Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas
ABSTRACT 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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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.Clinical Cancer Research 12/2014; 21(5). DOI:10.1158/1078-0432.CCR-14-1088 · 8.19 Impact Factor
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ABSTRACT: Glutamine (Gln) and glutamate (Glu) play pivotal roles in the malignant phenotype of brain tumors via multiple mechanisms. Glutaminase (GA, EC 22.214.171.124) metabolizes Gln to Glu and ammonia. Human GA isoforms are encoded by two genes: GLS gene codes for kidney-type isoforms, KGA and GAC, whereas GLS2 codes for liver-type isoforms, GAB and LGA. The expression pattern of both genes in different neoplastic cell lines and tissues implicated that the kidney-type isoforms are associated with cell proliferation, while the liver-type isoforms dominate in, and contribute to the phenotype of quiescent cells. GLS gene has been demonstrated to be regulated by oncogene c-Myc, whereas GLS2 gene was identified as a target gene of p53 tumor suppressor. In glioblastomas (GBM, WHO grade IV), the most aggressive brain tumors, high levels of GLS and only traces or lack of GLS2 transcripts were found. Ectopic overexpression of GLS2 in human glioblastoma T98G cells decreased their proliferation and migration and sensitized them to the alkylating agents often used in the chemotherapy of gliomas. GLS silencing reduced proliferation of glioblastoma T98G cells and strengthen the antiproliferative effect evoked by previous GLS2 overexpression.Neurochemistry International 12/2014; DOI:10.1016/j.neuint.2014.11.004 · 2.65 Impact Factor
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ABSTRACT: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all subregions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.IEEE Transactions on Medical Imaging 12/2014; DOI:10.1109/TMI.2014.2377694 · 3.80 Impact Factor