Porter, Alyx

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 Purpose: Monitoring disease progression in patients with high-grade gliomas (HGG) is challenging due to treatment-related changes in imaging and the requirement for neurosurgical intervention to obtain diagnostic tissue. DNA junctions in HGG often amplify oncogenes, making these DNA fragments potentially more abundant in blood than monoallelic mutations. In this study, we piloted a cell-free DNA approach for disease detection in the plasma of patients with HGG by leveraging patient-specific DNA junctions associated with oncogene amplifications. Experimental Design: Whole-genome sequencing of grade 3 or 4 isocitrate dehydrogenase–mutant or wild-type astrocytomas was utilized to identify amplified junctions. Individualized qPCR assays were developed using patient-specific primers designed for the amplified junction. ctDNA levels containing these junctions were measured in patient plasma samples. Results: Unique amplified junctions were evaluated by individualized semi-qPCR assays in presurgical plasma of 18 patients, 15 with tumor-associated focal amplifications and three without tumor-associated focal amplifications. high copy-number junctions were robustly detected in the plasma of 14 of 15 (93.3%) patients with amplified junctions and none of the controls. Changes in junction abundance correlated with disease trajectory in serial plasma samples from five patients, including increased abundance of amplified junctions preceding radiographic disease progression. Conclusions: In patients with grade 3 or 4 astrocytomas who had tumor-associated amplifications, patient-specific amplified junctions were successfully detected in assayed plasma from most patients. Longitudinal analysis of plasma samples correlated with disease trajectory, including cytoreduction and progression.

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.