Ohgaki H, Kleihues PPopulation-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J Neuropathol Exp Neurol 64: 479-489

Pathology Group, International Agency for Research on Cancer (HO), F-69372, Lyon, France.
Journal of Neuropathology and Experimental Neurology (Impact Factor: 3.8). 06/2005; 64(6):479-89. DOI: 10.1097/01.jnen.0000166799.76946.08
Source: PubMed


Published data on prognostic and predictive factors in patients with gliomas are largely based on clinical trials and hospital-based studies. This review summarizes data on incidence rates, survival, and genetic alterations from population-based studies of astrocytic and oligodendrogliomas that were carried out in the Canton of Zurich, Switzerland (approximately 1.16 million inhabitants). A total of 987 cases were diagnosed between 1980 and 1994 and patients were followed up at least until 1999. While survival rates for pilocytic astrocytomas were excellent (96% at 10 years), the prognosis of diffusely infiltrating gliomas was poorer, with median survival times (MST) of 5.6 years for low-grade astrocytoma WHO grade II, 1.6 years for anaplastic astrocytoma grade III, and 0.4 years for glioblastoma. For oligodendrogliomas the MSTwas 11.6 years for grade II and 3.5 years for grade III. TP53 mutations were most frequent in gemistocytic astrocytomas (88%), followed by fibrillary astrocytomas (53%) and oligoastrocytomas (44%), but infrequent (13%) in oligodendrogliomas. LOH 1p/19q typically occurred in tumors without TP53 mutations and were most frequent in oligodendrogliomas (69%), followed by oligoastrocytomas (45%), but were rare in fibrillary astrocytomas (7%) and absent in gemistocytic astrocytomas. Glioblastomas were most frequent (3.55 cases per 100,000 persons per year) adjusted to the European Standard Population, amounting to 69% of total incident cases. Observed survival rates were 42.4% at 6 months, 17.7% at one year, and 3.3% at 2 years. For all age groups, survival was inversely correlated with age, ranging from an MST of 8.8 months (<50 years) to 1.6 months (>80 years). In glioblastomas, LOH 10q was the most frequent genetic alteration (69%), followed by EGFR amplification (34%), TP53 mutations (31%), p16INK4a deletion (31%), and PTEN mutations (24%). LOH 10q occurred in association with any of the other genetic alterations, and was the only alteration associated with shorter survival of glioblastoma patients. Primary (de novo) glioblastomas prevailed (95%), while secondary glioblastomas that progressed from low-grade or anaplastic gliomas were rare (5%). Secondary glioblastomas were characterized by frequent LOH 10q (63%) and TP53 mutations (65%). Of the TP53 mutations in secondary glioblastomas, 57% were in hot-spot codons 248 and 273, while in primary glioblastomas, mutations were more evenly distributed. G:C-->A:T mutations at CpG sites were more frequent in secondary than primary glioblastomas, suggesting that the acquisition of TP53 mutations in these glioblastoma subtypes may occur through different mechanisms.

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    • "For example, the mutational load of ATRX mutated gliomas increased from 21.6 ± 10.3 and 26.0 ± 11.2 to 65.4 ±The mutational load is associated with patient age Because age is a well-known prognostic factor in diffuse glioma patients, we included age in the analysis. Similar to previously reported[1,32,33], grade II tumors occur in patients that were younger than those with grade III or grade IV tumors, 39.6 ± 12.5 (n = 137), 45.6 ± 13.5 (n = 144) and 61.3 ± 13.0 (n = 261) years respectively (average ± standard deviation (SD), P < 0.0001 for any comparison , ANOVA). As patient age and tumor grade were correlated, and tumor grade was correlated to the mutational load, it is not surprizing that age was also correlated with the mutational load of the tumor (Fig. 3). "
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    ABSTRACT: Introduction: Recent advances in molecular diagnostics allow diffuse gliomas to be classified based on their genetic changes into distinct prognostic subtypes. However, a systematic analysis of all molecular markers has thus far not been performed; most classification schemes use a predefined and select set of genes/molecular markers. Here, we have analysed the TCGA dataset (combined glioblastoma (GBM) and lower grade glioma (LGG) datasets) to identify all prognostic genetic markers in diffuse gliomas in order to generate a comprehensive classification scheme. Results: Of the molecular markers investigated (all genes mutated at a population frequency >1.7 % and frequent chromosomal imbalances) in the entire glioma dataset, 57 were significantly associated with overall survival. Of these, IDH1 or IDH2 mutations are associated with lowest hazard ratio, which confirms IDH as the most important prognostic marker in diffuse gliomas. Subsequent subgroup analysis largely confirms many of the currently used molecular classification schemes for diffuse gliomas (ATRX or TP53 mutations, 1p19q codeletion). Our analysis also identified PI3-kinase mutations as markers of poor prognosis in IDH-mutated + ATRX/TP53 mutated diffuse gliomas, median survival 3.7 v. 6.3 years (P = 0.02, Hazard rate (HR) 2.93, 95 % confidence interval (CI) 1.16 - 7.38). PI3-kinase mutations were also prognostic in two independent datasets. In our analysis, no additional molecular markers were identified that further refine the molecular classification of diffuse gliomas. Interestingly, these molecular classifiers do not fully explain the variability in survival observed for diffuse glioma patients. We demonstrate that tumor grade remains an important prognostic factor for overall survival in diffuse gliomas, even within molecular glioma subtypes. Tumor grade was correlated with the mutational load (the number of non-silent mutations) of the tumor: grade II diffuse gliomas harbour fewer genetic changes than grade III or IV, even within defined molecular subtypes (e.g. ATRX mutated diffuse gliomas). Conclusion: We have identified PI3K mutations as novel prognostic markers in gliomas. We also demonstrate that the mutational load is associated with tumor grade. The increase in mutational load may partially explain the increased aggressiveness of higher grade diffuse gliomas when a subset of the affected genes actively contributes to gliomagenesis and/or progression.
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    • "More recently, the development of next-generation sequencing (NGS) based techniques, RNA-seq[5], is enabling gene expression analysis to yield a much greater resolution for accurate identification of different isoforms. While several genome-wide expression profiling studies have dramatically improved our collective understanding of cancer biology and led to numerous clinical advancements[6,7], the use of genomics based molecular diagnostics, such as OncotypeDX89101112, in clinical practice still remains largely unmet for majority of human cancers[13]. A crucial step in the translation of gene signatures derived from high-throughput platforms is validation in a clinical setting, using robust and quantitative assay platforms (e.g., RT-qPCR based assay) without loss of any classification accuracy[14]. "
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    ABSTRACT: Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms.
    Full-text · Article · Nov 2015 · BMC Genomics
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    • "Malignant gliomas such as glioblastoma multiforme being characterized by aggressive proliferation of undifferentiated cells, pervasive invasion into distant healthy brain tissue and a high penchant to recur is among the most recalcitrant tumors to be treated. In spite of access to state-of-the-art imaging, neurosurgery, radiotherapy and chemotherapy, patients have a median survival time of around 3 months and most of them die within 6 months to 2 years post-diagnosis [1] [2]. This is probably suggestive of the fact that local recurrence of the tumor is the primary etiology of death which is confirmed from observed local glioma recurrence within 2 cm of the original tumor resection field [3]. "
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    ABSTRACT: Purpose: We have previously demonstrated that ritonavir targeting of glycolysis is growth inhibitory and cytotoxic in a subset of MM cells. In this study our objective was to investigate the metabolic basis of resistance to ritonavir and to determine the utility of co-treatment with the mitochondrial complex I inhibitor metformin to target compensatory metabolism. Experimental Design: We determined combination indices for ritonavir and metformin, impact on myeloma cell lines, patient samples and myeloma xenograft growth. Additional evaluation in breast, melanoma, and ovarian cancer cell lines was also performed. Signaling connected to suppression of the pro-survival BCL2 family member MCL-1 was evaluated in MM cell lines and tumor lysates. Reliance on oxidative metabolism was determined by evaluation of oxygen consumption and dependence on glutamine was assessed by estimation of viability upon metabolite withdrawal in the context of specific metabolic perturbations. Results: Ritonavir-treated MM cells exhibited increased reliance on glutamine metabolism. Ritonavir sensitized MM cells to metformin, effectively eliciting cytotoxicity both in vitro and in an in vivo xenograft model of MM and in breast, ovarian and melanoma cancer cell lines. Ritonavir and metformin effectively suppressed AKT and mTORC1 phosphorylation and pro-survival BCL-2 family member MCL-1 expression in MM cell lines in vitro and in vivo. Conclusions: FDA-approved ritonavir and metformin effectively target MM cell metabolism to elicit cytotoxicity in MM. Our studies warrant further investigation into repurposing ritonavir and metformin to target the metabolic plasticity of myeloma to more broadly target myeloma heterogeneity and prevent the re-emergence of chemo-resistant aggressive MM. Copyright © 2014, American Association for Cancer Research.
    Full-text · Article · Dec 2014 · Clinical Cancer Research
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