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      Genetic variations in EGF and EGFR and glioblastoma outcome

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          Abstract

          Few prognostic factors have been associated with glioblastoma survival. We analyzed a complete tagging of the epidermal growth factor (EGF) and EGF receptor (EGFR) gene polymorphisms as potential prognostic factors. Thirty tagging single-nucleotide polymorphisms (SNPs) in EGF and 89 tagging SNPs in EGFR were analyzed for association with survival in 176 glioblastoma cases. Validation analyses were performed for 4 SNPs in a set of 638 glioblastoma patients recruited at The University of Texas M. D. Anderson Cancer Center (MDACC). Three hundred and seventy-four glioblastoma patients aged 50 years or older at diagnosis were subanalyzed to enrich for de novo arising glioblastoma. We found 7 SNPs in haplotype 4 in EGF that were associated with prognosis in glioblastoma patients. In EGFR, 4 of 89 SNPs were significantly associated with prognosis but judged as false positives. Four of the significantly associated EGF polymorphisms in haplotype block 4 were validated in a set from MDACC; however, none of the associations were clearly replicated. rs379644 had a hazard ratio (HR) of 1.19 (0.94–1.51) in the whole population with 18.6 months survival in the risk genotype compared with 24.5 in the reference category. As the median age differed slightly between the 2 study sets, the MDACC cases aged 50 or older at diagnosis were analyzed separately (rs379644, HR 1.32 [0.99–1.78]), which is marginally significant and partially validates our findings. This study is, to our knowledge, the first to perform a comprehensive tagging of the EGF and EGFR genes, and the data give some support that EGF polymorphisms might be associated with poor prognosis. Further confirmation in independent data sets of prospective studies is necessary to establish EGF as prognostic risk factor.

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          Genetic pathways to glioblastoma: a population-based study.

          We conducted a population-based study on glioblastomas in the Canton of Zurich, Switzerland (population, 1.16 million) to determine the frequency of major genetic alterations and their effect on patient survival. Between 1980 and 1994, 715 glioblastomas were diagnosed. The incidence rate per 100,000 population/year, adjusted to the World Standard Population, was 3.32 in males and 2.24 in females. Observed survival rates were 42.4% at 6 months, 17.7% at 1 year, and 3.3% at 2 years. For all of the age groups, younger patients survived significantly longer, ranging from a median of 8.8 months ( 80 years). Loss of heterozygosity (LOH) 10q was the most frequent genetic alteration (69%), followed by EGFR amplification (34%), TP53 mutations (31%), p16(INK4a) deletion (31%), and PTEN mutations (24%). LOH 10q occurred in association with any of the other genetic alterations and was predictive of shorter survival. Primary (de novo) glioblastomas prevailed (95%), whereas 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 hotspot codons 248 and 273, whereas in primary glioblastomas, mutations were more equally distributed. G:C-->A:T mutations at CpG sites were more frequent in secondary than primary glioblastomas (56% versus 30%; P = 0.0208). This suggests that the acquisition of TP53 mutations in these glioblastoma subtypes occurs through different mechanisms.
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            Long-term survival with glioblastoma multiforme.

            The median survival of glioblastoma patients is approximately 12 months. However, 3-5% of the patients survives for more than 3 years and are referred to as long-term survivors. The clinical and molecular factors that contribute to long-term survival are still unknown. To identify specific parameters that might be associated with this phenomenon, we performed a detailed clinical and molecular analysis of 55 primary glioblastoma long-term survivors recruited at the six clinical centres of the German Glioma Network and one associated centre. An evaluation form was developed and used to document demographic, clinical and treatment-associated parameters. In addition, environmental risk factors, associated diseases and occupational risks were assessed. These patients were characterized by young age at diagnosis and a good initial Karnofsky performance score (KPS). None of the evaluated socioeconomic, environmental and occupational factors were associated with long-term survival. Molecular analyses revealed MGMT hypermethylation in 28 of 36 tumours (74%) investigated. TP53 mutations were found in 9 of 31 tumours (29%) and EGFR amplification in 10 of 38 tumours (26%). Only 2 of 32 tumours (6%) carried combined 1p and 19q deletions. Comparison of these data with results from an independent series of 141 consecutive unselected glioblastoma patients registered in the German Glioma Network revealed significantly more frequent MGMT hypermethylation in the long-term survivor group. Taken together, our findings underline the association of glioblastoma long-term survival with prognostically favourable clinical factors, in particular young age and good initial performance score, as well as MGMT promoter hypermethylation.
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              Recursive partitioning analysis of prognostic factors in three Radiation Therapy Oncology Group malignant glioma trials.

              Despite notable technical advances in therapy for malignant gliomas during the past decade, improved patient survival has not been clearly documented, suggesting that pretreatment prognostic factors influence outcome more than minor modifications in therapy. Age, performance status, and tumor histopathology have been identified as the pretreatment variables most predictive of survival outcome. However, an analysis of the association of survival with both pretreatment characteristics and treatment-related variables is necessary to assure reliable evaluation of new approaches for treatment of malignant glioma. This study of malignant glioma patients used a non-parametric statistical technique to examine the associations of both pretreatment patient and tumor characteristics and treatment-related variables with survival duration. This technique was used to identify subgroups with survival rates sufficiently different to create improvements in the design and stratification of clinical trials. We used a recursive partitioning technique to analyze survival in 1578 patients entered in three Radiation Therapy Oncology Group malignant glioma trials from 1974 to 1989 that used several radiation therapy (RT) regimens with and without chemotherapy or a radiation sensitizer. This approach creates a regression tree according to prognostic variables that classifies patients into homogeneous subsets by survival. Twenty-six pretreatment characteristics and six treatment-related variables were analyzed. The years). Patients younger than 50 years old were categorized by histology (astrocytomas with anaplastic or atypical foci [AAF] versus glioblastoma multiforme [GBM]) and subsequently by normal or abnormal mental status for AAF patients and by performance status for those with GBM. For patients aged 50 years or older, performance status was the most important variable, with normal or abnormal mental status creating the only significant split in the poorer performance status group. Treatment-related variables produced a subgroup showing significant differences only for better performance status GBM patients over age 50 (by extent of surgery and RT dose). Median survival times were 4.7-58.6 months for the 12 subgroups resulting from this analysis, which ranged in size from 32 to 256 patients. This approach permits examination of the interaction between prognostic variables not possible with other forms of multivariate analysis. The recursive partitioning technique can be employed to refine the stratification and design of malignant glioma trials.
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                Author and article information

                Journal
                Neuro Oncol
                neuonc
                neuonc
                Neuro-Oncology
                Oxford University Press
                1522-8517
                1523-5866
                August 2010
                2 March 2010
                : 12
                : 8
                : 815-821
                Affiliations
                Department of Radiation Sciences, Oncology, simpleUmeå University Hospital , Umeå, Sweden (S.S., U.A., R.H., B.M.); Department of Epidemiology, simplethe M. D. Anderson Cancer Center , Texas (Y.L., M.B.); Department of Medical Biosciences, Pathology, simpleUmeå University , Umeå, Sweden (T.B.); Department of Pathology, the Cancer of Diagnostic Investigations, Rigshospitalet, simpleCopenhagen University Hospital , Copenhagen, Denmark (H.B.); Institute of Cancer Epidemiology, simpleDanish Cancer Society , Copenhagen, Denmark (C.J., H.C-L.); Department of ENT, simpleSlagelse Hospital , Slagelse, Denmark (H.C-L.)
                Author notes
                Corresponding Author:Beatrice Melin, MD, PhD, Department of Radiation Sciences, Oncology, simpleUmeå University , 90187 Umeå, Sweden ( beatrice.melin@ 123456onkologi.umu.se ).
                Article
                noq018
                10.1093/neuonc/noq018
                2940681
                20197289
                34a3be5e-568c-435b-bf2f-e038f3f54feb
                © The Author(s) 2010. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 2 October 2009
                : 4 January 2010
                Categories
                Basic and Translational Investigations

                Oncology & Radiotherapy
                egfr,polymorphism,outcome,egf,glioblastoma
                Oncology & Radiotherapy
                egfr, polymorphism, outcome, egf, glioblastoma

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