SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Atopic conditions and brain tumor risk in children and adolescents--an international case-control study (CEFALO)

BACKGROUND A number of epidemiological studies indicate an inverse association between atopy and brain tumors in adults, particularly gliomas. We investigated the association between atopic disorders and intracranial brain tumors in children and adolescents, using international collaborative CEFALO data. PATIENTS AND METHODS CEFALO is a population-based case-control study conducted in Denmark, Norway, Sweden, and Switzerland, including all children and adolescents in the age range 7-19 years diagnosed with a primary brain tumor between 2004 and 2008. Two controls per case were randomly selected from population registers matched on age, sex, and geographic region. Information about atopic conditions and potential confounders was collected through personal interviews. RESULTS In total, 352 cases (83%) and 646 controls (71%) participated in the study. For all brain tumors combined, there was no association between ever having had an atopic disorder and brain tumor risk [odds ratio 1.03; 95% confidence interval (CI) 0.70-1.34]. The OR was 0.76 (95% CI 0.53-1.11) for a current atopic condition (in the year before diagnosis) and 1.22 (95% CI 0.86-1.74) for an atopic condition in the past. Similar results were observed for glioma. CONCLUSIONS There was no association between atopic conditions and risk of all brain tumors combined or of glioma in particular. Stratification on current or past atopic conditions suggested the possibility of reverse causality, but may also the result of random variation because of small numbers in subgroups. In addition, an ongoing tumor treatment may affect the manifestation of atopic conditions, which could possibly affect recall when reporting about a history of atopic diseases. Only a few studies on atopic conditions and pediatric brain tumors are currently available, and the evidence is conflicting.

A Hybrid Model for Multimodal Brain Tumor Segmentation

We present a fully automatic segmentation method for multi-modal brain tumor segmentation. The proposed generative-discriminative hybrid model generates initial tissue probabilities, which are used subsequently for enhancing the classi�cation and spatial regularization. The model has been evaluated on the BRATS2013 training set, which includes multimodal MRI images from patients with high- and low-grade gliomas. Our method is capable of segmenting the image into healthy (GM, WM, CSF) and pathological tissue (necrotic, enhancing and non-enhancing tumor, edema). We achieved state-of-the-art performance (Dice mean values of 0.69 and 0.8 for tumor subcompartments and complete tumor respectively) within a reasonable timeframe (4 to 15 minutes).

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.

CCDC26, CDKN2BAS, RTEL1 and TERT Polymorphisms in pediatric brain tumor susceptibility.

The role of genetic polymorphisms in pediatric brain tumor (PBT) etiology is poorly understood. We hypothesized that single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) on adult glioma would also be associated with PBT risk. The study is based on the Cefalo study, a population-based multicenter case-control study. Saliva DNA from 245 cases and 489 controls, aged 7-19 years at diagnosis/reference date, was extracted and genotyped for 29 SNPs reported by GWAS to be significantly associated with risk of adult glioma. Data were analyzed using unconditional logistic regression. Stratified analyses were performed for two histological subtypes: astrocytoma alone and the other tumor types combined. The results indicated that four SNPs, CDKN2BAS rs4977756 (p = 0.036), rs1412829 (p = 0.037), rs2157719 (p = 0.018) and rs1063192 (p = 0.021), were associated with an increased susceptibility to PBTs, whereas the TERT rs2736100 was associated with a decreased risk (p = 0.018). Moreover, the stratified analyses showed a decreased risk of astrocytoma associated with RTEL1 rs6089953, rs6010620 and rs2297440 (p trend = 0.022, p trend = 0.042, p trend = 0.029, respectively) as well as an increased risk of this subtype associated with RTEL1 rs4809324 (p trend = 0.033). In addition, SNPs rs10464870 and rs891835 in CCDC26 were associated with an increased risk of non-astrocytoma tumor subtypes (p trend = 0.009, p trend = 0.007, respectively). Our findings indicate that SNPs in CDKN2BAS, TERT, RTEL1 and CCDC26 may be associated with the risk of PBTs. Therefore, we suggest that pediatric and adult brain tumors might share common genetic risk factors and similar etiological pathways.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

Development and validation of the Neuro Symptom Inventory in the primary brain tumor patient population

Symptoms has been shown to predict quality of life, treatment course and survival in solid tumor patients. Currently, no instrument exists that measures both cancer-related symptoms and the neurologic symptoms that are unique to persons with primary brain tumors (PBT). The aim of this study was to develop and validate an instrument to measure symptoms in patients who have PBT. A conceptual analysis of symptoms and symptom theories led to defining the symptoms experience as the perception of the frequency, intensity, distress, and meaning that occurs as symptoms are produced, perceived, and expressed. The M.D. Anderson Symptom Inventory (MDASI) measures both symptoms and how they interfere with daily functioning in patients with cancer, which is similar to the situational meaning defined in the analysis. A list of symptoms pertinent to the PBT population was added to the core MDASI and reviewed by a group of experts for validity. As a result, 18 items were added to the core MDASI (the MDASI-BT) for the next phase of instrument development, establishing validity and reliability through a descriptive, cross-sectional approach with PBT patients. Data were collected with a patient completed demographic data sheet, an investigator completed clinician checklist, and the MDASI-BT. Analysis evaluated the reliability and validity of the MDASI-BT in PBT patients. Data were obtained from 201 patients. The number of items was reduced to 22 by evaluation of symptom severity as well as cluster analysis. Regression analysis showed more than half (56%) of the variability in symptom severity was explained by the brain tumor module items. Factor analysis confirmed that the 22 item MDASI-BT measured six underlying constructs: (a) affective; (b) cognitive; (c) focal neurologic deficits; (d) constitutional symptoms; (e) treatment-related symptoms; and (f) gastrointestinal symptoms. The MDASI-BT was sensitive to disease severity and if the patient was hospitalized. The MDASI-BT is the first instrument to measure symptoms in PBT patients that has demonstrated reliability and validity. It is the first step in a program of research to evaluate the occurrence of symptoms and plan and evaluate interventions for PBT patients. ^

Occupation and glioma: The Harris County Brain Tumor Study, 2001--2010

The relationship between occupational exposures and glioma has not been adequately assessed due to the lack of studies in current scientific literature. To address this disparity, the Harris County Brain Tumor Study, an ongoing population-based case-control study, began in January 2001. Longest-held occupation for 382 cases and 629 controls were frequency matched on age (within 5 years), sex, and race and placed into 14 predetermined occupational categories. Adjusted odds ratios and 95% confidence intervals were calculated for each category using multiple logistic regression. Potential confounders assessed included sex, age, smoking status, education and income. For all subjects, significantly elevated adjusted odds ratios were found in health-related (aOR=1.66; 95%CI=1.03, 2.68), teaching (aOR=1.84; 95%CI=1.17, 2.88), and protective service (aOR=3.6; 95%CI=1.05, 12.31) occupational categories after controlling for sex and education. A significantly lowered odds ratio was seen in the writers, artists, and entertainers category (aOR=0.14; 95%CI=0.03, 0.58). In the stratified analyses, which controlled for education, males had a significantly elevated odds ratio for protective service workers (aOR=4.83; 95%CI=1.24, 18.83) while a significantly lower odds ratio was found in mechanics and machine operators (aOR=0.33; 95%CI=0.12,0.87). In females, we observed a significantly elevated odds ratio in teachers (aOR=1.99; 95%CI=1.20,3.31) and a significantly lower odds ratio in clerical workers (aOR=0.63; 95%CI=0.45,0.90). These analyses revealed several significant associations and allowed for separate analyses by gender, distinguishing this study from many glioma studies. Further analyses should provide a large enough sample size to stratify by gender as well as histological subtype.^

Identification of genetic risk factors for cerebellar mutism in pediatric brain tumor patients

Following posterior fossa surgery for resection of childhood medulloblastoma and primitive neuroectodermal tumor (M/PNET), cerebellar mutism (CM) may develop. This is a condition of absent or diminished speech in a conscious patient with no evidence of oral apraxia, which can be accompanied by other symptoms of the posterior fossa syndrome complex, which includes ataxia and hypotonia. Little is known about the etiology. Therefore, we conducted a SNP, gene, and pathway-level analysis to assess the role of host genetic variation on the risk of CM in M/PNET subjects following treatment. Cases (n= 20) and controls (n= 53) were recruited from the Childhood Cancer Epidemiology and Prevention Center, in Houston, TX. DNA samples were genotyped using the Illumina Human 1M Quad SNP chip. Ten pathways were identified from logistic regression used to identify the marginal effect of each SNP on CM risk. The minP test was conducted to identify associations between SNPs categorized to genes and CM risk. Pathways were assessed to determine if there was a significant enrichment of genes in the pathway compared to all other pathways. There were 78 genes that reached the threshold of min P ≤0.05 in 948 genes. The Neurotoxicity pathway was the most significant pathway after adjusting for multiple comparisons (q=0.040 and q=0.005, using Fisher's exact test and a test of proportions, respectively). Most genes within the Neurotoxicity pathway that reached a threshold of minP ≤0.05 were known to have an apoptosis function, possibly inducing neuronal apoptosis in the dentatothalamocortical pathway, and may be important in CM etiology in this population. This is the first study to assess the potential role of genetic risk factors on CM. As an exploratory study, these results should be replicated in a larger sample. ^

Cationic Bovine Serum Albumin (CBA) conjugated Poly Lactic-co-Glycolic Acid (PLGA) nanoparticles for extended delivery of methotrexate into brain tumor

Current treatment strategies for the treatment of brain tumor have been hindered primarily by the presence of highly lipophilic insurmountable blood-brain barrier (BBB). The purpose of current research was to investigate the efficiency of engineered biocompatible polymeric nanoparticles (NPs) as drug delivery vehicle to bypass the BBB and enhance biopharmaceutical attributes of anti-metabolite methotrexate (MTX) encapsulated NPs. The NPs were prepared by solvent diffusion method using cationic bovine serum albumin (CBA), and characterized for physicochemical parameters such as particle size, polydispersity index, and zeta-potential; while the surface modification was confirmed by FTIR, and NMR spectroscopy. Developed NPs exhibited zestful relocation of FITC tagged NPs across BBB in albino rats. Further, hemolytic studies confirmed them to be non-toxic and biocompatible as compared to free MTX. In vitro cytotoxicity assay of our engineered NPs on HNGC1 tumor cells proved superior uptake in tumor cells; and elicited potent cytotoxic effect as compared to plain NPs and free MTX solution. The outcomes of the study evidently indicate the prospective of CBA conjugated poly (D,L-lactide-co-glycolide) (PLGA) NPs loaded with MTX in brain cancer bomber with amplified capability to circumvent BBB.

Atlas-based Segmentation of Tumor-bearing Brain Images

In diagnostic neuroradiology as well as in radiation oncology and neurosurgery, there is an increasing demand for accurate segmentation of tumor-bearing brain images. Atlas-based segmentation is an appealing automatic technique thanks to its robustness and versatility. However, atlas-based segmentation of tumor-bearing brain images is challenging due to the confounding effects of the tumor in the patient image. In this article, we provide a brief background on brain tumor imaging and introduce the clinical perspective, before we categorize and review the state of the art in the current literature on atlas-based segmentation for tumor-bearing brain images. We also present selected methods and results from our own research in more detail. Finally, we conclude with a short summary and look at new developments in the field, including requirements for future routine clinical use.