Porter, Alyx B.

Abstract BACKGROUND Selinexor (SEL) is a first-in-class XPO1 inhibitor with potent antitumor activity through the nuclear retention and reactivation of tumor suppressor proteins, reduced translation of oncogenes, and decreased expression of anti-apoptotic proteins. As a single agent, SEL has demonstrated brain penetration and clinically relevant responses in glioblastoma. We seek to enhance the effect and benefit of SEL by priming with temozolomide (TMZ). METHODS Through an NCI Project Team, we developed a multi-institutional phase I/II clinical trial which opened across the NCI Experimental Therapeutics Clinical Trial Network. Eligibility included histologically confirmed 1st recurrent MGMT promoter methylated glioblastoma. We evaluated safety, tolerability, and dose finding with an IQ 3 + 3 design of TMZ 150mg/m2 on days 1-5 of a 28-day cycle plus SEL on days 8 and 15. RESULTS From October 2022 to March 2024, we enrolled 12 patients (n=11 evaluable) into phase I of the study. Patient characteristics included 82% male, median age 62 (range 50-77), 64% white. The median number of cycles administered was 6 (range 1-12). Two dose levels (SEL 60mg and SEL 80mg) were evaluated. No dose-limiting toxicities (DLTs) were observed. The most common treatment related adverse events were nausea (58%), decreased platelet count (50%), fatigue (50%), constipation (42%), vomiting (25%), decreased lymphocyte count (25%), and decreased neutrophil count (25%). Grade 3+ treatment related adverse events included nausea, grade 3 (n=1); decreased lymphocyte count, grades 3-4 (n=2); and anorexia, grade 3 (n=1). Phase I has completed enrollment and the recommended phase II dose will be SEL 80mg. Additional results will be presented. CONCLUSION Results suggest that a sequential dosing regimen of SEL+TMZ is feasible, well-tolerated, and may minimize the cumulative toxicity of SEL. The phase II randomized portion of this trial is enrolling nationwide and will investigate efficacy and candidate molecular signatures of vulnerability (NCI #10505, NCT05432804).

Abstract Accurate response assessment in patients with high-grade gliomas (HGGs) is complicated by post-treatment radiographic changes that are equivocal for treatment effect versus progression. Plasma-based liquid biopsy assays for circulating tumor DNA (ctDNA) have had limited sensitivity to date, due in part to the presence of the blood-brain barrier that limits the diffusion of tumor-derived analytes out of the central nervous system. To address this challenge, we developed a ctDNA plasma-based assay to detect individualized tumor-derived amplified junctions in patients with HGGs. These junctions occur due to large-scale chromosomal rearrangements, such as those associated with epidermal growth factor receptor (EGFR) or cyclin-dependent kinase 4 (CDK4) amplification. To identify these amplified junctions, whole genome sequencing was performed on resected tissue. Primers were designed to flank junction breakpoints and were then utilized on each patient’s plasma samples to detect the amplified junction via qPCR. Tumor-specific amplified junctions were robustly detected in plasma from 14/14 (100%) patients with IDH-mutant or wild-type grade 4 astrocytomas whose tumor genomes demonstrated amplification-related junctions, located at chromosomes 7, 11, or 12. In comparison, ctDNA could not be detected in three control cases that did not have an amplification or did not have high copy number junctions. Importantly, serial plasma samples obtained prior to and at disease recurrence in 4 patients demonstrated an increasing abundance of amplified junctions that accurately correlated with disease progression in 4/4 patients (100%). In two of these patients, increasing abundance of amplified junctions preceded radiographic changes compatible with disease progression by approximately 2 or 4 months, respectively. In conclusion, tumor-specific amplified junctions could reliably be detected in plasma from patients with IDH-mutant or wild-type grade 4 astrocytomas and correlated with changes in disease burden. Further work is currently ongoing to evaluate these promising results in a larger cohort of patients with serial plasma samples.

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<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 <80, OR 3.16, p=0.003; men: 90 vs <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<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 <80, OR 3.21, p<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.