Clinical actionability in gliomas revealed by real-world next-generation sequencing: a multicentric study

Clinical actionability in gliomas revealed by real-world next-generation sequencing: a multicentric study | 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 Clinical actionability in gliomas revealed by real-world next-generation sequencing: a multicentric study Oriol Mirallas, Fiorella Ruiz-Pace, Gabriel Velilla, Diego Gómez-Puerto, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6903120/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in npj Precision Oncology → Version 1 posted 10 You are reading this latest preprint version Abstract Treatment options for patients with gliomas remain limited, and prognosis is generally poor. While next-generation sequencing (NGS) is increasingly used to stratify glioma patients and guide therapy, its implementation in routine clinical practice remains variable. We conducted a multicenter retrospective study across seven Spanish hospitals to evaluate the clinical utility of NGS in glioma management, focusing on its impact on diagnosis and treatment selection based on the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). A total of 541 glioma patients diagnosed between 2018 and 2022 were included; 76% had glioblastomas and 24% other glioma subtypes. Among glioblastoma patients, 9% harbored ESCAT tier 1/2 alterations and 74% tier 3/4. Molecularly matched therapy was administered in 10.2% of glioblastoma cases. Objective responses were observed in 17.6% of glioblastoma and 33% of non-glioblastoma patients with ESCAT tier 1/2 alterations. Patients with tier 1/2 alterations experienced significantly longer progression-free survival compared to those with tier 3/4. These findings support the integration of NGS into standard care for glioma, facilitating precise molecular classification, expanding therapeutic options, and improving access to matched clinical trials. Health sciences/Oncology/Cancer/Cns cancer Health sciences/Oncology/Cancer/Tumour biomarkers Health sciences/Oncology/Cancer/Tumour heterogeneity Health sciences/Molecular medicine Health sciences/Medical research/Drug development Next-Generation Sequencing glioma molecular targeted therapy molecular diagnostics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Central nervous system (CNS) tumors comprise a diverse group of over 40 entities. Even if considered as a single entity, they would meet the criteria for rare tumors, with an estimated incidence of 308,102 new cases and 251,329 deaths worldwide in 2020 1 . The World Health Organization (WHO) classification of malignant CNS tumors used to rely on histological findings, such as morphology and immunohistochemical features. However, new molecular biomarkers have improved tumor classification, leading to more precise diagnoses 2 – 4 . The WHO 2021 classification integrated molecular data as key elements to guide diagnosis and treatment. Glioblastoma, the most common malignant tumor in adults, accounts for 49% of malignant brain tumors. Treatment remains challenging and varies by tumor type and location. Surgery is the first-line treatment, with complete resection as the primary goal 5 . Adjuvant treatment with radiation and temozolomide-based chemotherapy remains the standard, based on the phase III trial by Stupp et al 6 . Targeted therapies have emerged for specific brain tumors, often developed in tumor-agnostic clinical trials. For example, the tissue-agnostic approval of dabrafenib and trametinib for BRAFV600E-mutated gliomas followed the positive results of the ROAR basket trial 7 , 8 . More recently, the INDIGO phase III clinical trial with vorasidenib demonstrated efficacy in IDH1/2- mutant type 2 gliomas surgery 9 , 10 . The ESMO Scale of Clinical Actionability for molecular Targets (ESCAT) 11 prioritizes genomic alterations for targeted therapies. The ESMO Magnitude of Clinical Benefit Scale (MCBS) assess the clinical benefit of treatments based on progression-free survival (PFS) and overall response rate (ORR) 12 . An initial ESCAT proposal for gliomas was presented in ESMO 2022 13 , followed by the European Association of Neuro-Oncology (EANO) guidelines on rational molecular testing for gliomas. Notably, these guidelines do not recommend NGS due to its limited clinical actionability 14 . This initial assessment will evolve as more targeted treatments receive regulatory approval. The Cancer Genome Atlas Research Network (TCGA) established the genomic and transcriptomic landscape of glioblastoma 15 . In 2010, Verhaak et al. identified four distinct molecular subtypes (proneural, neural, classical, and mesenchymal) with different clinical outcomes and treatment sensitivities 16 . Subsequent work by Brennan et al. expanded this classification using DNA methylation, miRNA expression, and protein analysis, identifying key pathways like EGFR and PDGFRA . Despite advances in glioma molecular profiling, real-world implementation remains limited due to challenges in standardizing multiparametric testing, though key mutations aid clinical distinction 17 . While NGS is not standard-of-care for glioma diagnosis, the WHO 2021 classification requires pathologists to conduct a series of tests, which are costly and require substantial tissue. In contrast, NGS can detect multiple alterations in a single test, offering new opportunities for molecularly matched therapies. Early-phase trials with BRAF, NTRK and IDH inhibitors have shown safety and efficacy in glioma patients 7 , 10 , 18 . Despite this, the number of glioma patients included in trials remains low, highlighting the need for increased molecular testing and real-world evidence supporting targeted therapies. To address this gap, we analyzed a large CNS tumor cohort undergoing routine NGS to assess ESCAT's role in therapy prioritization and the clinical impact of molecular matched treatments. Results Patient population From January 2018 to May 2022, 580 primary brain tumors patients were included; 541 glioma patients met eligibility criteria (Fig. 1 ). The median number of treatment lines was 3 (range 1–10). Median age was 51 years (range 3–84), with glioblastoma patients being older than non-glioblastoma [55 vs 36 years, p 0.05). Over 80% of patients underwent surgery, with no significant in complete vs incomplete resection between glioblastoma vs non-glioblastoma (p = 0.055). After surgery, 79% of glioblastoma patients received Stupp protocol chemoradiation vs 46% of non-glioblastoma patients ( Supplementary Table 1 ). ESCAT proposal for glioma The ESCAT proposal for gliomas and MCBS is based on published efficacy data, supplemented by the real-world distribution of mutations in our cohort (Table 1 ). Among 409 glioblastoma cases with NGS, 83% had actionable alterations: 9% (n = 36) were tier 1/2 (IDH1/2, BRAF V600E, FGFR1-3 mutations/rearrangements, NTRK1-3 fusions), and 74% (n = 303) were tier 3/4 alterations (Table 1 ). Matched therapy was given to 42 glioblastoma (10.2%) and 5 non-glioblastoma (3.8%) patients. These results show that integrating NGS increases eligibility for targeted therapies. Table 1 ESCAT proposal for glioma tumors according to the most recent evidence. ESCAT Alteration Matched Therapy and ESMO MCBS Patients with alteration Patients tested Prevalence Altered patients by ESCAT Total by group Tier 1 BRAF - V600E mutation Dabra-Trame: 3 7 497 1,4% 144 168 IDH1 mutation Vorasidenib: 3 123 535 23,0% 31,1% IDH2 mutation Vorasidenib: 3 10 492 2,0% NTRK (1–3) fusion Larotrecitnib/ Entrectinib: ND 10 489 2,0% Tier 2 FGFR (1–3) pathogenic mutations Erdafitinib: 3 14 482 2,9% 24 FGFR rearrangement (FGFR-TACC) Futibatinib/ Pemigatinib: 3 13 473 2,7% Tier 3 AKT1 - E17K mutation - 0 467 0,0% 153 303 BRAF fusions** Selumitinib: ND 8 495 1,6% 56,0% FGFR (1–3) amplification Erda/Futi/Pemi: ND 3 459 0,7% H3K27M mutation - 11 455 2,4% MET mutation Vebreltinib/ Capmatinib: ND 10 469 2,1% PI3K mutations (PIK3CA, PTEN) Capmatinib: ND 149 492 30,3% PTEN loss - 19 479 4,0% Tier 4 ATM mutation - 14 349 4,0% 150 ATRX mutation ATR inhibitor: ND 80 459 17,4% CDKN2A loss - 127 445 28,5% CDKN2B loss - 108 393 27,5% EGFR amplification Depatuxizumab mafodotin/Dacomitinib: ND 128 482 26,6% EGFR mutation - 69 497 13,9% EGFR vIII rearrangement EGFRvIII-TCBA/Rindopepimut: ND 72 492 14,6% pTERT mutation - 205 437 46,9% Wild type 40 541 7,4% Unknown Incomplete results 30 541 5,5% Total 541 *Dabra-Trame: Dabrafenib-Trametinib; ND: No data. **Pending results of FIREFLY-2. Uncovering molecular alterations and co-mutations in real-world data The most common TERT co-mutations in glioblastoma patients were CDKN2A loss (20%), EGFR amplification (20%), PIK3CA mutation (19%), and CDKN2B loss (19%) ( Supplementary Fig. 1A ). Most patients with CDKN2A loss also had CDKN2B loss [101 patients (25%)]. In patients with non-glioblastoma tumors, the most common co-mutation for IDH1 was ATRX loss (48%), followed by TERT mutation (19%) and PIK3CA mutation (12%) ( Supplementary Fig. 1B ). For the entire cohort, the most common mutations were found in TERT (47%), TP53 (33%), PI3KCA (30%), and CDKN2A / B (28%). In the GBM cohort exclusively, the most common mutations were found in TERT (54%), CDKN2A (37%), PI3KCA (36%), CDKN2B and EGFR amplified (35% each, Supplementary Fig. 2A ). The non-GBM cohort's most common mutations were IDH1 (93%), TP53 (61%), ATRX (58%), and TERT (26%, Supplementary Fig. 2B ). OS in our real-world data population Survival was significantly better in non-glioblastoma than in glioblastoma patients [126.7 months (95% CI 106.8–146) versus 22.9 months (95% CI 20.2–25.4; p < 0.001)] (Fig. 2 A). In the glioblastoma cohort, regardless of treatment, OS was superior in ESCAT tier 1/2 (median 43.5 months, 95% CI 21.3 – NR) than in tier 3/4 (22.2 months, 95% CI 19.5–25.3; p = 0.01) [Figure 2 B]. Clinical actionability of targeted therapy in glioma patients Overall, the ORR for targeted therapies was 11%. The ORR in ESCAT tier 1/2 patients was 26% (DCR 80%), compared to 8% in tier 3/4 (DCR 34%) population, which exhibited a DCR of 34%. FGFR 1–3 fusions/rearrangements treated with covalent FGFR inhibitor (futibatinib, pemigatinib, erdafitinib) achieved 60% ORR and 80% DCR, whereas FGFR 1–4 mutations had 20% ORR and 60% DCR. BRAF V600E- mutant patients receiving BRAF/MEK inhibitors had 20% ORR and 80% DCR, with one partial response with BRAF inhibitor alone. One BRAF fusion patient treated with selumetinib responded. IDH1 and NTRK achieved 100% and 50% DCR, respectively, but no PRs. Capmatinib failed to elicit responses in MET fusions or PIK3CA / PTEN- mutant cases (Fig. 3 ). Patients with ESCAT tier 1/2 alterations had significantly longer PFS (6.36 vs. 1.64 months, HR 2.69, 95% CI 1.39–5.21, p = 0.003) (Fig. 4 B). Treatment in early-phase trials did not negatively impact PFS (HR 1.15, 95% CI 0.75–1.74, p = 0.518) (Fig. 4 A). The longest PFS was observed in patients with FGFR 1–4 mutations, BRAF V600E , IDH1 R132H , BRAF fusion, EGFR amplification, with MAPK-targeted drugs showing efficacy (Fig. 5 ). Classification of glioblastoma patients using real-world NGS data As an exploratory analysis, we aimed to determine whether the 409 patients in our database classified as glioblastoma could be reassigned to an adapted molecular classification based solely on their genomic features identified by NGS, without the inclusion of RNA or methylation profiling ( Supplementary Fig. 3A ). Of 409 glioblastoma patients, 12% exhibited proneural, 20% mesenchymal, and 68% classical molecular hallmarks, enabling classification of 72% of cases ( Supplementary Fig. 3A, Table 2 ). No differences in age, gender, MGMT methylation, ECOG PS, or OS were found among subtypes ( Supplementary Fig. 3B ). Discussion Our multicenter study exploring the potential of real-world data for identifying clinically actionable targets underscores the clinical utility of NGS in glioma diagnosis and treatment decision-making. Per ESMO recommendations, NGS should be prioritized for tumors with a high likelihood of ESCAT tier 1 alterations and in hospitals with drug development programs for tiers 2–4 19 . NGS is essential not only for identifying targeted therapies in glioma but also for accurate diagnosis per updated guidelines. However, clinically actionable targets in CNS tumors remain limited. As recommended by EANO, we must carefully assess when and which molecular alterations to test in adult primary brain tumors 14 . Pathological assessment should include actionable mutations such as IDH1/2 , CDKN2A/B loss, TERT mutations, EGFR amplifications, and nuclear ATRX loss. IHC detects canonical IDH mutations and ATRX loss, while ISH identifies CDKN2A/B deletions and EGFR amplifications. However, NGS enhances diagnosis by detecting rare IDH mutations and identifying “molecular glioblastoma” in IDH -wildtype gliomas without microvascular proliferation or necrosis. Moreover, our dataset showed ESCAT tier 1 in 28% and tier 2 in 6.5%, exceeding prior glioma studies (3–7%) 20 . Notably, 10.2% of our glioblastoma patients received molecularly matched therapy through clinical trials or as compassionate use, surpassing the 4.1% reported in the NCI-match trial across tumor types 21 . Considering the limited enrollment of glioma patients in clinical trials and the scarcity of targeted therapies, there is potential for expanding precision medicine and improve glioma patients outcomes. In our real-world glioma cohort, we identified clinical and molecular factors associated with an increased likelihood of detecting relevant molecular events. Patients with ESCAT 1/2 alterations had superior PFS (6.4 vs 1.6 months) compared to those with tier 3/4 alterations (Fig. 4 B). Notably, FGFR fusions/rearrangements showed the highest ORR with covalent FGFR inhibitors, consistent with NCI-MATCH trial findings 21 (Fig. 3 ). These insights underscore the value of understanding glioma molecular alterations in improving treatment strategies. ESCAT profiling may better stratify patients for novel therapies, potentially leading to superior clinical outcomes (Fig. 5 ). TCGA molecular subtypes require more than NGS alone 17 , necessitating additional molecular layers. The proneural group, which includes glioblastomas with IDH promoter mutations or epigenetic alterations, is now classified separately by the 2021 WHO guidelines. While our data align with Verhaak’s subtypes, they do not replicate the OS reported for each group. Our study has limitations, including selection bias, variable NGS timing, and platform heterogeneity, which may affect the generalizability of results. However, with over 500 glioma patients from seven hospitals across Spain, our cohort reflects real-life oncology. The inclusion of diverse treatment types strengthens the findings, and our study presents the largest clinical and genomic glioma dataset published to date. Despite NGS heterogeneity, molecular characterization allowed for accurate diagnoses and increased matched therapies, supporting its broader adoption. Our findings suggest that NGS should be integrated into glioma management to enhance diagnostic precision and identify targeted therapy opportunities. ESCAT profiling aids in selecting molecularly matched therapies, advancing personalized treatment approaches. Methods Population and Study Design We conducted a retrospective analysis of clinical characteristics, NGS parameters, and matched therapies in a multicentric Spanish cohort of glioma patients treated from 2008 to 2023. Participating centers included Vall d'Hebron University Hospital, Catalan Institute of Oncology (ICO) Hospitalet, ICO Badalona, Hospital del Mar, Hospital Clinic de Barcelona, ​​Hospital de la Santa Creu i Sant Pau, Hospital 12 de Octubre, and Hospital Ramon y Cajal in Madrid. All glioma patients with NGS performed at these centers were included. Institutional review board (IRB) approval was obtained at each site. Living patients provided informed consent, while the IRB exempted deceased patients. The inclusion criteria were patients with glioma diagnosis, available NGS analysis and who underwent systemic treatment. The primary goal of NGS was to detect targetable mutations for clinical trial enrollment or molecularly matched targeted therapy. Data were collected via REDCap and included demographics, tumor type, surgery, systemic therapy, targeted therapy, clinical trial participation, and survival status as of May 2023. Patients were classified as glioblastoma or non-glioblastoma per WHO 2021 and further categorized using an adapted molecular classification (TCGA/Verhaak). Molecular alterations were classified by ESCAT and MCBS criteria. Sample collection and NGS genomic profiling. Molecular profiling was performed on tumor tissue from biopsies or surgical samples. DNA was analyzed using in-house NGS panels (21–61 genes for mutations, 26 genes for fusions using NanoString nCounter) or the FoundationOne CDx platform (324 genes, Foundation Medicine, Inc.). Additional data on mismatch repair deficiency (dMMR), microsatellite instability (MSI), and tumor mutational burden (TMB) were collected. Statistical analysis Descriptive statistics assessed baseline characteristics of patients, comparing groups and testing patterns. Continuous variables were expressed as median (IQR), and categorical variables as absolute values and percentages. Progression-Free survival (PFS) PFS, our main objective of the study, was defined as the time from the treatment initiation to disease progression or death from any cause. We compared PFS between patients receiving ESCAT tier 1/2 versus tier 3/4 therapies and between molecularly matched versus non-matched treatments. For patients who did not receive a molecularly matched therapy, treatment initiation was the first systemic therapy after completing or discontinuing the Stupp regimen, regardless of prior therapies. To control selection bias, we applied propensity score weighting generated through logistic regression. To control for selection bias, propensity score weighting through logistic regression was applied, including age, therapy line, tumor grade, and surgery outcome. Treatment effect was estimated using "average treatment effect for the treated". Overall survival The study's secondary objectives were to assess the overall survival (OS) of the glioblastoma and non-glioblastoma cohorts, compare OS between ESCAT tier 1/2 and tier 3/4 groups, and evaluate OS according to molecularly adapted classification. Time-to-event endpoints were estimated using Kaplan-Meier, with comparisons via log-rank test. To mitigate immortal bias arising from differences in patient entry times based on reported NGS test results, all estimates were adjusted using the risk set method, with the NGS test date serving as the index date. Univariate Cox proportional hazards models were applied to calculate hazard ratios (HRs) with 95% confidence intervals (CIs). Objective response rate and Disease Control Rate: ORR was defined as the proportion of patients achieving a complete response or partial response, disease control rate (DCR) included patients with stable disease (SD). P-values were two-sided and adjusted by the Benjamini and Hochberg (BH) method to account for multiple comparisons. Statistical analyses were conducted in R (v4.3.0). Declarations Funding The development of this publication was supported by Hoffman-La Roche&Co, Spain, through a grant for the publishing fees. The views and opinions contained in this publication do not necessarily reflect the ideas of the awarding entity of the scholarship. The founder of the study had no role in the study design, data collection, analysis, interpretation of the data, and writing of the report. Acknowledgements The authors would like to thank all the patients and families who agreed to participate in this study; progress in oncology care would be impossible without them. The first author would like to thank all the health staff at the different centers for working together to make this study possible. The first author wishes to express his sincere gratitude to Javier Carmona Cortés, PhD, for his input to this manuscript. Declaration of Interest: OM reports writing aid from Merck and Roche, and travel aid from Almirall, Kyowa Kirin, and Recordati. DG reports honoraria for lectures from LEO Pharma. TG reports disclose lectures/educational activities GSK, and travel expenses Pfizer, Reddy’s, BMS, MSD. MAVS reports grants from Pfizer, honoraria for lectures from Novocure and Servier, and advisory board from Servier. MCH reports consulting fees from Genomic Health, payments for expert testimony from Novartis, Daichii-Sankyo, Pfizer, Gilead, Astra-Zeneca, travel expenses from Lilly, fizer, Daichii-Sankyo, Novartis, Astra-Zeneca, Roche. MG reports writing aid Merck Sharp & Dohme, Bayer, Pfizer, Ipsen pharma and travel aid from Roche, Sanofi Aventis, Astellas, Pfizer, Janssen, Merck Sharp & Dohme, Bayer, and Lilly. MMG reports consulting fees from Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly, travel aid from Pfizer, Roche, Gilead, Astra Zeneca-Daiichi Sankyo, and participation on Advisory Board of Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly. MD reports grants from Roche, honoraria for lectures from BMS, and travel aid from Takeda and Lilly. JC reports consulting fees from Astellas Pharma; AstraZeneca; Bayer; Bristol-Myers Squibb; Exelixis; Ipsen; Johnson & Johnson; MSD Oncology; Novartis (AAA); Pfizer; Sanofi, payment or honoraria for lectures from Astellas Pharma; Bayer; Johnson & Johnson; Sanofi, and support for attending meetings from BMS, Ipsen, Roche, AstraZéneca, Bayer. RD reports grants from Merck, Novartis, Daiichi-Sankyo, GlaxoSmithKline, and AstraZeneca; consulting fees from Roche, Foundation Medicine, and AstraZeneca; payment or honoraria for lectures from Roche, Ipsen, Amgen, Servier, Sanofi, Libbs, Merck Sharp & Dohme, Lilly, AstraZeneca, Janssen, Takeda, Bristol-Myers Squibb, GlaxoSmithKline, and Gilead; and holds stocks in Trialing Health. JM reports grants from Cantex and IDP Pharma, consulting fees from Cantex, GSK, Servier, and Novocure, payment or honoraria for lectures from GSK and Novocure, travel aid from Pfizer, and participation on Advisory Board of Cantex and GSK. MV reports. CB reports support for attending meeting from Servier and payment or honoraria as for DSMB member by Laminar Pharmaceuticals. The rest of the authors declare no conflicts of interest. Author Contributions: Conception and design: Oriol Mirallas, Rodrigo Dienstmann, Maria Vieito. Provision of study materials or patients: Gabriel Velilla, Diego Gomez-Puerto, Teresa Gorria, Jesus Yaringaño, Alvaro Martinez-Monino, Antonio Di Muzio, Daniel López-Valbuena, Maria Angeles Vaz, Ainhoa Hernandez, Elena Martínez-Saez, Maria Aguado Sorolla, Maria Castro Henriques, María Martínez-García, Marta Domenech, Carmen Balaña, Estela Pineda, Juan Manuel Sepúlveda. Collection and assembly of data: Fiorella Ruiz, Oriol Mirallas, Gabriel Velilla, Diego Gomez-Puerto, Teresa Gorria, Jesus Yaringaño, Antonio Di Muzio, Alvaro Martinez-Monino, Daniel López-Valbuena, Maria Angeles Vaz, Ainhoa Hernandez, Elena Martínez-Saez, Maria Aguado Sorolla, Maria Castro Henriques, María Martínez-García, Marta Domenech, Estela Pineda. Data analysis and interpretation: Fiorella Ruiz, Oriol Mirallas, Rodrigo Dientsmann, Maria Vieito. Manuscript writing: All authors. Final approval of manuscript: All authors. Accountable for all aspects of the work: All authors. Data Availability Deidentified patient data from this study can be made available to qualified investigators who provide a methodologically sound research proposal and sign a data access agreement. Please email [email protected] for information. The study protocol, statistical analysis plan, and informed consent form will also be made available upon request. Data will be shared via a secure online platform; REDcap and public repository on the VHIO website. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A Cancer J Clinicians . 2021;71(3):209-249. doi:10.3322/caac.21660 Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology . 2021;23(8):1231-1251. doi:10.1093/neuonc/noab106 Weller M, Van Den Bent M, Preusser M, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol . 2021;18(3):170-186. doi:10.1038/s41571-020-00447-z Louis DN, Ohgaki H, Wiestler OD, Cavenee WK. WHO Classification of Tumours of the Central Nervous System. 5th ed. Lyon, France: International Agency for Research on Cancer; 2021. Ius T, Sabatino G, Panciani PP, et al. Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review. J Neurooncol . 2023;162(2):267-293. doi:10.1007/s11060-023-04274-x Stupp R, Weller M, Belanger K, et al. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma. The New England Journal of Medicine . Published online 2005. Wen PY, Stein A, Van Den Bent M, et al. Dabrafenib plus trametinib in patients with BRAFV600E-mutant low-grade and high-grade glioma (ROAR): a multicentre, open-label, single-arm, phase 2, basket trial. The Lancet Oncology . 2022;23(1):53-64. doi:10.1016/S1470-2045(21)00578-7 U.S. Food and Drug Administration. FDA grants accelerated approval to dabrafenib in combination with trametinib for unresectable or metastatic solid tumors with BRAF V600E mutation [Internet]. 2022. Available from: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-grants-accelerated-approval-dabrafenib-combination-trametinib-unresectable-or-metastatic-solid Mellinghoff IK, Van Den Bent MJ, Blumenthal DT, et al. Vorasidenib in IDH1- or IDH2-Mutant Low-Grade Glioma. N Engl J Med . 2023;389(7):589-601. doi:10.1056/NEJMoa2304194 Mellinghoff IK, Penas-Prado M, Peters KB, et al. Vorasidenib, a Dual Inhibitor of Mutant IDH1/2, in Recurrent or Progressive Glioma; Results of a First-in-Human Phase I Trial. Clinical Cancer Research . 2021;27(16):4491-4499. doi:10.1158/1078-0432.CCR-21-0611 Mateo J, Chakravarty D, Dienstmann R, et al. 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Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Annals of Oncology . 2020;31(11):1491-1505. doi:10.1016/j.annonc.2020.07.014 Padovan M, Maccari M, Bosio A, et al. Next-generation sequencing (NGS) for identifying actionable molecular alterations in newly diagnosed and recurrent IDH wild-type glioblastoma patients. Neuro Oncol. 2022;24(Suppl 7). doi:10.1093/neuonc/noac200.014. Flaherty KT, Gray R, Chen A, et al. The Molecular Analysis for Therapy Choice (NCI-MATCH) Trial: Lessons for Genomic Trial Design. J Natl Cancer Inst . 2020;112(10):1021-1029. doi:10.1093/jnci/djz245 Additional Declarations Competing interest reported. OM reports writing aid from Merck and Roche, and travel aid from Almirall, Kyowa Kirin, and Recordati. DG reports honoraria for lectures from LEO Pharma. TG reports disclose lectures/educational activities GSK, and travel expenses Pfizer, Reddy’s, BMS, MSD. MAVS reports grants from Pfizer, honoraria for lectures from Novocure and Servier, and advisory board from Servier. MCH reports consulting fees from Genomic Health, payments for expert testimony from Novartis, Daichii-Sankyo, Pfizer, Gilead, Astra-Zeneca, travel expenses from Lilly, fizer, Daichii-Sankyo, Novartis, Astra-Zeneca, Roche. MG reports writing aid Merck Sharp & Dohme, Bayer, Pfizer, Ipsen pharma and travel aid from Roche, Sanofi Aventis, Astellas, Pfizer, Janssen, Merck Sharp & Dohme, Bayer, and Lilly. MMG reports consulting fees from Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly, travel aid from Pfizer, Roche, Gilead, Astra Zeneca-Daiichi Sankyo, and participation on Advisory Board of Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly. MD reports grants from Roche, honoraria for lectures from BMS, and travel aid from Takeda and Lilly. JC reports consulting fees from Astellas Pharma; AstraZeneca; Bayer; Bristol-Myers Squibb; Exelixis; Ipsen; Johnson & Johnson; MSD Oncology; Novartis (AAA); Pfizer; Sanofi, payment or honoraria for lectures from Astellas Pharma; Bayer; Johnson & Johnson; Sanofi, and support for attending meetings from BMS, Ipsen, Roche, AstraZéneca, Bayer. RD reports grants from Merck, Novartis, Daiichi-Sankyo, GlaxoSmithKline, and AstraZeneca; consulting fees from Roche, Foundation Medicine, and AstraZeneca; payment or honoraria for lectures from Roche, Ipsen, Amgen, Servier, Sanofi, Libbs, Merck Sharp & Dohme, Lilly, AstraZeneca, Janssen, Takeda, Bristol-Myers Squibb, GlaxoSmithKline, and Gilead; and holds stocks in Trialing Health. JM reports grants from Cantex and IDP Pharma, consulting fees from Cantex, GSK, Servier, and Novocure, payment or honoraria for lectures from GSK and Novocure, travel aid from Pfizer, and participation on Advisory Board of Cantex and GSK. MV reports. CB reports support for attending meeting from Servier and payment or honoraria as for DSMB member by Laminar Pharmaceuticals. The rest of the authors declare no conflicts of interest. Supplementary Files SupplementaryTables.docx SupplementaryFigures.docx Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2026 Read the published version in npj Precision Oncology → Version 1 posted Editorial decision: Revision requested 11 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor assigned by journal 06 Jul, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 16 Jun, 2025 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-6903120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488820398,"identity":"837c6f3d-eda4-4d35-b241-a1d703baa50e","order_by":0,"name":"Oriol 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Universitari","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"López-Valbuena","suffix":""},{"id":488820410,"identity":"ca681377-7d55-44b5-9ef8-4568b6c8c9f0","order_by":8,"name":"Maria Angeles Vaz","email":"","orcid":"","institution":"Hospital Ramón y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Angeles","lastName":"Vaz","suffix":""},{"id":488820411,"identity":"9e148249-2431-448c-bcf6-d84a7eaf9c95","order_by":9,"name":"Ainhoa Hernandez","email":"","orcid":"","institution":"Institut Catala d’Oncologia (ICO) Badalona, Institut Investigació Germans Trias i Pujol (IGTP)","correspondingAuthor":false,"prefix":"","firstName":"Ainhoa","middleName":"","lastName":"Hernandez","suffix":""},{"id":488820412,"identity":"c92e2594-98fd-4722-a425-21a6e77a6cbd","order_by":10,"name":"Elena Martínez-Saez","email":"","orcid":"","institution":"Vall d'Hebron Hospital Universitari","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Martínez-Saez","suffix":""},{"id":488820413,"identity":"2b97cfc8-06f7-471a-a184-2bc47397361b","order_by":11,"name":"Maria Aguado Sorolla","email":"","orcid":"","institution":"Hospital de de la Santa Creu i Sant Pau","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Aguado","lastName":"Sorolla","suffix":""},{"id":488820414,"identity":"f138cdc3-ab3d-4512-9f60-b91d58cc60bb","order_by":12,"name":"Maria Castro-Henriques","email":"","orcid":"","institution":"Hospital del Mar","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Castro-Henriques","suffix":""},{"id":488820416,"identity":"32be554f-a8ed-40bf-9bd1-a5c99fdcbc6a","order_by":13,"name":"Julia Lostes Bardaji","email":"","orcid":"","institution":"Vall d'Hebron Hospital Universitari","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"Lostes","lastName":"Bardaji","suffix":""},{"id":488820417,"identity":"67e0ab80-47b0-43f7-8183-4db5401eff87","order_by":14,"name":"Antonio Muzio","email":"","orcid":"","institution":"Humanitas University","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Muzio","suffix":""},{"id":488820419,"identity":"b6dee5dc-1be6-4aa8-acbe-bc0e96289246","order_by":15,"name":"Oscar Gallego","email":"","orcid":"","institution":"Hospital de de la Santa Creu i Sant Pau","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Gallego","suffix":""},{"id":488820421,"identity":"03f61104-d153-4302-98e6-dc7eb925bc85","order_by":16,"name":"Maria Martinez-Garcia","email":"","orcid":"","institution":"Hospital del Mar","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Martinez-Garcia","suffix":""},{"id":488820422,"identity":"83fa5730-e29c-4328-8756-a6af2bcf91cc","order_by":17,"name":"Marta Domenech","email":"","orcid":"","institution":"Institut Catala d’Oncologia (ICO) Badalona, Institut Investigació Germans Trias i Pujol (IGTP)","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Domenech","suffix":""},{"id":488820424,"identity":"38992469-2335-42ab-aec2-d6388d1f6f4f","order_by":18,"name":"Joan Carles","email":"","orcid":"","institution":"Vall d'Hebron Hospital Universitari","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"","lastName":"Carles","suffix":""},{"id":488820426,"identity":"ca6fe952-fc6a-4327-8d2c-1180d7cda134","order_by":19,"name":"Carmen Balaña","email":"","orcid":"","institution":"Institut Catala d’Oncologia (ICO) Badalona, Institut Investigació Germans Trias i Pujol (IGTP)","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"","lastName":"Balaña","suffix":""},{"id":488820428,"identity":"67378659-521d-43b0-b927-9b65940290fb","order_by":20,"name":"Estela Pineda","email":"","orcid":"","institution":"IDIBAPS, Hospital Clinic","correspondingAuthor":false,"prefix":"","firstName":"Estela","middleName":"","lastName":"Pineda","suffix":""},{"id":488820431,"identity":"371f88b8-5996-4ef5-8067-81f524ab4958","order_by":21,"name":"Rodrigo Dienstmann","email":"","orcid":"","institution":"Vall d'Hebron Institute of Oncology (VHIO)","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Dienstmann","suffix":""},{"id":488820434,"identity":"568c345f-faab-458d-b2c5-dc3d848e40f8","order_by":22,"name":"Juan Manuel Sepúlveda","email":"","orcid":"","institution":"Instituto de Investigación 12 de Octubre","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Manuel","lastName":"Sepúlveda","suffix":""},{"id":488820437,"identity":"7929ea08-4158-496b-9c8d-dcfcfc3137c1","order_by":23,"name":"Maria Vieito","email":"","orcid":"","institution":"Vall d'Hebron Hospital Universitari","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Vieito","suffix":""}],"badges":[],"createdAt":"2025-06-16 08:08:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6903120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6903120/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41698-025-01247-3","type":"published","date":"2026-01-06T15:58:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87574652,"identity":"3998a83a-f60a-4b85-aec8-c8e3f29da447","added_by":"auto","created_at":"2025-07-25 11:31:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41565,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/7e07c613e41c7746229d5d81.jpg"},{"id":87574651,"identity":"3a8c60cb-969d-4940-9854-70f95390f15d","added_by":"auto","created_at":"2025-07-25 11:31:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48594,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival Kaplan-Meier curves for gliomas subtypes (2A) and OS for GBM patients harboring ESCAT tier 1/2 and tier 3/4. *\u003cem\u003eLegend: *GBM: Glioblastoma; Non-GBM: Non-glioblastoma. Curve A corresponds to GBM, curve B to non-GBM.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/bd49f3675f68e141b293efd2.jpg"},{"id":87574653,"identity":"fe45df84-604e-483a-b944-465f1480f1a9","added_by":"auto","created_at":"2025-07-25 11:31:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123482,"visible":true,"origin":"","legend":"\u003cp\u003eObjective response rate and disease control rate of all targeted therapy administered per molecular alteration and tier.\u003cstrong\u003e *\u003c/strong\u003e\u003cem\u003eLegend: Targeted treatments are ordered per tier and number of patients treated. BRAF fusion is considered as tier 3 pending results of FIREFLY-2.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/64562fc7c92c44646b33944f.jpg"},{"id":87573778,"identity":"940bb1e4-7fe8-499e-9449-520aabeda787","added_by":"auto","created_at":"2025-07-25 11:23:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68391,"visible":true,"origin":"","legend":"\u003cp\u003eProgression-free survival curves according to molecular matched therapy (4A) and progression-free survival curves according to ESCAT (4B).\u003cstrong\u003e *\u003c/strong\u003e\u003cem\u003eLegend: In Figure 4A, the red line represents patients receiving molecularly matched therapy, while the green line represents those not receiving molecularly matched therapy. In Figure 4B, the blue line corresponds to patients with ESCAT tiers 1/2 molecular alterations, and the orange line corresponds to patients with tiers 3/4 molecular alterations.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/99805866f4e93bac1cea9053.jpg"},{"id":87574655,"identity":"5815a3da-d511-4984-ad5f-0b57d25a320f","added_by":"auto","created_at":"2025-07-25 11:31:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83903,"visible":true,"origin":"","legend":"\u003cp\u003eProgression-free survival according to therapy administered and matched molecular alterations, its tier according to ESCAT, and its best response rate.\u003cstrong\u003e *\u003c/strong\u003e\u003cem\u003eLegend: Progression disease is labeled in red, stable disease in yellow, partial response in green, and complete response in blue. Black triangle stands for ongoing treatment, and black square stands for halted treatment. Blue circle stands for tiers 1/2, and orange circle stands for tiers 3/4 molecular alterations. Dot circled stand for non-GBM, and empty circle stands for GBM.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/f240e94e0f086aeda6d8d088.jpg"},{"id":100069767,"identity":"4cee6cb1-14ef-4074-a753-83aa3736fea4","added_by":"auto","created_at":"2026-01-12 16:15:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1410145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/d1aeca87-f5c1-4bf7-943b-56e8eb627907.pdf"},{"id":87573772,"identity":"a538a6fa-24ef-4cb3-a626-2325af3710bf","added_by":"auto","created_at":"2025-07-25 11:23:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26630,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/ddd6fd3e1b468863d7f8f964.docx"},{"id":87573779,"identity":"e6c1f3f6-b90c-48b7-8a7a-6cc65c1c3527","added_by":"auto","created_at":"2025-07-25 11:23:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":380776,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6903120/v1/b4cb43bc28fb9332ed707153.docx"}],"financialInterests":"Competing interest reported. OM reports writing aid from Merck and Roche, and travel aid from Almirall, Kyowa Kirin, and Recordati. DG reports honoraria for lectures from LEO Pharma. TG reports disclose lectures/educational activities GSK, and travel expenses Pfizer, Reddy’s, BMS, MSD. MAVS reports grants from Pfizer, honoraria for lectures from Novocure and Servier, and advisory board from Servier. MCH reports consulting fees from Genomic Health, payments for expert testimony from Novartis, Daichii-Sankyo, Pfizer, Gilead, Astra-Zeneca, travel expenses from Lilly, fizer, Daichii-Sankyo, Novartis, Astra-Zeneca, Roche. MG reports writing aid Merck Sharp \u0026 Dohme, Bayer, Pfizer, Ipsen pharma and travel aid from Roche, Sanofi Aventis, Astellas, Pfizer, Janssen, Merck Sharp \u0026 Dohme, Bayer, and Lilly. MMG reports consulting fees from Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly, travel aid from Pfizer, Roche, Gilead, Astra Zeneca-Daiichi Sankyo, and participation on Advisory Board of Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly. MD reports grants from Roche, honoraria for lectures from BMS, and travel aid from Takeda and Lilly. JC reports consulting fees from Astellas Pharma; AstraZeneca; Bayer; Bristol-Myers Squibb; Exelixis; Ipsen; Johnson \u0026 Johnson; MSD Oncology; Novartis (AAA); Pfizer; Sanofi, payment or honoraria for lectures from Astellas Pharma; Bayer; Johnson \u0026 Johnson; Sanofi, and support for attending meetings from BMS, Ipsen, Roche, AstraZéneca, Bayer. RD reports grants from Merck, Novartis, Daiichi-Sankyo, GlaxoSmithKline, and AstraZeneca; consulting fees from Roche, Foundation Medicine, and AstraZeneca; payment or honoraria for lectures from Roche, Ipsen, Amgen, Servier, Sanofi, Libbs, Merck Sharp \u0026 Dohme, Lilly, AstraZeneca, Janssen, Takeda, Bristol-Myers Squibb, GlaxoSmithKline, and Gilead; and holds stocks in Trialing Health. JM reports grants from Cantex and IDP Pharma, consulting fees from Cantex, GSK, Servier, and Novocure, payment or honoraria for lectures from GSK and Novocure, travel aid from Pfizer, and participation on Advisory Board of Cantex and GSK. MV reports. CB reports support for attending meeting from Servier and payment or honoraria as for DSMB member by Laminar Pharmaceuticals. The rest of the authors declare no conflicts of interest.","formattedTitle":"Clinical actionability in gliomas revealed by real-world next-generation sequencing: a multicentric study","fulltext":[{"header":"Background","content":"\u003cp\u003eCentral nervous system (CNS) tumors comprise a diverse group of over 40 entities. Even if considered as a single entity, they would meet the criteria for rare tumors, with an estimated incidence of 308,102 new cases and 251,329 deaths worldwide in 2020 \u003csup\u003e1\u003c/sup\u003e. The World Health Organization (WHO) classification of malignant CNS tumors used to rely on histological findings, such as morphology and immunohistochemical features. However, new molecular biomarkers have improved tumor classification, leading to more precise diagnoses \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The WHO 2021 classification integrated molecular data as key elements to guide diagnosis and treatment.\u003c/p\u003e\u003cp\u003eGlioblastoma, the most common malignant tumor in adults, accounts for 49% of malignant brain tumors. Treatment remains challenging and varies by tumor type and location. Surgery is the first-line treatment, with complete resection as the primary goal \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Adjuvant treatment with radiation and temozolomide-based chemotherapy remains the standard, based on the phase III trial by Stupp \u003cem\u003eet al\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Targeted therapies have emerged for specific brain tumors, often developed in tumor-agnostic clinical trials. For example, the tissue-agnostic approval of dabrafenib and trametinib for BRAFV600E-mutated gliomas followed the positive results of the ROAR basket trial \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. More recently, the INDIGO phase III clinical trial with vorasidenib demonstrated efficacy in \u003cem\u003eIDH1/2-\u003c/em\u003emutant type 2 gliomas surgery \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe ESMO Scale of Clinical Actionability for molecular Targets (ESCAT)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e prioritizes genomic alterations for targeted therapies. The ESMO Magnitude of Clinical Benefit Scale (MCBS) assess the clinical benefit of treatments based on progression-free survival (PFS) and overall response rate (ORR) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. An initial ESCAT proposal for gliomas was presented in ESMO 2022 \u003csup\u003e13\u003c/sup\u003e, followed by the European Association of Neuro-Oncology (EANO) guidelines on rational molecular testing for gliomas. Notably, these guidelines do not recommend NGS due to its limited clinical actionability \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This initial assessment will evolve as more targeted treatments receive regulatory approval.\u003c/p\u003e\u003cp\u003eThe Cancer Genome Atlas Research Network (TCGA) established the genomic and transcriptomic landscape of glioblastoma \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In 2010, Verhaak \u003cem\u003eet al.\u003c/em\u003e identified four distinct molecular subtypes (proneural, neural, classical, and mesenchymal) with different clinical outcomes and treatment sensitivities \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Subsequent work by Brennan \u003cem\u003eet al.\u003c/em\u003e expanded this classification using DNA methylation, miRNA expression, and protein analysis, identifying key pathways like \u003cem\u003eEGFR\u003c/em\u003e and \u003cem\u003ePDGFRA\u003c/em\u003e. Despite advances in glioma molecular profiling, real-world implementation remains limited due to challenges in standardizing multiparametric testing, though key mutations aid clinical distinction \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile NGS is not standard-of-care for glioma diagnosis, the WHO 2021 classification requires pathologists to conduct a series of tests, which are costly and require substantial tissue. In contrast, NGS can detect multiple alterations in a single test, offering new opportunities for molecularly matched therapies. Early-phase trials with BRAF, NTRK and IDH inhibitors have shown safety and efficacy in glioma patients \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Despite this, the number of glioma patients included in trials remains low, highlighting the need for increased molecular testing and real-world evidence supporting targeted therapies.\u003c/p\u003e\u003cp\u003eTo address this gap, we analyzed a large CNS tumor cohort undergoing routine NGS to assess ESCAT's role in therapy prioritization and the clinical impact of molecular matched treatments.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient population\u003c/h2\u003e\u003cp\u003eFrom January 2018 to May 2022, 580 primary brain tumors patients were included; 541 glioma patients met eligibility criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median number of treatment lines was 3 (range 1\u0026ndash;10). Median age was 51 years (range 3\u0026ndash;84), with glioblastoma patients being older than non-glioblastoma [55 vs 36 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No differences were found by sex, MGMT methylation, or ECOG/Karnofsky performance status (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Over 80% of patients underwent surgery, with no significant in complete vs incomplete resection between glioblastoma vs non-glioblastoma (p\u0026thinsp;=\u0026thinsp;0.055). After surgery, 79% of glioblastoma patients received Stupp protocol chemoradiation vs 46% of non-glioblastoma patients (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eESCAT proposal for glioma\u003c/h3\u003e\n\u003cp\u003eThe ESCAT proposal for gliomas and MCBS is based on published efficacy data, supplemented by the real-world distribution of mutations in our cohort (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among 409 glioblastoma cases with NGS, 83% had actionable alterations: 9% (n\u0026thinsp;=\u0026thinsp;36) were tier 1/2 (IDH1/2, BRAF V600E, FGFR1-3 mutations/rearrangements, NTRK1-3 fusions), and 74% (n\u0026thinsp;=\u0026thinsp;303) were tier 3/4 alterations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Matched therapy was given to 42 glioblastoma (10.2%) and 5 non-glioblastoma (3.8%) patients. These results show that integrating NGS increases eligibility for targeted therapies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eESCAT proposal for glioma tumors according to the most recent evidence.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESCAT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlteration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMatched Therapy and ESMO MCBS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePatients with alteration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePatients tested\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAltered patients by ESCAT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal by group\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eTier 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBRAF - V600E mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDabra-Trame: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIDH1 mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVorasidenib: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23,0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31,1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIDH2 mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVorasidenib: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNTRK (1\u0026ndash;3) fusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLarotrecitnib/ Entrectinib: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eTier 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFGFR (1\u0026ndash;3) pathogenic mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eErdafitinib: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFGFR rearrangement (FGFR-TACC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFutibatinib/ Pemigatinib: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eTier 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAKT1 - E17K mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBRAF fusions**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelumitinib: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e56,0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFGFR (1\u0026ndash;3) amplification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eErda/Futi/Pemi: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH3K27M mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMET mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVebreltinib/ Capmatinib: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePI3K mutations (PIK3CA, PTEN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCapmatinib: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30,3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePTEN loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eTier 4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATM mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATRX mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATR inhibitor: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17,4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDKN2A loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28,5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDKN2B loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27,5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEGFR amplification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDepatuxizumab mafodotin/Dacomitinib: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26,6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEGFR mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13,9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEGFR vIII rearrangement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEGFRvIII-TCBA/Rindopepimut: ND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14,6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epTERT mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46,9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWild type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7,4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncomplete results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e541\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Dabra-Trame: Dabrafenib-Trametinib; ND: No data.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e**Pending results of FIREFLY-2.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eUncovering molecular alterations and co-mutations in real-world data\u003c/h3\u003e\n\u003cp\u003eThe most common \u003cem\u003eTERT\u003c/em\u003e co-mutations in glioblastoma patients were \u003cem\u003eCDKN2A\u003c/em\u003e loss (20%), EGFR amplification (20%), \u003cem\u003ePIK3CA\u003c/em\u003e mutation (19%), and CDKN2B loss (19%) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). Most patients with \u003cem\u003eCDKN2A\u003c/em\u003e loss also had CDKN2B loss [101 patients (25%)]. In patients with non-glioblastoma tumors, the most common co-mutation for \u003cem\u003eIDH1\u003c/em\u003e was \u003cem\u003eATRX\u003c/em\u003e loss (48%), followed by \u003cem\u003eTERT\u003c/em\u003e mutation (19%) and \u003cem\u003ePIK3CA\u003c/em\u003e mutation (12%) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eFor the entire cohort, the most common mutations were found in \u003cem\u003eTERT\u003c/em\u003e (47%), \u003cem\u003eTP53\u003c/em\u003e (33%), \u003cem\u003ePI3KCA\u003c/em\u003e (30%), and \u003cem\u003eCDKN2A\u003c/em\u003e/\u003cem\u003eB\u003c/em\u003e (28%). In the GBM cohort exclusively, the most common mutations were found in \u003cem\u003eTERT\u003c/em\u003e (54%), \u003cem\u003eCDKN2A\u003c/em\u003e (37%), \u003cem\u003ePI3KCA\u003c/em\u003e (36%), \u003cem\u003eCDKN2B\u003c/em\u003e and \u003cem\u003eEGFR\u003c/em\u003e amplified (35% each, \u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). The non-GBM cohort's most common mutations were \u003cem\u003eIDH1\u003c/em\u003e (93%), \u003cem\u003eTP53\u003c/em\u003e (61%), \u003cem\u003eATRX\u003c/em\u003e (58%), and \u003cem\u003eTERT\u003c/em\u003e (26%, \u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eOS in our real-world data population\u003c/h3\u003e\n\u003cp\u003eSurvival was significantly better in non-glioblastoma than in glioblastoma patients [126.7 months (95% CI 106.8\u0026ndash;146) versus 22.9 months (95% CI 20.2\u0026ndash;25.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the glioblastoma cohort, regardless of treatment, OS was superior in ESCAT tier 1/2 (median 43.5 months, 95% CI 21.3 \u0026ndash; NR) than in tier 3/4 (22.2 months, 95% CI 19.5\u0026ndash;25.3; p\u0026thinsp;=\u0026thinsp;0.01) [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eClinical actionability of targeted therapy in glioma patients\u003c/h3\u003e\n\u003cp\u003eOverall, the ORR for targeted therapies was 11%. The ORR in ESCAT tier 1/2 patients was 26% (DCR 80%), compared to 8% in tier 3/4 (DCR 34%) population, which exhibited a DCR of 34%. \u003cem\u003eFGFR 1\u0026ndash;3\u003c/em\u003e fusions/rearrangements treated with covalent FGFR inhibitor (futibatinib, pemigatinib, erdafitinib) achieved 60% ORR and 80% DCR, whereas \u003cem\u003eFGFR 1\u0026ndash;4\u003c/em\u003e mutations had 20% ORR and 60% DCR. \u003cem\u003eBRAF V600E-\u003c/em\u003emutant patients receiving BRAF/MEK inhibitors had 20% ORR and 80% DCR, with one partial response with BRAF inhibitor alone. One BRAF fusion patient treated with selumetinib responded. IDH1 and NTRK achieved 100% and 50% DCR, respectively, but no PRs. Capmatinib failed to elicit responses in \u003cem\u003eMET\u003c/em\u003e fusions or \u003cem\u003ePIK3CA\u003c/em\u003e/\u003cem\u003ePTEN-\u003c/em\u003emutant cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePatients with ESCAT tier 1/2 alterations had significantly longer PFS (6.36 vs. 1.64 months, HR 2.69, 95% CI 1.39\u0026ndash;5.21, p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Treatment in early-phase trials did not negatively impact PFS (HR 1.15, 95% CI 0.75\u0026ndash;1.74, p\u0026thinsp;=\u0026thinsp;0.518) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The longest PFS was observed in patients with \u003cem\u003eFGFR 1\u0026ndash;4\u003c/em\u003e mutations, \u003cem\u003eBRAF V600E\u003c/em\u003e, \u003cem\u003eIDH1 R132H\u003c/em\u003e, \u003cem\u003eBRAF\u003c/em\u003e fusion, \u003cem\u003eEGFR\u003c/em\u003e amplification, with MAPK-targeted drugs showing efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eClassification of glioblastoma patients using real-world NGS data\u003c/h2\u003e\u003cp\u003eAs an exploratory analysis, we aimed to determine whether the 409 patients in our database classified as glioblastoma could be reassigned to an adapted molecular classification based solely on their genomic features identified by NGS, without the inclusion of RNA or methylation profiling (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u003c/b\u003e). Of 409 glioblastoma patients, 12% exhibited proneural, 20% mesenchymal, and 68% classical molecular hallmarks, enabling classification of 72% of cases (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A, Table\u0026nbsp;2\u003c/b\u003e). No differences in age, gender, MGMT methylation, ECOG PS, or OS were found among subtypes (\u003cb\u003eSupplementary Fig.\u0026nbsp;3B\u003c/b\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur multicenter study exploring the potential of real-world data for identifying clinically actionable targets underscores the clinical utility of NGS in glioma diagnosis and treatment decision-making. Per ESMO recommendations, NGS should be prioritized for tumors with a high likelihood of ESCAT tier 1 alterations and in hospitals with drug development programs for tiers 2\u0026ndash;4 \u003csup\u003e19\u003c/sup\u003e. NGS is essential not only for identifying targeted therapies in glioma but also for accurate diagnosis per updated guidelines. However, clinically actionable targets in CNS tumors remain limited. As recommended by EANO, we must carefully assess when and which molecular alterations to test in adult primary brain tumors \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePathological assessment should include actionable mutations such as \u003cem\u003eIDH1/2\u003c/em\u003e, \u003cem\u003eCDKN2A/B\u003c/em\u003e loss, \u003cem\u003eTERT\u003c/em\u003e mutations, \u003cem\u003eEGFR\u003c/em\u003e amplifications, and nuclear ATRX loss. IHC detects canonical \u003cem\u003eIDH\u003c/em\u003e mutations and \u003cem\u003eATRX\u003c/em\u003e loss, while ISH identifies \u003cem\u003eCDKN2A/B\u003c/em\u003e deletions and \u003cem\u003eEGFR\u003c/em\u003e amplifications. However, NGS enhances diagnosis by detecting rare \u003cem\u003eIDH\u003c/em\u003e mutations and identifying \u0026ldquo;molecular glioblastoma\u0026rdquo; in \u003cem\u003eIDH\u003c/em\u003e-wildtype gliomas without microvascular proliferation or necrosis.\u003c/p\u003e\u003cp\u003eMoreover, our dataset showed ESCAT tier 1 in 28% and tier 2 in 6.5%, exceeding prior glioma studies (3\u0026ndash;7%) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Notably, 10.2% of our glioblastoma patients received molecularly matched therapy through clinical trials or as compassionate use, surpassing the 4.1% reported in the NCI-match trial across tumor types \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Considering the limited enrollment of glioma patients in clinical trials and the scarcity of targeted therapies, there is potential for expanding precision medicine and improve glioma patients outcomes.\u003c/p\u003e\u003cp\u003eIn our real-world glioma cohort, we identified clinical and molecular factors associated with an increased likelihood of detecting relevant molecular events. Patients with ESCAT 1/2 alterations had superior PFS (6.4 vs 1.6 months) compared to those with tier 3/4 alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Notably, FGFR fusions/rearrangements showed the highest ORR with covalent FGFR inhibitors, consistent with NCI-MATCH trial findings \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These insights underscore the value of understanding glioma molecular alterations in improving treatment strategies. ESCAT profiling may better stratify patients for novel therapies, potentially leading to superior clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTCGA molecular subtypes require more than NGS alone \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, necessitating additional molecular layers. The proneural group, which includes glioblastomas with \u003cem\u003eIDH\u003c/em\u003e promoter mutations or epigenetic alterations, is now classified separately by the 2021 WHO guidelines. While our data align with Verhaak\u0026rsquo;s subtypes, they do not replicate the OS reported for each group. Our study has limitations, including selection bias, variable NGS timing, and platform heterogeneity, which may affect the generalizability of results. However, with over 500 glioma patients from seven hospitals across Spain, our cohort reflects real-life oncology. The inclusion of diverse treatment types strengthens the findings, and our study presents the largest clinical and genomic glioma dataset published to date. Despite NGS heterogeneity, molecular characterization allowed for accurate diagnoses and increased matched therapies, supporting its broader adoption.\u003c/p\u003e\u003cp\u003eOur findings suggest that NGS should be integrated into glioma management to enhance diagnostic precision and identify targeted therapy opportunities. ESCAT profiling aids in selecting molecularly matched therapies, advancing personalized treatment approaches.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePopulation and Study Design\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective analysis of clinical characteristics, NGS parameters, and matched therapies in a multicentric Spanish cohort of glioma patients treated from 2008 to 2023. Participating centers included Vall d'Hebron University Hospital, Catalan Institute of Oncology (ICO) Hospitalet, ICO Badalona, Hospital del Mar, Hospital Clinic de Barcelona, ​​Hospital de la Santa Creu i Sant Pau, Hospital 12 de Octubre, and Hospital Ramon y Cajal in Madrid. All glioma patients with NGS performed at these centers were included. Institutional review board (IRB) approval was obtained at each site. Living patients provided informed consent, while the IRB exempted deceased patients. The inclusion criteria were patients with glioma diagnosis, available NGS analysis and who underwent systemic treatment. The primary goal of NGS was to detect targetable mutations for clinical trial enrollment or molecularly matched targeted therapy. Data were collected via REDCap and included demographics, tumor type, surgery, systemic therapy, targeted therapy, clinical trial participation, and survival status as of May 2023. Patients were classified as glioblastoma or non-glioblastoma per WHO 2021 and further categorized using an adapted molecular classification (TCGA/Verhaak). Molecular alterations were classified by ESCAT and MCBS criteria.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSample collection and NGS genomic profiling.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMolecular profiling was performed on tumor tissue from biopsies or surgical samples. DNA was analyzed using in-house NGS panels (21\u0026ndash;61 genes for mutations, 26 genes for fusions using NanoString nCounter) or the FoundationOne CDx platform (324 genes, Foundation Medicine, Inc.). Additional data on mismatch repair deficiency (dMMR), microsatellite instability (MSI), and tumor mutational burden (TMB) were collected.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics assessed baseline characteristics of patients, comparing groups and testing patterns. Continuous variables were expressed as median (IQR), and categorical variables as absolute values and percentages.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProgression-Free survival (PFS)\u003c/h2\u003e\u003cp\u003ePFS, our main objective of the study, was defined as the time from the treatment initiation to disease progression or death from any cause. We compared PFS between patients receiving ESCAT tier 1/2 versus tier 3/4 therapies and between molecularly matched versus non-matched treatments. For patients who did not receive a molecularly matched therapy, treatment initiation was the first systemic therapy after completing or discontinuing the Stupp regimen, regardless of prior therapies. To control selection bias, we applied propensity score weighting generated through logistic regression. To control for selection bias, propensity score weighting through logistic regression was applied, including age, therapy line, tumor grade, and surgery outcome. Treatment effect was estimated using \"average treatment effect for the treated\".\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eOverall survival\u003c/h2\u003e\u003cp\u003eThe study's secondary objectives were to assess the overall survival (OS) of the glioblastoma and non-glioblastoma cohorts, compare OS between ESCAT tier 1/2 and tier 3/4 groups, and evaluate OS according to molecularly adapted classification. Time-to-event endpoints were estimated using Kaplan-Meier, with comparisons via log-rank test. To mitigate immortal bias arising from differences in patient entry times based on reported NGS test results, all estimates were adjusted using the risk set method, with the NGS test date serving as the index date. Univariate Cox proportional hazards models were applied to calculate hazard ratios (HRs) with 95% confidence intervals (CIs).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eObjective response rate and Disease Control Rate:\u003c/h2\u003e\u003cp\u003eORR was defined as the proportion of patients achieving a complete response or partial response, disease control rate (DCR) included patients with stable disease (SD). P-values were two-sided and adjusted by the Benjamini and Hochberg (BH) method to account for multiple comparisons. Statistical analyses were conducted in R (v4.3.0).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe development\u0026nbsp;of this publication was supported by\u0026nbsp;Hoffman-La Roche\u0026amp;Co, Spain, through a grant for the publishing fees. The views and opinions contained in this publication do not necessarily reflect the ideas of the awarding entity of the scholarship. The founder of the study had no role in the study design, data collection, analysis, interpretation of the data, and writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the patients and families who agreed to participate in this study; progress in oncology care would be impossible without them. The first author would like to thank all the health staff at the different centers for working together to make this study possible. The first author wishes to express his sincere gratitude to Javier Carmona Cort\u0026eacute;s, PhD, for his input to this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOM reports writing aid from Merck and Roche, and travel aid from Almirall, Kyowa Kirin, and Recordati. DG reports honoraria for lectures from LEO Pharma. TG reports disclose lectures/educational activities GSK, and travel expenses Pfizer, Reddy\u0026rsquo;s, BMS, MSD. MAVS reports grants from Pfizer, honoraria for lectures from Novocure and Servier, and advisory board from Servier. MCH reports consulting fees from Genomic Health, payments for expert testimony from Novartis, Daichii-Sankyo, Pfizer, Gilead, Astra-Zeneca, travel expenses from Lilly, fizer, Daichii-Sankyo, Novartis, Astra-Zeneca, Roche. MG reports writing aid Merck Sharp \u0026amp; Dohme, Bayer, Pfizer, Ipsen pharma and travel aid from Roche, Sanofi Aventis, Astellas, Pfizer, Janssen, Merck Sharp \u0026amp; Dohme, Bayer, and Lilly. MMG reports consulting fees from Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly, travel aid from Pfizer, Roche, Gilead, Astra Zeneca-Daiichi Sankyo, and participation on Advisory Board of Gilead, Novocure, Seagen, Boehringer Ingelheim, Roche, Celgene, and Lilly. MD reports grants from Roche, honoraria for lectures from BMS, and travel aid from Takeda and Lilly. JC reports consulting fees from Astellas Pharma; AstraZeneca; Bayer; Bristol-Myers Squibb; Exelixis; Ipsen; Johnson \u0026amp; Johnson; MSD Oncology; Novartis (AAA); Pfizer; Sanofi, payment or honoraria for lectures from Astellas Pharma; Bayer; Johnson \u0026amp; Johnson; Sanofi, and support for attending meetings from BMS, Ipsen, Roche, AstraZ\u0026eacute;neca, Bayer. RD reports grants from Merck, Novartis, Daiichi-Sankyo, GlaxoSmithKline, and AstraZeneca; consulting fees from Roche, Foundation Medicine, and AstraZeneca; payment or honoraria for lectures from Roche, Ipsen, Amgen, Servier, Sanofi, Libbs, Merck Sharp \u0026amp; Dohme, Lilly, AstraZeneca, Janssen, Takeda, Bristol-Myers Squibb, GlaxoSmithKline, and Gilead; and holds stocks in Trialing Health. JM reports grants from Cantex and IDP Pharma, consulting fees from Cantex, GSK, Servier, and Novocure, payment or honoraria for lectures from GSK and Novocure, travel aid from Pfizer, and participation on Advisory Board of Cantex and GSK. MV reports. CB reports support for attending meeting from Servier and payment or honoraria as for DSMB member by\u0026nbsp;Laminar Pharmaceuticals.\u0026nbsp; The rest of the authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Oriol Mirallas, Rodrigo Dienstmann, Maria Vieito.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProvision of study materials or patients: Gabriel Velilla, Diego Gomez-Puerto, Teresa Gorria, Jesus Yaringa\u0026ntilde;o, Alvaro Martinez-Monino, Antonio Di Muzio, Daniel L\u0026oacute;pez-Valbuena, Maria Angeles Vaz, Ainhoa Hernandez, Elena Mart\u0026iacute;nez-Saez, Maria Aguado Sorolla, Maria Castro Henriques, Mar\u0026iacute;a Mart\u0026iacute;nez-Garc\u0026iacute;a, Marta Domenech, Carmen Bala\u0026ntilde;a, Estela Pineda, Juan Manuel Sep\u0026uacute;lveda.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollection and assembly of data: Fiorella Ruiz, Oriol Mirallas, Gabriel Velilla, Diego Gomez-Puerto, Teresa Gorria, Jesus Yaringa\u0026ntilde;o, Antonio Di Muzio, Alvaro Martinez-Monino, Daniel L\u0026oacute;pez-Valbuena, Maria Angeles Vaz, Ainhoa Hernandez, Elena Mart\u0026iacute;nez-Saez, Maria Aguado Sorolla, Maria Castro Henriques, Mar\u0026iacute;a Mart\u0026iacute;nez-Garc\u0026iacute;a, Marta Domenech, Estela Pineda.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation: Fiorella Ruiz, Oriol Mirallas, Rodrigo Dientsmann, Maria Vieito.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eManuscript writing: All authors.\u003c/p\u003e\n\u003cp\u003eFinal approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003eAccountable for all aspects of the work: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeidentified patient data from this study can be made available to qualified investigators who provide a methodologically sound research proposal and sign a data access agreement. Please email [email protected] for information. The study protocol, statistical analysis plan, and informed consent form will also be made available upon request. Data will be shared via a secure online platform; REDcap and public repository on the VHIO website.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA A Cancer J Clinicians\u003c/em\u003e. 2021;71(3):209-249. doi:10.3322/caac.21660\u003c/li\u003e\n\u003cli\u003eLouis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. \u003cem\u003eNeuro-Oncology\u003c/em\u003e. 2021;23(8):1231-1251. doi:10.1093/neuonc/noab106\u003c/li\u003e\n\u003cli\u003eWeller M, Van Den Bent M, Preusser M, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. \u003cem\u003eNat Rev Clin Oncol\u003c/em\u003e. 2021;18(3):170-186. doi:10.1038/s41571-020-00447-z\u003c/li\u003e\n\u003cli\u003eLouis DN, Ohgaki H, Wiestler OD, Cavenee WK. WHO Classification of Tumours of the Central Nervous System. 5th ed. Lyon, France: International Agency for Research on Cancer; 2021.\u003c/li\u003e\n\u003cli\u003eIus T, Sabatino G, Panciani PP, et al. Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch\u0026reg;): a systematic review. \u003cem\u003eJ Neurooncol\u003c/em\u003e. 2023;162(2):267-293. doi:10.1007/s11060-023-04274-x\u003c/li\u003e\n\u003cli\u003eStupp R, Weller M, Belanger K, et al. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma. \u003cem\u003eThe New England Journal of Medicine\u003c/em\u003e. Published online 2005.\u003c/li\u003e\n\u003cli\u003eWen PY, Stein A, Van Den Bent M, et al. Dabrafenib plus trametinib in patients with BRAFV600E-mutant low-grade and high-grade glioma (ROAR): a multicentre, open-label, single-arm, phase 2, basket trial. \u003cem\u003eThe Lancet Oncology\u003c/em\u003e. 2022;23(1):53-64. doi:10.1016/S1470-2045(21)00578-7\u003c/li\u003e\n\u003cli\u003eU.S. Food and Drug Administration. FDA grants accelerated approval to dabrafenib in combination with trametinib for unresectable or metastatic solid tumors with BRAF V600E mutation [Internet]. 2022. Available from: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-grants-accelerated-approval-dabrafenib-combination-trametinib-unresectable-or-metastatic-solid\u003c/li\u003e\n\u003cli\u003eMellinghoff IK, Van Den Bent MJ, Blumenthal DT, et al. Vorasidenib in IDH1- or IDH2-Mutant Low-Grade Glioma. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2023;389(7):589-601. doi:10.1056/NEJMoa2304194\u003c/li\u003e\n\u003cli\u003eMellinghoff IK, Penas-Prado M, Peters KB, et al. Vorasidenib, a Dual Inhibitor of Mutant IDH1/2, in Recurrent or Progressive Glioma; Results of a First-in-Human Phase I Trial. \u003cem\u003eClinical Cancer Research\u003c/em\u003e. 2021;27(16):4491-4499. doi:10.1158/1078-0432.CCR-21-0611\u003c/li\u003e\n\u003cli\u003eMateo J, Chakravarty D, Dienstmann R, et al. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). \u003cem\u003eAnnals of Oncology\u003c/em\u003e. 2018;29(9):1895-1902. doi:10.1093/annonc/mdy263\u003c/li\u003e\n\u003cli\u003eCherny NI, Dafni U, Bogaerts J, et al. ESMO-Magnitude of Clinical Benefit Scale version 1.1. \u003cem\u003eAnn Oncol\u003c/em\u003e. 2017;28(10):2340-2366. doi:10.1093/annonc/mdx310\u003c/li\u003e\n\u003cli\u003eMirallas O. Potential enrichment strategies for next-generation sequencing (NGS) in primary brain cancers (pBCs) in a clinical series according to ESMO scale for clinical actionability of molecular targets (ESCAT). Presented at: ESMO Congress 2022. https://www.annalsofoncology.org/article/S0923-7534(22)02275-X/pdf\u003c/li\u003e\n\u003cli\u003eCapper D, Reifenberger G, French PJ, et al. EANO guideline on rational molecular testing of gliomas, glioneuronal, and neuronal tumors in adults for targeted therapy selection. \u003cem\u003eNeuro-Oncology\u003c/em\u003e. 2023;25(5):813-826. doi:10.1093/neuonc/noad008\u003c/li\u003e\n\u003cli\u003eThe Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. \u003cem\u003eNature\u003c/em\u003e. 2008;455(7216):1061-1068. doi:10.1038/nature07385\u003c/li\u003e\n\u003cli\u003eVerhaak RGW, Hoadley KA, Purdom E, et al. Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1. \u003cem\u003eCancer Cell\u003c/em\u003e. 2010;17(1):98-110. doi:10.1016/j.ccr.2009.12.020\u003c/li\u003e\n\u003cli\u003eScheer M, Leisz S, Sorge E, et al. Neurofibromatosis Type 1 Gene Alterations Define Specific Features of a Subset of Glioblastomas. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2021;23(1):352. doi:10.3390/ijms23010352\u003c/li\u003e\n\u003cli\u003eDesai AV, Robinson GW, Gauvain K, et al. Entrectinib in children and young adults with solid or primary CNS tumors harboring \u003cem\u003eNTRK\u003c/em\u003e , \u003cem\u003eROS1\u003c/em\u003e , or \u003cem\u003eALK\u003c/em\u003e aberrations (STARTRK-NG). \u003cem\u003eNeuro-Oncology\u003c/em\u003e. 2022;24(10):1776-1789. doi:10.1093/neuonc/noac087\u003c/li\u003e\n\u003cli\u003eMosele F, Remon J, Mateo J, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. \u003cem\u003eAnnals of Oncology\u003c/em\u003e. 2020;31(11):1491-1505. doi:10.1016/j.annonc.2020.07.014\u003c/li\u003e\n\u003cli\u003ePadovan M, Maccari M, Bosio A, et al. Next-generation sequencing (NGS) for identifying actionable molecular alterations in newly diagnosed and recurrent IDH wild-type glioblastoma patients. \u003cem\u003eNeuro Oncol.\u003c/em\u003e 2022;24(Suppl 7). doi:10.1093/neuonc/noac200.014.\u003c/li\u003e\n\u003cli\u003eFlaherty KT, Gray R, Chen A, et al. The Molecular Analysis for Therapy Choice (NCI-MATCH) Trial: Lessons for Genomic Trial Design. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 2020;112(10):1021-1029. doi:10.1093/jnci/djz245\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Next-Generation Sequencing, glioma, molecular targeted therapy, molecular diagnostics","lastPublishedDoi":"10.21203/rs.3.rs-6903120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6903120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTreatment options for patients with gliomas remain limited, and prognosis is generally poor. While next-generation sequencing (NGS) is increasingly used to stratify glioma patients and guide therapy, its implementation in routine clinical practice remains variable. We conducted a multicenter retrospective study across seven Spanish hospitals to evaluate the clinical utility of NGS in glioma management, focusing on its impact on diagnosis and treatment selection based on the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). A total of 541 glioma patients diagnosed between 2018 and 2022 were included; 76% had glioblastomas and 24% other glioma subtypes. Among glioblastoma patients, 9% harbored ESCAT tier 1/2 alterations and 74% tier 3/4. Molecularly matched therapy was administered in 10.2% of glioblastoma cases. Objective responses were observed in 17.6% of glioblastoma and 33% of non-glioblastoma patients with ESCAT tier 1/2 alterations. Patients with tier 1/2 alterations experienced significantly longer progression-free survival compared to those with tier 3/4. These findings support the integration of NGS into standard care for glioma, facilitating precise molecular classification, expanding therapeutic options, and improving access to matched clinical trials.\u003c/p\u003e","manuscriptTitle":"Clinical actionability in gliomas revealed by real-world next-generation sequencing: a multicentric study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 11:22:55","doi":"10.21203/rs.3.rs-6903120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-11T17:39:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T15:17:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56881197847031293490045809890317345794","date":"2025-08-19T17:02:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T21:54:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133090023381073180344985750934121135777","date":"2025-07-30T13:15:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42697304090791829094936405694617260592","date":"2025-07-25T09:49:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T19:44:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-06T12:01:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T17:14:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Precision Oncology","date":"2025-06-16T08:06:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"769cc432-06de-4891-8b5e-ceb38d694258","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51890484,"name":"Health sciences/Oncology/Cancer/Cns cancer"},{"id":51890485,"name":"Health sciences/Oncology/Cancer/Tumour biomarkers"},{"id":51890486,"name":"Health sciences/Oncology/Cancer/Tumour heterogeneity"},{"id":51890487,"name":"Health sciences/Molecular medicine"},{"id":51890488,"name":"Health sciences/Medical research/Drug development"}],"tags":[],"updatedAt":"2026-01-12T16:08:10+00:00","versionOfRecord":{"articleIdentity":"rs-6903120","link":"https://doi.org/10.1038/s41698-025-01247-3","journal":{"identity":"npj-precision-oncology","isVorOnly":false,"title":"npj Precision Oncology"},"publishedOn":"2026-01-06 15:58:13","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-07-25 11:22:55","video":"","vorDoi":"10.1038/s41698-025-01247-3","vorDoiUrl":"https://doi.org/10.1038/s41698-025-01247-3","workflowStages":[]},"version":"v1","identity":"rs-6903120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6903120","identity":"rs-6903120","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}