Citations

Abstract Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment response. Apparent tumor progression on MRI can be very difficult to manage clinically as it is unclear the degree to which the imaging changes are actually tumor progression vs response to treatment (including inflammatory response and necrosis). In this analysis, we study a unique cohort of patients for whom image localized-biopsies reveal heterogeneity in response vs progression. Our dataset included 70 biopsy samples from 32 patients with GBM each histolopatholgically characterized for tumor abundance vs immune infiltrate. Six multiparametric MRI contrasts were available, including T1, T1gd, T2, FLAIR, SWI, and ADC. Images were co-registered. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. Machine learning models were built to predict each biomarker using radiomic features. Leave-one-out cross validation was used to evaluate the prediction accuracy. Radiomic features were found to be informative to the prediction of biomarkers. ANOVA tests show significant improvement of using radiomic features compared with the null model. The prediction accuracy was higher when considering the biomarkers on a binary scale using the median as the cutoff than on a numerical scale. Spatially-informed radiomics models for tumor progression vs treatment effect are possible and can play an instrumental role in navigating confounding imaging changed common during treatment progression.

Abstract High-grade gliomas (HGGs) nearly always recur after standard initial treatment, and the resulting mixture of recurrent tumor and treatment-induced reactive changes presents major diagnostic challenges. Anatomical imaging, such as MRI, cannot adequately distinguish progressive disease from treatment effect (pseudo-progression). Furthermore, there is marked intra-tumoral heterogeneity, such that some areas of a tumor may demonstrate necrotic treatment effect and others frank recurrence. Due to this difficulty reliably differentiating between these two clinical findings, analytic methods using multiple modalities are necessary to further our understanding of this disease process. To this end, we sought to correlate radiographic, histopathologic and molecular features of surgically sampled post-treatment suspected recurrence to identify markers distinguishing tumor growth from treatment effect. We performed Stimulated Raman Histology (SRH) imaging and highly multiplexed RNA-sequencing (PLATE-seq) on 84 MRI-localized biopsies from 39 patients with clinically suspected recurrent HGG. The SRH images were classified as recurrent tumor or gliotic/reactive tissue using a convolutional neural network trained on an independent cohort including a large set of recurrent HGG, and an automated cell-counting algorithm was used to quantify cellularity from the SRH image of each sample. Differential gene expression analysis of the PLATE-seq data was used to identify gene sets that distinguish recurrent tumor from treatment effect, and single sample gene set variation analysis (GSVA) was used to further assess the molecular and cellular composition of each MRI-localized sample. The histopathologic and molecular features of each sample were also correlated with the MRI features of the corresponding biopsy sites, and this data is currently being used to train machine learning models that predict the distribution of recurrent tumor and treatment-induced reactive changes within a patient’s radiographic lesion. These predictive radiomic models will help to guide neurosurgical sampling, and improve our ability to monitor glioma progression and response to therapy.

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.

INTRODUCTION: Treatment options for patients with moderate-severe ulcerative colitis (UC) are increasing. The appropriateness of their use in mild-moderate UC is less clear, as is the role of surgical intervention. Our aim was to define the appropriateness of therapeutic options in a range of clinical scenarios in mild-moderate UC METHODS: We applied the RAND/UCLA Appropriateness Method to rate appropriateness of treatment choices in patients with mild-moderate UC; severe disease (likely to need hospitalization or surgery within 2 weeks) was excluded. A literature review was presented to the BRIDGe group, a panel of 13 IBD specialists, as well as 1 external IBD expert and 2 IBD surgical experts. 12 chapters were constructed including failed 5-ASA, steroid or thiopurine therapy, and primary/secondary non-response to biologics or tofacitinib. Scenarios were also divided by disease extent (proctitis vs more extensive) and activity (mild vs moderate) creating 260 scenarios in all. Panellists used a modified Delphi method to anonymously rate the appropriateness of a therapy on a 1-9 scale (1-3 not appropriate, 4-6 uncertain, 7-9 appropriate). Disagreement was assessed using a validated index (DI) and was defined as a DI > 1. The panellists then convened in person to discuss areas of disagreement, followed by a second round of rating. Scenarios rated a median of 7 or higher were deemed appropriate and those rated 3 or lower were deemed inappropriate. Scenarios rated 4-6 were considered uncertain, as were any with a DI > 1 RESULTS: Appropriateness was agreed in 131 and inappropriateness in 47 scenarios. Greatest disagreement was around the use of thiopurines, but no scenario had a DI > 1. There were 82 scenarios were rated uncertain. Biologics and tofacitinib were more likely to be rated appropriate for treatment resistance and greater disease activity; disease extent less likely to influence decision making. Surgery was felt to be appropriate in 8 scenarios, generally in more extensive and active disease that had failed to respond to medical therapy. Surgery was felt to be inappropriate in 16 scenarios and was uncertain in 24 CONCLUSION: Although there was little disagreement amongst an expert panel of IBD physicians and surgeons, there remained uncertainty about the use of medical and surgical therapy in nearly one third of situations in patients with mild-moderate UC. These results could be used to guide the design of clinical trials for these patient groups in order to guide best practice.

Abstract Background : Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods : Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results : Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival. Conclusion : Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

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.

Significance Glioblastoma, the most common and lethal of primary brain tumors, can diffusely infiltrate brain. Because the components of the cytoskeleton that drive tumor invasion are nonredundant, they have been attractive targets for blocking glioblastoma dispersion. However, we find that while targeting one of these—the molecular motor myosin IIA—effectively blocks glioblastoma invasion, it enhances tumor proliferation and lethality. In this study, we have found that myosin IIA functions as a tumor suppressor by reducing signaling of two oncogenes—ERK1/2 and NF-κB—in a manner regulated by the mechanics of the tumor and surrounding brain. Our results also argue that effective treatment of glioblastoma will require inhibiting targets that drive both invasion and proliferation.

Abstract Background: Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods: Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results: Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival. Conclusion: Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

Humans are sexually dimorphic, with sex being the most persistent difference among humans over the course of our evolutionary history. Beyond the visible sex differences that can be considered true dimorphisms, there are also sex differences at the molecular and cellular scales. The role of these biological sex differences for human health, while being increasingly recognized, have long been underappreciated and underexplored. Frequently, these differences are only recognized in sex–specific diseases, such as genitourinary diseases and cancers. However, given the evidence for sex differences in the most basic aspects of human biology, including metabolism, cellular composition, and immune activity, these differences could have consequences for the etiology and pathophysiology of a majority of diseases. It is thus essential to consider the extent to which these differences may influence the various mechanisms underlying disease processes, response to treatment, and the maintenance of health in order to better improve patient outcomes. Here we review the evidence for a broad array of biological sex differences in humans and discuss how they may relate to observed sex differences in various diseases, with an emphasis on cancer, specifically glioblastoma. We further propose that mathematical approaches can be useful for exploring the extent to which sex differences affect disease outcomes and accounting for those in the development of therapeutic strategies.