Hervey‐Jumper, Shawn L.

PURPOSE Age ≥40 years is regarded as a high-risk feature and an indication for adjuvant chemoradiotherapy for patients with lower-grade glioma in clinical practice guidelines. It is unclear whether age remains a relevant prognostic factor for contemporary definitions of lower-grade gliomas in the molecular era. METHODS The Prospective Gliomas Research (PROGRES) database contains individual patient-level data from 11 prospective clinical trials or observational registries of histologically defined lower-grade 2-3 oligodendroglioma or astrocytoma. We determined the association of age (18-39 years v ≥40 years) with progression-free survival (PFS) stratified by isocitrate dehydrogenase 1 or 2 ( IDH1/2 ) status, using log-rank tests and Cox regression models. We validated our findings in a separate multi-institutional retrospective cohort (Retrospective Glioma Research [REGRES] database). RESULTS We identified 1,619 and 1,292 eligible patients in the PROGRES and REGRES cohorts, respectively. IDH -wildtype tumors were more common in patients 40 years and older (38% v 5%, odds ratio: 11.3 [95% CI, 6.5 to 19.7]). Age was associated with PFS in IDH -wildtype (5-year PFS for ≥40 v 18-39 years: 6% v 24%, hazard ratio [HR], 1.74 [95% CI, 1.21 to 2.50]) but not in IDH -mutant glioma (60% v 59%, HR, 0.89 [95% CI, 0.76 to 1.05], P interaction < .001). In IDH -wildtype tumors, older age predicted aggressive molecular features, including TERT promoter mutation (65% v 28%), EGFR amplification (41% v 15%), and chromosome +7/–10 alteration (57% v 25%). In a pooled analysis of four clinical trials, age was not predictive of a benefit from chemoradiotherapy versus radiotherapy alone for IDH -mutant glioma. CONCLUSION In the absence of additional clinical or molecular risk factors, age alone should not be considered an indication for administration or deferral of adjuvant treatment. Practice guidelines should be revised to reflect contemporary prognostic factors in the molecular era.

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 Oligodendrogliomas are initially slow-growing brain tumors that are prone to malignant transformation despite surgery and cytotoxic therapy. Understanding of oligodendroglioma evolution and new treatments for patients have been encumbered by a paucity of patient-matched newly diagnosed and recurrent tumor samples for multiplatform analyses, and by a lack of preclinical models for interrogation of therapeutic vulnerabilities that drive oligodendroglioma growth. Here we integrate spatial and functional analyses of tumor samples and patient-derived organoid co-cultures to show that synaptic connectivity is a hallmark of oligodendroglioma evolution and recurrence. We find that patient-matched recurrent oligodendrogliomas are enriched in synaptic gene expression programs irrespective of previous therapy or histologic grade. Analyses of spatial, single-cell, and clinical data reveal epigenetic misactivation of synaptic genes that are concentrated in regions of cortical infiltration and can be used to predict eventual oligodendroglioma recurrence. To translate these findings to patients, we show that local field potentials from tumor-infiltrated cortex at the time of resection and neuronal hyperexcitability and synchrony in patient-derived organoid co-cultures are associated with oligodendroglioma proliferation and recurrence. In preclinical models, we find that neurophysiologic drugs block oligodendroglioma growth and pathologic electrophysiology. These results elucidate mechanisms underlying oligodendroglioma evolution from an indolent tumor to a fatal disease and shed light on new biomarkers and new treatments for patients.

Abstract BACKGROUND Despite changes in national policy, women and minority patients remain under-accrued in glioma clinical trials. Supervised machine learning may serve as a novel approach to understanding which variables and their complex interplay most influence enrollment for underrepresented patients. We aimed to design and externally validate machine learning models to predict therapeutic clinical trial enrollment for a) all patients, b) women, and c) NIH-designated minority patients with low- and high-grade glioma. METHODS In a 20-year retrospective cohort of 1042 adult patients with low- and high-grade glioma, boosted neural network (BNN) models for the whole, women, and minority cohorts were used to analyze the combined effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment. These models were validated using an external cohort (n=103). RESULTS In the development cohort (445 [42.7%] women; 151 [14.5%] minorities; median age, 51.5 [interquartile range: 39.4-62.5] years), 350 patients (33.6%) enrolled in a therapeutic clinical trial. The whole cohort BNN (AUC: 0.827, misclassification rate (MR): 0.175), the women cohort BNN (AUC: 0.895, MR: 0.125), and the minority cohort BNN (AUC: 0.983, MR: 0.053) models accurately predicted trial enrollment on external validation. For the whole cohort and women models, several demographic (race, ethnicity, minority status, sex), socioeconomic (insurance, employment status), and oncologic variables (seizure, chemotherapy, KPS, WHO grade, tumor lobe) were equally influential (Main effect (ME): 1.000, Total effect (TE): 1.000) on enrollment. For the minority model, socioeconomic variables including insurance status (ME: 0.089, TE: 0.417) and occupation classification (ME: 0.087, TE: 0.381) drove enrollment. CONCLUSION We designed and externally validated the first machine learning model for predicting therapeutic clinical trial enrollment for patients with diffuse glioma. Model features driving trial enrollment varied for underrepresented patients. These results may help prioritize personalized patient-specific initiatives to address barriers to equitable patient accrual.

Abstract Genomic alterations and molecular subgroups between extracranial disease sites and brain metastases often differ, yet the prevalence and clinical significance of divergent evolution in brain metastases is not fully understood. This study examined genomic alterations and molecular subgroup divergence rates between matched brain metastases and extracranial disease sites. A retrospective, two-center study was conducted on patients who underwent resection of brain metastases between 2014-2022 with clinical and matched genomic data and/or molecular markers available. The overall cohort comprised 256 patients with matched tumors from multiple cancer types, most commonly from breast (n=108, 42.2%), melanoma (n=83, 32.4%), and NSCLC (n=43, 16.8%). Targeted next-generation sequencing of over 500 oncogenes was performed on resected tumors as part of clinical care (n=41 patients with matched specimens). Molecular subgroups for breast, melanoma, and non-small cell lung cancer were also assessed by immunohistochemistry to evaluate rates of molecular subgroup discordance between sites. Subgroup discordance by immunohistochemistry was observed most frequently across breast cancer (22.2%, n=24/108) with loss of the estrogen receptor (ER) and progesterone receptor (PR) and gain of HER2 most frequently observed within brain metastases. Molecular subgroup discordance was rare within melanoma (7.3%, n=6/82) and NSCLC (14.0%, n=6/43) patients. When evaluating oncogenomic alterations from targeted DNA sequencing in 41 patients with matched specimens, 80.5% (33/41) of brain metastases demonstrated a unique oncogene alteration profile compared to an extracranial disease site. Fourteen patients (34.1%) had a gain of at least 1 targetable alteration within a brain metastasis that was not observed in an extracranial disease site. Brain metastases demonstrated high rates of genomic divergence compared to primary and extracranial metastatic disease sites. This data supports obtaining brain metastasis tissue and conducting molecular profiling of this tissue to help select optimal CNS penetrant systemic therapies for each patient.

Abstract Current standard of care for IDH-wildtype glioblastoma (GBM) includes maximal safe resection, adjuvant radiation, and chemotherapy with the alkylating agent temozolomide. A subset of gliomas treated with temozolomide develop somatic hypermutation with a predominance of C&gt;T transitions due to the alkylation effects on DNA. However, the frequency of occurrence, clinical ramifications, and predictive biomarkers for developing temozolomide-induced hypermutation (TMZ-HM) in GBM are unknown. Here we performed comprehensive histopathologic, genomic, and epigenomic evaluation of paired initial and recurrent GBM from 106 patients. Seven patients (7%) developed hypermutation at time of first recurrence, while an additional five patients (5%) developed hypermutation at a subsequent recurrence. Patients who developed TMZ-HM had a significantly longer interval between initial surgery and first recurrence (27.8 vs. 9.9 months, p&lt;0.001). In contrast to IDH-mutant gliomas where TMZ-HM is associated with poor prognosis, those patients with IDH-wildtype GBM who developed TMZ-HM had significantly longer overall survival from both initial surgery (67.9 vs. 20.3 months, p&lt;0.001) and from time of recurrence after development of hypermutation (30.9 vs. 9.5 months, p=0.001). There was no difference in oncogenic alteration frequency in initial GBM that subsequently developed TMZ-HM. However, 4 specific CpG sites out of 12 total interrogated sites in the promoter region of MGMT (a DNA repair enzyme) and 5 specific CpG sites in the promoter region of KCNQ1DN (a negative regulator of c-Myc) were significantly more hypermethylated in those GBM that developed TMZ-HM. We then generated a weighted risk score across these CpG sites that can be used to prospectively identify GBM likely to develop TMZ-HM associated with favorable survival. In summary, patients with GBM that develop TMZ-HM follow a more favorable clinical course, and specific MGMT and KCNQ1DN promoter hypermethylation patterns could serve as a biomarker to identify these patients who may potentially benefit from extended alkylating chemotherapy treatment.

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.

Abstract Harnessing the neuroplastic potential of the human brain is being increasingly recognized as an important neuro-oncological paradigm to facilitate safe resection of brain tumors while preserving neurological function and quality of life. Interventional neurorehabilitation, employing both invasive and noninvasive neuromodulation techniques, represents an important emerging therapeutic strategy to induce or enhance neural plasticity to promote functional recovery in brain tumor patients. This study aimed to conduct a comprehensive review of interventional neurorehabilitation techniques for glioma patients. Methods In accordance with PRISMA guidelines, searches of Medline, Embase, Web of Science, APA PsycINFO, and Cochrane were undertaken from database inception to November 28, 2023. Studies reporting on neuromodulation applied to glioma patients were included. Results Seven studies reporting findings from 118 patients met the inclusion criteria. Three neuromodulation techniques were identified and included transcranial magnetic stimulation (TMS) reported in 5 out of 7 (71.4%) studies; transcranial direct current stimulation (tDCS); and continuous cortical electrical stimulation (cCES) using grid electrodes, reported in one study each. All studies applying noninvasive stimulation to ameliorate postoperative deficits demonstrated an improvement on at least one outcome measure. The 2 studies applying tDCS and cCES to induce plasticity reported evidence of functional reorganization. Conclusions There is emerging evidence of benefits of neuromodulation to improve postoperative outcome in glioma patients. In the current literature, noninvasive stimulation has shown to have a favorable safety profile. Large-scale, double-blind, sham-controlled trials are warranted to further investigate the effectiveness of these interventions for modulating different cognitive networks in patients undergoing glioma surgery.

1614 Background: Despite the unique ability of machine learning (ML) techniques to uncover complex interactive effects in datasets, studies exploring predictors of clinical trial enrollment have largely used standard statistical methods to discern associations. Furthermore, there have been limited applications of ML to close existing disparities in clinical trial enrollment of underrepresented patients in neuro-oncology. This study aims to use supervised machine learning to define and validate primary contributors of therapeutic clinical trial enrollment for a) all patients, b) women and c) NIH-designated minority patients with low- and high-grade glioma. Methods: Adult glioma patients who received care from the UCSF Brain Tumor Center between 1997- 2017 were identified in a prospective registry. Bootstrap forest (BF) and Recursive Partitioning (RP) models were created by randomly dividing patients in a 70/30 split into development (DEV) and validation (VAL) cohorts among all patients, women, and NIH-designated minority patients, separately. Model performance was assessed using the area under the curve (AUC) in the DEV and VAL cohorts. Results: Among 1042 patients, 350 patients (33.6%) enrolled in a therapeutic clinical trial. There were 445 women (42.7%), of which 144 (32.4%) enrolled, and 141 minority patients (13.5%), of which 39 (27.7%) enrolled. For all patients, in order of decreasing influence, the BF model selected median neighborhood household income, age, neighborhood poverty level, distance from hospital, tumor location, tumor volume, treatment with chemotherapy, occupation, KPS, insurance status, employment status, seizure at presentation, and volumetric extent of resection (EOR) to predict trial enrollment (DEV AUC: 0.984; VAL AUC: 0.746). For women, the BF model selected distance from hospital, age, household income, neighborhood poverty level, tumor location, treatment with chemotherapy, and KPS for trial prediction (DEV AUC: 0.972; VAL AUC: 0.746), whereas for minority patients, the BF model selected neighborhood poverty level, age, occupation, distance from hospital, tumor volume, insurance status, employment status, and EOR to predict trial enrollment (DEV AUC: 0.9830; VAL AUC: 0.769). The RP model for women designated those with non-White race as less likely to enroll (DEV AUC: 0.735; VAL AUC: 0.665) while the RP model for minority patients characterized those who preferred a non-English language or who were divorced or widowed as less likely to enroll (DEV AUC: 0.775; VAL AUC: 0.601). Conclusions: Supervised machine learning models achieved similar predictive performance among patient subgroups, while uncovering interactions between gender identity, minority status, and sociodemographic variables. These findings can guide targeted recruiting efforts to advance equitable trial enrollment for women and minorities.