Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.

BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.

A Phase I Trial of VEGF-A Inhibition Combined with PD-L1 Blockade for Recurrent Glioblastoma.

Purpose: The treatment of glioblastoma (GBM) poses challenges. The use of immune checkpoint inhibition (ICI) has been disappointing as GBM is characterized by low mutational burden and low T-cell infiltration. The combination of ICI with other treatment modalities may improve efficacy. Patient and Methods: Patients with recurrent GBM were treated with avelumab, a human IgG1 antibody directed against PD-L1 (part A), or avelumab within a week after laser interstitial thermal therapy (LITT) and continuation of avelumab (part B). Bevacizumab was allowed to be combined with ICI to spare steroid use. The primary objective was to characterize the tolerability and safety of the regimens. The secondary objectives included overall survival, progression-free survival (PFS), signatures of plasma analytes, and immune cells. Results: A total of 12 patients (median age 64; range, 37-73) enrolled, five in part A and seven in part B. Two serious adverse events occurred in the same patient, LITT treated, not leading to death. The median survival from enrollment was 13 months [95% confidence interval (CI), 4-16 months] with no differences for part A or B. The median PFS was 3 months (95% CI, 1.5-4.5 months). The decrease in MICA/MICB, γδT cells, and CD4+ T cell EMRA correlated with prolonged survival. Conclusions: Avelumab was generally well tolerated. Adding bevacizumab to ICI may be beneficial by lowering cytokine and immune cell expression. The development of this combinatorial treatment warrants further investigation. Exploring the modulation of adaptive and innate immune cells and plasma analytes as biomarker signatures may instruct future studies in this dismal refractory disease. Significance: Our phase I of PD-L1 inhibition combined with LITT and using bevacizumab to spare steroids had a good safety profile for recurrent GBM. Developing combinatory treatment may help outcomes. In addition, we found significant immune modulation of cytokines and immune cells by bevacizumab, which may enhance the effect of ICI.

Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign.

PURPOSE: The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. METHODS: In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. RESULTS: A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. CONCLUSION: The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.

Using EGFR amplification to stratify recurrent glioblastoma treated with immune checkpoint inhibitors.

PURPOSE: While immune checkpoint inhibitors (ICI) have had success with various malignancies, their efficacy in brain cancer is still unclear. Retrospective and prospective studies using PD-1 inhibitors for recurrent glioblastoma (GBM) have not established survival benefit. This study evaluated if ICI may be effective for select patients with recurrent GBM. METHODS: This was a single-center retrospective study of adult patients diagnosed with first recurrence GBM and received pembrolizumab or nivolumab with or without concurrent bevacizumab. Archival tissue was used for immunohistochemistry (IHC) and targeted DNA next-generation sequencing (NGS) analysis. RESULTS: Median overall survival (mOS) from initial diagnosis was 24.5 months (range 10-42). mOS from onset of ICI was 10 months (range 1-31) with 75% surviving > 6 months and 46% > 12 months. Additional IHC analysis on tumors from eight patients demonstrated a trend of longer survival after ICI for those with elevated PD-L1 expression. NGS of samples from 15 patients identified EGFR amplification at initial diagnosis and at any time point to be associated with worse survival after ICI (HR 12.2, 95% CI 1.37-108, p = 0.025 and HR 3.92, 95% CI 1.03-14.9, p = 0.045, respectively). This significance was corroborated with previously tested EGFR amplification via in situ hybridization. CONCLUSION: ICI did not extend overall survival for recurrent GBM. However, molecular sequencing identified EGFR amplification as associated with worse survival. Prospective studies can validate if EGFR amplification is a biomarker of ICI resistance and determine if its use can stratify responders from non-responders.

U-Net Based Segmentation and Characterization of Gliomas.

(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40−50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of <18 months demonstrated AUC of 0.75. (4) Conclusions: Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.

Phase I/randomized phase II trial of TRC105 plus bevacizumab versus bevacizumab in recurrent glioblastoma: North Central Cancer Treatment Group N1174 (Alliance).

Background: Patients with glioblastoma (GBM) have a poor prognosis and limited effective treatment options. Bevacizumab has been approved for treatment of recurrent GBM, but there is questionable survival benefit. Based on preclinical and early clinical data indicating that CD105 upregulation may represent a mechanism of resistance to bevacizumab, we hypothesized that combining bevacizumab with the anti-CD105 antibody TRC105 may improve efficacy in recurrent GBM. Methods: Phase I dose-escalation/comparative randomized phase II trial in patients with GBM. During phase I, the maximum tolerated dose (MTD) of TRC105 in combination with bevacizumab was determined. In phase II, patients were randomized 1:1 to TRC105 and bevacizumab or bevacizumab monotherapy. Patients received TRC105 (10 mg/kg) weekly and bevacizumab (10 mg/kg) every 2 weeks. Efficacy, as assessed by progression-free survival (PFS), was the primary endpoint; safety, quality of life, and correlative outcomes were also evaluated. Results: In total, 15 patients were enrolled in phase I and 101 in phase II; 52 patients were randomized to TRC105 with bevacizumab and 49 to bevacizumab monotherapy. The MTD was determined to be 10 mg/kg TRC105 weekly plus bevacizumab 10 mg/kg every 2 weeks. An increased occurrence of grade ≥3 adverse events was seen in the combination arm, including higher incidences of anemia. Median PFS was similar in both treatment arms: 2.9 months for combination versus 3.2 months for bevacizumab monotherapy (HR = 1.16, 95% CI = 0.75-1.78, P = .51). Quality of life scores were similar for both treatment arms. Conclusions: TRC105 in combination with bevacizumab was well tolerated in patients with recurrent GBM, but no difference in efficacy was observed compared to bevacizumab monotherapy.

The future of cancer immunotherapy for brain tumors: a collaborative workshop.

Harnessing the effector mechanisms of the immune system to combat brain tumors with antigen specificity and memory has been in research and clinical testing for many years. Government grant mechanisms and non-profit organizations have supported many innovative projects and trials while biotech companies have invested in the development of needed tools, assays and novel clinical approaches. The National Brain Tumor Society and the Parker Institute for Cancer Immunotherapy partnered to host a workshop to share recent data, ideas and identify both hurdles and new opportunities for harnessing immunotherapy against pediatric and adult brain tumors. Adoptively transferred cell therapies have recently shown promising early clinical results. Local cell delivery to the brain, new antigen targets and innovative engineering approaches are poised for testing in a new generation of clinical trials. Although several such advances have been made, several obstacles remain for the successful application of immunotherapies for brain tumors, including the need for more representative animal models that can better foreshadow human trial outcomes. Tumor and tumor microenvironment biopsies with multiomic analysis are critical to understand mechanisms of response and patient stratification, yet brain tumors are especially challenging for such biopsy collection. These workshop proceedings and commentary shed light on the status of immunotherapy in pediatric and adult brain tumor patients, including current research as well as opportunities for improving future efforts to bring immunotherapy to the forefront in the management of brain tumors.

Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion.

BACKGROUND: Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma. METHODS: In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed. RESULTS: From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR). CONCLUSION: Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

The CNS and the Brain Tumor Microenvironment: Implications for Glioblastoma Immunotherapy.

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor in adults. Its aggressive nature is attributed partly to its deeply invasive margins, its molecular and cellular heterogeneity, and uniquely tolerant site of origin-the brain. The immunosuppressive central nervous system (CNS) and GBM microenvironments are significant obstacles to generating an effective and long-lasting anti-tumoral response, as evidenced by this tumor's reduced rate of treatment response and high probability of recurrence. Immunotherapy has revolutionized patients' outcomes across many cancers and may open new avenues for patients with GBM. There is now a range of immunotherapeutic strategies being tested in patients with GBM that target both the innate and adaptive immune compartment. These strategies include antibodies that re-educate tumor macrophages, vaccines that introduce tumor-specific dendritic cells, checkpoint molecule inhibition, engineered T cells, and proteins that help T cells engage directly with tumor cells. Despite this, there is still much ground to be gained in improving the response rates of the various immunotherapies currently being trialed. Through historical and contemporary studies, we examine the fundamentals of CNS immunity that shape how to approach immune modulation in GBM, including the now revamped concept of CNS privilege. We also discuss the preclinical models used to study GBM progression and immunity. Lastly, we discuss the immunotherapeutic strategies currently being studied to help overcome the hurdles of the blood-brain barrier and the immunosuppressive tumor microenvironment.

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting.

Patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status have been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffused Weighted Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.

Exploiting the Complexities of Glioblastoma Stem Cells: Insights for Cancer Initiation and Therapeutic Targeting.

The discovery of glioblastoma stem cells (GSCs) in the 2000s revolutionized the cancer research field, raising new questions regarding the putative cell(s) of origin of this tumor type, and partly explaining the highly heterogeneous nature of glioblastoma (GBM). Increasing evidence has suggested that GSCs play critical roles in tumor initiation, progression, and resistance to conventional therapies. The remarkable oncogenic features of GSCs have generated significant interest in better defining and characterizing these cells and determining novel pathways driving GBM that could constitute attractive key therapeutic targets. While exciting breakthroughs have been achieved in the field, the characterization of GSCs is a challenge and the cell of origin of GBM remains controversial. For example, the use of several cell-surface molecular markers to identify and isolate GSCs has been a challenge. It is now widely accepted that none of these markers is, per se, sufficiently robust to distinguish GSCs from normal stem cells. Finding new strategies that are able to more efficiently and specifically target these niches could also prove invaluable against this devastating and therapy-insensitive tumor. In this review paper, we summarize the most relevant findings and discuss emerging concepts and open questions in the field of GSCs, some of which are, to some extent, pertinent to other cancer stem cells.

Multiparametric MRI for early identification of therapeutic response in recurrent glioblastoma treated with immune checkpoint inhibitors.

BACKGROUND: Physiologic changes quantified by diffusion and perfusion MRI have shown utility in predicting treatment response in glioblastoma (GBM) patients treated with cytotoxic therapies. We aimed to investigate whether quantitative changes in diffusion and perfusion after treatment by immune checkpoint inhibitors (ICIs) would determine 6-month progression-free survival (PFS6) in patients with recurrent GBM. METHODS: Inclusion criteria for this retrospective study were: (i) diagnosis of recurrent GBM treated with ICIs and (ii) availability of diffusion and perfusion in pre and post ICI MRI (iii) at ≥6 months follow-up from treatment. After co-registration, mean values of the relative apparent diffusion coefficient (rADC), Ktrans (volume transfer constant), Ve (extravascular extracellular space volume) and Vp (plasma volume), and relative cerebral blood volume (rCBV) were calculated from a volume-of-interest of the enhancing tumor. Final assignment of stable/improved versus progressive disease was determined on 6-month follow-up using modified Response Assessment in Neuro-Oncology criteria. RESULTS: Out of 19 patients who met inclusion criteria and follow-up (mean ± SD: 7.8 ± 1.4 mo), 12 were determined to have tumor progression, while 7 had treatment response after 6 months of ICI treatment. Only interval change of rADC was suggestive of treatment response. Patients with treatment response (6/7: 86%) had interval increased rADC, while 11/12 (92%) with tumor progression had decreased rADC (P = 0.001). Interval change in rCBV, Ktrans, Vp, and Ve were not indicative of treatment response within 6 months. CONCLUSIONS: In patients with recurrent GBM, interval change in rADC is promising in assessing treatment response versus progression within the first 6 months following ICI treatment. KEY POINTS: • In recurrent GBM treated with ICIs, interval change in rADC suggests early treatment response.• Interval change in rADC can be used as an imaging biomarker to determine PFS6.• Interval change in MR perfusion and permeability measures do not suggest ICI treatment response.

Widely metastatic glioblastoma with BRCA1 and ARID1A mutations: a case report.

BACKGROUND: Glioblastoma (GBM) is a highly malignant brain neoplasm with poor survival. Despite its aggressive nature, metastatic spread of GBM is identified only rarely. While the molecular alterations associated with GBM and its subtypes are well-described, there remains a gap in understanding which alterations may predispose towards metastasis. In this report, we present a case of GBM with multi-organ metastases and discuss its genomic alterations. CASE PRESENTATION: A 74-year-old woman was diagnosed with left occipital glioblastoma (IDH-wildtype, MGMT-unmethylated), for which she underwent resection, standard chemoradiation, and then stereotactic radiosurgery (SRS) for local recurrence. One month after SRS, work-up for a pathologic hip fracture revealed a left breast mass, lytic lesions involving pelvic bones, and multiple pulmonary and hepatic lesions. Biopsies of the breast and bone lesions both demonstrated metastatic IDH-wildtype GBM. For worsening neurologic symptoms, the patient underwent debulking of a large right temporal lobe recurrence and expired shortly thereafter. Autopsy confirmed metastatic GBM in multiple systemic sites, including bilateral lungs, heart, liver, thyroid, left breast, small bowel, omentum, peritoneal surfaces, visceral surfaces, left pelvic bone, and hilar lymph nodes. Targeted sequencing was performed on tissue samples obtained pre- and postmortem, as well as on cell cultures and an orthotopic mouse xenograft derived from premortem surgical specimens. A BRCA1 mutation (p.I571T) was the only variant found in common among the primary, recurrence, and metastatic specimens, suggesting its likely status as an early driver mutation. Multiple subclonal ARID1A mutations, which promote genomic instability through impairment of DNA mismatch repair, were identified only in the recurrence. Mutational spectrum analysis demonstrated a high percentage of C:G to T:A transitions in the post-treatment samples but not in the primary tumor. CONCLUSION: This case report examines a rare case of widely metastatic IDH-wildtype GBM with a clonal somatic mutation in BRCA1. Post-treatment recurrent tumor in the brain and in multiple systemic organs exhibited evidence of acquired DNA mismatch repair deficiency, which may be explained by functional loss of ARID1A. We identify a potential role for immune checkpoint and PARP inhibitors in the treatment of metastatic GBM.

Tumoral and immune heterogeneity in an anti-PD-1-responsive glioblastoma: a case study.

Clinical benefit of immune checkpoint blockade in glioblastoma (GBM) is rare, and we hypothesize that tumor clonal evolution and the immune microenvironment are key determinants of response. Here, we present a detailed molecular characterization of the intratumoral and immune heterogeneity in an IDH wild-type, MGMT-negative GBM patient who plausibly benefited from anti-PD-1 therapy with an unusually long 25-mo overall survival time. We leveraged multiplex immunohistochemistry, RNA-seq, and whole-exome data from the primary tumor and three resected regions of recurrent disease to survey regional tumor-immune interactions, genomic instability, mutation burden, and expression profiles. We found significant regional heterogeneity in the neoantigenic and immune landscape, with a differential T-cell signature among recurrent sectors, a uniform loss of focal amplifications in EGFR, and a novel subclonal EGFR mutation. Comparisons with recently reported correlates of checkpoint blockade in GBM and with TCGA-GBM revealed appreciable intratumoral heterogeneity that may have contributed to a differential PD-1 blockade response.

Multiparametric MRI for Differentiation of Radiation Necrosis From Recurrent Tumor in Patients With Treated Glioblastoma.

OBJECTIVE: Differentiation of radiation necrosis (RN) from recurrent tumor (RT) in treated patients with glioblastoma remains a diagnostic challenge. The purpose of this study is to evaluate the diagnostic performance of multiparametric MRI in distinguishing RN from RT in patients with glioblastoma, with the use of a combination of MR perfusion and diffusion parameters. MATERIALS AND METHODS: Patients with glioblastoma who had a new enhancing mass develop after completing standard treatment were retrospectively evaluated. Apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), and relative cerebral blood volume (rCBV) values were calculated from the MR images on which the enhancing lesions first appeared. Repeated measure of analysis, logistic regression, and ROC analysis were performed. RESULTS: Of a total of 70 patients evaluated, 46 (34 with RT and 12 with RN) met our inclusion criteria. Patients with RT had significantly higher mean rCBV (p < 0.001) and Ktrans (p = 0.006) values and lower ADC values (p = 0.004), compared with patients with RN. The overall diagnostic accuracy was 85.8% for rCBV, 75.5% for Ktrans, and 71.3% for ADC values. The logistic regression model showed a significant contribution of rCBV (p = 0.024) and Ktrans (p = 0.040) as independent imaging classifiers for differentiation of RT from RN. Combined use of rCBV and Ktrans at threshold values of 2.2 and 0.08 min-1, respectively, improved the overall diagnostic accuracy to 92.8%. CONCLUSION: In patients with treated glioblastoma, rCBV outperforms ADC and Ktrans as a single imaging classifier to predict recurrent tumor versus radiation necrosis; however, the combination of rCBV and Ktrans may be used to improve overall diagnostic accuracy.

Planning TTFields treatment using the NovoTAL system-clinical case series beyond the use of MRI contrast enhancement.

BACKGROUND: Gliomas are the most common primary brain tumors in adults and invariably carry a poor prognosis. Recent clinical studies have demonstrated the safety and compelling survival benefit when tumor treating fields (TTFields) are added to temozolomide for patients with newly diagnosed glioblastoma. TTFields are low-intensity, intermediate frequency (200 kHz) alternating electric fields, delivered directly to a patient's brain through the local application of non-invasive transducer arrays. Experimental simulations have demonstrated that TTFields distribute in a non-uniform manner within the brain. To ensure patients receive the maximal therapeutic level of TTFields at the site of their tumor, tumor burden is mapped and an optimal array layout is personalized using the NovoTAL software. The NovoTAL software utilizes magnetic resonance imaging (MRI) measurements for head size and tumor location obtained from axial and coronal T1 postcontrast sequences to determine the optimal paired transducer array configuration that will deliver the maximal field intensity at the site of the tumor. In clinical practice, physicians planning treatment with TTFields may determine that disease activity is more accurately represented in noncontrast-enhancing sequences. Here we present and discuss a series of 8 cases where a treating physician has utilized non-contrast enhancement and advanced imaging to inform TTFields treatment planning based on a clinical evaluation of where a patient is believed to have active tumor. This case series is, to our knowledge, the first report of this kind in the literature. CASE PRESENTATIONS: All patients presented with gliomas (grades 2-4) and ranged in age from 49 to 65 years; 5 were male and 3, female. Each patient had previously received standard therapy including surgery, radiation therapy and/or chemotherapy prior to initiation of TTFields. The majority had progressed on prior therapy. A standard pre- and postcontrast MRI scan was acquired and used for TTFields treatment planning. CONCLUSION: This paper details important approaches for integrating clinical considerations, nonmeasurable disease and advanced imaging into the treatment planning workflow for TTFields. As TTFields become integrated into standard care pathways for glioblastoma, this case series demonstrates that treatment planning beyond the extent of contrast enhancement is clinically feasible and should be prospectively compared to standard treatment planning in a clinical trial setting, in order to determine the impact on patient outcomes.

Molecular and Clinical Effects of Notch Inhibition in Glioma Patients: A Phase 0/I Trial.

PURPOSE: High-grade gliomas are associated with a dismal prognosis. Notch inhibition via the gamma-secretase inhibitor RO4929097 has emerged as a potential therapeutic option based on modulation of the cancer-initiating cell (CIS) population and a presumed antiangiogenic role. EXPERIMENTAL DESIGN: In this phase 0/I trial, 21 patients with newly diagnosed glioblastoma or anaplastic astrocytoma received RO4929097 combined with temozolomide and radiotherapy. In addition to establishing the MTD, the study design enabled exploratory studies evaluating tumor and brain drug penetration and neuroimaging parameters. We also determined functional effects on the Notch pathway and targeting of CISs through analysis of tumor tissue sampled from areas with and without blood-brain barrier disruption. Finally, recurrent tumors were also sampled and assessed for Notch pathway responses while on treatment. RESULTS: Treatment was well tolerated and no dose-limiting toxicities were observed. IHC of treated tumors showed a significant decrease in proliferation and in the expression of the Notch intracellular domain (NICD) by tumor cells and blood vessels. Patient-specific organotypic tumor explants cultures revealed a specific decrease in the CD133+ CIS population upon treatment. Perfusion MRI demonstrated a significant decrease in relative plasma volume after drug exposure. Gene expression data in recurrent tumors suggested low Notch signaling activity, the upregulation of key mesenchymal genes, and an increase in VEGF-dependent angiogenic factors. CONCLUSIONS: The addition of RO4929097 to temozolomide and radiotherapy was well tolerated; the drug has a variable blood-brain barrier penetration. Evidence of target modulation was observed, but recurrence occurred, associated with alterations in angiogenesis signaling pathways. Clin Cancer Res; 22(19); 4786-96. ©2016 AACR.

Disparities in receipt of modern concurrent chemoradiotherapy in glioblastoma.

Temozolomide given concurrently with radiation after resection/biopsy improves survival in glioblastoma (GBM). The disparities in receipt of adjuvant single-agent chemotherapy and their association with outcome have not been well established. Observational study of a prospectively collected database, the National Cancer Database (NCDB), from 1998 to 2012 with median follow-up 12.4 months. Among the 114,979 patients in the NCDB with GBM, 44,531 patients were analyzed for disparities, and 28,279 patients were analyzed for overall survival (OS). Associations were assessed in a multivariable Cox proportional hazards regression model. Survival was estimated using the Kaplan-Meier method. Median age was 58 years. Chemotherapy use was associated with male gender, white race, younger age (≤50), higher performance status (≥70), more extensive surgery, insurance status, higher income/education, and treatment at academic centers (all p < 0.05). We found improved OS associated with type of insurance (private insurance HR 0.91, 95 % CI 0.85-0.96 and Medicare HR 1.24, 95 % CI 1.16-1.33, both p < 0.01 compared to uninsured) and treatment at academic programs (HR 0.86; p < 0.01). MGMT methylation status predicted improved OS (HR 0.54; 95 % CI 0.41-0.70, p < 0.01). 1-year OS for patients receiving chemotherapy was 55.9 % versus 35.3 % for those without (p < 0.0001). After adjustment for confounders, chemotherapy use remained associated with improved OS (HR 0.64, 95 % CI 0.63-0.66, p < 0.01). Chemotherapy utilization increased from 26.9 to 93.3 % during the study period. We have identified specific disparities in the use of chemotherapy that may be targeted to improve patient access to care. Widespread adoption of adjuvant chemoradiotherapy after resection or biopsy for GBM appears to improve OS.

Phase II study of bevacizumab, temozolomide, and hypofractionated stereotactic radiotherapy for newly diagnosed glioblastoma.

PURPOSE: Bevacizumab is associated with decreased vascular permeability that allows for more aggressive radiotherapy schedules. We conducted a phase II trial in newly diagnosed glioblastoma utilizing a novel hypofractionated stereotactic radiotherapy (HFSRT) schedule combined with temozolomide and bevacizumab. EXPERIMENTAL DESIGN: Patients with tumor volume ≤60 cc were treated with HFSRT (6 × 6 Gy to contrast enhancement and 6 × 4 Gy to FLAIR hyperintensity with dose painting) combined with concomitant/adjuvant temozolomide and bevacizumab at standard doses. Primary endpoint was 1-year overall survival (OS): promising = 70%; nonpromising = 50%; α = 0.1; ß = 0.1. RESULTS: Forty patients were enrolled (median age: 55 years; methylated MGMT promoter: 23%; unmethylated: 70%). The 1-year OS was 93% [95% confidence interval (CI), 84-100] and median OS was 19 months. The median PFS was 10 months, with no pseudo-progression observed. The objective response rate (ORR) was 57%. Analysis of The Cancer Genome Atlas glioblastoma transcriptional subclasses (Nanostring assay) suggested patients with a proneural phenotype (26%) fared worse (ORR = 14%, vs. 77% for other subclasses; P = 0.009). Dynamic susceptibility-contrast perfusion MRI showed marked decreases in relative cerebral blood volume over time (P < 0.0001) but had no prognostic value, whereas higher baseline apparent diffusion coefficient (ADC) ratios and persistent hypermetabolism at the 6-month FDG-PET predicted poor OS (P = 0.05 and 0.0001, respectively). Quality-of-life (FACT-BR-4) and neuropsychological test scores were stable over time, although some domains displayed transient decreases following HFSRT. CONCLUSIONS: This aggressive radiotherapy schedule was safe and more convenient for patients, achieving an OS that is comparable with historical controls. Analysis of advanced neuroimaging parameters suggests ADC and FDG-PET as potentially useful biomarkers, whereas tissue correlatives uncovered the poor prognosis associated with the proneural signature in non-IDH-1-mutated glioblastoma.