Porter, Alyx B.

Abstract Determining effective therapies for patients with recurrent glioblastoma remains difficult especially in small early phase clinical trials. Currently utilized estimators of treatment response rely on two-dimensional measurements of tumor progression (e.g. RANO, RECIST, MacDonald) that have often fallen short in reliably connecting patients who respond with positive survival outcomes. This has prompted consideration of advanced imaging and volumetric measurements by the RANO working group and others. Our proposed approach, Days Gained (DG), uses volumetric measurements to calculate a personalized response metric based on change in tumor growth characteristics pre/post-treatment. DG has shown promise for connecting imageable treatment response with clinically meaningful (statistically significant) survival benefit when applied in upfront and recurrent therapy settings, even in the context of coincident bevacizumab treatment. In this study, we seek to consider a new cohort of patients receiving bevacizumab in combination with other novel therapies to distinguish any subpopulation of patients with meaningful response. In this work, we identified a subset of 16 patients from bevacizumab clinical trials with data available for DG analysis requiring serial imaging (T1Gd and FLAIR) prior to and after the first cycle of therapy. Each trial evaluated bevacizumab alone versus bevacizumab plus either dasatinib or TRC105 in recurrent disease. Discrimination of time to progression (TTP) was evaluated at DG thresholds identified from our prior analysis of patients treated with bevacizumab. Using previously identified DG thresholds for clinically significant treatment benefit, in our cohort, DG measured by serial FLAIR imaging was significant in predicting longer TTP (120 DG, p=0.014), but T1Gd DG scores did not attain significance (p=0.630). Given the limited insights in connecting response metrics to accurately predicted outcomes, these data further support the use of DG as a burgeoning marker of treatment response (or lack of response) that may be useful for more routine integration into clinical trials.

Abstract PURPOSE Brain tumor related epilepsy (BTE) is a major co-morbidity in patients with glioma. It is difficult to determine whether the use of anti-epileptic drugs is necessary. We attempted to build a machine-learning model to predict the probability of seizure presentation (SP) with glioma. METHODS We trained a random forest classifier using the following variables: volumetric data of pre-treatment MR images (T1Gd and T2-FLAIR sequences), patient demographics (age; sex), and measurements of tumor proliferation (log(ρ)), invasiveness (log(D)) and their relative ratio (log(ρ/D)). Our cohort consisted of 221 patients total. Using an 80-20 ratio, we used 176 patients (76 SP, 100 nSP) for training and the remaining 45 patients (19 SP, 26 nSP) were used for testing. We also trained on male-only and female-only cohorts to evaluate any sex differences in prediction. For training, 108 males (53 SP, 55 nSP) were used and 28 for testing (14 SP, 14 nSP). We used 72 females (21 SP, 49 nSP) for training and 15 (7 SP, 8 nSP) for testing. We corrected for class imbalance in the female cohort before training. Using 10-fold cross-validation and a separate testing set, we measured performance by ROC curve (AUC), accuracy, sensitivity, and specificity of predictions (average of folds in cross validation). RESULTS The female model achieved the highest AUC (0.853) followed by the mixed model (0.726) and the male model (0.651). In the validation set, the accuracy/sensitivity/specificity of the three cohorts were as follows: mixed (0.726/0.696/0.750), female (0.853/0.830/0.875), and male (0.651/0.577/0.722). The performance of the testing set, in terms of accuracy/sensitivity/specificity were: mixed (0.733/0.74/0.73), female (0.8/0.57/1), and male (0.714/0.64/0.79). CONCLUSION We found a negative correlation between seizure probability and size and invasiveness of tumors. Our model shows promising performance on testing set data. Further cohort studies and training is warranted.

Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.

Abstract BACKGROUND The face of neuro-oncology providers has evolved over the century. We sought to evaluate if its culture has evolved concurrently. Women in NeuroOncology (“WiN.”) is a SNO subcomittee that identifies the needs of women in neuro-oncology and promotes advancement of the careers of women in neuro-oncology. We created this survey to examine bias within our field with the goal to implement future change. METHODS Following IRB approval, we surveyed both male and female SNO members and analyzed de-identified responses. RESULTS Of the 206 respondents, 25% identified as male, 84.9% were in academics, 73.7% were primarily research focused (69% clinical), and 59% were medical neuro-oncology focused. The majority of respondents were 36–45 years old (47%), followed by 46–55 (32%), and 21% aged 56+. 69.8% self-identified as white, followed by 22% Asian, 7.8% Latin, and 2.9% Black. The majority reported general professional bias at least “not infrequently”, with 11.7% reporting it “very commonly.” 59.9% reported “not being invited to participate and or being considered for an opportunity”, followed by 46.1% “being minimized, teased or disrespected”, 44.3% “being passed over for professional opportunities”, and 41.9% salary disparity. Furthermore, of those reporting general professional bias, 42.9% reported a lack of effective mentorship and 94% reported some frequency of burnout, with 34% “commonly, very commonly”, followed by 28.3% “not infrequently.” Notably, although only 25% of responders were men, 82.4% of total respondents reported some frequency of gender bias in the workplace, with “not infrequently” being the most commonly reported (48%). CONCLUSIONS In this survey, we identified SNO members who have experienced bias. Based on quantitative and qualitative responses, many reported bias manifesting as missing academic opportunities, salary disparity, burnout, and lack of mentorship. As we cultivate the next generation, we will use these results to engage those in leadership to promote unbiased equality.

Abstract BACKGROUND Radiation is an integral component of the multidisciplinary treatment of patients with intracranial metastasis (ICM) from melanoma. The risk of radiation necrosis (RN) post-treatment can range from 5–25%. We retrospectively evaluated pre- and post-radiation clinical and radiographical characteristics in patients with ICM from melanoma to identify potential risk factors for RN. METHODS After IRB approval, patients with ICM from melanoma who received radiation at our institution between 2013 and 2018 were retrospectively reviewed. We recorded demographics, intracranial metastasis (burden and location), systemic therapy, resection, stereotactic radiosurgery versus whole brain radiation, and outcomes. Brain MRI was evaluated using the Response Assessment in Neuro-Oncology criteria (RANO). RESULTS A total of 27 patients were included in the study. RN was diagnosed in 14 patients (52%) at one month to three years following radiation. Cerebellar location (n=7) was significantly associated with RN (p=0.0058). Metastasis-associated hemorrhage was present in 7 patients, 6 of which developed RN (43% vs 8%, p=0.08). Surgical resection prior to radiation was performed in 9 patients, 7 of which developed RN (50% vs 15%, p=0.1). RN appeared to be less often diagnosed when ICM were treated with higher doses of radiation (24 Gy vs 20 Gy, p=0.07). Systemic treatment was administered prior to radiation in 25 patients and we found no relationship with systemic therapy type and RN. Presumed RN was treated in 12 of 14 patients (86%). Steroids or surgical resection were the primary treatment modalities, with laser ablation used in 1 case. Following treatment, improvement was noted in 3 patients radiographically and in 1 of these clinically. CONCLUSIONS This series of patients with radiotherapy-treated brain metastases from melanoma reveals cerebellar involvement, hemorrhage, and prior surgical resection as potential risk factors for radiation necrosis. Neoadjuvant systemic treatment did not appear to be a risk factor in our review.

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 Background: Central nervous system (CNS) lymphoma manifests as a highly aggressive non-Hodgkins lymphoma, either confined to the central nervous system or secondary to spread of systemic diffuse large B cell lymphoma. Rising incidence of CNS lymphoma has been observed, especially in elderly populations. Presentation depends on the burden and location of disease, with a varying spectrum of symptoms ranging from headaches and cognitive changes to focal neurological deficits and seizures. Neuropsychiatric presentation of CNS lymphoma has been seen and anecdotally reported but appears to be under-represented in the literature. In this retrospective review of patients with CNS lymphoma at MCA we report a cohort of patients who presented with primarily neuropsychiatric symptoms Methods: After receiving IRB approval, we retrospectively identified 135 patients with ICD 9 and 10 codes consistent with CNS lymphoma, seen at Mayo Clinic Arizona, between 1998-2018. Symptoms identified prior to presentation with the CNS component of the lymphoma were recorded with attention to those with depression, anxiety, agitation, psychosis, disinhibition, and apathy. Patients with long term premorbid psychiatric disease or symptoms that developed later in the course as a result the stressors of disease and treatment were excluded. Results: A total of 19 out of 136 patients (14%) were noted to have primarily neuropsychiatric symptoms prior to diagnosis of CNS lymphoma. Symptom onset to diagnosis of PCNSL ranged from 3 weeks to 11 months. The average age was 65, and 47% of subjects were male. Depression, apathy, agitation and features of parkinsonism were the most common neuropsychiatric symptoms identified, with psychosis and disinhibition being the least frequent. The majority of these patients had frontal lobe lesions (42%), although several also had multifocal disease at time of diagnosis. Improvement of neuropsychiatric disease was not explicitly discussed or noted in the treatment and post-treatment notes. In fact, we found it notable that follow up notes generally did not comment on these psychiatric symptoms at all, and there was no clear management plan separate from treating the disease process. Parkinsonian features were observed in 5 patients (26%), with onset range from 2 weeks to 29 months prior to PCNSL diagnosis. The associated lesions were mostly deep basal ganglia or corpus callosum except in one case of left parietal lobe tumor. Notably, all patients had resolution of their parkinsonism with treatment of the lymphoma. Conclusions: Neuropsychiatric symptoms are a rare but noteworthy presentation of CNS lymphoma, distinct from cognitive changes that have been previously described with this condition. There is an increasing awareness of neurological illness presenting as pure psychiatric disturbance, prompting exclusion of organic disease, particularly in elderly patients who present with new psychiatric complaints. Disclosures No relevant conflicts of interest to declare.

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.