and aggressive CNS tumor subtype associated with PRC dysregulation and recurrent 3p chromosome loss. These findings support the designation "meningioma, BAP1-altered."

Introduction:Patients with suspected neuro-oncological disease on radiographic images and no histopathological evidence of a tumor on the surgically retrieved tissue, pose a great challenge for clinicians and neuropathologists. Meanwhile, genome-wide DNA methylation-based molecular profiling has been established to allow robust brain tumor classification. Research question:Does DNA methylation-based molecular profiling make a relevant contribution to the diagnosis and resolution of these non-specific neuro-oncological cases. Materials and methods:We screened all neurosurgical cases at our institution between 2009 and 2021 with suspected neuro-oncological diseases on MRI but negative or unspecific histopathological diagnosis. We differentiated two groups: cases with cell-enriched, reactive tissue (with or without suspected single tumor cells), insufficient to classify the lesion according to WHO 2021 diagnostic criteria for CNS tumors (group 1) and cases that were not cell-enriched, without reactive changes and no suspected tumor cells (group 2). The primary endpoint of the study was to assess the feasibility of establishing a molecular diagnosis in accordance with the WHO 2021 diagnostic criteria for CNS tumors. Results:23 cases with unspecified histopathological diagnosis were identified, 16 cases were assigned to group 1, seven cases to group 2. DNA-methylation-based profiling and copy number variations enabled a tumor diagnosis in nine (56.3 %) cases in group 1 and three (42.9 %) cases in group 2, adding up to 12 tumors (52.2 %). Five cases were identified as physiological cortex. Discussion and conclusion:Our findings underscore the potential of integrating DNA methylation-based profiling into diagnostic workflows, contributing to an accurate diagnosis in challenging cases.

DNA methylation analysis is an important diagnostic method in neuropathology and often enables a precise WHO diagnosis; however, some cases remain unclassifiable by current methylation-based brain tumor classifiers. Factors influencing classifiability of samples are incompletely understood and performance evaluations of new machine learning tools in challenging specimens are limited. We retrospectively analyzed a cohort of 1,853 CNS tissue samples diagnosed between 2017 and 2024 using genome-wide DNA methylation profiling and identified a cohort of 393 cases (21%) unclassifiable by the Heidelberg Brain Tumor Classifier v12.8. We examined preanalytic, histological, and molecular features of these samples and analyzed them using the new Bethesda Classifier v3 (Bv3). Younger age (P = 1.352 × 10⁻⁶), lower tumor purity (P = 2.539 × 10⁻¹¹) and reduced DNA input (P = 3.458 × 10⁻¹⁶) negatively affected the v12.8 classifier accuracy, while unclassifiable sample rates were identical for the 850k and 935k Illumina EPIC arrays (22% each; p = 0.9505). Bv3 was able to classify 213/393 unclassifiable cases (54%), including 196/325 neuroectodermal tumors (60%), 6/21 mesenchymal tumors (29%), 6/20 metastases (30%) and 5/27 non-tumor specimens (19%). A WHO diagnosis was established in 288/325 neuroectodermal tumors (89%; NEC/NOS: n = 37); the Bv3 achieved a classifier score above the threshold of 0.9 in 186 cases (186/288;65%): in 163/186 cases (88%) the classification matched the WHO diagnosis, in 23/186 cases the Bv3 suggested an alternative diagnosis (12%). Notably, the Bv3 correctly identified 117/174 glioblastomas, IDH-wildtype (67%), including 86/102 histological GBM and 16/102 molecular GBM. The RF_purify estimated tumor cell content of 100 GBM samples correctly classified by the Bv3 was significantly lower compared to 458 GBM samples correctly classified by the v12.8 (P = 0.01086). The Bv3 classifier is a valuable tool in currently unclassifiable, diagnostically challenging brain specimens and especially useful in low tumor cell content samples.

DNA methylation-based classification is now central to contemporary neuro-oncology, as highlighted by the World Health Organization (WHO) classification of central nervous system (CNS) tumors. We present the Heidelberg CNS Tumor Methylation Classifier version 12.8 (v12.8), trained on 7,495 methylation profiles, which expands recognized entities from 91 classes in version 11 (v11) to 184 subclasses. This expansion is a result of newly identified tumor types discovered through our large online repository and global collaborations, underscoring CNS tumor heterogeneity. The random forest-based classifier achieves 95% subclass-level accuracy, with its well-calibrated probabilistic scores providing a reliable measure of confidence for each classification. Its hierarchical output structure enables interpretation across subclass, class, family, and superfamily levels, thereby supporting clinical decisions at multiple granularities. Comparative analyses demonstrate that v12.8 surpasses previous versions and conventional WHO-based approaches. These advances highlight the improved precision and practical utility of the updated classifier in personalized neuro-oncology.

Molecular data integration plays a central role in central nervous system (CNS) tumor diagnostics but currently used assays pose limitations due to technical complexity, equipment and reagent costs, as well as lengthy turnaround times. We previously reported the development of Rapid-CNS2, an adaptive-sampling-based nanopore sequencing workflow. Here we comprehensively validated and further developed Rapid-CNS2 for intraoperative use. It now offers real-time methylation classification and DNA copy number information within a 30-min intraoperative window, followed by comprehensive molecular profiling within 24 h, covering the complete spectrum of diagnostically and therapeutically relevant information for the respective entity. We validated Rapid-CNS2 in a multicenter setting on 301 archival and prospective samples including 18 samples sequenced intraoperatively. To broaden the utility of methylation-based CNS tumor classification, we developed MNP-Flex, a platform-agnostic methylation classifier encompassing 184 classes. MNP-Flex achieved 99.6% accuracy for methylation families and 99.2% accuracy for methylation classes with clinically applicable thresholds across a global validation cohort of more than 78,000 frozen and formalin-fixed paraffin-embedded samples spanning five different technologies. Integration of these tools has the potential to advance CNS tumor diagnostics by providing broad access to rapid, actionable molecular insights crucial for personalized treatment strategies. Application of a nanopore sequencing workflow for real-time analysis of brain tumors results in molecular classification within a 30-minute intraoperative window, followed by comprehensive profiling within 24 hours.

Central neurocytomas (CN) are intraventricular brain tumors predominantly occurring in young adults. Although prognosis is usually favorable, tumor recurrence is common, particularly following subtotal resection (STR). Currently, the risk of progression is evaluated using atypical features and an elevated Ki67 proliferation index. However, these markers lack consistent definitions, raising the need for objective criteria. Genome-wide DNA methylation profiles were examined in 136 tumors histologically classified as CN. Clinical/histopathological characteristics were assessed in 93/90 cases, and whole-exome sequencing was conducted in 12 cases. Clinical and molecular characteristics were integrated into a survival model to predict progression-free survival (PFS). A diagnosis of CN was epigenetically confirmed in 125 of 136 cases (92%). No DNA methylation subgroups were identified, but global DNA hypomethylation emerged as a hallmark feature of CN associated with higher recurrence risk. Risk stratification based on histological features of atypia and Ki67 proliferation index was not reproducible across neuropathologists. Hypomethylation at the FGFR3 locus, accompanied by increased FGFR3 protein expression, was observed in 97% of cases. Gross total resection was associated with significantly improved PFS compared to STR, while patients undergoing STR receiving radiotherapy had a better outcome (p = 0.0001). Younger patients were identified as having a higher risk of recurrence (p = 0.026). Patient age and treatment strategy were key factors associated with survival outcomes in this cohort. These findings underscore the importance of closer follow-up for younger patients and radiotherapy for STR cases. Furthermore, FGFR3 represents a hallmark feature and potential therapeutic target, warranting further investigation.