Molinaro, Annette M.

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 The CCDC26 germline variant rs55705857 is causal for development of IDH mutant (IDHmut) adult glioma. However, ~60% of IDHmut patients do not carry the rs55705857 risk allele. We aimed to identify variants associated with developing IDHmut glioma among patients that do not have the rs55705857 risk allele and to further understand development of IDHwt glioma. Methods We used three datasets that included 1216 IDHmut and 1442 IDHwt glioma patients and a case–case design to perform genome-wide association (GWAS) analyses comparing IDHmut versus IDHwt glioma. Analyses were performed overall and stratified by rs55705857 genotype and sex. Multivariable logistic regression and regression trees were used to develop models to predict IDH status using germline variants, age, and contrast enhancement on MRI. Results Three regions were identified comparing IDHmut versus IDHwt: rs55705857 (meta P-value [P] = 1.35 × 10−43), PHLDB1 (rs7125115, P = 3.46 × 10−17), and D2HGDH (rs71430382, P = 2.43 × 10−12). When analyzing only patients that do not have the rs55705857 risk allele, PHLDB1 (rs7125115, P = 1.73 × 10−13) and D2HGDH (rs71430382, P = 8.86 × 10−10) were identified. Among patients who have the rs55705857 risk allele, four variants between ROBO1 and ROBO2 (rs4680975, P = 4.65 × 10−7) increased the likelihood of having an IDHwt tumor. Tumor expression of ROBO1 was associated with rs4680975 genotype in IDHwt patients that have the rs55705857 risk allele (P = 0.034). Multivariable logistic analysis demonstrated that rs55705857, rs71430382 (D2HGDH), and age predicted IDH mutation status. Conclusions To understand the development of adult glioma, we demonstrate that D2HGDH and PHLDB1 should be prioritized for functional studies in IDHmut tumors. The ROBO1 region warrants further investigation in IDHwt tumors.

Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factors remains limited. To test the interactive effects of demographic, socioeconomic, and oncologic variables on trial enrollment, we designed boosted neural networks (BNNs) for all glioma patients ( n = 1042), women ( n = 445, 42.7%), and minorities ( n = 151, 14.5%) and externally validated these models [whole cohort, n = 230; women, n = 89 (38.7%); minority, n = 66 (28.7%)]. For the whole-cohort BNN, the most influential variables on enrollment were oncologic variables, including KPS [total effect (TE), 0.327], chemotherapy (TE, 0.326), tumor location (TE, 0.322), and seizures (TE, 0.239). The women-only BNN exhibited a similar trend. Conversely, for the minority-only BNN, socioeconomic variables [insurance status (TE, 0.213), occupation classification (TE, 0.204), and employment status (TE, 0.150)] were most influential. These results may help prioritize patient-specific initiatives to increase accrual.

Abstract Background Temozolomide (TMZ) treatment has demonstrated a variable impact on glioma prognosis. This study examines associations of survival with DNA repair gene germline polymorphisms among glioma patients who did and did not have TMZ treatment. Identifying genetic markers which sensitize tumor cells to TMZ could personalize therapy and improve outcomes. Methods We evaluated TMZ-related survival associations of pathogenic germline SNPs and genetically predicted transcript levels within 34 DNA repair genes among 1504 glioma patients from the University of California San Francisco Adult Glioma Study (UCSF AGS) and Mayo Clinic, whose diagnoses spanned pre- and post-TMZ eras within the major known glioma prognostic molecular subtypes. Results Among those who received TMZ, 5 SNPs were associated with overall survival, but not in those who did not receive TMZ. Only rs2308321-G, in MGMT, was associated with decreased survival (hazard ratio = 1.21, P = .019) for all glioma subtypes. Rs73191162-T (near UNG), rs13076508-C (near PARP3), rs7840433-A (near NEIL2), and rs3130618-A (near MSH5) were only associated with survival and TMZ treatment for certain subtypes, suggesting subtype-specific germline chemosensitization. Genetically predicted elevated expression of PNKP, compared to normal brain expression, was associated with markedly poor survival in TMZ-treated patients with isocitrate dehydrogenase (IDH)-mutant and 1p/19q non-codeleted gliomas (P = .015), with a median difference of over 70 months in overall survival times. Similarly, NEIL2 and TDG expressions were associated with altered TMZ-related survival only among certain subtypes. Conclusions Functional germline alterations within DNA repair genes were associated with TMZ sensitivity, measured by overall survival among adults with glioma. These variants should be evaluated in prospective analyses and functional studies.

Abstract Acquired tumor alterations are part of the WHO diagnosis of glioma. However, germline predisposition to these alterations is unknown. GWAS in European and Asian populations identified germline variants in 30 regions, many in or near genes that also acquire alterations, suggesting that interactions between inherited and acquired genetics are involved in glioma development. To evaluate such interactions, we used 3686 glioma cases and 1889 controls. A case-control design and multinomial logistic regression were used to identify germline variants associated with development of TERT-promoter mutant gliomas, generating two interesting observations. First, TERT germline variants were associated with predisposition to IDHwt glioma that have a TERT promoter mutation, but not with IDH-mutant codeleted gliomas that have a TERT promoter mutation. Second, two novel regions were observed in this more homogeneous subgroup of TERT mutated tumors: variants in TXNDC11 (OR=2.66, p=5.7x10-8) and RUVBL2 (OR=3.1, p=5.8x10-8). High tumor expression of TXNDC11 is associated with poor prognosis in glioma and RUVBL2 interacts with TERT to affect telomerase activity. Similar analyses are being performed on additional clinically relevant acquired alterations. In parallel, case-case GWAS analyses were performed to evaluate germline variants associated with other tumor alterations. Comparing IDH-mutant versus IDHwt stratified by rs55705857 genotype, the most significant variants were PHLDB1 (p=1.2x10-13) and D2HGDH (p=2.1x10-10), highlighting the importance of these two variants independent of CCDC26 for the development of IDH-mutant glioma. As a parallel approach, we utilized recursive partitioning and regression trees (rpart) to classify IDH mutation status using the 30 known variants. The top four branches of the regression tree included CCDC26, D2HGDH, PHLDB1, and MAML2, as well as two Asian variants (RAB27A, CYP4F12). This highlights the importance of utilizing variants discovered in non-European populations in analyses. Overall, understanding these interactions are necessary to develop therapeutics, stratify clinical trials, and to develop relevant tumor models.

Abstract The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient’s demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.

PURPOSE The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve reliability of response assessment in glioma trials. Over time, some limitations of these criteria were identified, and challenges emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. METHODS Informed by data from studies evaluating the different criteria, updates to the RANO criteria are proposed (RANO 2.0). RESULTS We recommend a standard set of criteria for both high- and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the postradiotherapy magnetic resonance imaging (MRI), rather than the postsurgical MRI, will be used as the baseline for comparison with subsequent scans. Since the incidence of pseudoprogression is high in the 12 weeks after radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, are required to define tumor progression. However, confirmation scans are not mandatory after this period nor for the evaluation of treatment for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is highly recommended. The primary measurement remains the maximum cross-sectional area of tumor (two-dimensional) but volumetric measurements are an option. For IDH wild-type glioblastoma, the nonenhancing disease will no longer be evaluated except when assessing response to antiangiogenic agents. In IDH-mutated tumors with a significant nonenhancing component, clinical trials may require evaluating both the enhancing and nonenhancing tumor components for response assessment. CONCLUSION The revised RANO 2.0 criteria refine response assessment in gliomas.

Abstract BACKGROUND The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve reliability of response assessment in glioma trials. Over time some limitations of these criteria were identified, and uncertainty emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. MATERIAL AND METHODS Informed by data from a cohort of glioblastoma patients and other evaluations of RANO criteria that allowed evaluation of features of the different criteria, the RANO working group developed updates to the RANO criteria (RANO 2.0). RESULTS Based on the 2021 WHO classification of gliomas, we recommend a standard set of criteria for both high and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the post-radiotherapy MRI, rather than the post-surgical MRI, will be used as the baseline for future comparison. Since the incidence of pseudoprogression is high in the 12 weeks following radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, is required to define tumor progression. However, confirmation scans are not mandatory after this period nor for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is highly recommended. The primary measurement remains the maximum cross-sectional area of tumor (2-dimensional) but volumetric measurements are an option. For IDH-wildtype glioblastoma, the non-enhancing disease will no longer be evaluated. In IDH-mutated tumors with a significant non-enhancing component, clinical trials may require evaluating both the enhancing and non-enhancing tumor components for response assessment. CONCLUSION These criteria represent a work in progress that will hopefully improve the assessment of response in glioma trials. Future updates will incorporate novel developments, advanced imaging techniques, and endpoints as they become validated.

Abstract The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve the reliability of response assessment in glioma trials. Over time some limitations of these criteria were identified, and uncertainty emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. Informed by data from a cohort of glioblastoma patients and other evaluations of RANO criteria that allowed evaluation of features of the different criteria, we propose updates to the RANO criteria (RANO 2.0). Based on the 2021 WHO classification of gliomas, we recommend a standard set of criteria for both high and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the post-radiotherapy MRI, rather than the post-surgical MRI, will be used as the baseline for future comparison. Since the incidence of pseudoprogression is high in the 12 weeks following radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, is required to define tumor progression. However, confirmation scans are not mandatory after this period nor for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is an option. The primary measurement remains the maximum cross-sectional area of tumor (2-dimensional) but volumetric measurements are an option. For IDH-wildtype glioblastoma, the non-enhancing disease will no longer be evaluated. In IDH-mutated tumors with a significant non-enhancing component, clinical trials may require evaluating both the enhancing and non-enhancing tumor components for response assessment.

2017 Background: Response Assessment in Neuro-Oncology (RANO) criteria for high-grade and low-grade glioma (HGG and LGG) were developed to improve reliability of response assessment in glioma trials. Several limitations of the original RANO criteria have been reported. Methods: To address limitations of the original RANO criteria, a large cohort of patients with newly diagnosed and recurrent glioblastoma were evaluated comparing RANO-HGG with modified RANO (mRANO) and immunotherapy RANO (iRANO) criteria to inform the following proposed updates (RANO 2.0). Results: Based on the 2021 WHO classification of glioma, we recommend a single common set of criteria for all gliomas regardless of WHO grade and IDH mutational status. These criteria will be used for all trials regardless of the treatment modalities being evaluated. In newly diagnosed glioblastoma, the post-radiotherapy MRI, usually obtained approximately 4 weeks after completion of radiotherapy, rather than the post-surgical MRI as proposed in original RANO criteria, will be used as the baseline for comparison of subsequent scans. Since the incidence of pseudoprogression is high in the first 3 months following radiotherapy, confirmation of progression during this period with a repeat MRI (in 4-8 weeks) will be required for determining radiographic progression of disease (PD) if the patient is clinically stable. However, confirmation scans are not mandatory after this period nor for recurrent tumors since these scans do not appear to improve reliability in determining progression. For agents with a high likelihood of producing pseudoprogression such as viral therapies and some other immunotherapies, mandatory confirmation of progression with a repeat MRI is an option. The primary measurement will remain the 2-dimensional maximum cross-sectional area of tumor as the simplest method, but where resources are available, volumetric measurements are an option. For IDH wild-type tumors with contrast enhancement, non-enhancing tumor will no longer be evaluated with the possible exception of patients receiving antiangiogenic therapies. However, in tumors with a significant non-enhancing component, both the contrast enhancing and non-enhancing components will be evaluated as target lesions. Conclusions: We hope that these and other proposed changes by the RANO working group will improve response assessment in glioma clinical trials and help the development of more effective therapy for patients.