SUMMARY Glioblastoma harbors a dynamic subpopulation of glioblastoma stem-like cells (GSCs) that can propagate tumors in vivo and is resistant to standard chemoradiation. Identification of the cell-intrinsic mechanisms governing this clinically important cell state may lead to the discovery of novel therapeutic strategies for this challenging malignancy. Here, we demonstrate that the mitotic E3 ubiquitin ligase CDC20-Anaphase-Promoting Complex (CDC20-APC) drives invasiveness and self-renewal in patient tumor-derived GSCs. Moreover, CDC20 knockdown inhibited and CDC20 overexpression increased the ability of human GSCs to generate brain tumors in an orthotopic xenograft model in vivo. CDC20-APC control of GSC invasion and self-renewal operates through pluripotency-related transcription factor SOX2. Our results identify a CDC20-APC/SOX2 signaling axis that controls key biological properties of GSCs, with implications for CDC20-APC-targeted strategies in the treatment of glioblastoma.

Biomechanical models that describe soft tissue deformation provide a relatively inexpensive way to correct registration errors in image-guided neurosurgical systems caused by nonrigid brain shift. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based methods have been developed recently that allow for uncertainty, yet still capture the first-order effects associated with deformation. The inverse solution is driven by sparse intraoperative surface measurements, which could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies using intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The dural septa act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. A novel method to segment the tentorium cerebelli will be described, along with the procedure for modeling the dural septa. Results in seven clinical cases show a qualitative improvement in subsurface shift accuracy making the predicted deformation more congruous with previous observations in the literature. The results also suggest a considerably more important role for hyperosmotic drug modeling for the intraoperative shift correction environment.

Background: Primary gliosarcoma is a rare malignant brain tumor with dismal prognosis. Previous reports are limited to case reports and small retrospective case series. Objective: To evaluate treatment and survival outcomes in a large cohort of primary gliosarcoma patients treated in the United States. Results: 1622 patients met the inclusion criterion. Median age was 63 years. The 3-year OS rate for the entire cohort was 11.9%. Patients aged 18 to 60 years were significantly more likely to receive trimodality therapy (defined as the use of surgery, radiotherapy [RT] and chemotherapy [CT]) than patients older than 60 (68.1% vs. 56.7%, p < 0.001). The utilization of trimodality therapy significantly increased during the study period (57.5% in 2004-2008 vs. 65.1% in 2009-2013; p = 0.002). On multivariate Cox regression analysis, GTR, surgery followed by RT and the use of trimodality therapy were associated with longer OS, while older age, Charlson-Deyo score ≥ 1 and multi-focal tumor were associated with shorter OS. The use of trimodality therapy was consistently associated with longer OS in subgroup analyses based on age and extent of resection. Materials and Methods: The National Cancer Database was used to identify all primary gliosarcoma patients aged 18 to 90 years who were diagnosed between 2004 and 2013. Overall survival (OS) was evaluated by Kaplan-Meir analysis, univariate and multivariate Cox proportional hazard regression analysis. Conclusions: The use of trimodality therapy significantly increased during the study period and was associated with improved outcomes regardless of age and extent of resection.

Purpose Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases. Methods Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients. Results The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm. Conclusions Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.

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