Precision Prognostication in Adult-Type Diffuse Gliomas through Multi-Cohort Validation of Key Genomic Alterations

Precision Prognostication in Adult-Type Diffuse Gliomas through Multi-Cohort Validation of Key Genomic Alterations | 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 Research Article Precision Prognostication in Adult-Type Diffuse Gliomas through Multi-Cohort Validation of Key Genomic Alterations Yongjae Kim, Seong-Eun Kim, Ji Eun Park, Chang Ohk Sung, Do Kyung Yoon, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8790042/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Despite advances in molecular classification of adult-type diffuse gliomas, limited data exist on the prognostic implications of specific genetic alterations. We integrated molecular profiles of adult-type diffuse glioma subtypes in a multicenter cohort and examined the prognostic significance, especially in glioblastoma, IDH -wildtype. Methods After analyzing 470 primary adult-type diffuse gliomas using next-generation sequencing, multivariable Cox regression and Kaplan–Meier survival analyses were performed. Associations between molecular profiles and overall survival were independently validated using 840 cases of primary glioblastoma, IDH -wildtype, from four external cohorts: CPTAC, GLASS, MSKCC, and TCGA. Results Distinct genetic landscapes were identified in oligodendroglioma, IDH-mutant, 1p/19q co-deleted, astrocytoma, IDH -mutant, and glioblastoma, IDH -wildtype. In glioblastoma, PIK3R1 mutation (hazard ratio [HR] = 1.53, 95% confidence interval [CI] 1.06–2.20; P = 0.022), CDKN2A/B deletion (1.41 [1.09–1.83]; P = 0.009), and MET amplification (2.78 [1.40–5.51]; P = 0.003) were significant prognostic markers, along with negative O6-methylguanine-DNA methyltransferase promoter methylation status (1.51 [1.14–2.00]; P = 0.004). External validation confirmed the prognostic impact of PIK3R1 in CPTAC (HR [95% CI] = 3.72 [1.44–9.58]; P = 0.007) and MET amplification in MSKCC (2.74 [1.12–6.68]; P = 0.027); CDKN2A/B showed cohort-specific effects. Tumor mutational burden lacked prognostic significance across all cohorts. Although 45.4% of glioblastomas exhibited MTAP deletion, no significant association with overall survival was detected. Conclusions CDKN2A/B deletion, MET amplification, and PIK3R1 mutation were identified as independent markers of poor prognosis in glioblastoma, potentially enabling molecular-based risk stratification. Glioblastoma High-Throughput Nucleotide Sequencing Gene amplification Gene deletion Phosphatidylinositol 3-Kinases Figures Figure 1 Figure 2 Figure 3 Introduction Recent WHO classification incorporates molecular analysis and categorizes adult diffuse-type gliomas into three primary tumor groups: oligodendroglioma, IDH-mutant, and 1p/19q-co-deleted (oligodendroglioma); astrocytoma, IDH-mutant (astrocytoma); and glioblastoma, IDH-wildtype (glioblastoma) [ 1 ]. The identification of these markers often requires the application of next-generation sequencing (NGS), enabling comprehensive genomic profiling, detecting single nucleotide variants, small insertions/deletions, copy number variations (CNVs), gene fusions, and intragenic deletions, as well as quantifying tumor mutational burden (TMB). As NGS has become an essential diagnostic tool, it plays a crucial role in discovering potential therapeutic targets, leading to the development of novel therapeutic agents, known as precision medicine. Accordingly, comprehensive NGS analyses of actionable genes with prognostic or therapeutic potential are crucial for advancing precision medicine in diffuse gliomas. Despite recent advancements in understanding the genetic landscape of glioblastoma [ 1 ], comprehensive studies exploring the molecular epidemiology of glioblastoma and its correlation with clinical outcomes remain limited. Current prognostic models predominantly rely on clinical parameters such as age and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, with minimal integration of molecular biomarkers. While individual alterations—such as CDKN2A/B homozygous deletion—have been reported to correlate with inferior overall and progression-free survival, these associations have not been consistently validated across larger datasets [ 2 – 4 ]. Likewise, although earlier studies found that MET amplification is a poor prognostic factor in glioblastoma, the data needs to be updated [ 5 – 7 ]. Other emerging alterations, including PIK3R1 mutations [ 8 ] and MTAP deletions [ 9 ], demonstrate promising preclinical relevance but lack robust prognostic implication and translational potential. Furthermore, while TMB serves as a useful biomarker in other solid tumors [ 10 ], its role in glioblastoma is not well-defined [ 11 ]. These gaps in molecular epidemiology have hindered the development of rational, biomarker-driven clinical trials in glioblastoma, largely owing to inadequate patient stratification and limited incorporation of molecular features and clinical outcomes. Considering the intrinsically poor prognosis of patients with glioblastoma and persistent challenges in developing effective therapies, a comprehensive molecular epidemiologic analysis integrating clinical outcome data is needed to advance precision medicine in glioblastoma. To address these critical gaps, we conducted a comprehensive molecular characterization of newly diagnosed adult-type diffuse gliomas. This study identified prognostic genetic alterations associated with survival and evaluated the translational potential of emerging molecular targets. Furthermore, we examined the relationship between TMB and clinical outcomes in patients with newly diagnosed glioblastoma. To ensure reproducibility and strengthen the clinical relevance of our findings, we also validated our results using an independent external cohort. Methods Study populations This retrospective study, conducted at the Asan Medical Center in Seoul, Korea, between May 2017 and November 2021, enrolled 470 patients with primary adult-type diffuse gliomas, including 68 with oligodendrogliomas, 63 with astrocytomas, and 339 with glioblastomas. The classification adhered to the 2021 WHO standards for central nervous system tumors, v5 criteria [ 1 ]. For glioblastoma, cases were identified as wildtype for IDH1/2 genes, with IDH mutations defined as any missense mutation altering codon 132 of IDH1 or codon 172 of IDH2 . Oligodendrogliomas were selected to detect IDH mutations and 1p/19q co-deletion, whereas astrocytomas were selected based on IDH mutations without 1p/19q co-deletion. Each 1p and 19q deletion occurred at the chromosome arm level. Deletions were detected using a targeted NGS panel, which did not cover the entire chromosome. Thus, chromosome 1p and 19q deletions were defined as complete segmental losses within specific ranges based on the hg19 human reference genome. Clinical data, including age at diagnosis, sex, tumor grade, MGMT status, KPS score, tumor location, laterality, eloquent area, extent of resection, and postoperative treatment, were collected from pathology reports, ensuring a comprehensive dataset for analysis. For validation, we utilized four independent glioblastoma cohorts obtained from the cBioPortal database [ 12 – 14 ]: CPTAC [ 15 ], GLASS [ 16 ], MSKCC [ 17 ], and TCGA [ 18 ]. Each cohort comprised clinical data, mutation data, and copy number alterations (CNA) data. We selected only patients with primary glioblastoma, resulting in final sample sizes of 90 (CPTAC), 106 (GLASS), 410 (MSKCC), and 234 (TCGA). Genomic analysis Targeted NGS was conducted using an in-house panel at Asan Medical Center, utilizing versions 3, 4, and 4.5 of the OncoPanel [ 19 , 20 ]. Version 3 covered 382 genes, including exons of 199 genes and 184 hotspots involved in rearrangements. Version 4 targeted 323 genes, encompassing exons of 225 genes and 99 hotspots. The specifics for version 4.5 included exons of 244 genes and 99 hotspots. Initially, tumor samples were meticulously selected during histopathologic review. Pathologists labeled tumor areas, estimating tumor purity, and performed manual dissection on five to ten whole sections of each formalin-fixed paraffin-embedded tissue (FFPE) tumor block. DNA extraction was conducted using either the Maxwell FFPE Plus DNA kit or NEXprep FFPE Tissue kit. The quality and quantity of extracted DNA were assessed using NanoDrop and Quant-iT dsDNA Assay kits. Of the 217 initially enrolled patients, samples from 43 were excluded owing to insufficient DNA quality, underscoring the rigorous quality control measures established. For sequencing and analysis, acceptable DNA samples were sheared and enriched using custom target enrichment probes, followed by sequencing using NextSeq 550Dx. Reads were aligned to the reference genome (GRCh37) with BWA, Picard, and GATK to generate BAM files. Nucleotide variations, CNA, and structural variations were identified using VarDict [ 21 ], CNVkit [ 22 ], and BreaKmer [ 23 ]. An experienced pathologist manually reviewed the variants using the IGV genomic viewer to ensure accuracy. Genomic metrics were calculated, including the mean copy numbers (CN) of genome segments using log2 ratios, and tumor purity was estimated. CNVs were defined based on estimated CN, and the altered fraction of the genome was calculated. Gene amplification was defined as alterations with CN > 10. TMB was calculated based on identified nucleotide variations, including only variants with a variant allele frequency of ≥ 0.05. The Panel of Normals approach was employed for variant calling in the somatic variant analysis. This comprehensive approach ensures reliable and robust findings, crucial for understanding the genetic landscape of the examined tumors. Defining hypermutation thresholds The cutoffs for hypermutation were determined by examining the TMB distribution in each cohort using segmented linear regression analysis [ 24 , 25 ]. The distribution of TMB values is presented in Supplementary Fig. 1 . Cutoff values for hypermutation were generally higher in panel sequencing datasets (ASAN and MSKCC) than in whole-genome or whole-exome sequencing datasets (TCGA, GLASS, and CPTAC; Supplementary Table 1 ). A summary of the hypermutation cutoff values for each cohort is provided in Supplementary Table 1 . Additionally, we calculated the I-index (InDel-to-total mutation ratio), an established method for detecting microsatellite instability in colorectal cancer [ 26 ]. Validation using independent glioblastoma cohorts From genomic data of each validation cohort, we extracted samples with mutations or CNAs for four target genes ( PIK3R1 , MET , CDK4 , and CDKN2A/B ). Samples were labeled as mutation/wildtype for PIK3R1 , amplification/wildtype for MET and CDK4 , and deletion/wildtype for CDKN2A/B . PIK3R1 classification was based on mutation data, whereas CNAs were defined using GISTIC 2.0 values (-2 to 2) or for GLASS, chromosomal coordinates and segment mean values (AMP: seg.mean ≥ 0.5; DEL: seg.mean ≤-0.5). Clinical variables The extent of resection was assessed by two neuroradiologists (J.E.P. and H.S.K., with 12 and 27 years of clinical experience in neuro-oncologic imaging, respectively) using T2-weighted FLAIR and CE-T1-weighted images based on remnant contrast-enhancing lesion on postoperative MRI acquired within 72 h (preferably 48 h) postoperatively. A measurable contrast-enhancing lesion was defined as a bidimensional contrast-enhancing lesion with clearly defined margins, with two perpendicular diameters of at least 10 mm, visible on two or more axial slices. A non-measurable contrast-enhancing lesion was defined as the absence of clearly defined margins, unidimensional measurable lesions, or a lesion with maximal perpendicular diameters < 10 mm [ 27 ]. Outcome definition All patients with glioblastoma received standard treatment, including surgery, concurrent chemoradiotherapy, and adjuvant temozolomide. Patients were assessed by performing MRI scans at regular 2–3-month intervals. Overall survival (OS) was calculated from the date of diagnosis until the date of death due to any cause. Progression-free survival (PFS) was defined as the time from diagnosis to radiological or clinical progression or death, whichever occurred first, as assessed by the attending physician. In the cBioPortal cohort, PFS data were obtained from the “progression-free survival” columns for MSKCC and TCGA cohorts, whereas for GLASS and CPTAC cohorts, the "Time to 2nd operation" column was used. As the DFCI cohort did not contain PFS data, it was excluded from this analysis. Death was confirmed via an institutional linkage to the national healthcare system. Patients were censored at the date of medical record abstraction or date of last imaging report, whichever occurred first. Statistical analysis To evaluate differences in continuous variables, the Mann–Whitney U test was employed for comparisons between two groups, whereas the Kruskal–Wallis test was used for analyzing differences among three or more groups. For categorical variables, differences in frequencies were assessed using the chi-square test, with Fisher’s exact test applied when expected frequencies were markedly small for the chi-square test. Survival analysis involved univariable analysis of factors associated with survival in the Kaplan–Meier method, with comparisons performed using the log-rank test. Hazard ratios (HRs) and corresponding P-values were estimated using the Cox proportional hazards regression model. The same methodology was applied to the external validation cohorts. Significant clinicogenomic factors identified in the univariable analysis were further examined in a multivariable survival analysis using a Cox proportional hazards regression model. HRs with 95% confidence intervals (CIs) were calculated for each variable in the model. All statistical evaluations were performed using R (v4.2.2, R Core Development Team, Vienna, Austria), with P-values < 0.05 considered statistically significant. Results Patient demographics and glioma classification The current study involves 470 patients with primary adult-type diffuse glioma ( Table 1 ), with a median patient age of 60 years (range 22‒88 years). The cohort comprised 268 males (57.0%) and 202 females (43.0%). Overall, 192 patients (40.9%) had MGMT methylation, and 268 patients (57.0%) received the Stupp protocol as their post-operation initial treatment. Patients were categorized into subtypes following the WHO classification: 68 had oligodendroglioma, 63 had astrocytoma, and 339 had glioblastoma (Fig. 1 ). Glioblastoma accounted for the largest proportion (72.1%) and was associated with the lowest OS, with a median OS of 15.7 months ( Supplementary Fig. 2 ). Genomic landscape of adult-type diffuse gliomas Each adult-type diffuse glioma subtype exhibited specific genetic alterations that aligned with established molecular profiles (Fig. 1 ). In oligodendrogliomas, the presence of IDH mutations combined with a 1p/19q co-deletion (100%) confirmed their canonical genetic signature, including mutations in NOTCH1 (27.9%), FUBP1 (20.6%), and ARID1A (19.1%). Astrocytomas displayed a distinct profile characterized by IDH mutations (100%), frequently accompanied by TP53 (92.1%) and ATRX (79.4%) mutations, differentiating them from oligodendrogliomas. Glioblastomas predominantly comprised IDH-wildtype tumors, with common genetic alterations such as CDKN2A/B homozygous deletion (52.5%), EGFR amplification (30.4%), and TP53 mutation (35.1%). MTAP deletion was identified in 45.4% of glioblastoma samples, as detected using OncoPancel v.4.0 and v.4.5. Fusion genes were also more prevalent in glioblastoma, with EGFR -intergenic fusion (5.3%) and FGFR3-TACC3 fusion (3.2%) being the most frequent. Hypermutation was identified in 7.3% of oligodendrogliomas, 23.8% of astrocytomas, and 10.9% of glioblastomas, as determined by segmented linear regression analysis. TMB and survival in glioblastoma Hypermutation was observed in approximately 10% of glioblastomas in this study, aligning with the consistent rates detected across four independent cohorts, ranging from 9.4% to 13.3% (Supplementary Table 1). Hypermutated samples were confirmed by a significantly higher mean insertion-deletion mutation rate (I-index) than that of non-hypermutated samples ( Supplementary Fig. 3A, 5 .61 vs 1.05 per Mb, P = 3.63 × 10 ⁻11 ). Among genes more frequently mutated in hypermutated samples, MSH2 (27.0% vs 2.3%, P fisher = 8.12 × 10 − 07 ), TP53 (70.3% vs 30.8%, P fisher = 5.07 × 10 − 06 ), and PTPN11 (24.3% vs 3.6%, P fisher = 5.91 × 10 − 05 ) showed the most notable differences ( Supplementary Fig. 3B). Despite these differences, no significant variations were observed in OS or PFS between hypermutated and non-hypermutated cases ( Supplementary Fig. 3C, D ). The median OS was similar between hypermutated cases and non-hypermutated cases (14.2 vs 15.9 months, respectively; P = 0.95; Supplementary Fig. 3C ). Consistent with these findings, analysis across four cohorts also revealed no significant differences in OS and PFS between hypermutated and non-hypermutated cases ( Supplementary Fig. 4 ). Genetic alterations and survival in glioblastoma To identify genetic alterations that impact the survival of patients with adult-type diffuse glioma, samples were stratified into two groups based on the top 50% survival rates for each subtype. Genetic alteration frequencies were compared between these groups using Fisher's exact test ( P < 0.05). Gene variants with a combined frequency of ≤ 10 in the two groups were excluded from further analysis owing to the limited statistical power associated with the small sample size for these variants. In glioblastoma, the frequencies of CDKN2A/B deletion ( P fisher = 0.002), PIK3R1 mutation ( P fisher = 0.005), CDK4 amplification (CN ≥ 10, P fisher = 0.031), TP53 mutation ( P fisher = 0.031), and MET amplification (CN ≥ 10, P fisher = 0.020) differed significantly (Supplementary Table 2) . Considering the frequencies of identified genetic alterations, no significant differences between the high- and low-survival cohorts in either oligodendroglioma or astrocytoma subtypes (Supplementary Tables 3 and 4) . Considering the notably worse survival rates associated with glioblastoma compared with that of other adult-type diffuse glioma subtypes, subsequent analyses focused on this subtype. Univariable analysis of glioblastoma revealed substantial associations between survival and several genetic alterations (Fig. 2 ). Subsequent multivariable analysis demonstrated that PIK3R1 mutation (HR = 1.53, 95% CI: 1.04–2.13, P = 0.022; median OS: 13.1 vs 16.1), CDKN2A/B deletion (HR = 1.41, 95% CI: 1.09–1.83, P = 0.009; median OS: 13.4 vs 19.9), and MET amplification with a CN of ≥ 10 (HR = 2.78, 95% CI: 1.40–5.51, P = 0.003; median OS: 9.6 vs 16.3) remained independent predictors of poor prognosis, along with clinical factors, such as MGMT methylation status, age, KPS < 80, and tumor bilaterality (Fig. 3 ) [ 28 ]. PIK3R1 mutations were detected in 12.7% of patients with glioblastoma, predominantly in the inter-SH2 domain, which binds p110α ( PIK3CA ) ( Supplementary Fig. 5) . CDKN2A/B deletion and MET amplification were detected in 52.5% and 3.5% of patients with glioblastoma, respectively. External validation of prognostic significance of genetic alterations To validate the impact of identified genetic alterations on survival outcomes, we performed survival analyses across four independent glioblastoma cohorts obtained from the cBioPortal database: CPTAC, GLASS, MSKCC, and TCGA (Fig. 2 and Supplementary Fig. 6 ). The CPTAC cohort revealed a significant association between PIK3R1 mutation and poor survival outcome (HR = 3.72, 95% CI: 1.44–9.58, P = 0.007; median OS: 7.82 vs 16.79). PIK3R1 mutations were detected in 5.56% of patients with glioblastoma in the CPTAC cohort. Likewise, the MSKCC cohort revealed a significant correlation between MET amplification and inferior survival (HR = 2.74, 95% CI: 1.12–6.68, P = 0.027, median OS: 10.5 vs 21.7). MET amplification was detected in 1.71% of patients with glioblastoma in the MSKCC cohort. Kaplan–Meier analyses in these cohorts confirmed lower survival probabilities in patients with these alterations. While other cohorts showed trends toward reduced survival for PIK3R1 mutation and MET amplification, these did not reach significance. CDKN2A/B deletion exhibited inconsistent results, with only a modest trend toward reduced survival across cohorts. Genomic alterations with therapeutic potential Next, we evaluated genomic alterations for clinical actionability using OncoKB Knowledge Base v4.20 ( Table 2 ) [ 29 ]. We identified 11 targetable alterations in glioblastoma, 8 in astrocytoma, and 5 in oligodendrogliomas (OncoKB Level 1–4). BRAF V600E and EGFR alterations were predominantly detected in patients with glioblastoma (3.2% and 25.1%, respectively). Notably, FGFR3 - TACC3 fusions were identified exclusively in patients with glioblastoma (3.2%), and MET amplification (CN ≥ 10) was found in a subset of glioblastoma (3.5%), indicating their potential as therapeutic targets in these aggressive gliomas. Consideirng that most mutations were classified as Levels 3 and 4, the identified genetic alterations may be linked to potential investigational therapies or have preclinical implications, and hence, may predict response to potential therapeutics. Discussion Our comprehensive molecular profiling of 470 adult-type diffuse gliomas provided insights into the molecular basis of prognosis and potential therapeutic strategies. In glioblastoma, PIK3R1 mutations, MET amplification, and CDKN2A/B deletion were independently associated with worse OS, a finding that was validated across multiple independent cohorts. Despite the growing interest in immunotherapy, we found that TMB was not associated with survival in newly diagnosed glioblastoma across multiple cohorts. Although 10% of glioblastomas exhibited a hypermutated phenotype, this subset did not demonstrate improved outcomes. While high TMB was associated with poorer survival but improved outcomes following immune checkpoint inhibitor (ICI) therapy in various solid cancers [ 10 , 30 ], its impact on glioblastoma remains elusive [ 31 , 32 ]. Previous studies have primarily focused on recurrent glioblastoma [ 24 , 31 ], where post-treatment effects, particularly those of alkylating agents such as temozolomide, contributes to hypermutation. Certain patients, such as those with de novo replication repair deficiency linked to constitutional defects in DNA polymerase and mismatch repair genes, reportedly benefit from ICI therapy [ 33 , 34 ]. However, data regarding newly diagnosed glioblastoma with high TMB remain limited. In our dataset (n = 339) and four additional cohorts totaling 570 patients with newly diagnosed glioblastoma, approximately 10% of patients in each cohort showed evidence of hypermutation. Despite this observation, glioblastomas are known for their lack of prominent T cell infiltration, which may limit their response to ICI therapy. A clinical trial on neoadjuvant combination ICI therapy in patients with newly diagnosed glioblastoma reported the occurrence of T cell activation and no recurrence after 17 months, albeit in patients with low TMB [ 11 ]. This contrast emphasizes the need for further investigations to determine the potential benefits of ICIs in patients with glioblastoma across different TMB profiles. Therefore, identifying targetable genomic alterations may offer a more tangible strategy for improving outcomes in glioblastoma. Beyond TMB, our genomic analysis revealed a broad spectrum of actionable alterations in GBM, as categorized by OncoKB. Several alterations were classified as Level 1, including EGFR amplification, BRAF V600E, and IDH mutations. Among Level 4 alterations, EGFR aberrations were the most common (25.1%), encompassing both hotspot mutations (e.g., A289V, R108K) and amplifications (24.5%). Other frequently altered genes included PTEN (46.0%), CDKN2A (54.0%), and NF1 (9.1%). Along with these alterations, we identified PIK3R1 mutation, MET amplification, and CDKN2A/B deletion as independent markers of poor prognosis in patients with glioblastoma. PIK3R1 mutations, primarily localized in the inter-SH2 domain, likely result in aberrant activation of the PI3K pathway, a mechanism known to enhance tumor growth and resistance to apoptosis. CDKN2A/B deletion, a hallmark of cell cycle dysregulation, is linked to aggressive tumor behavior and poor prognosis. In this cohort, CDKN2A/B deletion was associated with shorter survival (median OS: 13.4 vs 19.9 months; HR = 1.41). The prognostic significance of CDKN2A/B deletion was validated by both a recent meta-analysis (HR = 1.50) [ 3 ] and large-scale UK study (HR = 1.49) [ 4 ], despite inconsistent results in some validation cohorts. These findings, along with other recurrent alterations, highlight a subset of patients with glioblastoma harboring molecular vulnerabilities that may be amenable to targeted therapeutic strategies. A subset of patients with glioblastoma harbored molecular alterations with direct translational potential, including MTAP deletion (45.4%), MET amplification (3.5%), and FGFR3-TACC3 fusion (3.2%). These three alterations are of particular interest because of their mechanistic roles in tumorigenesis and the availability of matched targeted agents currently in clinical or preclinical development. Although several other alterations in glioblastoma are deemed biologically relevant, these three stand out as promising for biomarker-driven therapeutic approaches. Alterations in MTAP —a gene involved in the methionine salvage pathway—have recently emerged as potential biomarkers for targeted therapy, particularly in tumors with deletion of this gene. The outcomes of ongoing Phase I trials evaluating AMG 193 monotherapy and the AMG 193/MRTX1719 combination are anticipated to be of clinical interest [ 9 , 35 ]. AMG 193 is a selective PRMT5 inhibitor that exploits synthetic lethality in MTAP-deficient tumors. Meanwhile, MRTX1719 targets MTA-cooperative binding of PRMT5 via the substrate adaptor protein, further enhancing selectivity. If these and other early trials with MTAP-directed compounds demonstrate promising efficacy, MTAP deletion may emerge as a clinically actionable biomarker in glioblastoma. MET amplification, identified in 3.5% of glioblastoma cases in this study, is another alteration of interest. Although glioma-specific data remain limited, MET amplification has been validated as a therapeutic target in other solid tumors, particularly non-small cell lung cancer. In the GEOMETRY trial, treatment with the MET inhibitor capmatinib resulted in an objective response rate (ORR) of 29% in previously treated patients and an ORR of 40% in treatment-naïve patients with high MET copy number (≥ 10). Additionally, capmatinib demonstrated a 92% intracranial disease control rate in patients with lung cancer and brain metastases. These findings support further investigation of MET inhibitors in patients with glioblastoma harboring MET amplification. Finally, FGFR3-TACC3 fusion, detected in 3.2% of glioblastomas in our dataset, defines a mutually exclusive and potentially actionable molecular subtype. Although response rates to FGFR inhibitors have been modest—10% with erdafitinib in the RAGNAR trial and 3.8% with infigratinib—case reports have described durable benefit in select patients. Therefore, recent EANO guidelines recommend FGFR inhibitors as a treatment consideration for fusion-positive glioblastomas [ 36 ]. These findings emphasize the importance of comprehensive genomic profiling to identify patients who may benefit from targeted therapies directed against potentially actionable alterations and underscore the need for ongoing clinical trials to further define their therapeutic potential in this challenging disease. This study has several limitation. First, our use of targeted sequencing, similar to that performed by Foundation Medicine, restricted analysis to a defined set of cancer-associated genes, potentially excluding the discovery of novel drivers and limiting the detection of structural variations such as large genomic rearrangements and complex fusions. Second, the oligodendroglioma and astrocytoma analyses had poor statistical power, owing to their smaller sample sizes compared with that of glioblastoma. Third, the amplification cutoff for genes such as MET , although carefully selected and consistent across analyses, is inherently sensitive to variations in tumor purity, requiring careful data adjustment. Nevertheless, the prognostic relevance of the MET amplification and other key drivers was confirmed through external validation using independent glioblastoma cohorts. In conclusion, our study highlights the importance of molecular profiling in elucidating the biology of adult-type diffuse gliomas, particularly glioblastomas, for which prognosis remains dismal. Identifying actionable genetic alterations paves the way for future research on targeted therapies that may enhance survival outcomes. Continued exploration into the molecular mechanisms driving glioma progression, along with validation using larger and more diverse cohorts, is crucial to advancing precision medicine in the treatment of adult-type diffuse gliomas. Declarations Ethical statement This study was approved by the Institutional Review Board of Asan Medical Center (IRB no: 2020-1204). The need for informed consent was waived owing to the retrospective nature of this study. Funding This study was supported by a grant (2023IP0146) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea to S. Y. This study was supported by a grant from the National Research Foundation of Korea grant (NRF-RS-2023-00211481) to H.-S.L. funded by the Ministry of Science and ICT, the Republic of Korea. Conflict of interests The authors declare no conflicts of interest. Author contributions S.Y. and H.S.L. conceptualized the study and designed the methodology. Data curation was performed by S.Y. and H.S.L. Formal analysis and validation were conducted by Y.K., D.K.Y., S.Y., and H.S.L. Visualization of the results was carried out by Y.K. and D.K.Y. The original draft was written by Y.K., S.E.K., D.K.Y., S.Y., and H.S.L. All authors, including Y.K., S.E.K., J.E.P., C.O.S., D.K.Y., D.P., S.J.N., S.W.S., C.K.H., Y.H.K., S.P., D.K., K.S.P., H.C., J.H., S.W.K., J.H.K., H.S.K., S.Y., and H.S.L., contributed to the review and editing of the manuscript. Supervision was provided by S.Y. and H.S.L. Data Availability Clinical and genomic data supporting this study were collected during routine patient care and are subject to Korean privacy laws, which restrict public sharing of patient genomic information. These data cannot be publicly deposited. Data requests must be directed to the corresponding author and require approval of the Asan Medical Center IRB. References Wen PY, Packer RJ (2021) The 2021 WHO classification of tumors of the central nervous system: Clinical implications. 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JCO Precis Oncol 2017:PO. https://doi.org/10.1200/po.17.00011 . .17.00011 Valero C, Lee M, Hoen D et al (2021) The association between tumor mutational burden and prognosis is dependent on treatment context. Nat Genet 53:11–15. https://doi.org/10.1038/s41588-020-00752-4 Wang L, Ge J, Lan Y et al (2020) Tumor mutational burden is associated with poor outcomes in diffuse glioma. BMC Cancer 20:213. https://doi.org/10.1186/s12885-020-6658-1 Hadad S, Gupta R, Oberheim Bush NA et al (2023) De novo replication repair deficient glioblastoma, IDH-wildtype is a distinct glioblastoma subtype in adults that may benefit from immune checkpoint blockade. Acta Neuropathol 147:3. https://doi.org/10.1007/s00401-023-02654-1 Johanns TM, Miller CA, Dorward IG et al (2016) Immunogenomics of hypermutated glioblastoma: A patient with germline POLE deficiency treated with checkpoint blockade immunotherapy. Cancer Discov 6:1230–1236. https://doi.org/10.1158/2159-8290.Cd-16-0575 Bouffet E, Larouche V, Campbell BB et al (2016) Immune checkpoint inhibition for hypermutant glioblastoma multiforme resulting from germline biallelic mismatch repair deficiency. J Clin Oncol 34:2206–2211. https://doi.org/10.1200/jco.2016.66.6552 Engstrom LD, Aranda R, Waters L et al (2023) MRTX1719 Is an MTA-cooperative PRMT5 inhibitor that exhibits synthetic lethality in preclinical models and patients with MTAP-deleted cancer. Cancer Discov 13:2412–2431. https://doi.org/10.1158/2159-8290.Cd-23-0669 Weller M, van den Bent M, Preusser M et al (2021) EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol 18:170–186. https://doi.org/10.1038/s41571-020-00447-z Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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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-8790042","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587797577,"identity":"08ba984b-8244-4309-8bfa-744855fc421c","order_by":0,"name":"Yongjae Kim","email":"","orcid":"","institution":"Department of Biochemistry and Molecular Biology, Asan Medical Center, Brain Korea 21 Project, University of Ulsan College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongjae","middleName":"","lastName":"Kim","suffix":""},{"id":587797578,"identity":"50623bdb-639f-40bd-a77b-da869eb8e7f2","order_by":1,"name":"Seong-Eun 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2","display":"","copyAsset":false,"role":"figure","size":1259912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival of patients with glioblastoma harboring alterations in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePIK3R1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMET\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCDKN2A/B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e(a) Asan (b) CPTAC (c) GLASS (d) MSKCC (e) TCGA\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8790042/v1/3aa149116f2d07196c043bfe.png"},{"id":102379680,"identity":"bd10e2fb-8951-4a2f-8ad0-c038f639875f","added_by":"auto","created_at":"2026-02-11 06:26:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable survival analysis in glioblastoma: Forest plot of hazard ratios \u003c/strong\u003eAbbreviations: MGMT, O6-methylguanine-DNA methyltransferase; KPS, Karnofsky performance scale; GTR, Gross total resection; NTR, Near total resection; STR, subtotal resection; PR, partial resection\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8790042/v1/8d4a78d5ed6558f0b0226e02.png"},{"id":102964089,"identity":"25b0b84b-3394-4b3d-9213-8b1520bb3bd8","added_by":"auto","created_at":"2026-02-19 04:21:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3151881,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8790042/v1/c22b35f0-0baa-4e5c-b97a-577a2bcf1e6d.pdf"},{"id":102379661,"identity":"9aa6c765-a6a1-4875-b1f1-5fcb8978d360","added_by":"auto","created_at":"2026-02-11 06:25:56","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1706767,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementfile.zip","url":"https://assets-eu.researchsquare.com/files/rs-8790042/v1/1b35a05bc84fe9e12eebfcb5.zip"},{"id":102379696,"identity":"89e3cb8d-3900-4fc5-a34e-413aef9abf21","added_by":"auto","created_at":"2026-02-11 06:26:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":88361,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8790042/v1/3d293fe3a982df6cf0558592.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Precision Prognostication in Adult-Type Diffuse Gliomas through Multi-Cohort Validation of Key Genomic Alterations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent WHO classification incorporates molecular analysis and categorizes adult diffuse-type gliomas into three primary tumor groups: oligodendroglioma, IDH-mutant, and 1p/19q-co-deleted (oligodendroglioma); astrocytoma, IDH-mutant (astrocytoma); and glioblastoma, IDH-wildtype (glioblastoma) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The identification of these markers often requires the application of next-generation sequencing (NGS), enabling comprehensive genomic profiling, detecting single nucleotide variants, small insertions/deletions, copy number variations (CNVs), gene fusions, and intragenic deletions, as well as quantifying tumor mutational burden (TMB). As NGS has become an essential diagnostic tool, it plays a crucial role in discovering potential therapeutic targets, leading to the development of novel therapeutic agents, known as precision medicine. Accordingly, comprehensive NGS analyses of actionable genes with prognostic or therapeutic potential are crucial for advancing precision medicine in diffuse gliomas.\u003c/p\u003e \u003cp\u003eDespite recent advancements in understanding the genetic landscape of glioblastoma [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], comprehensive studies exploring the molecular epidemiology of glioblastoma and its correlation with clinical outcomes remain limited. Current prognostic models predominantly rely on clinical parameters such as age and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, with minimal integration of molecular biomarkers. While individual alterations\u0026mdash;such as \u003cem\u003eCDKN2A/B\u003c/em\u003e homozygous deletion\u0026mdash;have been reported to correlate with inferior overall and progression-free survival, these associations have not been consistently validated across larger datasets [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Likewise, although earlier studies found that \u003cem\u003eMET\u003c/em\u003e amplification is a poor prognostic factor in glioblastoma, the data needs to be updated [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other emerging alterations, including \u003cem\u003ePIK3R1\u003c/em\u003e mutations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and \u003cem\u003eMTAP\u003c/em\u003e deletions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], demonstrate promising preclinical relevance but lack robust prognostic implication and translational potential. Furthermore, while TMB serves as a useful biomarker in other solid tumors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], its role in glioblastoma is not well-defined [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These gaps in molecular epidemiology have hindered the development of rational, biomarker-driven clinical trials in glioblastoma, largely owing to inadequate patient stratification and limited incorporation of molecular features and clinical outcomes. Considering the intrinsically poor prognosis of patients with glioblastoma and persistent challenges in developing effective therapies, a comprehensive molecular epidemiologic analysis integrating clinical outcome data is needed to advance precision medicine in glioblastoma.\u003c/p\u003e \u003cp\u003eTo address these critical gaps, we conducted a comprehensive molecular characterization of newly diagnosed adult-type diffuse gliomas. This study identified prognostic genetic alterations associated with survival and evaluated the translational potential of emerging molecular targets. Furthermore, we examined the relationship between TMB and clinical outcomes in patients with newly diagnosed glioblastoma. To ensure reproducibility and strengthen the clinical relevance of our findings, we also validated our results using an independent external cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy populations\u003c/h2\u003e \u003cp\u003eThis retrospective study, conducted at the Asan Medical Center in Seoul, Korea, between May 2017 and November 2021, enrolled 470 patients with primary adult-type diffuse gliomas, including 68 with oligodendrogliomas, 63 with astrocytomas, and 339 with glioblastomas. The classification adhered to the 2021 WHO standards for central nervous system tumors, v5 criteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For glioblastoma, cases were identified as wildtype for \u003cem\u003eIDH1/2\u003c/em\u003e genes, with \u003cem\u003eIDH\u003c/em\u003e mutations defined as any missense mutation altering codon 132 of \u003cem\u003eIDH1\u003c/em\u003e or codon 172 of \u003cem\u003eIDH2\u003c/em\u003e. Oligodendrogliomas were selected to detect \u003cem\u003eIDH\u003c/em\u003e mutations and 1p/19q co-deletion, whereas astrocytomas were selected based on \u003cem\u003eIDH\u003c/em\u003e mutations without 1p/19q co-deletion. Each 1p and 19q deletion occurred at the chromosome arm level. Deletions were detected using a targeted NGS panel, which did not cover the entire chromosome. Thus, chromosome 1p and 19q deletions were defined as complete segmental losses within specific ranges based on the hg19 human reference genome. Clinical data, including age at diagnosis, sex, tumor grade, MGMT status, KPS score, tumor location, laterality, eloquent area, extent of resection, and postoperative treatment, were collected from pathology reports, ensuring a comprehensive dataset for analysis.\u003c/p\u003e \u003cp\u003eFor validation, we utilized four independent glioblastoma cohorts obtained from the cBioPortal database [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]: CPTAC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], GLASS [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], MSKCC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and TCGA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Each cohort comprised clinical data, mutation data, and copy number alterations (CNA) data. We selected only patients with primary glioblastoma, resulting in final sample sizes of 90 (CPTAC), 106 (GLASS), 410 (MSKCC), and 234 (TCGA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenomic analysis\u003c/h3\u003e\n\u003cp\u003eTargeted NGS was conducted using an in-house panel at Asan Medical Center, utilizing versions 3, 4, and 4.5 of the OncoPanel [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Version 3 covered 382 genes, including exons of 199 genes and 184 hotspots involved in rearrangements. Version 4 targeted 323 genes, encompassing exons of 225 genes and 99 hotspots. The specifics for version 4.5 included exons of 244 genes and 99 hotspots.\u003c/p\u003e \u003cp\u003eInitially, tumor samples were meticulously selected during histopathologic review. Pathologists labeled tumor areas, estimating tumor purity, and performed manual dissection on five to ten whole sections of each formalin-fixed paraffin-embedded tissue (FFPE) tumor block. DNA extraction was conducted using either the Maxwell FFPE Plus DNA kit or NEXprep FFPE Tissue kit. The quality and quantity of extracted DNA were assessed using NanoDrop and Quant-iT dsDNA Assay kits. Of the 217 initially enrolled patients, samples from 43 were excluded owing to insufficient DNA quality, underscoring the rigorous quality control measures established.\u003c/p\u003e \u003cp\u003eFor sequencing and analysis, acceptable DNA samples were sheared and enriched using custom target enrichment probes, followed by sequencing using NextSeq 550Dx. Reads were aligned to the reference genome (GRCh37) with BWA, Picard, and GATK to generate BAM files. Nucleotide variations, CNA, and structural variations were identified using VarDict [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], CNVkit [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and BreaKmer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. An experienced pathologist manually reviewed the variants using the IGV genomic viewer to ensure accuracy. Genomic metrics were calculated, including the mean copy numbers (CN) of genome segments using log2 ratios, and tumor purity was estimated. CNVs were defined based on estimated CN, and the altered fraction of the genome was calculated. Gene amplification was defined as alterations with CN\u0026thinsp;\u0026gt;\u0026thinsp;10. TMB was calculated based on identified nucleotide variations, including only variants with a variant allele frequency of \u0026ge;\u0026thinsp;0.05. The Panel of Normals approach was employed for variant calling in the somatic variant analysis. This comprehensive approach ensures reliable and robust findings, crucial for understanding the genetic landscape of the examined tumors.\u003c/p\u003e\n\u003ch3\u003eDefining hypermutation thresholds\u003c/h3\u003e\n\u003cp\u003eThe cutoffs for hypermutation were determined by examining the TMB distribution in each cohort using segmented linear regression analysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The distribution of TMB values is presented in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. Cutoff values for hypermutation were generally higher in panel sequencing datasets (ASAN and MSKCC) than in whole-genome or whole-exome sequencing datasets (TCGA, GLASS, and CPTAC; \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). A summary of the hypermutation cutoff values for each cohort is provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Additionally, we calculated the I-index (InDel-to-total mutation ratio), an established method for detecting microsatellite instability in colorectal cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eValidation using independent glioblastoma cohorts\u003c/h3\u003e\n\u003cp\u003eFrom genomic data of each validation cohort, we extracted samples with mutations or CNAs for four target genes (\u003cem\u003ePIK3R1\u003c/em\u003e, \u003cem\u003eMET\u003c/em\u003e, \u003cem\u003eCDK4\u003c/em\u003e, and \u003cem\u003eCDKN2A/B\u003c/em\u003e). Samples were labeled as mutation/wildtype for \u003cem\u003ePIK3R1\u003c/em\u003e, amplification/wildtype for \u003cem\u003eMET\u003c/em\u003e and \u003cem\u003eCDK4\u003c/em\u003e, and deletion/wildtype for \u003cem\u003eCDKN2A/B\u003c/em\u003e. \u003cem\u003ePIK3R1\u003c/em\u003e classification was based on mutation data, whereas CNAs were defined using GISTIC 2.0 values (-2 to 2) or for GLASS, chromosomal coordinates and segment mean values (AMP: seg.mean\u0026thinsp;\u0026ge;\u0026thinsp;0.5; DEL: seg.mean \u0026le;-0.5).\u003c/p\u003e\n\u003ch3\u003eClinical variables\u003c/h3\u003e\n\u003cp\u003eThe extent of resection was assessed by two neuroradiologists (J.E.P. and H.S.K., with 12 and 27 years of clinical experience in neuro-oncologic imaging, respectively) using T2-weighted FLAIR and CE-T1-weighted images based on remnant contrast-enhancing lesion on postoperative MRI acquired within 72 h (preferably 48 h) postoperatively. A measurable contrast-enhancing lesion was defined as a bidimensional contrast-enhancing lesion with clearly defined margins, with two perpendicular diameters of at least 10 mm, visible on two or more axial slices. A non-measurable contrast-enhancing lesion was defined as the absence of clearly defined margins, unidimensional measurable lesions, or a lesion with maximal perpendicular diameters\u0026thinsp;\u0026lt;\u0026thinsp;10 mm [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcome definition\u003c/h2\u003e \u003cp\u003eAll patients with glioblastoma received standard treatment, including surgery, concurrent chemoradiotherapy, and adjuvant temozolomide. Patients were assessed by performing MRI scans at regular 2\u0026ndash;3-month intervals. Overall survival (OS) was calculated from the date of diagnosis until the date of death due to any cause. Progression-free survival (PFS) was defined as the time from diagnosis to radiological or clinical progression or death, whichever occurred first, as assessed by the attending physician. In the cBioPortal cohort, PFS data were obtained from the \u0026ldquo;progression-free survival\u0026rdquo; columns for MSKCC and TCGA cohorts, whereas for GLASS and CPTAC cohorts, the \"Time to 2nd operation\" column was used. As the DFCI cohort did not contain PFS data, it was excluded from this analysis. Death was confirmed via an institutional linkage to the national healthcare system. Patients were censored at the date of medical record abstraction or date of last imaging report, whichever occurred first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo evaluate differences in continuous variables, the Mann\u0026ndash;Whitney U test was employed for comparisons between two groups, whereas the Kruskal\u0026ndash;Wallis test was used for analyzing differences among three or more groups. For categorical variables, differences in frequencies were assessed using the chi-square test, with Fisher\u0026rsquo;s exact test applied when expected frequencies were markedly small for the chi-square test. Survival analysis involved univariable analysis of factors associated with survival in the Kaplan\u0026ndash;Meier method, with comparisons performed using the log-rank test. Hazard ratios (HRs) and corresponding P-values were estimated using the Cox proportional hazards regression model. The same methodology was applied to the external validation cohorts. Significant clinicogenomic factors identified in the univariable analysis were further examined in a multivariable survival analysis using a Cox proportional hazards regression model. HRs with 95% confidence intervals (CIs) were calculated for each variable in the model. All statistical evaluations were performed using R (v4.2.2, R Core Development Team, Vienna, Austria), with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatient demographics and glioma classification\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe current study involves 470 patients with primary adult-type diffuse glioma (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e), with a median patient age of 60 years (range 22‒88 years). The cohort comprised 268 males (57.0%) and 202 females (43.0%). Overall, 192 patients (40.9%) had MGMT methylation, and 268 patients (57.0%) received the Stupp protocol as their post-operation initial treatment. Patients were categorized into subtypes following the WHO classification: 68 had oligodendroglioma, 63 had astrocytoma, and 339 had glioblastoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Glioblastoma accounted for the largest proportion (72.1%) and was associated with the lowest OS, with a median OS of 15.7 months (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenomic landscape of adult-type diffuse gliomas\u003c/h2\u003e \u003cp\u003eEach adult-type diffuse glioma subtype exhibited specific genetic alterations that aligned with established molecular profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In oligodendrogliomas, the presence of \u003cem\u003eIDH\u003c/em\u003e mutations combined with a 1p/19q co-deletion (100%) confirmed their canonical genetic signature, including mutations in \u003cem\u003eNOTCH1\u003c/em\u003e (27.9%), \u003cem\u003eFUBP1\u003c/em\u003e (20.6%), and \u003cem\u003eARID1A\u003c/em\u003e (19.1%). Astrocytomas displayed a distinct profile characterized by \u003cem\u003eIDH\u003c/em\u003e mutations (100%), frequently accompanied by \u003cem\u003eTP53\u003c/em\u003e (92.1%) and \u003cem\u003eATRX\u003c/em\u003e (79.4%) mutations, differentiating them from oligodendrogliomas. Glioblastomas predominantly comprised IDH-wildtype tumors, with common genetic alterations such as \u003cem\u003eCDKN2A/B\u003c/em\u003e homozygous deletion (52.5%), \u003cem\u003eEGFR\u003c/em\u003e amplification (30.4%), and \u003cem\u003eTP53\u003c/em\u003e mutation (35.1%). \u003cem\u003eMTAP\u003c/em\u003e deletion was identified in 45.4% of glioblastoma samples, as detected using OncoPancel v.4.0 and v.4.5. Fusion genes were also more prevalent in glioblastoma, with \u003cem\u003eEGFR\u003c/em\u003e-intergenic fusion (5.3%) and \u003cem\u003eFGFR3-TACC3\u003c/em\u003e fusion (3.2%) being the most frequent. Hypermutation was identified in 7.3% of oligodendrogliomas, 23.8% of astrocytomas, and 10.9% of glioblastomas, as determined by segmented linear regression analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTMB and survival in glioblastoma\u003c/h2\u003e \u003cp\u003eHypermutation was observed in approximately 10% of glioblastomas in this study, aligning with the consistent rates detected across four independent cohorts, ranging from 9.4% to 13.3% (Supplementary Table\u0026nbsp;1). Hypermutated samples were confirmed by a significantly higher mean insertion-deletion mutation rate (I-index) than that of non-hypermutated samples (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A, 5\u003c/b\u003e.61 vs 1.05 per Mb, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.63 \u0026times; 10\u003csup\u003e⁻11\u003c/sup\u003e). Among genes more frequently mutated in hypermutated samples, \u003cem\u003eMSH2\u003c/em\u003e (27.0% vs 2.3%, \u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 8.12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e), \u003cem\u003eTP53\u003c/em\u003e (70.3% vs 30.8%, \u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 5.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e), and \u003cem\u003ePTPN11\u003c/em\u003e (24.3% vs 3.6%, \u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 5.91 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e) showed the most notable differences (\u003cb\u003eSupplementary Fig.\u0026nbsp;3B).\u003c/b\u003e Despite these differences, no significant variations were observed in OS or PFS between hypermutated and non-hypermutated cases (\u003cb\u003eSupplementary Fig.\u0026nbsp;3C, D\u003c/b\u003e). The median OS was similar between hypermutated cases and non-hypermutated cases (14.2 vs 15.9 months, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95; \u003cb\u003eSupplementary Fig.\u0026nbsp;3C\u003c/b\u003e). Consistent with these findings, analysis across four cohorts also revealed no significant differences in OS and PFS between hypermutated and non-hypermutated cases (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alterations and survival in glioblastoma\u003c/h2\u003e \u003cp\u003eTo identify genetic alterations that impact the survival of patients with adult-type diffuse glioma, samples were stratified into two groups based on the top 50% survival rates for each subtype. Genetic alteration frequencies were compared between these groups using Fisher's exact test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Gene variants with a combined frequency of \u0026le;\u0026thinsp;10 in the two groups were excluded from further analysis owing to the limited statistical power associated with the small sample size for these variants. In glioblastoma, the frequencies of \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion (\u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 0.002), \u003cem\u003ePIK3R1\u003c/em\u003e mutation (\u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 0.005), \u003cem\u003eCDK4\u003c/em\u003e amplification (CN\u0026thinsp;\u0026ge;\u0026thinsp;10, \u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 0.031), \u003cem\u003eTP53\u003c/em\u003e mutation (\u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 0.031), and \u003cem\u003eMET\u003c/em\u003e amplification (CN\u0026thinsp;\u0026ge;\u0026thinsp;10, \u003cem\u003eP\u003c/em\u003e\u003csub\u003efisher\u003c/sub\u003e = 0.020) differed significantly \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Considering the frequencies of identified genetic alterations, no significant differences between the high- and low-survival cohorts in either oligodendroglioma or astrocytoma subtypes \u003cb\u003e(Supplementary Tables\u0026nbsp;3 and 4)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eConsidering the notably worse survival rates associated with glioblastoma compared with that of other adult-type diffuse glioma subtypes, subsequent analyses focused on this subtype. Univariable analysis of glioblastoma revealed substantial associations between survival and several genetic alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequent multivariable analysis demonstrated that \u003cem\u003ePIK3R1\u003c/em\u003e mutation (HR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.04\u0026ndash;2.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022; median OS: 13.1 vs 16.1), \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion (HR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.09\u0026ndash;1.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; median OS: 13.4 vs 19.9), and \u003cem\u003eMET\u003c/em\u003e amplification with a CN of \u0026ge;\u0026thinsp;10 (HR\u0026thinsp;=\u0026thinsp;2.78, 95% CI: 1.40\u0026ndash;5.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; median OS: 9.6 vs 16.3) remained independent predictors of poor prognosis, along with clinical factors, such as MGMT methylation status, age, KPS\u0026thinsp;\u0026lt;\u0026thinsp;80, and tumor bilaterality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. \u003cem\u003ePIK3R1\u003c/em\u003e mutations were detected in 12.7% of patients with glioblastoma, predominantly in the inter-SH2 domain, which binds p110α (\u003cem\u003ePIK3CA\u003c/em\u003e) (\u003cb\u003eSupplementary Fig.\u0026nbsp;5)\u003c/b\u003e. \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion and \u003cem\u003eMET\u003c/em\u003e amplification were detected in 52.5% and 3.5% of patients with glioblastoma, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eExternal validation of prognostic significance of genetic alterations\u003c/h2\u003e \u003cp\u003eTo validate the impact of identified genetic alterations on survival outcomes, we performed survival analyses across four independent glioblastoma cohorts obtained from the cBioPortal database: CPTAC, GLASS, MSKCC, and TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand Supplementary Fig.\u0026nbsp;6\u003c/b\u003e). The CPTAC cohort revealed a significant association between \u003cem\u003ePIK3R1\u003c/em\u003e mutation and poor survival outcome (HR\u0026thinsp;=\u0026thinsp;3.72, 95% CI: 1.44\u0026ndash;9.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007; median OS: 7.82 vs 16.79). \u003cem\u003ePIK3R1\u003c/em\u003e mutations were detected in 5.56% of patients with glioblastoma in the CPTAC cohort. Likewise, the MSKCC cohort revealed a significant correlation between \u003cem\u003eMET\u003c/em\u003e amplification and inferior survival (HR\u0026thinsp;=\u0026thinsp;2.74, 95% CI: 1.12\u0026ndash;6.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027, median OS: 10.5 vs 21.7). \u003cem\u003eMET\u003c/em\u003e amplification was detected in 1.71% of patients with glioblastoma in the MSKCC cohort. Kaplan\u0026ndash;Meier analyses in these cohorts confirmed lower survival probabilities in patients with these alterations. While other cohorts showed trends toward reduced survival for \u003cem\u003ePIK3R1\u003c/em\u003e mutation and \u003cem\u003eMET\u003c/em\u003e amplification, these did not reach significance. \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion exhibited inconsistent results, with only a modest trend toward reduced survival across cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGenomic alterations with therapeutic potential\u003c/h2\u003e \u003cp\u003eNext, we evaluated genomic alterations for clinical actionability using OncoKB Knowledge Base v4.20 (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We identified 11 targetable alterations in glioblastoma, 8 in astrocytoma, and 5 in oligodendrogliomas (OncoKB Level 1\u0026ndash;4). \u003cem\u003eBRAF\u003c/em\u003e V600E and \u003cem\u003eEGFR\u003c/em\u003e alterations were predominantly detected in patients with glioblastoma (3.2% and 25.1%, respectively). Notably, \u003cem\u003eFGFR3\u003c/em\u003e-\u003cem\u003eTACC3\u003c/em\u003e fusions were identified exclusively in patients with glioblastoma (3.2%), and \u003cem\u003eMET\u003c/em\u003e amplification (CN\u0026thinsp;\u0026ge;\u0026thinsp;10) was found in a subset of glioblastoma (3.5%), indicating their potential as therapeutic targets in these aggressive gliomas. Consideirng that most mutations were classified as Levels 3 and 4, the identified genetic alterations may be linked to potential investigational therapies or have preclinical implications, and hence, may predict response to potential therapeutics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur comprehensive molecular profiling of 470 adult-type diffuse gliomas provided insights into the molecular basis of prognosis and potential therapeutic strategies. In glioblastoma, \u003cem\u003ePIK3R1\u003c/em\u003e mutations, \u003cem\u003eMET\u003c/em\u003e amplification, and \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion were independently associated with worse OS, a finding that was validated across multiple independent cohorts. Despite the growing interest in immunotherapy, we found that TMB was not associated with survival in newly diagnosed glioblastoma across multiple cohorts. Although 10% of glioblastomas exhibited a hypermutated phenotype, this subset did not demonstrate improved outcomes.\u003c/p\u003e \u003cp\u003eWhile high TMB was associated with poorer survival but improved outcomes following immune checkpoint inhibitor (ICI) therapy in various solid cancers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], its impact on glioblastoma remains elusive [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Previous studies have primarily focused on recurrent glioblastoma [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], where post-treatment effects, particularly those of alkylating agents such as temozolomide, contributes to hypermutation. Certain patients, such as those with \u003cem\u003ede novo\u003c/em\u003e replication repair deficiency linked to constitutional defects in DNA polymerase and mismatch repair genes, reportedly benefit from ICI therapy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, data regarding newly diagnosed glioblastoma with high TMB remain limited. In our dataset (n\u0026thinsp;=\u0026thinsp;339) and four additional cohorts totaling 570 patients with newly diagnosed glioblastoma, approximately 10% of patients in each cohort showed evidence of hypermutation. Despite this observation, glioblastomas are known for their lack of prominent T cell infiltration, which may limit their response to ICI therapy. A clinical trial on neoadjuvant combination ICI therapy in patients with newly diagnosed glioblastoma reported the occurrence of T cell activation and no recurrence after 17 months, albeit in patients with low TMB [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This contrast emphasizes the need for further investigations to determine the potential benefits of ICIs in patients with glioblastoma across different TMB profiles. Therefore, identifying targetable genomic alterations may offer a more tangible strategy for improving outcomes in glioblastoma.\u003c/p\u003e \u003cp\u003eBeyond TMB, our genomic analysis revealed a broad spectrum of actionable alterations in GBM, as categorized by OncoKB. Several alterations were classified as Level 1, including \u003cem\u003eEGFR\u003c/em\u003e amplification, \u003cem\u003eBRAF\u003c/em\u003e V600E, and \u003cem\u003eIDH\u003c/em\u003e mutations. Among Level 4 alterations, \u003cem\u003eEGFR\u003c/em\u003e aberrations were the most common (25.1%), encompassing both hotspot mutations (e.g., A289V, R108K) and amplifications (24.5%). Other frequently altered genes included \u003cem\u003ePTEN\u003c/em\u003e (46.0%), \u003cem\u003eCDKN2A\u003c/em\u003e (54.0%), and \u003cem\u003eNF1\u003c/em\u003e (9.1%). Along with these alterations, we identified \u003cem\u003ePIK3R1\u003c/em\u003e mutation, \u003cem\u003eMET\u003c/em\u003e amplification, and \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion as independent markers of poor prognosis in patients with glioblastoma. \u003cem\u003ePIK3R1\u003c/em\u003e mutations, primarily localized in the inter-SH2 domain, likely result in aberrant activation of the PI3K pathway, a mechanism known to enhance tumor growth and resistance to apoptosis. \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion, a hallmark of cell cycle dysregulation, is linked to aggressive tumor behavior and poor prognosis. In this cohort, \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion was associated with shorter survival (median OS: 13.4 vs 19.9 months; HR\u0026thinsp;=\u0026thinsp;1.41). The prognostic significance of \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion was validated by both a recent meta-analysis (HR\u0026thinsp;=\u0026thinsp;1.50) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and large-scale UK study (HR\u0026thinsp;=\u0026thinsp;1.49) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], despite inconsistent results in some validation cohorts. These findings, along with other recurrent alterations, highlight a subset of patients with glioblastoma harboring molecular vulnerabilities that may be amenable to targeted therapeutic strategies.\u003c/p\u003e \u003cp\u003eA subset of patients with glioblastoma harbored molecular alterations with direct translational potential, including \u003cem\u003eMTAP\u003c/em\u003e deletion (45.4%), \u003cem\u003eMET\u003c/em\u003e amplification (3.5%), and \u003cem\u003eFGFR3-TACC3\u003c/em\u003e fusion (3.2%). These three alterations are of particular interest because of their mechanistic roles in tumorigenesis and the availability of matched targeted agents currently in clinical or preclinical development. Although several other alterations in glioblastoma are deemed biologically relevant, these three stand out as promising for biomarker-driven therapeutic approaches. Alterations in \u003cem\u003eMTAP\u003c/em\u003e\u0026mdash;a gene involved in the methionine salvage pathway\u0026mdash;have recently emerged as potential biomarkers for targeted therapy, particularly in tumors with deletion of this gene. The outcomes of ongoing Phase I trials evaluating AMG 193 monotherapy and the AMG 193/MRTX1719 combination are anticipated to be of clinical interest [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. AMG 193 is a selective PRMT5 inhibitor that exploits synthetic lethality in MTAP-deficient tumors. Meanwhile, MRTX1719 targets MTA-cooperative binding of PRMT5 via the substrate adaptor protein, further enhancing selectivity. If these and other early trials with MTAP-directed compounds demonstrate promising efficacy, \u003cem\u003eMTAP\u003c/em\u003e deletion may emerge as a clinically actionable biomarker in glioblastoma. \u003cem\u003eMET\u003c/em\u003e amplification, identified in 3.5% of glioblastoma cases in this study, is another alteration of interest. Although glioma-specific data remain limited, \u003cem\u003eMET\u003c/em\u003e amplification has been validated as a therapeutic target in other solid tumors, particularly non-small cell lung cancer. In the GEOMETRY trial, treatment with the MET inhibitor capmatinib resulted in an objective response rate (ORR) of 29% in previously treated patients and an ORR of 40% in treatment-na\u0026iuml;ve patients with high \u003cem\u003eMET\u003c/em\u003e copy number (\u0026ge;\u0026thinsp;10). Additionally, capmatinib demonstrated a 92% intracranial disease control rate in patients with lung cancer and brain metastases. These findings support further investigation of MET inhibitors in patients with glioblastoma harboring \u003cem\u003eMET\u003c/em\u003e amplification. Finally, \u003cem\u003eFGFR3-TACC3\u003c/em\u003e fusion, detected in 3.2% of glioblastomas in our dataset, defines a mutually exclusive and potentially actionable molecular subtype. Although response rates to FGFR inhibitors have been modest\u0026mdash;10% with erdafitinib in the RAGNAR trial and 3.8% with infigratinib\u0026mdash;case reports have described durable benefit in select patients. Therefore, recent EANO guidelines recommend FGFR inhibitors as a treatment consideration for fusion-positive glioblastomas [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These findings emphasize the importance of comprehensive genomic profiling to identify patients who may benefit from targeted therapies directed against potentially actionable alterations and underscore the need for ongoing clinical trials to further define their therapeutic potential in this challenging disease.\u003c/p\u003e \u003cp\u003eThis study has several limitation. First, our use of targeted sequencing, similar to that performed by Foundation Medicine, restricted analysis to a defined set of cancer-associated genes, potentially excluding the discovery of novel drivers and limiting the detection of structural variations such as large genomic rearrangements and complex fusions. Second, the oligodendroglioma and astrocytoma analyses had poor statistical power, owing to their smaller sample sizes compared with that of glioblastoma. Third, the amplification cutoff for genes such as \u003cem\u003eMET\u003c/em\u003e, although carefully selected and consistent across analyses, is inherently sensitive to variations in tumor purity, requiring careful data adjustment. Nevertheless, the prognostic relevance of the \u003cem\u003eMET\u003c/em\u003e amplification and other key drivers was confirmed through external validation using independent glioblastoma cohorts.\u003c/p\u003e \u003cp\u003eIn conclusion, our study highlights the importance of molecular profiling in elucidating the biology of adult-type diffuse gliomas, particularly glioblastomas, for which prognosis remains dismal. Identifying actionable genetic alterations paves the way for future research on targeted therapies that may enhance survival outcomes. Continued exploration into the molecular mechanisms driving glioma progression, along with validation using larger and more diverse cohorts, is crucial to advancing precision medicine in the treatment of adult-type diffuse gliomas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Asan Medical Center (IRB no: 2020-1204). The need for informed consent was waived owing to the retrospective nature of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant (2023IP0146) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea to S. Y. This study was supported by a grant from the National Research Foundation of Korea grant (NRF-RS-2023-00211481) to H.-S.L. funded by the Ministry of Science and ICT, the Republic of Korea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.Y. and H.S.L. conceptualized the study and designed the methodology. Data curation was performed by S.Y. and H.S.L. Formal analysis and validation were conducted by Y.K., D.K.Y., S.Y., and H.S.L. Visualization of the results was carried out by Y.K. and D.K.Y. The original draft was written by Y.K., S.E.K., D.K.Y., S.Y., and H.S.L. All authors, including Y.K., S.E.K., J.E.P., C.O.S., D.K.Y., D.P., S.J.N., S.W.S., C.K.H., Y.H.K., S.P., D.K., K.S.P., H.C., J.H., S.W.K., J.H.K., H.S.K., S.Y., and H.S.L., contributed to the review and editing of the manuscript. Supervision was provided by S.Y. and H.S.L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and genomic data supporting this study were collected during routine patient care and are subject to Korean privacy laws, which restrict public sharing of patient genomic information. These data cannot be publicly deposited. Data requests must be directed to the corresponding author and require approval of the Asan Medical Center IRB.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWen PY, Packer RJ (2021) The 2021 WHO classification of tumors of the central nervous system: Clinical implications. 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Nat Rev Clin Oncol 18:170\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41571-020-00447-z\u003c/span\u003e\u003cspan address=\"10.1038/s41571-020-00447-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioblastoma, High-Throughput Nucleotide Sequencing, Gene amplification, Gene deletion, Phosphatidylinositol 3-Kinases","lastPublishedDoi":"10.21203/rs.3.rs-8790042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8790042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite advances in molecular classification of adult-type diffuse gliomas, limited data exist on the prognostic implications of specific genetic alterations. We integrated molecular profiles of adult-type diffuse glioma subtypes in a multicenter cohort and examined the prognostic significance, especially in glioblastoma, \u003cem\u003eIDH\u003c/em\u003e-wildtype.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAfter analyzing 470 primary adult-type diffuse gliomas using next-generation sequencing, multivariable Cox regression and Kaplan\u0026ndash;Meier survival analyses were performed. Associations between molecular profiles and overall survival were independently validated using 840 cases of primary glioblastoma, \u003cem\u003eIDH\u003c/em\u003e-wildtype, from four external cohorts: CPTAC, GLASS, MSKCC, and TCGA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDistinct genetic landscapes were identified in oligodendroglioma, IDH-mutant, 1p/19q co-deleted, astrocytoma, \u003cem\u003eIDH\u003c/em\u003e-mutant, and glioblastoma, \u003cem\u003eIDH\u003c/em\u003e-wildtype. In glioblastoma, \u003cem\u003ePIK3R1\u003c/em\u003e mutation (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;1.53, 95% confidence interval [CI] 1.06\u0026ndash;2.20; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion (1.41 [1.09\u0026ndash;1.83]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and \u003cem\u003eMET\u003c/em\u003e amplification (2.78 [1.40\u0026ndash;5.51]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) were significant prognostic markers, along with negative O6-methylguanine-DNA methyltransferase promoter methylation status (1.51 [1.14\u0026ndash;2.00]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). External validation confirmed the prognostic impact of \u003cem\u003ePIK3R1\u003c/em\u003e in CPTAC (HR [95% CI]\u0026thinsp;=\u0026thinsp;3.72 [1.44\u0026ndash;9.58]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and \u003cem\u003eMET\u003c/em\u003e amplification in MSKCC (2.74 [1.12\u0026ndash;6.68]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027); \u003cem\u003eCDKN2A/B\u003c/em\u003e showed cohort-specific effects. Tumor mutational burden lacked prognostic significance across all cohorts. Although 45.4% of glioblastomas exhibited \u003cem\u003eMTAP\u003c/em\u003e deletion, no significant association with overall survival was detected.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eCDKN2A/B\u003c/em\u003e deletion, \u003cem\u003eMET\u003c/em\u003e amplification, and \u003cem\u003ePIK3R1\u003c/em\u003e mutation were identified as independent markers of poor prognosis in glioblastoma, potentially enabling molecular-based risk stratification.\u003c/p\u003e","manuscriptTitle":"Precision Prognostication in Adult-Type Diffuse Gliomas through Multi-Cohort Validation of Key Genomic Alterations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 06:24:26","doi":"10.21203/rs.3.rs-8790042/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62ab6b0c-6af0-4006-aa5f-8de3258ad232","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T01:39:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 06:24:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8790042","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8790042","identity":"rs-8790042","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}