Citations

Abstract INTRODUCTION Glioblastoma (GBM) is an aggressive primary brain tumor, known for its poor prognosis. Due to its diffuse invasiveness into normal-appearing brain, localized treatments such as surgical resection and radiotherapy are typically supplemented with chemotherapy. However, to reach invading tumor cells, such antineoplastic drugs must cross the blood-brain barrier (BBB). That is, while angiogenesis induces BBB breakdown in dense tumor regions, the BBB remains rather intact for invading GBM cells. As a result, it is unclear whether BBB-impermeable drugs are delivered at a sufficient level to be effective. METHODS In order to study heterogeneity in BBB breakdown, experiments were conducted using both flank and intracranial patient-derived xenografts (PDXs) treated with the EGFR-targeted monoclonal antibody drug conjugate, depatuxizumab mafodotin (ABT-414). Time-series bioluminescence imaging (BLI) data was used to develop a differential equation model of tumor growth for three PDX cell lines. Data from untreated PDXs, both in flank and intracranially, were used to parameterize tumor proliferation rates. Flank PDX data were used to parameterize individual sensitivity to ABT-414, whereas intracranial PDX data were used to determine the proportion of drug exposed to the tumor. RESULTS Each PDX line differed in response to the study drug ABT-414. As expected, such heterogeneous responses can primarily be attributed to differences in both drug sensitivity and the proportion of drug that reached the tumor. Notably, the estimated proportion of drug that reached the tumor was highest in the PDX line with the longest survival times, despite also having higher estimates of resistance. This suggests that PDXs with greater overall BBB breakdown may respond better to this agent. CONCLUSIONS Although more cell lines are needed to validate our approach, parameterizing this model for PDXs gives valuable insight into the extent of BBB breakdown in patient GBMs and may aid in determining optimal therapies for individual patients.

Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of only 15 months with standard of care treatment. Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, another morphological measure of the complexity of pixel arrangement, of segmented necrotic regions on gadolinium-enhanced T1 weighted (T1gd) MRI have previously been shown to distinguish both overall survival (OS) and progression free survival (PFS) in GBM (n = 95). In our larger patient cohort (n = 389), we sought to validate or refute previously published results connecting morphological metrics and patient survival. We identified pretreatment necrotic regions of our retrospective first-diagnosis GBM patient cohort using segmented T1gd MRI enhancing regions. We calculated lacunarity and fractal dimension across all T1gd MRI slices with enhancing tumor, and used the median lacunarity and fractal dimension values for our analysis. We find that a lacunarity threshold can significantly distinguish OS (14 months vs 19 months median, log-rank p = 0.015, n = 389) and a fractal dimension threshold can significantly distinguish PFS (8 months vs 11 months median, log-rank p = 0.015, n = 123). We believe that morphological metrics such as lacunarity and fractal dimension could play a role in standard-of-care prognostic considerations at tumor presentation. This link between morphological and survival metrics could be driven by underlying biological phenomena or microenvironmental factors that should be further explored.

Abstract INTRODUCTION Glioblastoma (GBM) is the most common malignant primary brain tumor in adults with a median overall survival (OS) of 15months. Despite advancements in treatments, prognosis is dismal and the prognostic significance of tumor location is not entirely understood. METHODOLOGY: In our study, we investigated sex-specific volumetric, tumor growth kinetics, and outcome differences among GBMs in various brain locations. Primary GBM patients with pretreatment magnetic resonance imaging (MRI) data (N=289, 173 males, 116 females) were selected from our brain tumor repository. Tumor abnormality was segmented on T1-weighted post-gadolinium contrast agent (T1Gd) MRIs. We utilized the Harvard-Oxford brain atlases to determine the location of GBMs. RESULTS Overall, our study found smaller tumors in the left hemisphere. This may be expected as left-hemispheric GBM symptoms could present earlier, leading to earlier diagnosis and treatment. However, when the cohort was split by sex, we found this observation significant for females only in the parietal lobe (p < 0.0001). Further, female GBMs demonstrated smaller necrotic volume in the left hemisphere (p = 0.030). Sex-specific differences in incidence were noted in the temporal and occipital lobes (2M:1F). Comparing tumor growth kinetics in different brain locations and hemispheres, females had significantly lower tumor proliferation rates in the left hemisphere (p = 0.009) and lower tumor proliferation rates in the left frontal lobe (p = 0.031). Controlling for treatment, patients with frontal lobe tumors had significantly longer OS compared to those with GBMs in the temporal lobe (p = 0.046, 312 days). Differences in growth velocities were noted between frontal and parietal lobe with frontal GBMs having lower velocities in comparison to parietal lobe GBMs. CONCLUSION Together, our results demonstrate that tumor growth and proliferation rates may vary based on location and sex. Additional research is needed to further explore the clinical significance of tumor location.

Abstract Sex differences in brain tumor related epilepsy (BTE) as an initial symptom of glioma are rarely investigated. Sex differences are being explored with increased interest more recently. Epidemiological studies have generally indicated overall incidence of epilepsy to be slightly higher in males than females, but a higher incidence of idiopathic generalized epilepsy has been seen in females in some studies. Clinicians regularly face the challenge of anti-epileptic drug prescription determination in patients with newly identified brain mass and would greatly benefit from knowing clinical characteristics in BTE. We analyzed clinical characteristics in patients with pathologically-determined glioma with known seizure status at clinical presentation using non-parametric and Fisher’s Exact tests with the entire cohort and male and female separately. From our clinical data repository, we identified 223 patients (females, n=86 (39%); males, n=137 (61%)) divided by 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). In sex-specific analysis males had a significantly (x2=6.28, p=0.01) higher proportion of SP (49.6%) than females (32.6%). There was a dependence between seizure presentation status and tumor grade in males (x2=5.94, p=0.015). Tumor kinetics in the female cohort showed significantly (p< 0.0001) more diffuse tumors in SP compared to nSP. Right-sided tumors were predominantly nSP, while left-sided tumors were split more evenly between SP and nSP. The predominance of nSP among right-sided tumors seems to be driven by females (74%), male right-sided tumors were split more evenly between SP and nSP. Seizure status is dependent on tumor location for the combined cohort, however not for males (x2=5.23, p=0.16) or females (x2=4.34, p=0.23). Clearly there are sex differences and similarities in clinical characteristics at glioma presentation. Further characterization could lead to improved adherence of national standards for AED prescription.

Abstract INTRODUCTION Immunotherapy with engineered CAR T cells is a promising new therapy for glioblastoma, for which predictive and prognostic biomarkers are needed to inform effective intervention. Recently, our group analyzed standard-of-care (SOC) MRI images of long-term and short-term glioblastoma survivors and identified six intratumoral “habitats” of which “Habitat 6” was correlated with survival at diagnosis in high-grade glioma. Based on the MRI characteristics of “Habitat 6”, viz. high enhancement and high edema, we hypothesized that it could be a marker of tumor immune infiltrates. We are studying longitudinal changes in tumor “habitat” composition on MRIs of subjects with recurrent high-grade glioma treated with CAR T cells engineered to target IL13Ra2. METHODS MRI scans of the brain were acquired in 6 subjects at 3.0 T at baseline and various times before and after initiation of CAR T cell therapy. FLAIR, T1W and T1W-CE MRI images were registered to T2W images and six intratumoral “habitats” were computed as per our recently published methodology. The six habitats generated at the end of the tumor segmentation process were: “Habitat 1” (low FLAIR, low enhancement), “Habitat 2” (high FLAIR, low enhancement), “Habitat 3” (low FLAIR, medium enhancement), “Habitat 4” (high FLAIR, medium enhancement), “Habitat 5” (low FLAIR, high enhancement), and “Habitat 6” (high FLAIR, high enhancement). RESULTS Analysis of temporal changes in the six “habitats” shows an initial increase in both “Habitat 4” and “Habitat 6” following CAR T cell therapy initiation. Subjects with higher absolute volumes of “Habitat 6” at the baseline (pre-treatment) showed longer overall survival. Overall survival is a function of absolute “Habitat 6” volume at baseline, its direction of change immediately post-therapy, the duration of any increase in “Habitat 6” post-treatment, and the “Habitat 6” to “Habitat 4” ratio. Additional subjects are being evaluated to further understand these preliminary observations.

Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor with an average survival of 15 months with standard of care treatment. GBM patients typically present with necrosis surrounded by enhancement on T1-weighted post gadolinium magnetic resonance imaging (T1gd MRI), however some patients present with a significant cystic component. Cysts are caused by different underlying biological mechanisms to necrosis and are important to identify for future clinical investigations. These cystic components can be manually identified through MRI but this process can be time consuming for large patient cohorts. Over the last two decades, our lab has collected serial MRI data of brain tumor patients. With over 70,000 images now in the database and that number increasing daily, it is clear that we have a unique resource for clinical investigation and a need to automate this process. To this end, the aim of this work was to develop and assess the performance of a convolution neural network (CNN) model for automatic detection of cystic GBMs. In this retrospective IRB-approved work, we collected pretreatment MRIs of a patient cohort consisting of 85 patients with a significant cystic component at presentation along with 400 non-cystic GBM, both identified manually through MRI. Image slices with a view of the cystic component were used as positive samples for training. Data were randomly split into training, validation, and test sets using a 70:15:15 ratio. The proportion of positive to negative cases was comparable between sets. Prior to training, we used image augmentation techniques to compensate for the class imbalance in our data. Our results showed that deep learning networks can automatically detect cystic GBMs on MRIs with high accuracy and thus illustrates the potential use of this technique in clinically relevant settings.

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.

Background: Temporary mechanical circulatory support (MCS) devices provide hemodynamic assistance for shock refractory to pharmacological treatment. Most registries have focused on single devices or specific etiologies of shock, limiting data regarding overall practice patterns with temporary MCS in cardiac intensive care units. Methods: The CCCTN (Critical Care Cardiology Trials Network) is a multicenter network of tertiary CICUs in North America. Between September 2017 and September 2018, each center (n=16) contributed a 2-month snapshot of consecutive medical CICU admissions. Results: Of the 270 admissions using temporary MCS, 33% had acute myocardial infarction-related cardiogenic shock (CS), 31% had CS not related to acute myocardial infarction, 11% had mixed shock, and 22% had an indication other than shock. Among all 585 admissions with CS or mixed shock, 34% used temporary MCS during the CICU stay with substantial variation between centers (range: 17%–50%). The most common temporary MCS devices were intraaortic balloon pumps (72%), Impella (17%), and veno-arterial extracorporeal membrane oxygenation (11%), although intraaortic balloon pump use also varied between centers (range: 40%–100%). Patients managed with intraaortic balloon pump versus other forms of MCS (advanced MCS) had lower Sequential Organ Failure Assessment scores and less severe metabolic derangements. Illness severity was similar at high- versus low-MCS utilizing centers and at centers with more advanced MCS use. Conclusions: There is wide variation in the use of temporary MCS among patients with shock in tertiary CICUs. While hospital-level variation in temporary MCS device selection is not explained by differences in illness severity, patient-level variation appears to be related, at least in part, to illness severity.

Gestational trophoblastic neoplasia (GTN), a subset of gestational trophoblastic disease (GTD), occurs when tumors develop in the cells that would normally form the placenta during pregnancy. The NCCN Guidelines for Gestational Trophoblastic Neoplasia provides treatment recommendations for various types of GTD including hydatidiform mole, persistent post-molar GTN, low-risk GTN, high-risk GTN, and intermediate trophoblastic tumor.

Disease monitoring in nonalcoholic steatohepatitis (NASH) is limited by absence of noninvasive biomarkers of disease regression or progression. We aimed to examine the role of multiparametric three‐dimensional magnetic resonance elastography (3D‐MRE) and magnetic resonance imaging proton density fat fraction (MRI‐PDFF) in the detection of NASH regression after interventions. This is a single‐center prospective clinical trial of 40 patients who underwent bariatric surgery. Imaging and liver biopsies were obtained at baseline and 1 year after surgery. The imaging protocol consisted of multifrequency 3D‐MRE to determine the shear stiffness at 60 Hz and damping ratio at 40 Hz, and MRI‐PDFF to measure the fat fraction. A logistic regression model including these three parameters was previously found to correlate with NASH. We assessed the model performance in the detection of NASH resolution after surgery by comparing the image‐predicted change in NAFLD activity score (delta NAS) to the histologic changes. A total of 38 patients (median age 43, 87% female, 30 of 38 with NAS ≥ 1, and 13 of 38 with NASH) had complete data at 1 year. The NAS decreased in all subjects with NAS ≥ 1 at index biopsy, and NASH resolved in all 13. There was a strong correlation between the predicted delta NAS by imaging and the delta NAS by histology (r = 0.73, P < 0.001). The strength of correlation between histology and the predicted delta NAS using single conventional parameters, such as the fat fraction by MRI‐PDFF or shear stiffness at 60 Hz by MRE, was r = 0.69 (P < 0.001) and r = 0.43 (P = 0.009), respectively. Conclusion: Multiparametric 3D‐MRE and MRI‐PDFF can detect histologic changes of NASH resolution after bariatric surgery. Studies in a nonbariatric setting are needed to confirm the performance as a composite noninvasive biomarker for longitudinal NASH monitoring.