Warrier, Gayathri

Abstract IDH-wildtype diffuse astrocytic gliomas with lower-grade histological features alongside specific genomic alterations (e.g., TERT promoter mutation, EGFR amplification, gain Chr7/loss Chr10) are presently diagnosed as glioblastoma, IDH-wildtype (GBM) under WHO 2021 criteria. Previous analyses categorized these into “early/evolving” or “surgically undersampled” GBMs. However, it remains unclear whether these tumors share identical clinical and prognostic features with histologic GBM. Data from IDH-wt molecular GBMs across six institutions were aggregated. Astrocytic IDH-wt tumors of any grade that lacked necrosis or microvascular proliferation on initial histopathology yet exhibited molecular alterations consistent with WHO 2021 GBM diagnosis, were included. Recursive partitioning analysis (RPA) was used to categorize patients into risk groups based on overall survival (OS). Among the 264 patients included, the median age was 60 years (IQR: 51-67), with 162 (61%) being male. Additionally, 210 (80%) were diagnosed since 2016, and 216 (82%) received chemoradiation plus adjuvant temozolomide (GBM-SOC). The median follow-up period was 3.9 years (95% CI: 3.4, 4.6). Identified univariate risk factors for worse survival included older age, biopsy-only status, EGFR amplification, absence of initial adjuvant treatment, low post-operative KPS, and tumor histologic grade of 3. RPA divided patients into four distinct survival groups (p<0.0001). The “best” risk group comprised lower grade (<=2) tumors (n=58, median OS (mOS): 40 months (95% CI: 33, NA)). The “second best” group included Grade 3 patients receiving GBM-SOC without steroids during initial RT (n=139, mOS: 25 months (95% CI: 22, 28)). The “second worst” group encompassed Grade 3 patients who were on steroids during initial RT while receiving GBM-SOC (n=38, mOS: 15 months (95% CI: 13, 20)). The “worst” group contained Grade 3 tumors that had not undergone SOC (n=29, mOS: 9 months (95% CI: 4, 26)). These data show that histologic features and treatment are strong indicators of patient outcomes.

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.

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 BACKGROUND The prognostic importance of maximal resection of contrast enhancing and non-contrast enhancing disease has been established. Nonetheless, glioblastomas exist within the framework of complex neural circuitry serving cognition, movement, and behavior consequential leading to neurological impairments. The prognostic importance of neurological impairments on survival remains poorly understood. METHODS This is a retrospective, single cohort study from UCSF including 316 eligible patients diagnosed over 20 years with 9.6 years of follow-up. All patients underwent surgical resection for newly diagnosed glioblastoma for whom survival, molecular, preoperative and postoperative MRI images, and clinical data were available. All patients had chemoradiation treated IDH-wild-type glioblastoma with available preoperative and 1-month post-surgical resection neurological outcomes. We employed survival models and recursive partitioning (RPA) to investigate multivariate relationships of overall survival (OS). RESULTS Preoperative neurological impairments were present in 75.6% (n= 239) and new post resection impairments were identified in 37.3% (n=117). Univariate analysis confirmed that new postoperative cognitive impairment [HR 7.91, 95% CI 2.47-25.33] and hemiplegia [HR 3.38, 95% CI 0.83-13.67] (not hemiparesis) impact OS. Risk stratified grouping by RPA demonstrated that gross total resection of contrast enhancing tumor in patients with no new postoperative neurological impairments confers the longest OS (median OS 27.1 months 95%CI 21.5-33.7). Patients with any residual tumor volume after surgery but no new neurological deficits experience a similar survival to younger patients (under 65) with 1 or more new postoperative neurological deficits (median OS 16.6 months 95%CI 15.2-19.2). Shortest OS is identified in patients with any volume of residual tumor plus 1 or more new postoperative neurological deficits and age over 65 (median OS 11.4 months, 95%CI 9.3-13.5). CONCLUSIONS This study confirms that new postoperative neurological impairments impact overall survival in patients with chemoradiation treated IDH-wild-type glioblastoma.