Jenkins, Robert B.

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Abstract Background Isocitrate dehydrogenase (IDH)-mutant gliomas are infiltrative tumors with limited treatment options at recurrence. Early studies suggest IDH inhibition has therapeutic activity. We evaluated clinical outcomes associated with IDH-1 inhibitor ivosidenib in recurrent IDH-1-mutant gliomas and molecular predictors of response. Methods We retrospectively analyzed adults treated with ivosidenib from January 2021 to August 2025 with predominantly recurrent, pretreated CNS WHO grade 2-4 astrocytoma IDH-mutant or grade 2-3 oligodendroglioma, IDH-mutant and 1p/19q codeleted. Prior anticancer treatments, toxicities, enhancement status at ivosidenib initiation, and genomic alterations were collected. The primary endpoint was progression-free survival (PFS); secondary analyses evaluated associations between PFS, genomic complexity, and CDKN2A/B homozygous deletion. Results Ninety-two adults, median age 43, were included: 61 astrocytomas and 31 oligodendrogliomas, mostly grade 2. Before ivosidenib, 69 had surgery, 64 chemotherapy, and 64 radiation. Median time from diagnosis to ivosidenib was 4.8 years, with median of two prior progressions. Median treatment duration was 5.8 months. Median PFS was 15.2, 4.6, and 2.1 months for astrocytoma grades 2-4, 14.0 and 6.9 months for oligodendroglioma grades 2-3. Increasing genomic complexity was associated with shorter PFS in univariate analysis. Patients without CDKN2A/B homozygous deletion had median PFS of 19.2 months. Two patients with homozygous deletion had median PFS of 0.6 months. Conclusions In this retrospective analysis of predominantly recurrent IDH-1-mutant glioma, ivosidenib was associated with longer PFS in lower-grade, non-enhancing tumors that received little or no prior anticancer treatment and less complex genomic profiles. These findings support a context-dependent benefit of IDH inhibition in recurrent disease.

ABSTRACT IDH-mutant astrocytomas maintain telomeres through the alternative lengthening of telomeres (ALT) pathway, producing extreme inter-arm telomere length heterogeneity, yet how this heterogeneity shapes structural genome evolution remains unknown. Using Oxford Nanopore long-read sequencing of 20 IDH-mutant astrocytomas, we profiled structural variants (SVs), copy number variants, extrachromosomal DNA (ecDNA) and measured allele-specific telomere lengths from individual long reads. We identified pervasive complex rearrangements, including chromothripsis and foldback events consistent with breakage-fusion-bridge cycles, and widespread ecDNAs. SV breakpoints were enriched at telomeric and centromeric regions regardless of local telomere length, revealing constitutive structural fragility. Arm-level telomere length analysis uncovered a dual-mode model: arms with short telomeres preferentially harbored breakage-associated events, while arms with long ALT-maintained telomeres were enriched for ecDNA and amplification-associated events. These findings identify chromosome-arm-specific telomere length as a determinant of structural genome evolution in ALT-driven tumors.

Abstract Background: Alternative lengthening of telomeres (ALT) is common in isocitrate dehydrogenase (IDH) mutant astrocytomas, but how telomere length variability contributes to genome instability remains unclear. We hypothesized that chromosome arm–specific telomere heterogeneity underlies distinct patterns of somatic structural variation (SV), copy-number alteration (CNA), and extrachromosomal DNA (ecDNA) formation. Methods: Oxford Nanopore long-read whole-genome sequencing (∼20–40x coverage) was performed in a clinical cohort of n = 20 IDH-mutant astrocytomas. Somatic SVs were called with Severus and filtered to a high-confidence tumor-specific set. CNAs were inferred by read-depth segmentation (DNAcopy) and further refined using a long-read CNA framework (Wakhan). Chromosome arm–specific telomere length was estimated with Telogator, and SV breakpoints were annotated for enrichment near subtelomeric and pericentromeric regions. Associations between arm-level telomere length and SV/CNA features (rate and proportion) were tested using linear mixed-effects models with patient as a random effect. High-level amplification regions were defined directly from CNA segments with high copy number, and ecDNA-like circular amplicons were subsequently reconstructed from amplified intervals using Decoil. Results: IDH-mutant astrocytomas displayed pronounced SV heterogeneity, with complex breakends (BNDs) dominating the somatic SV landscape. SVs were spatially patterned, as deletions and insertions preferentially accumulated near subtelomeric and pericentromeric domains, implicating chromosome ends and repeat-rich regions as recurrent substrates for rearrangement. At the tumor level, markedly shortened telomeres were associated with increased SV burden and CNA burden, consistent with telomere attrition as a correlate of genome-wide structural instability in highly rearranged genomes. At higher resolution, telomere length varied substantially across chromosome arms and classified distinct structural outcomes: arms with shorter telomeres were enriched for complex insertional SVs, whereas arms with relatively elongated telomeres more frequently carried focal high-level amplifications and ecDNA-like circular amplicons. Conclusions: These findings reveal a link between arm-specific telomere heterogeneity and the emergence of structural and amplification architectures in IDH-mutant astrocytomas. Telomere remodeling may influence where rearrangements and ecDNA structures arise, offering potential biomarkers of genome instability and tumor evolution. Generative AI was used to improve language clarity; all content was verified by the authors. Citation Format: Maryam Jehangir, Kristen Drucker, T. Rhyker Ranallo-Benavidez, Thomas M. Kollmeyer, Ayse Keskus, Daniel Lachance, Jeanette E. Eckel-Passow, Ofer Shoshani, Mikhail Kolmogorov, Robert B. Jenkins, Floris P. Barthel. Telomere length heterogeneity shapes structural and amplification landscapes in IDH-mutant astrocytoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr PR009.

Abstract In adult-type diffuse gliomas CDKN2A and/or CDKN2B (CDKN2A/B) deletions often co-occur with deletion of MTAP, suggesting that MTAP immunohistochemistry (IHC) may be a surrogate marker of CDKN2A/B status. However, the association between CDKN2A/B and MTAP deletion at the genomic level remains unknown. We assessed CDKN2A/B and MTAP deletions by chromosomal microarray in 333 adult-type diffuse gliomas and performed MTAP IHC on a subset (n = 63). CDKN2A/B and MTAP deletions were detected in 216 and 215 cases, respectively, and were concurrent in 99.5% (215/216). While most tumors with CDKN2A/B homozygous deletion (n = 148) showed concurrent MTAP homozygous deletion (108/148; 73.0%), a subset harbored MTAP heterozygous deletion (39/148; 26.4%). By analyzing the size of the chromosomal alterations, we demonstrate that initial large chromosomal 9p losses result in concurrent heterozygous deletion of CDKN2A/B and MTAP whereas smaller “second hit” deletions leading to homozygous CDKN2A/B deletion do not always encompass the MTAP locus. Discordant CDKN2A/B and MTAP tumors affect the association between MTAP IHC and copy number status of MTAP and CDKN2A/B. These findings suggest that adult-type diffuse gliomas, regardless of IDH status, follow a stereotypic pathway involving concurrent CDKN2A/B and MTAP heterozygous deletion but may diverge for CDKN2A/B and MTAP homozygous deletion.

Abstract Background Glioblastoma IDH-wildtype (GBM) may be diagnosed histologically (hGBM) or based on molecular features when microvascular proliferation and histologic necrosis are absent (mGBM). Clinical and radiographic distinction between hGBM and mGBM remains unclear. Methods 101 hGBM cases were matched to 109 mGBM cases by age, sex, year of diagnosis, and TERT promoter mutation (TERTp) status. Clinical outcome, radiographic, and molecular features were compared across mGBM and hGBM. Results Median time from first MRI to diagnosis was longer for mGBM versus hGBM (34 vs 7 days, p < .001). mGBM more often presented with seizures (51% vs 26%, p < .001), gliomatosis pattern (12% vs 2%, p = .005), basal ganglia involvement (17% vs 0%, p < .001). Contrast enhancement appeared on MRI for 98% (96/98) of hGBM and 50% (54/107) of mGBM (p < .001). mGBM with non-enhancing MRI were rarely multifocal (2% vs 30%, p < .001). mGBM harbored fewer total copy number alterations (median 20 vs 16.5, p = .029), less often had EGFRamp (34% vs 48%, p = .051), isolated chromosome 10 loss (84% vs 96%, p = .013), and CDKN2A/B homozygous deletion (56% vs 77%, p = .005). Median overall survival was longer for mGBM (2.16 vs 1.44 years, p < .001), and for mGBM without MRI contrast enhancement versus hGBM with enhancement (3.11 vs 1.62 years, adjusted p = .003). mGBM without +7/−10 or without CDKN2A/B homozygous deletion had longer survival and time-to-progression versus hGBM with these alterations. Conclusion mGBM with MRI enhancement may represent undersampled or early/evolving hGBM. mGBM that are non-enhancing, present with seizures, prolonged time to diagnosis, absent CDKN2A/B alteration or +7/−10 may represent other IDH-wildtype entities.

Abstract Glioblastoma, IDH-wildtype (GBM), and primary central nervous system lymphoma (PCNSL) are two aggressive brain tumors with distinct treatment strategies but overlapping imaging features on MRI. Accurate preoperative differentiation remains a clinical challenge, due to their overlapping imaging features, even when advanced MRI techniques are employed. This study explored deep learning approaches leveraging multi-sequence MRI to distinguish GBM from PCNSL. Models were developed utilizing 146 PCNSL cases, which were matched to146 GBM by age, sex and year of diagnosis. A 3D-DenseNet121 convolutional neural network was trained using two analytical strategies: (i) loss-based model selection and (ii) ensemble prediction using five-fold cross-validation. Model performance was evaluated on an independent validation set of 256 GBM and 37 PCNSL from the same institution as well as 36 PCNSL cases from an external institution. All MRIs were preprocessed using the FeTS pipeline. Using the independent validation cohort, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI) were computed overall and stratified by demographic characteristics. Using both T1 post-contrast (T1Gd) and T2-weighted images, the loss-based model achieved AUC=0.83 (95% CI: 0.77-0.88) and the ensemble model yielded an AUC=0.82 (95% CI: 0.76-0.89). Model performance was similar across males and females; however, model performance was better for subjects older than 50 years of age versus younger than 50. When using only T1Gd images, the loss-based model dropped to AUC=0.55 (95% CI: 0.48-0.63), whereas the ensemble model had AUC=0.79 (95% CI: 0.72-0.85). Models trained with only T2 images achieved AUCs of 0.67 (95% CI: 0.60-0.73) (loss-based) and 0.75 (95% CI: 0.69-0.82) (ensemble). These results highlight the added value of the multi-sequence input. Our results confirm the feasibility of using automated deep learning models to differentiate GBM from PCNSL. Notably, the model generalized well to external MRI data, emphasizing its potential utility as a diagnostic aid in real-world clinical workflows.

Abstract Glioblastoma (GBM) diagnosis integrates morphology and tumor molecular alterations. Patients with classic morphology are designated as histologic GBM (hGBM). Patients without classic morphology harboring TERT promoter mutation (TERTp), or epidermal growth factor receptor amplification (EGFRamp), or combined gain of chromosome 7 and loss of 10 (+7/-10) designate molecular GBM (mGBM). We compared radiographic features, genomic alterations, and clinical outcome between hGBM and mGBM. 109 mGBM Mayo Clinic cases diagnosed between 1993-2021 were matched to 101 cGBM by age, sex, year of diagnosis, and TERT promoter (TERTp) status. Clinical and radiographic data abstraction was blinded. Progression-free and overall survival were compared across groups in the subset of patients who received standard of care. Contrast enhancement was present on index MRI (MRI at diagnosis) for 98% (96/98) hGBM and 50% (54/107) mGBM (p<0.0001) patients. mGBM tumors harbored fewer total copy number alterations (median of 16.5 vs 20, p=0.029) and were less likely to have CDKN2A/B homozygous deletion (56% vs 77%, p=0.005). Among the 87 mGBM and 82 hGBM receiving standard of care, median overall survival was longer for mGBM versus hGBM (2.24 vs 1.62 years, p=0.016). mGBM patients with +7/-10 (HR=2.60, p=0.035) or CDKN2A/B (HR=3.61, p=0.002) had poorer overall survival compared to mGBM patients without the corresponding alteration. On index MRI mGBM patients without contrast enhancement had better overall survival compared to hGBM (HR=0.50; p=0.003) whereas there was no survival difference between mGBM with enhancement and hGBM. mGBM with enhancement on index MRI likely represent undersampled, or early / evolving hGBM. mGBM patients with non-enhancing MRI, younger than 60, with long symptom profile including seizures, and some combination of non-anaplastic morphology, TERTp only, no homozygous deletion of CDKN2A/B, and low copy number complexity may represent other IDHwt glioma entities. Caution is advised in counseling, management, discussing prognosis, and inclusion in clinical trials.

Abstract The 2021 WHO classification redefined glioblastoma IDH-wildtype (GBM), integrating histopathological and tumor molecular features. Diffuse astrocytic glioma lacking histologic features of necrosis and microvascular proliferation are classified as (molecular) GBM if the tumor is IDH- and H3-wildtype and has one or more additional tumor molecular alterations: TERT promoter mutation, EGFR amplification, or +7/-10. Due to the absence of histopathological features of classic GBM and presentation without enhancement on MRI in approximately 50% of cases, molecular GBM frequently resemble low-grade IDH-mutant gliomas. We hypothesized that MRI-based deep learning models could distinguish IDH-mutant glioma from molecular GBM. We retrospectively identified 97 individuals with molecular GBM, who were matched to 97 individuals with IDH-mutant glioma by age, sex, year of diagnosis, and enhancement on MRI. Of the 97 IDH-mutant gliomas, 51% were 1p/19q codeleted, 42% grade 2, 47% grade 3, and 10% grade 4. Preoperative postcontrast T1-weighted (T1C) and T2-weighted (T2) MRI sequences were used to train a 3D-DenseNet121 model. The model was validated on an independent prospective set of 150 IDH-mutant gliomas and 71 molecular GBM. The MRI-based deep learning model achieved an area under the receiver operator curve (AUC) of 0.75 (95% CI: 0.68-0.82) in the prospective validation set. Sex-stratified AUCs were 0.72 (95% CI: 0.59-0.85) for females and 0.76 (95% CI: 0.68-0.85) for males. Age stratified AUCs were 0.71 (95% CI: 0.60-0.83) for patients under 55 years of age and 0.64 (95% CI: 0.50-0.79) for patients older than 55. These results suggest that discriminative MRI features of IDH-mutant glioma and molecular GBM exist and can be identified from deep learning models. However, radiographic ambiguity exists and underscores the need for further optimization of the deep learning architecture or by integrating additional non-invasive modalities. For example, future work entails integrating germline variants into the model, including rs55705857, which is a causal variant for IDH-mutant glioma. With further validation and integration of multi-modal data, a noninvasive tool may support more personalized decision-making.