Citations

Abstract INTRODUCTION Glioblastoma (GBM) is a diffusely invasive primary brain tumor with significant spread of tumor cells to the periphery of visible image abnormality. Enhancement of Gadolinium (Gd) contrast agent on magnetic resonance imaging (MRI) has historically been considered a confirmation of local breakdown of the blood brain barrier (BBB) and sufficient drug delivery to the bulk of tumors. In this work, we used GBM-derived xenograft (PDX) models to compare drug delivery in GBM brain for high and low BBB-permeable drugs. MATERIALS AND METHODS Five patient-derived orthotopic xenograft models from two GBM cell lines (GBM39 and GBM12) were co-dosed with erlotinib and osimertinib, two drugs with low and high BBB-permeability, respectively. T1Gd and T2-weighted MRIs were acquired from all animals prior to model sacrifice. Tumors were manually segmented on denoised and standardized MRIs and intensity patterns were captured using first and second order statistical features in the moving 3x3 kernel. We compared drug levels found in Matrix Assisted Laser Desorption Ionization (MALDI) in T1Gd enhancement, T2 enhancement, and normal brain. We also performed linear regression modeling to predict drug levels using MRI features. Model performance was measured using root mean squared error (RMSE). RESULTS Our analysis showed correlations between imaging features and MALDI drug levels. Osimertinib had a uniform distribution across the brain for all animals and all cell lines, consistent with our expectation for a high BBB-penetrant drug. Erlotinib showed the highest drug levels in T2 for GBM39 and in T1Gd for GBM12. Regression models showed promising results for predicting Erlotinib with a low RMSE of 0.037. CONCLUSION Our preliminary results suggest MRI can be predictive of drug levels for low-BBB penetrant drugs. Understanding the relationship between MRIs and drug distribution in diffuse tumors can be beneficial to developing effective treatment.

Abstract Choosing effective chemotherapies for intravenous delivery to brain tumors is challenging, especially given the protective nature of the blood brain barrier (BBB). Connecting drug distribution to non-invasive, pre-surgical magnetic resonance imaging (MRI) could allow for predictive insight into drug distribution. In a previous study, we found that T2Gd images were predictive of a low BBB penetrant drug (Cefazolin), and FLAIR images were predictive of a high BBB penetrant drug (Levetiracetam). While these results are promising, we further seek to explore how advanced MRI sequences might inform image-based models of drug distribution. Prior to surgery, we acquired advanced dynamic contrast enhanced (DCE) and diffusion weighted imaging (DWI) MRI sequences for eight brain tumor patients (7 gliomas and 1 metastatic adenocarcinoma) in addition to the anatomic MRIs. All resulting quantitative maps and acquired images were co-registered. Prior to incision, patients received injections of cefazolin and levetiracetam. Next, multiple blood samples and biopsies were collected during surgery. Biopsies and plasma samples were analyzed for drug concentration using liquid chromatography mass spectrometry (LCMS), and biopsy drug levels were reported as Brain-Plasma Ratio (BPR). Mean image intensity was extracted from a 15x15 voxel window surrounding the biopsy location. We performed linear regression analyses to determine which combination of images were predictive of BPR. We found that considering quantitative imaging improved our initial ability to predict BPR for both drugs. For cefazolin, the third diffusion tensor eigenvalue (L3) map was significantly correlated with BPR (p< 0.001, R2= 0.36). For levetiracetam, the best model consisted of a combination of images and maps with the L3 map and the isotropic diffusion map (P) being the most influential (p= 0.001, R2= 0.63). Advanced MRI-based modeling is a promising tool for forecasting drug distribution in brain tumors and could be of great importance for understanding efficacy and selecting therapeutic strategies.

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

Objective: To evaluate the incidence of and characterize the presentation of neuropsychiatric symptoms and/or Parkinsonism as a presentation of central nervous system lymphoma (CNSL) in either its primary CNSL form or when it spreads to the brain in systemic diffuse large B-cell lymphoma (secondary CNSL). Patients and Methods: With Institutional Review Board approval we identified patients who had been treated at Mayo Clinic from 1998 to 2018 and were recorded to have a combination of ICD 9/10 codes for CNSL and various psychiatric diagnoses. Results: A total of 20 of the 232 patients (9%) were noted to have neuropsychiatric symptoms preceding diagnosis. The average age at diagnosis was 62, with even split for sex. The majority (85%) of patients had primary CNSL. The average duration of symptoms before the diagnosis was 4.8 months. Confusion (80%), depression (40%), apathy (30%), anxiety (30%), and agitation (30%) were the most common symptoms identified. The majority (65%) of patients had subcortical lesions followed by the frontal lobe (50%). Parkinsonism was identified in 5 of the 20 patients with 4 demonstrating resolution of symptoms with treatment of the lymphoma. Conclusions: Neuropsychiatric symptoms are a rare but notable symptom before the presentation of CNSL. There is an increasing awareness of neurological illness presenting as pure psychiatric disturbance, prompting the need to exclude organic and treatable diseases, particularly in elderly patients. Acknowledgment and diagnosis are important for an appropriate management as there is a significant impact on patient and caregiver quality of life.

Abstract Background Accurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies. Methods DG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy (N = 62). Kaplan–Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM. Results In patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan–Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients. Conclusion DG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.

Abstract INTRODUCTION Neuroimaging dogma for glioblastoma asserts that hyperintensity on T1Gd MRI reveals the bulk of the tumor, while T2/FLAIR signal indicates edema. However, it is unclear whether this edema results from immune response or increased tumor cells. Further, one significant driver of the known sex differences in glioblastoma may be differences in immune response, due to the X-linkage of many immune genes. Based on this, we hypothesized that assumptions regarding tumor cellularity in T2/FLAIR images should be tailored to the biological sex of the patient. METHODS Using a retrospective cohort of 18 primary glioblastoma patients receiving multiple image-localized biopsies (82 total) and standard MRI, we assessed: distance of biopsy from T1Gd and T2 areas; a pathologist’s score of percent tumor cell density; and an imaging-based invasion metric, D/ρ. This metric is derived from the biomathematical Proliferation-Invasion model of glioma growth, which features two parameters, net growth rate (ρ) and net invasion rate (D). Their ratio D/ρ is related to degree of invasion, and is estimated from volumetric measurements of MRI abnormalities. Additionally, 25 patient-derived xenograft models implanted in females were grown until moribund, at which point brains were excised and stained for DAPI (to show all cells) and Lamin (to highlight tumor cells). Image processing of lamin-stained sections defines contours of intensity correlating with cell density. RESULTS Outside both the T1Gd and T2 region, male patient biopsies had higher tumor cell densities than females. Males also tended to have higher invasion metrics. Although each set derived from different patients, preclinical metrics of invasion were positively correlated with clinical invasion in females but negatively correlated in males. CONCLUSION Our preliminary finding that cell distribution patterns correlate with imaging metrics differently between the sexes supports the hypothesis that the degree of tumor cell density represented on certain MRI sequences may be sex-specific.

Abstract There have been increasing efforts to standardize MRI scanning protocols for both routine and clinical trial imaging of glioma. The recent BTIP guidelines propose acquisition of T2-weighted images after gadiolinium (Gd) based contrast injection and before post-contrast 3D T1-weighted (T1W) images to control timing of images after contrast administration. While Gd-contrast shortens T1W signal and appears hyperintense on magnetic resonance imaging (MRI), Gd-contrast also shortens T2W signal, which could result in unintended alternations in T2W signal intensities. This has potential implications for model-based approaches for GBM diagnosis and the differential diagnosis of other tumor types (e.g., lymphoma) that rely on T2W signal characteristics. To our knowledge, no neuroimaging studies have formally evaluated the impact of Gd contrast on T2W imaging. We acquired multi-echo T2 sequences (TE=14, 28, 41, 55, 69 ms) both prior to and after the administration of Gd for 5 patients suspected of having a brain tumor. Quantitative T2 maps were created by fitting the signal decay to a signal exponential with the Levenburg-Marqurdt algorithm. Percent difference maps were calculated to compare the quantitative T2 changes between the pre- and post-Gd T2 maps. Additionally, we performed a Wilcoxon Rank-Sum test comparing T2 values within regions of interest (ROIs) representing the tumor and normal appearing white matter (NAWM). Significant differences between the T2 values in the tumor and NAWM ROIs were seen for 4 and 3 of the patients, respectively (all p-values < 0.01). Our initial data suggests that the administration of Gd contrast may affect T2 values in both the tumor and NAWM, which could impact quantitative image-based models and diagnoses in glioma. We are currently replicating these preliminary results in an expanded cohort.

Abstract OBJECTIVES Dogma suggests that for brain tumors, regions of enhancement on T1-weighted gadolinium contrast enhanced (T1Gd) magnetic resonance imaging (MRI) correlate with intravenously delivered drug distribution as enhancement indicates a compromised blood-brain barrier (BBB). However, poor response to intravenous therapies highlights the importance of the diffuse disease beyond enhancing regions. This study investigated whether imaging features can provide an accurate prediction of drug distribution. METHODS Eight brain tumor patients (7 gliomas and 1 metastatic adenocarcinoma) were included in this Phase 0 trial. Presurgery T1-weighted, T1Gd, T2-weighted gadolinium contrast enhanced (T2Gd), and T2-weighted Fluid Attenuated Inversion Recovery (T2FLAIR) MRIs were acquired. All images underwent bias correction using the N4 algorithm, standardization of intensities, and registration. Prior to incision, patients received both an antibiotic cefazolin (6% BBB penetrance) and levetiracetam (80% BBB penetrance), an anti-seizure drug. Subsequently, multiple blood samples and image-guided biopsies were taken and analyzed for drug concentration using liquid chromatography mass spectrometry. Biopsy drug levels are reported as Brain-Plasma Ratio (BPR), the ratio of biopsy concentration relative to plasma concentration. Mean image intensity was extracted from an 8x8 mm window surrounding each biopsy location. Regression analysis was performed to determine which combination of image types were linearly predictive of BPR for both drugs. Correlations were also analyzed according to the biopsy location radiographic appearance. RESULTS Regression analysis revealed that T2Gd intensity was linearly predictive of cefazolin BPR and FLAIR intensity was linearly predictive of levetiracetam BPR (p=0.009 and 0.041, respectively). Grouping samples according the the radiographic appearance revealed that levetiracetam BPR had a similar pattern of values to that of FLAIR intensities and cefazolin BPR had a similar pattern to T2, further supporting the regression analysis results. CONCLUSIONS Local concentrations of drug may be related to T2-weighted signals (T2Gd and T2FLAIR) rather than the gadolinium distribution on T1Gd images.

Although glioblastoma (GBM) is a fatal primary brain cancer with short median survival of 15 months, a small number of patients survive >5 years after diagnosis; they are known as extreme survivors (ES). Because of their rarity, very little is known about what differentiates these outliers from other patients with GBM. For the purpose of identifying unknown drivers of extreme survivorship in GBM, the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of GBM) was developed. This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histologic parameters. Leveraging our combined resources, the goals of the ENDURES consortium are 2-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with GBM; and (2) to leverage the ENDURES repository for new insights into tumor behavior and novel targets for prolonging survival for all patients with GBM. In this article, the authors review the available literature and discuss what is already known about ES. The authors then describe the creation of their consortium and some preliminary results.