Egan, K. | Search bioRxiv

bioRxiv2022

The MIF SNP rs755622 is a germline determinant of tumor immune activation in Glioblastoma

While immunotherapies have shown durable responses for multiple tumors, their efficacy remains limited in some advanced cancers, including glioblastoma. This may be due to differences in the immune landscape, as the glioblastoma microenvironment strongly favors immunosuppressive myeloid cells, which are linked to an elevation in immune-suppressive cytokines, including macrophage migration inhibitory factor (MIF). We now find that a single-nucleotide polymorphism (SNP) rs755622 in the MIF promoter associates with increased leukocyte infiltration in glioblastoma. Furthermore, we identified lactotransferrin expression as being associated with the rs755622 SNP, which could also be used as a biomarker for immune infiltrated tumors. These findings provide the first example in glioblastoma of a germline SNP that underlies differences in the immune microenvironment and identifies high lactotransferrin as a potential factor promoting immune activation.

cancer biology↗

bioRxiv2019

Sex Differences in Predictors of Seizure in Contrast-Enhancing Gliomas at Clinical Presentation: A Network Approach

BackgroundBrain tumor related epilepsy (BTE) is a major co-morbidity related to the management of patients with brain cancer. Despite published practice guidelines recommending against anti-epileptic drug (AED) utilization in patients with gliomas, there is heterogeneity in prescription practices of AEDs in these patients. In an attempt to impact BTE management, we statistically analyzed clinically relevant attributes (sex, age, tumor size, tumor growth kinetics, and tumor location) pertaining to seizure at presentation and used them to build a computational machine learning model to predict the probability of a seizure (at presentation).\n\nMethodsFrom our clinical data repository, we identified 223 patients (females, n=86; males, n=137) with pathologically-determined glioma and known seizure status at clinical presentation. Non-parametric and Fishers Exact tests were used to identify statistical differences in clinical characteristics. We utilized a random forest machine learning method for generating our predictive models by entire cohort and separated by male and female.\n\nFindingsPatients were divided into those that presented with seizure (SP, n=96, 43%; F, n= 28; M, n= 68) and those that presented without seizure (nSP, n=127, 57%, F n=58, M n=69). Females presented with seizures significantly less often than males (x2=6{middle dot}28, p=0{middle dot}01). SP patients had significantly smaller T1Gd radius compared to nSP (SP 11{middle dot}30mm, nSP 18.66mm, p<0{middle dot}0001). Tumor size and patient age were significant negative predictors for SP; patients with larger tumors, older age and less tumor diffusivity (p/D) were at lower risk for SP.\n\nInterpretationDespite heterogeneity across our patient cohort, there is strong evidence of a role for patient sex, tumor size, tumor invasion, and patient age in predicting the incidence of seizures at diagnosis. Future studies, with prospectively detailed data collection, may provide clearer insights into the incidence of seizures through a patients treatment course.

cancer biology↗