Search

Introduction: Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. Methods: We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. Results: We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings. Discussion: Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival. [ABSTRACT FROM AUTHOR]

During the first year of the COVID-19 pandemic there was a global disruption in the provision of healthcare. Grade 4 gliomas are rapidly progressive tumors, and these patients are at risk of poorer outcomes due to delays in diagnosis or treatment. We retrospectively evaluated the impact of the pandemic on treatment patterns and outcomes of patients with grade 4 gliomas in British Columbia. We identified a cohort of 85 patients treated with radiotherapy between March 2020–2021 (COVID era) and compared baseline characteristics, treatments, and outcomes with a control cohort of 79 patients treated between March 2018–2019 (pre-COVID era). There were fewer patients treated with radiotherapy over age 65 in the COVID era compared to the pre-COVID era (p = 0.037). Significantly more patients were managed with biopsy relative to partial or gross total resection during the COVID era compared to the pre-COVID era (p = 0.04), but there were no other significant differences in time to assessment, time to treatment, or administration of adjuvant therapy. There was no difference in overall survival between eras (p = 0.189). In this assessment of outcomes of grade 4 gliomas during the pandemic, we found that despite less aggressive surgical intervention during the COVID era, outcomes were similar between eras. [ABSTRACT FROM AUTHOR]

Diffuse gliomas are incurable brain tumors, yet there is significant heterogeneity in patient survival. Advanced computational techniques such as radiomics show potential for presurgical prediction of survival and other outcomes from neuroimaging. However, these techniques ignore non-lesioned brain features that could be essential for improving prediction accuracy. Gray matter covariance network (connectome) features were retrospectively identified from the T1-weighted MRIs of 305 adult patients diagnosed with diffuse glioma. These features were entered into a Cox proportional hazards model to predict overall survival with 10-folds cross-validation. The mean time-dependent area under the curve (AUC) of the connectome model was compared with the mean AUCs of clinical and radiomic models using a pairwise t-test with Bonferroni correction. One clinical model included only features that are known presurgery (clinical) and another included an advantaged set of features that are not typically known presurgery (clinical +). The median survival time for all patients was 134.2 months. The connectome model (AUC 0.88 ± 0.01) demonstrated superior performance (P < 0.001, corrected) compared to the clinical (AUC 0.61 ± 0.02), clinical + (AUC 0.79 ± 0.01) and radiomic models (AUC 0.75 ± 0.02). These findings indicate that the connectome is a feasible and reliable early biomarker for predicting survival in patients with diffuse glioma. Connectome and other whole-brain models could be valuable tools for precision medicine by informing patient risk stratification and treatment decision-making. [ABSTRACT FROM AUTHOR]

Background: Patients with high‐grade glioma (HGG) face unique challenges toward the end of life (EoL), given their aggressive trajectory and neurologic deterioration. Aggressiveness of medical care at EoL has been identified as an important quality metric for oncology patients. At this time, limited data exist around the nature of EoL care of patients with HGG. Methods: Patients with HGG and palliative care (PC) referral seen between 2010 and 2015 were identified (N = 80). Of these, N = 52 met inclusion criteria. Random selections of patients with (1) HGG not referred to PC (n = 80), and (2) non‐CNS cancers with PC referral (n = 80) were identified for comparison. A composite score of aggressiveness of medical care at EoL was calculated for each patient from predetermined variables. A time of eligibility for PC was defined for each patient when predetermined criteria based on symptom burden, functional status, and prognosis were met. Results: Among the patients analyzed with HGG referred to PC, 59.6% (N = 31) were referred as inpatients, and 53.8% (N = 28) were referred within the last 12 weeks of life. Patients with HGG had similar aggressiveness of care at EoL regardless of PC referral, and HGG patients had less aggressive care at EoL than patients with non‐CNS cancers (p = 0.007). Care was more aggressive at EoL in HGG patients who received late versus early PC referrals (p = 0.012). Motor weakness at time of eligibility (OR = 2.55, p = 0.002) and more disease progressions (OR = 1.25, p = 0.043) were associated with less aggressive care at EoL. Conclusions: Early clinical‐ and disease‐related features predict the aggressiveness of medical care at EoL in patients with HGG. Formal PC consultation is used infrequently and suboptimally in patients with HGG. Our data suggest that the role of PC in improving EoL outcomes in HGG warrants further evaluation. [ABSTRACT FROM AUTHOR]

It is estimated only 8–11% of patients with glioblastoma (GBM) enrol in clinical trials, limiting treatment development. We analysed the clinical and demographic features of patients with GBM enroled in clinical trials at the University of Texas MD Anderson Cancer Center (MDACC). We reviewed the records of adult patients treated for primary GBM between 2007 and 2012 at the MDACC. A total of 755 patients were identified: 133 were deemed non-eligible, 111 were deemed trial eligible but received standard care and 511 participated in a clinical trial (311 for newly diagnosed glioblastoma [nGBM] and 200 for recurrent glioblastoma [rGBM]). Population characteristics were analysed using descriptive statistics, and survival end-points were evaluated with the Kaplan–Meier method. The median age of clinical trial participants and trial eligible patients was 53.2 years (standard deviation 12.1). Most patients (49.4%) were enroled in a clinical trial protocol for nGBM. The majority of nGBM trial participants were male patients (65.1%), white (86.3%), married (84.4%) and in state (59.9%). Employment status, education, symptoms, tumour location, performance status, extent of resection and treatment facility differed between nGBM trial participants and non-participants. Patients who were eligible but did not enrol tended to be older, have worse performance status and live farther away from the MDACC. Numerous disease and demographic barriers exist in trial enrolment in patients with GBM. This study highlights some of these obstacles, which require attention to improve patient enrolment to clinical trials. Patient and physician engagement in novel therapeutic strategies is essential to improving outcomes in this disease. • A small minority of patients with glioblastoma enrol in therapeutic clinical trials. • Demographic, disease and treatment-related variables influence trial enrolment. • Older age and poor functional status are associated with not enroling in trials. • Patients living farther from the treating centre were less likely to enrol. [ABSTRACT FROM AUTHOR]

Clinical research in neuro-oncology frequently classifies patients over 60-70 years of age as ‘elderly’, a designation intended to identify patients with the disease characteristics, psychosocial changes, and susceptibility to treatment toxicities associated with advancing age. The elderly account for a large proportion of patients diagnosed with glioblastoma (GBM), and this population is projected to increase. Their prognosis is inferior to that of GBM patients as a whole, and concerns over treatment toxicity may limit the aggressiveness with which they are treated. Recent clinical studies have assisted with therapeutic decision making in this cohort. Hypofractionated radiation with concurrent and adjuvant temozolomide has been shown to increase survival without worsened quality of life in elderly patients with good functional status. Single modality radiation therapy or temozolomide therapy are frequently used in this population, and while neither has demonstrated superiority, O6-methylguanine-DNA methyltransferase (MGMT) methylation status is predictive of improved survival with temozolomide over radiation therapy. Despite these advances, ambiguity as to how to best define, assess, and treat this population remains. The specific response of elderly patients to emerging therapies, such as immunotherapies, is unclear. Advancing outcomes for elderly patients with GBM requires persistent efforts to include them in translational and clinical research endeavors, and concurrent dedication to the preservation of function and quality of life in this population. [ABSTRACT FROM AUTHOR]