Cancer stem cell assay-guided chemotherapy improves survival of patients with recurrent glioblastoma in a randomized trial.

Therapy-resistant cancer stem cells (CSCs) contribute to the poor clinical outcomes of patients with recurrent glioblastoma (rGBM) who fail standard of care (SOC) therapy. ChemoID is a clinically validated assay for identifying CSC-targeted cytotoxic therapies in solid tumors. In a randomized clinical trial (NCT03632135), the ChemoID assay, a personalized approach for selecting the most effective treatment from FDA-approved chemotherapies, improves the survival of patients with rGBM (2016 WHO classification) over physician-chosen chemotherapy. In the ChemoID assay-guided group, median survival is 12.5 months (95% confidence interval [CI], 10.2-14.7) compared with 9 months (95% CI, 4.2-13.8) in the physician-choice group (p = 0.010) as per interim efficacy analysis. The ChemoID assay-guided group has a significantly lower risk of death (hazard ratio [HR] = 0.44; 95% CI, 0.24-0.81; p = 0.008). Results of this study offer a promising way to provide more affordable treatment for patients with rGBM in lower socioeconomic groups in the US and around the world.

Integrin α3ß1 promotes vessel formation of glioblastoma-associated endothelial cells through calcium-mediated macropinocytosis and lysosomal exocytosis.

Therapeutic targeting of angiogenesis in glioblastoma has yielded mixed outcomes. Investigation of tumor-associated angiogenesis has focused on the factors that stimulate the sprouting, migration, and hyperproliferation of the endothelial cells. However, little is known regarding the processes underlying the formation of the tumor-associated vessels. To address this issue, we investigated vessel formation in CD31+ cells isolated from human glioblastoma tumors. The results indicate that overexpression of integrin α3ß1 plays a central role in the promotion of tube formation in the tumor-associated endothelial cells in glioblastoma. Blocking α3ß1 function reduced sprout and tube formation in the tumor-associated endothelial cells and vessel density in organotypic cultures of glioblastoma. The data further suggest a mechanistic model in which integrin α3ß1-promoted calcium influx stimulates macropinocytosis and directed maturation of the macropinosomes in a manner that promotes lysosomal exocytosis during nascent lumen formation. Altogether, our data indicate that integrin α3ß1 may be a therapeutic target on the glioblastoma vasculature.

From Clinical Trial Efficacy to Real-Life Effectiveness: Why Conventional Metrics do not Work.

BACKGROUND: Randomised, double-blind, clinical trial methodology minimises bias in the measurement of treatment efficacy. However, most phase III trials in non-orphan diseases do not include individuals from the population to whom efficacy findings will be applied in the real world. Thus, a translation process must be used to infer effectiveness for these populations. Current conventional translation processes are not formalised and do not have a clear theoretical or practical base. There is a growing need for accurate translation, both for public health considerations and for supporting the shift towards personalised medicine. OBJECTIVE: Our objective was to assess the results of translation of efficacy data to population efficacy from two simulated clinical trials for two drugs in three populations, using conventional methods. METHODS: We simulated three populations, two drugs with different efficacies and two trials with different sampling protocols. RESULTS: With few exceptions, current translation methods do not result in accurate population effectiveness predictions. The reason for this failure is the non-linearity of the translation method. One of the consequences of this inaccuracy is that pharmacoeconomic and postmarketing surveillance studies based on direct use of clinical trial efficacy metrics are flawed. CONCLUSION: There is a clear need to develop and validate functional and relevant translation approaches for the translation of clinical trial efficacy to the real-world setting.

Systematic Review of Safety and Cost-Effectiveness of Venous Thromboembolism Prophylaxis Strategies in Patients Undergoing Craniotomy for Brain Tumor.

BACKGROUND: Studies have evaluated various strategies to prevent venous thromboembolism (VTE) in neuro-oncology patients, without consensus. OBJECTIVE: To perform a systematic review with cost-effectiveness analysis (CEA) of various prophylaxis strategies in tumor patients undergoing craniotomy to determine the safest and most cost-effective prophylaxis regimen. METHODS: A literature search was conducted for VTE prophylaxis in brain tumor patients. Articles reporting the type of surgery, choice of VTE prophylaxis, and outcomes were included. Safety of prophylaxis strategies was determined by measuring rates of VTE and intracranial hemorrhage. Cost estimates were collected based on institutional data and existing literature. CEA was performed at 30 d after craniotomy, comparing the following strategies: mechanical prophylaxis (MP), low molecular weight heparin with MP (MP+LMWH), and unfractionated heparin with MP (MP+UFH) to prevent symptomatic VTE. All costs were reported in 2016 US dollars. RESULTS: A total of 34 studies were reviewed (8 studies evaluated LMWH, 12 for MP, and 7 for UFH individually or in combination; 4 studies used LMWH and UFH preoperatively). Overall probability of VTE was 1.49% (95% confidence interval (CI) 0.42-3.72) for MP+UFH, 2.72% [95% CI 1.23-5.15] for MP+LMWH, and 2.59% (95% CI 1.31-4.58) for MP, which were not statistically significant. Compared to a control of MP alone, the number needed to treat for MP+UFH is 91 and 769 for MP+LMWH. The risk of intracranial hemorrhage was 0.26% (95% CI 0.01-1.34) for MP, 0.74% (95% CI 0.09-2.61) for MP+UFH, and 2.72% (95% CI 1.23-5.15) for MP+LMWH, which were also not statistically significant. Compared to MP, the number needed to harm for MP+UFH was 208 and for MP+LMWH was 41. Fifteen studies were included in the final CEA. The estimated cost of treatment was $127.47 for MP, $142.20 for MP+UFH, and $169.40 for MP+LMWH. The average cost per quality-adjusted life-year for different strategies was $284.14 for MP+UFH, $338.39 for MP, and $722.87 for MP+LMWH. CONCLUSION: Although MP+LMWH is frequently considered the optimal prophylaxis for VTE risk reduction, our model suggests that MP+UFH is the safest and most cost-effective measure to balance VTE and hemorrhage risks in brain tumor patients at lower risk of hemorrhage. MP+LMWH may be more effective for patients at higher risk of VTE.

Aberrations in the Iron Regulatory Gene Signature Are Associated with Decreased Survival in Diffuse Infiltrating Gliomas.

Iron is a tightly regulated micronutrient with no physiologic means of elimination and is necessary for cell division in normal tissue. Recent evidence suggests that dysregulation of iron regulatory proteins may play a role in cancer pathophysiology. We use public data from The Cancer Genome Atlas (TCGA) to study the association between survival and expression levels of 61 genes coding for iron regulatory proteins in patients with World Health Organization Grade II-III gliomas. Using a feature selection algorithm we identified a novel, optimized subset of eight iron regulatory genes (STEAP3, HFE, TMPRSS6, SFXN1, TFRC, UROS, SLC11A2, and STEAP4) whose differential expression defines two phenotypic groups with median survival differences of 52.3 months for patients with grade II gliomas (25.9 vs. 78.2 months, p< 10-3), 43.5 months for patients with grade III gliomas (43.9 vs. 87.4 months, p = 0.025), and 54.0 months when considering both grade II and III gliomas (79.9 vs. 25.9 months, p < 10-5).

Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values.

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.

Comparative Effectiveness Analysis of Treatment Options for Single Brain Metastasis.

BACKGROUND: Brain metastases (BMs) occur in up to 30% of patients with cancer. Treatments include surgery, whole-brain radiotherapy (WBRT), and stereotactic radiosurgery (SRS), alone or in combination. Although guidelines exist, data to inform individualized approaches to therapy remain sparse. We sought to compare semiquantitatively the effectiveness of various modalities in the treatment of single brain metastasis. METHODS: We performed a comparative effectiveness analysis (CEA) that integrated efficacy, cost, and quality of life (QoL) data for alternate BM treatments. Efficacy data were obtained from a comprehensive review of current literature. Cost estimates were based on publicly available data. QoL data included the Karnofsky Performance Status (KPS) and other questionnaires. Six treatment strategies using combinations of surgery, WBRT, and SRS were compared with decision tree software. RESULTS: The clinical efficacy, cost, and QoL effects of each strategy were scored semiquantitatively. We constructed a model to integrate individual preferences regarding the relative importance of efficacy, QoL, and cost to provide personalized rankings of the effectiveness of each strategy. CONCLUSION: The choice of strategy must be individualized for patients with a single BM. Our CEA and decision model combines empirical data with patient priorities to produce a ranking of alternate management strategies.

Multi-modal management of acromegaly: a value perspective.

PURPOSE: The Acromegaly Consensus Group recently released updated guidelines for medical management of acromegaly patients. We subjected these guidelines to a cost analysis. METHODS: We conducted a cost analysis of the recommendations based on published efficacy rates as well as publicly available cost data. The results were compared to findings from a previously reported comparative effectiveness analysis of acromegaly treatments. Using decision tree software, two models were created based on the Acromegaly Consensus Group's recommendations and the comparative effectiveness analysis. The decision tree for the Consensus Group's recommendations was subjected to multi-way tornado analysis to identify variables that most impacted the value analysis of the decision tree. RESULTS: The value analysis confirmed the Consensus Group's recommendations of somatostatin analogs as first line therapy for medical management. Our model also demonstrated significant value in using dopamine agonist agents as upfront therapy as well. Sensitivity analysis identified the cost of somatostatin analogs and growth hormone receptor antagonists as having the most significant impact on the cost effectiveness of medical therapies. CONCLUSION: Our analysis confirmed the value of surgery as first-line therapy for patients with surgically accessible lesions. Surgery provides the greatest value for management of patients with acromegaly. However, in accordance with the Acromegaly Consensus Group's recent recommendations, somatostatin analogs provide the greatest value and should be used as first-line therapy for patients who cannot be managed surgically. At present, the substantial cost is the most significant negative factor in the value of medical therapies for acromegaly.

Evolving Patient Compliance Trends: Integrating Clinical, Insurance, and Extrapolated Socioeconomic Data.

Efforts toward improving patient compliance in medication focus on either identifying trends in patient features or studying changes through an intervention. Our study seeks to provide an important link between these two approaches through defining trends of evolving compliance. In addition to using clinical covariates provided through insurance claims and health records, we also extracted census based data to provide socioeconomic covariates such as income and population density. Through creating quadrants based on periods of medicine intake, we derive several novel definitions of compliance. These definitions revealed additional compliance trends through considering refill histories later in a patient's length of therapy. These results suggested that the link between patient features and compliance includes a temporal component, and should be considered in policymaking when identifying compliant subgroups.

Preoperative imaging to predict intraoperative changes in tumor-to-corticospinal tract distance: an analysis of 45 cases using high-field intraoperative magnetic resonance imaging.

BACKGROUND: Preoperative diffusion tensor imaging (DTI) is used to demonstrate corticospinal tract (CST) position. Intraoperative brain shifts may limit preoperative DTI value, and studies characterizing such shifts are lacking. OBJECTIVE: To examine tumor characteristics that could predict intraoperative shift in tumor-to-CST distance using high-field intraoperative magnetic resonance imaging. METHODS: We retrospectively evaluated preoperative and intraoperative DTIs, tumor pathology, and imaging characteristics of patients who underwent resection of an intra-axial tumor adjacent to the CST to identify covariates that significantly affected shift in tumor-to-CST distance. For validation, we analyzed data from a separate, 20-patient cohort. RESULTS: In the first cohort, the mean intraoperative shift in the tumor-to-CST distance was 3.18 ± 3.58 mm. The mean shift for the 20 patients with contrast and the 5 patients with non-contrast-enhancing tumors was 3.93 ± 3.64 and 0.18 ± 0.18 mm, respectively (P < .001). No association was found between intraoperative shift in tumor-to-CST distance and tumor pathology, tumor volume, edema volume, preoperative tumor-to-CST distance, or extent of resection. According to receiver-operating characteristic analysis, nonenhancement predicted a tumor-to-CST distance shift of ≤ 0.5 mm, with a sensitivity of 100% and a specificity of 75%. We validated these findings using the second cohort. CONCLUSION: For nonenhancing intra-axial tumors, preoperative DTI is a reliable method for assessing intraoperative tumor-to-CST distance because of minimal intraoperative shift, a finding that is important in the interpretation of subcortical motor evoked potential to maximize extent of resection and to preserve motor function. In resection of intra-axial enhancing tumors, intraoperative imaging studies are crucial to compensate for brain shift.

Extent of resection of glioblastoma revisited: personalized survival modeling facilitates more accurate survival prediction and supports a maximum-safe-resection approach to surgery.

PURPOSE: Approximately 12,000 glioblastomas are diagnosed annually in the United States. The median survival rate for this disease is 12 months, but individual survival rates can vary with patient-specific factors, including extent of surgical resection (EOR). The goal of our investigation is to develop a reliable strategy for personalized survival prediction and for quantifying the relationship between survival, EOR, and adjuvant chemoradiotherapy. PATIENTS AND METHODS: We used accelerated failure time (AFT) modeling using data from 721 newly diagnosed patients with glioblastoma (from 1993 to 2010) to model the factors affecting individualized survival after surgical resection, and we used the model to construct probabilistic, patient-specific tools for survival prediction. We validated this model with independent data from 109 patients from a second institution. RESULTS: AFT modeling using age, Karnofsky performance score, EOR, and adjuvant chemoradiotherapy produced a continuous, nonlinear, multivariable survival model for glioblastoma. The median personalized predictive error was 4.37 months, representing a more than 20% improvement over current methods. Subsequent model-based calculations yield patient-specific predictions of the incremental effects of EOR and adjuvant therapy on survival. CONCLUSION: Nonlinear, multivariable AFT modeling outperforms current methods for estimating individual survival after glioblastoma resection. The model produces personalized survival curves and quantifies the relationship between variables modulating patient-specific survival. This approach provides comprehensive, personalized, probabilistic, and clinically relevant information regarding the anticipated course of disease, the overall prognosis, and the patient-specific influence of EOR and adjuvant chemoradiotherapy. The continuous, nonlinear relationship identified between expected median survival and EOR argues against a surgical management strategy based on rigid EOR thresholds and instead provides the first explicit evidence supporting a maximum safe resection approach to glioblastoma surgery.

Bevacizumab for radiation necrosis following treatment of high grade glioma: a systematic review of the literature.

This review identifies the current literature on the use of bevacizumab for cerebral radiation necrosis in patients with high-grade gliomas, summarizes the clinical course and complications following bevacizumab, and discusses the relative costs and benefits of this therapeutic option. A Medline search was conducted of all clinical studies before September 2012 investigating outcomes following use of bevacizumab therapy for radiation necrosis in patients with high-grade gliomas. Clinical and radiographic outcomes are reviewed. Seven studies reported a total of 30 patients with high-grade gliomas treated with bevacizumab for radiation necrosis. All patients demonstrated decreased radiographic volume of edema on T1 and T2 MRI sequences. Clinical outcomes were reported for 23 patients: 16 (70 %) had improvement in neurologic signs or symptoms, 5 (22 %) had mixed results, and 2 (9 %) remained neurologically unchanged. Complications were documented in 5 of 7 studies (18 of 29 patients, 62 %) and included deep vein thrombosis, pulmonary embolism, visual field worsening, worsening hemiplegia, pneumonia, seizure, and fatigue. Only one study evaluated quality of life measures and none evaluated cost or cost effectiveness. Data regarding the use of bevacizumab to treat radiation necrosis in patients with high-grade gliomas is limited and primarily class III evidence. While bevacizumab improves neurological symptoms and reduces radiographic volume of necrosis-associated cerebral edema, it comes at the expense of a high rate of potentially serious complications. Definitive evidence for the utility, cost-effectiveness, and overall efficacy of this management strategy is currently lacking and additional investigation is warranted.

The molecular biology of WHO grade II gliomas.

The WHO grading scheme for glial neoplasms assigns Grade II to 5 distinct tumors of astrocytic or oligodendroglial lineage: diffuse astrocytoma, oligodendroglioma, oligoastrocytoma, pleomorphic xanthoastrocytoma, and pilomyxoid astrocytoma. Although commonly referred to collectively as among the "low-grade gliomas," these 5 tumors represent molecularly and clinically unique entities. Each is the subject of active basic research aimed at developing a more complete understanding of its molecular biology, and the pace of such research continues to accelerate. Additionally, because managing and predicting the course of these tumors has historically proven challenging, translational research regarding Grade II gliomas continues in the hopes of identifying novel molecular features that can better inform diagnostic, prognostic, and therapeutic strategies. Unfortunately, the basic and translational literature regarding the molecular biology of WHO Grade II gliomas remains nebulous. The authors' goal for this review was to present a comprehensive discussion of current knowledge regarding the molecular characteristics of these 5 WHO Grade II tumors on the chromosomal, genomic, and epigenomic levels. Additionally, they discuss the emerging evidence suggesting molecular differences between adult and pediatric Grade II gliomas. Finally, they present an overview of current strategies for using molecular data to classify low-grade gliomas into clinically relevant categories based on tumor biology.

Cerebral radiation necrosis: a review of the pathobiology, diagnosis and management considerations.

Radiation therapy forms one of the building blocks of the multi-disciplinary management of patients with brain tumors. Improved survival following radiation therapy may come with a cost, including the potential complication of radiation necrosis. Radiation necrosis impacts the quality of life in cancer survivors, and it is essential to detect and effectively treat this entity as early as possible. Significant progress in neuro-radiology and molecular pathology facilitate more straightforward diagnosis and characterization of cerebral radiation necrosis. Several therapeutic interventions, both medical and surgical, may halt the progression of radiation necrosis and diminish or abrogate its clinical manifestations, but there are still no definitive guidelines to follow explicitly that guide treatment of radiation necrosis. We discuss the pathobiology, clinical features, diagnosis, available treatment modalities, and outcomes in the management of patients with intracranial radiation necrosis that follows radiation used to treat brain tumors.

Relapsing, remitting hypercortisolism in Cushing's disease due to intratumoral hemorrhages in pituitary microadenoma.

We report two patients with Cushing's disease (CD) from adrenocorticotropic hormone-secreting microadenomas in whom intralesional hemorrhage caused an episodic, remitting and relapsing pattern of hypercortisolism. To our knowledge this is the first report of patients in whom hemorrhage within a microadenoma caused cyclic CD. Both patients were treated successfully with transsphenoidal surgery.

Role of Gamma Knife surgery in patients with 5 or more brain metastases.

OBJECT: The authors evaluated overall survival and factors predicting outcome in patients with ≥ 5 brain metastases who were treated with Gamma Knife surgery (GKS). METHODS: Medical records from patients with ≥ 5 brain metastases treated with GKS between 1997 and 2010 at the Cleveland Clinic Gamma Knife Center were retrospectively reviewed. Patient demographics, tumor characteristics, treatment-related factors, and outcome data were evaluated. RESULTS: One hundred seventy patients were identified, with a median age of 58 years. The female/male ratio was 1.2:1. Gamma Knife surgery was used as an upfront treatment in 35% of patients and as salvage treatment in 65% of patients with multiple brain metastases. The median overall survival after GKS was 6.7 months (95% CI 5.5-8.1). At the time of GKS, 128 patients (75%) had concurrent extracranial metastases, and in 69 patients (41%) multiple extracranial sites were involved. Ninety-two patients (54%) had a history of whole-brain radiation therapy, and 158 patients (93%) had a Karnofsky Performance Scale (KPS) score ≥ 70. The median total intracranial disease volume was 3.2 cm(3) (range 0.2-37.2 cm(3)). A total intracranial tumor volume ≥ 10 cm(3) was observed in 32 patients (19%). Lower KPS score at the time of treatment (p < 0.0001), patient age > 60 years (p = 0.004), multiple extracranial metastases (p = 0.0001), and greater intracranial burden of disease (p = 0.03) were prognostic factors for poor outcome in the univariate and multivariate analyses. CONCLUSIONS: In this study, GKS was safe and effective for upfront and salvage treatment in patients with ≥ 5 brain metastases. Gamma Knife surgery should be considered as an additional treatment modality for these patients, especially in the subset of patients with favorable prognostic factors.

Non-gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes.

INTRODUCTION: Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. METHODS: We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. RESULTS: Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. CONCLUSIONS: Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that "small" departures from normality in the expression data distributions are analytically-insignificant and that "robust" gene-calling algorithms can fully compensate for these effects.