Chukwueke, Ugonma

TPS2103 Background: Treatment for newly diagnosed IDH-mutant astrocytoma, WHO grade 3, is based on the results of the CATNON study, demonstrating benefit of radiotherapy followed by 12 months of adjuvant temozolomide. While temozolomide and radiotherapy significantly improved survival, the prognosis for these patients remains limited and more effective therapies are required. This trial will evaluate the benefit of the addition of the IDH1 and IDH-2 inhibitor vorasidenib to adjuvant temozolomide versus placebo following standard radiotherapy to evaluate possible improvement in outcomes. Methods: Alliance A072301 (NCT07215910) is a multicenter, double-blinded phase 3 randomized study to determine if the addition of vorasidenib to adjuvant temozolomide significantly improves progression-free survival (PFS), based on blinded central review in patients with newly diagnosed, IDH-mutant astrocytoma, WHO grade 3. Key inclusion criteria include age ≥ 12 years old, histological confirmation of astrocytoma (absence of 1p/19q codeletion), WHO grade 3, presence of any IDH mutation, plan for radiation and chemotherapy and surgery within 6 months. The presence of CDKN2A/B homozygous deletion, spinal or leptomeningeal disease, as well as prior chemotherapy, cranial irradiation or IDH inhibitor therapy, is exclusionary. 408 patients will be randomized 1:1 to receive vorasidenib 40 mg or placebo daily with 12 cycles of temozolomide following radiotherapy and then continued as monotherapy until disease progression or unacceptable toxicity. Stratification factors include age (< 40 years old vs ≥ 40 years old) and residual disease (< 2 cm vs ≥ 2 cm). Tumor assessments with MRI will be every three months for the first two years, every four months for the next two years and then every six months thereafter as per blinded independent central review using the Response Assessment in Neuro-Oncology (RANO) 2.0 criteria. Key secondary endpoints include PFS by local review, time to next treatment, safety and tolerability, quality of life measures. Exploratory measures will include seizure frequency. The trial will have 85% power to detect a hazard ratio of 0.625 with one-sided type I error rate of 0.025. Enrollment for the trial commenced February 2026. Support: U10CA180821, U10CA180882, U24CA196171. https://acknowledgments.alliancefound.org.

PURPOSE Integrating external control data into clinical trial designs and analyses has the potential to accelerate drug development processes. We reanalyzed the three experimental arms of the Individual Screening Trial of Innovative Glioblastoma Therapy (INSIGhT), a randomized phase II platform trial in newly diagnosed O 6 -methylguanine-DNA methyltransferase-unmethylated glioblastoma (ClinicalTrials.gov identifier: NCT02977780 ). To evaluate the validity of using external data sets, we compared treatment effect estimates based on internal INSIGhT control data and matched external control data. METHODS The three experimental arms of INSIGhT (abemaciclib [n = 72], neratinib [n = 80], and CC-115 [n = 12]) did not improve survival compared with internal controls (standard chemoradiation [n = 70]). We derived external control patient-level data from multiple real-world and clinical trial data sets. We applied propensity score matching and Cox proportional hazards models to estimate treatment effects with external controls. Additionally, using this glioblastoma (GBM) data collection, we specified simulation scenarios to evaluate trial designs that integrate external controls. RESULTS After matching to external controls, no survival benefit was observed for patients receiving abemaciclib (hazard ratio [HR], 1.00 [95% CI, 0.75 to 1.34]), neratinib (HR, 0.93 [95% CI, 0.70 to 1.24]), or CC-115 (HR, 0.88 [95% CI, 0.41 to 1.88]). Simulations, together with the INSIGhT data and a collection of GBM data sets, allowed us to examine efficiencies and risks of clinical trial designs that leverage external control data. CONCLUSION The use of carefully matched external controls, to replace or augment the internal controls of INSIGhT, produced treatment effect estimates that were similar to previously published analyses. Single-arm trial designs and hybrid randomized designs incorporating propensity score–matched external control data evaluated treatment effects in the early-phase testing of experimental therapies in newly diagnosed GBM. The validity of this approach and risks of bias depended on the availability of comprehensive and accurate data on all potential confounders, in the absence of unmeasured confounding.

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 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 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.

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.

Abstract Introduction: Despite the enactment of the 1993 NIH Revitalization Act to improve accrual of women and minorities in clinical trials, these groups remain underrepresented. This study aims to uncover drivers of therapeutic clinical trial enrollment that are unique to women and 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. Dividing the cohort into subgroups by gender and NIH-designated minority status, factors associated with therapeutic clinical trial enrollment on univariate logistic regression (p&lt;0.2) were selected for multivariate logistic regression. Results: There were 351 trial participants (41% women, 12% minority) and 627 non-trial participants (44% women, 17% minority). Among women only, seizure at presentation (OR 2.10, p=0.008) was associated with increased odds of trial enrollment. In contrast, among men only, higher median household income (OR 1.09 per $10,000 increase, p=0.006), higher WHO tumor grade (OR 1.89, p=0.045), and occupational status according to the International Standard Classification of Occupations [professionals (e.g., physicians) vs managers (e.g., supervisory positions), OR 2.87, p=0.043] were associated with increased odds of trial enrollment. Among both women and men, lack of insurance (women: OR 0.49, p=0.02; men: OR 0.55, p=0.031) and no treatment with chemotherapy (women: OR 0.18, p=0.001; men: OR 0.34, p=0.002) were associated with decreased odds of clinical trial enrollment, whereas higher KPS (women: 90 vs &lt;80, OR 3.16, p=0.003; men: 90 vs &lt;80, OR 3.15, p=0.002) was associated with increased odds of trial enrollment. Among NIH-designated minority patients, older age (OR 0.66 per 10-year increase, p=0.033) and non-English preferred language (OR: 0.30, p=0.035) were associated with decreased odds of trial enrollment on univariate analysis, but not on multivariate analysis. Among non-minority patients, lack of insurance (OR 0.58, p=0.008) and no chemotherapy treatment (OR 0.25, p&lt;0.001) were associated with decreased odds of trial enrollment, whereas seizure at presentation (OR 1.80, p=0.001), higher neighborhood household income (OR 1.07 per $10,000 increase, p=0.004), higher WHO grade (4 vs 2-3, OR 1.86, p=0.015), higher KPS (90 vs &lt;80, OR 3.21, p&lt;0.0001), and lower extent of tumor resection (80-89% vs 100%, OR 2.16, p=0.047) were associated with increased odds of trial enrollment. Conclusion: Drivers of trial enrollment differed for women and minority patients with glioma. Non-English-speaking minority patients may face additional barriers to trial enrollment requiring targeted interventions. Our findings can revitalize and tailor efforts to recruit women and minority patients to therapeutic clinical trials in neuro-oncology. Citation Format: Mulki Mehari, Gayathri Warrier, Sheantel Reihl, Abraham Dada, Aymen Kabir, Mikias Negussie, Cesar Nava Gonzales, Ugonma Chukwueke, Alyx Porter, Shawn Hervey-Jumper. Revitalizing the accrual of women and minorities: Unique drivers of therapeutic clinical trial enrollment for underrepresented patients with diffuse glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1006.

Abstract Despite major strides in cancer research and therapy, these advances have not been equitable across race and ethnicity. Historically marginalized groups (HMG) are more likely to have inadequate preventive screening, increased delays in diagnosis, and poor representation in clinical trials. Notably, Black, Hispanic, and Indigenous people represent 30% of the population but only 9% of oncology clinical trial participants. As a result, HMGs lack equitable access to novel therapies, contradicting the principle of distributive justice, as enshrined in the Belmont report, which demands the equitable selection of subjects in research involving human subjects. The lack of clinical trial diversity also leads to low generalizability and potentially harmful medical practices. Specifically, patients with brain cancer face unique barriers to clinical trial enrollment and completion due to disease-specific neurologic and treatment-induced conditions. Collectively, the intersection of these disease-specific conditions with social determinants of health fosters a lack of diversity in clinical trials. To ameliorate this disparity in neuro-oncology clinical trial participation, we present interventions focused on improving engagement of HMGs. Proposals range from inclusive trial design, decreasing barriers to care, expanding trial eligibility, access to tumor profiling for personalized medical trials, setting reasonable metrics and goals for accrual, working with patient community stakeholders, diversifying the neuro-oncology workforce, and development of tools to overcome biases with options to incentivize equity. The diversification of participation amongst neuro-oncology clinical trials is imperative. Equitable access and inclusion of HMG patients with brain tumors will not only enhance research discoveries but will also improve patient care.

Abstract BACKGROUND Leveraging external data has the potential to increase efficiency and precision of drug development in glioblastoma by complementing or replacing data from in-trial controls. We used an external control arm (ECA) to re-analyze data of the abemaciclib, neratinib and CC-115 arms of the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial. METHODS INSIGhT (NCT02977780) is an ongoing phase 2 adaptive platform trial in newly diagnosed MGMT unmethylated glioblastoma with a shared control arm of patients receiving radiation therapy with concurrent and maintenance temozolomide. The initial experimental arms (abemaciclib, neratinib, CC-115) have not shown a survival benefit relative to the shared control arm. We re-analyzed the experimental arm data and replaced the shared control arm with an ECA. The ECA was developed from a collection of trial datasets and real-world data available through the Glioblastoma External (GBM-X) Data Platform (). Propensity score matching was used to adjust for possible differences between pretreatment covariates (age, sex, resection, KPS) in the ECA and shared control arm. Cox proportional hazards models were used to estimate treatment effects. RESULTS On INSIGhT, patients were randomized to abemaciclib (n = 74), neratinib (n = 80), and CC-115 (n = 12). There were 687 unmethylated MGMT patients in the GBM-X ECA. After propensity score matching, there was no significant treatment effect associated with abemaciclib (HR 1.091, 95%CI:0.834-1.433, p = 0.536), neratinib (HR = 1.043, 95%CI:0.787-1.383, p = 0.770), or CC-115 (HR 1.088, 95%CI:0.834-1.433, p = 0.794) compared to the ECA. CONCLUSION In comparison to an ECA, there was no significant survival benefit with abemaciclib, neratinib and CC-115. Treatment effects estimates were similar to the primary analyses based on the original shared control arm of INSIGhT. The use of external control arms in early phase testing of new therapies is supported by our findings and could significantly reduce costs and time to conduct trials in newly diagnosed glioblastoma.