Decker, Paul A.

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 Introduction: Lung cancer rates differ across rural and urban areas, but few studies have evaluated rural-urban differences across the risk-screening continuum. We examined smoking, cessation interventions, and lung cancer screening patterns across the rural-urban continuum in a geographically defined area over 10 years. Materials and Methods: A retrospective cohort study (2014-2023) was conducted using clinical data from the Rochester Epidemiology Project, derived from healthcare encounters in a 27-county region of the midwestern United States. Patients ages 40-80 were included. We used Rural-Urban Commuting Area codes to assign residence as urban, rural, or highly rural. We examined yearly smoking prevalence, cessation intervention (pharmacotherapy, counseling), and low-dose computed tomography (LDCT) lung cancer screening. Results: Over the 10-year study period, the sample size ranged from 305,530 to 340,411 people annually with 36-38% urban, 56-57% rural, and 6-7% highly rural. Current smoking prevalence declined from 14 to 12% over the 10-year period (range=12-16%; p=0.06) and was consistently lower in urban areas (range=10-14%) than rural (range=12-17.0%) and highly rural areas (range=12-18%; p=<0.001). Among individuals who currently smoked with no history of lung cancer, yearly smoking cessation intervention ranged from 16% to 23%, increasing over the 10-year period (p=<0.001). Cessation medication (varenicline, bupropion, or nicotine replacement) prescription consistently increased overall over time but remained higher in urban than rural and highly rural areas (p=<0.001). Cessation counseling rates also increased overall over time and were similar between urban and rural areas and lower in highly rural areas (p=<0.001). Among people who had ever-smoked aged 50-80 years with no diagnosis of lung cancer, LDCT screening increased from 0.0% to 3.1% over the study period. Increases were higher in urban areas (0.0-3.6%) and similar in rural and highly rural areas (0.0-2.8% and 0.0-2.9%, respectively; p=<0.001). Among people who currently smoked, LDCT screening also increased over the study period (0.0-6.4%), with higher increases in urban (0.1-8.0%) than rural (0.0-5.7%) or highly rural (0.1-6.1%) areas (p=<0.001). After the new screening guidelines in 2021, rates in all areas increased year-on-year. Conclusion: In this cohort, we found that smoking prevalence, cessation interventions, and lung cancer screening improved from 2014-2023 across the rural-urban continuum. Unfortunately, smoking prevalence remained higher and cessation interventions and screening rates lower in rural compared to urban areas. The persistent patterns underscore the need for strategies to overcome barriers for rural residents and improve lung cancer prevention and early detection in all areas. Citation Format: Brianna Tranby, Paul A. Decker, Jiang Ruoxiang, David Midthun, Lori C. Sakoda, Melinda C. Aldrich, Debra Friedman, Adoma Manful, Oindrila Bhattacharyya, Christi Patten, Chyke A. Doubeni. Patterns of tobacco use, cessation interventions, and lung cancer screening in rural vs. urban areas over time; a 10-year retrospective cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5045.

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 Background Murine double minute 2 (MDM2) inhibitors reactivate wild-type p53 and are a promising therapy for glioblastoma, IDH-wildtype (GBM). Brigimadlin is a highly potent MDM2 inhibitor being tested in a phase 0/1 clinical trial in combination with radiation in GBM. Methods Brigimadlin pharmacokinetics, pharmacodynamics, and efficacy were evaluated in GBM patient-derived xenografts (PDXs). Results In vitro, brigimadlin impaired viability in TP53 wild-type GBM with an IC50 of 0.8-6.6 nmol/L, but sensitivity did not correlate with MDM2 amplification. In vivo, MDM2 amplification was highly correlated with efficacy. In subcutaneous PDXs, 1 or 2 mg/kg brigimadlin dosed weekly was highly effective in 2 MDM2-amplified PDXs. At 2 mg/kg, brigimadlin delayed tumor regrowth by >5-fold in the MDM2-amplified PDXs compared to 1.5-fold in a non-amplified PDX. In orthotopic PDXs, efficacy was more limited, but 2 mg/kg brigimadlin enhanced the response to fractionated radiation in MDM2-amplified PDXs. Consistent with blood-brain barrier efflux limiting drug distribution, 2 mg/kg brigimadlin extended survival by >5-fold in an MDM2-amplified orthotopic PDX established in Rag1−/−Abcb1a−/− Abcg2−/− mice. In pharmacodynamic studies, p53 target genes were upregulated at both subtherapeutic and therapeutic dose levels, and the extent of activation did not correlate with MDM2 status. Concentrations of brigimadlin in tumor tissue were approximately 10-fold higher in MDM2-amplified tumors, and intracellular drug levels directly correlated with drug-dependent MDM2 upregulation, suggesting target binding affects drug accumulation. Conclusions Brigimadlin is highly effective in MDM2-amplified GBM when adequate drug levels are achieved in tumor tissue. MDM2 amplification impacts both treatment efficacy and intratumoral drug accumulation.

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 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.

Abstract The WHO 2021 classification of central nervous system tumors incorporated molecular markers to better define diffuse glioma subgroups. We analyzed array-based copy number, targeted next-generation sequencing, germline genotyping, and Illumina EPIC array methylation data from 404 primary IDH-mutant astrocytomas to further understand the relationship between copy number and outcomes. Loss of chromosome arm 13q was associated with poor overall survival after adjusting for age, sex, tumor grade, and treatment status (hazard ratio [HR]=2.04 (95% confidence interval: 1.25-3.33), p=0.004). Chromosome 13q loss was also significantly associated with the total number of copy number alterations (p<0.001). Loss of arm 11p was associated with poor survival in the adjusted analysis (HR=2.29 (1.38-3.79), p=0.001). Unsupervised clustering of the raw copy number data identified 12 clusters, where each cluster was defined by distinct copy number alteration(s). Clusters that contained 13q loss exhibited worse overall survival compared to other clusters. The clusters were significantly associated with age at diagnosis (p=0.015), tumor grade (p<0.001) and copy number complexity (p<0.001). The clusters with a high proportion of grade 4 tumors and with the highest complexity had the poorest overall survival. These findings highlight the prognostic relevance of chromosome 13q and 11p losses in IDH-mutant astrocytomas and support the integration of additional copy number alterations into molecular classification frameworks of IDH-mutant astrocytomas to improve risk stratification and patient management.

Abstract Imaging-based monitoring of gliomas is hampered by treatment-related changes, as well as the insidious growth of gliomas harboring the isocitrate dehydrogenase (IDH) mutation. The IDH mutation results in elevated D-2-hydroxyglutarate (D-2-HG), which is detectable in cerebrospinal fluid (CSF) that can be accessed longitudinally from multiple compartments, including cranially via CSF access devices. As such, we evaluated the early utility of CSF D-2-HG as a serially accessible biomarker for IDH-mutant gliomas. A CLIA-approved gas chromatography mass spectrometry assay was developed for CSF D- and L-2-HG. Lumbar and cranial CSF samples were collected from patients with IDH-mutant gliomas or IDH-wild-type brain tumors and non-tumor pathologies via surgical field collection, lumbar punctures, Ommaya reservoirs, and ventriculoperitoneal shunts. First, to confirm the ability of CSF D-2-HG to detect IDH-mutant gliomas, concentrations were compared between IDH-mutant gliomas and wild-type lesions. Cranial, but not lumbar, CSF D-2-HG distinguished primary IDH-mutant gliomas from IDH-wild type lesions (cranial AUC= 0.89, 95% confidence interval (CI)= 0.80-0.97); lumbar AUC= 0.52, 95% CI=0.28-0.76). Indeed, CSF D-2-HG was significantly higher in cranial than lumbar samples from IDH-mutant glioma patients (median lumbar=0.20 μM, cranial = 1.72 μM; p<0.0001), confirming a significantly elevated abundance of CSF D-2-HG only in cranial CSF from IDH-mutant gliomas. Then, response to cytoreduction was evaluated, demonstrating that cranial CSF D-2-HG levels decreased to 0.54x of baseline with resection in seventeen patients (p=0.0129). Changes in tumor-to-CSF contact impacted the serial evaluation of CSF D-2-HG prior to versus after chemoradiation in some patients. Nevertheless, in patients with grade 4 IDH-mutant astrocytomas, serial cranial CSF D-2-HG increased with disease progression and differentiated stability from pseudoprogression when tumor-CSF contact was sufficient. In conclusion, serial cranial CSF D-2-HG shows promise as a monitoring biomarker in patients with IDH-mutant gliomas, although anatomic variables must be considered in parallel to CSF D-2-HG evaluation.

Abstract Failure to repair radiation (RT)-induced DNA damage results in cell lethality, and combined treatment with DNA repair inhibitors can markedly enhance tumor control achieved with radiation. These inhibitors also can potentiate acute RT toxicities by suppressing DNA repair. Capitalizing on this normal tissue radiosensitizing effect, we determined effective radiosensitizing dosing regimens in mice for the ATM inhibitor WSD0628 and the DNAPK inhibitor WNC0901 in mice. Drugs were administered via oral gavage prior to each RT dose, and RT was delivered using an anterior/posterior 225 kVp beam. In an initial iteration of this model, various combinations of WSD0628 and RT were administered to cohorts of mice (n=5/group) and body weight was followed for 2 weeks. The resulting data were modeled to define the total dose of radiation estimated to cause 50% of mice to lose >20% body weight (D50). There was an inverse relationship between the WSD0628 and D50 doses: 0 mg/kg WSD0628/24.0 Gy (95% confidence interval (CI) 24.5–24.7); 0.25 mg/kg/19.2 Gy (95% CI 16.7–21.8); 1 mg/kg/13.7 Gy (95% CI 13.2–14.2); 2.5 mg/kg/11.0 Gy (95% CI 10.5–11.5); 5 mg/kg/11.3 Gy (95% CI 6.7–15.9) and 7.5 mg/kg/7.8 Gy (95% CI 7.8–7.9). Histological review of tongues from animals treated with 15 Gy RT (5Gy x 3) + 7.5 mg/kg WSD0628 demonstrated epithelial denudation and acute inflammation consistent with oral mucositis, while these features were not seen with the same dose as RT alone. Similar WSD0628 doses were evaluated in combination with 8 Gy supratentorial RT (via lateral beams) in orthotopic GBM43 PDX tumors. By treatment arm, the median time to a moribund state was: sham (29 days (d)), RT (39d), 1 mg/kg (42d), 2.5 mg/kg (49d), 5 mg/kg (65d), and 10 mg/kg (123d, overall p<0.0001). A second mucositis model was used for evaluation of WNC0901. Cohorts of 5 mice/group were treated with a set dose of RT (4Gy x 3) to the oral cavity alone and in combination with various doses of WNC0901, and the fraction of mice with >20% weight loss within 2 weeks was determined. The results of this study are as follows: placebo (0 of 5 mice with >20% weight loss); 15 mg/kg (3 of 5 mice); 25, 50, or 100 mg/kg (5 of 5 mice). Based on these data, we defined 25 mg/kg WNC0901 as the minimally effective dose and predicted that 50 mg/kg would provide robust radiosensitization. Consistent with this prediction, co-treatment of B16 melanoma flank tumor xenografts with RT (8Gy x 1) and WNC0901 (50 mg/kg) more than doubled the median tumor control duration (median 42 [range 24-45] days; time from implantation for tumor to exceed 1500 mm3) as compared to RT alone (24 [13-30] days) or sham treatment (17 [13-17] days; overall p<0.0001). This study describes how a radiation-induced oral mucositis model can be used to identify effective radiosensitizing regimens faster and with fewer resources than relying on tumor control studies alone. Citation Format: Danielle Burgenske, Darwin Garcia, Jiayan Le, Wenjuan Zhang, Sneha Rathi, Katrina K. Bakken, Lauren L. Ott, Zeng Hu, Brett L. Carlson, Ann Mladek, Jessica L. Abraham, Paul A. Decker, Matthew L . Kosel, Jeanette E. Eckel-Passow, William F. Elmquist, Wei Zhong, Jann N. Sarkaria. Radiation-induced oral mucositis to rapidly determine effective radiosensitizing dosing regimens for ATM and DNAPK inhibitors [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr B090.