Gonzales, Cesar Nava

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 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.