Massey, Susan Christine

Abstract OBJECTIVE Glioblastoma (GBM) is the most common malignant primary brain tumor in adults, with males more commonly affected than females(1.6:1). Despite advancements in treatments, prognosis is dismal with a median overall survival of 15 months. Our aim was to investigate sex as a variable in GBM patient survival after receiving incremental levels of standard-of-care treatment regimens – different extents of surgical resection and different numbers of cycles of adjuvant temozolomide chemotherapy. METHODS Drawing from our extensive multi-institutional brain tumor repository, we investigated GBM subjects with overall survival (OS), extent of resection (EOR), number of temozolomide (TMZ) cycles, and sex data (n=620, males: n=387, females: n=233). Cox proportional hazard ratios were computed to investigate the multivariable predictive value of the patient variables with OS. Patients were then divided into groups based on their sex, EOR (either biopsy, subtotal resection (STR) or gross total resection (GTR)), and TMZ cycles (I: < 6 cycles, II: 7-11 cycles and III: >12 cycles). RESULTS We observed that STR was beneficial for females (HR=0.52; CI=0.33-0.83; p-value=0.013), while for males the benefit was not detected (HR=0.73; CI=0.46-1.15; p-value=0.173) for STR but was detectable for GTR (HR=0.58, CI=0.37-0.90; p-value=0.014). Females receiving 7-11 cycles of TMZ showed a survival benefit (HR=0.52; CI=0.12-0.53; p-value=0.048) while males in the same group did not (HR=0.74; CI=0.46-1.19; p-value=0.21), in comparison to those in group I of TMZ cycles. No sex differences were identified in patients receiving < =6 cycles or >=12 cycles. CONCLUSION Together, our results contribute to the growing literature that sex differences exist in GBM patients, even in response to standard-of-care therapies. This should be accounted for when designing clinical trials for GBM so that we may advance our pursuit to deliver personalized medicine.

Abstract Glioblastoma (GBM) is the one of the most aggressive and common primary brain malignancies, with a poor median overall survival of less than 15 months. While the immune system is activated and brain-resident microglia and blood-derived macrophages combat the tumor, the tumor can convert some microglia and macrophages to instead exhibit an immune-suppressive phenotype. These co-opted immune cells are thereby termed ‘glioma-associated microglia and macrophages’ (GAMMs), as they allow for continued tumor growth. However, limited clinical data has been collected to understand this phenomenon. As a result, we have collected spatially-distributed image-localized biopsies from a cohort of patients and performed RNA sequencing on each sample. Correlations between normalized RNA counts of genetic markers for macrophages (i.e., CD68, CD163), tumor populations (i.e., SOX2, OLIG2), and key cell functions (i.e., KI67, CASP3) were analyzed. To further investigate the temporal effects that GAMMs have on GBM growth, we proposed the Proliferation-Invasion-Macrophage (PIM) model. This system of partial differential equations incorporates the proliferative and invasive behavior of GBM, as well as populations for both ‘healthy’ and ‘glioma-associated’ macrophages. By exploring the parameter space, we classified the various dynamics of tumor progression and how they relate to the immune response. With further insights of the interactions between GBM and macrophage populations, we can begin to parameterize the model on a patient-specific basis and provide insights to personalized immunotherapies and other novel immune-targeted treatments.

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 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.

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.

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.