001     119640
005     20240228134946.0
024 7 _ |a 10.3324/haematol.2013.101170
|2 doi
024 7 _ |a pmid:25082786
|2 pmid
024 7 _ |a pmc:PMC4116826
|2 pmc
024 7 _ |a 0390-6078
|2 ISSN
024 7 _ |a 1592-8721
|2 ISSN
037 _ _ |a DKFZ-2017-00271
041 _ _ |a eng
082 _ _ |a 610
100 1 _ |a Benner, Axel
|0 P:(DE-He78)e15dfa1260625c69d6690a197392a994
|b 0
|e First author
|u dkfz
245 _ _ |a MDM2 promotor polymorphism and disease characteristics in chronic lymphocytic leukemia: results of an individual patient data-based meta-analysis.
260 _ _ |a Pavia
|c 2014
|b Ferrata Storti Foundation
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1522757712_32102
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a A number of single nucleotide polymorphisms have been associated with disease predisposition in chronic lymphocytic leukemia. A single nucleotide polymorphism in the MDM2 promotor region, MDM2SNP309, was shown to soothe the p53 pathway. In the current study, we aimed to clarify the effect of the MDM2SNP309 on chronic lymphocytic leukemia characteristics and outcome. We performed a meta-analysis of data from 2598 individual patients from 10 different cohorts. Patients' data and genetic analysis for MDM2SNP309 genotype, immunoglobulin heavy chain variable region mutation status and fluorescence in situ hybridization results were collected. There were no differences in overall survival based on the polymorphism (log rank test, stratified by study cohort; P=0.76; GG genotype: cohort-adjusted median overall survival of 151 months; TG: 153 months; TT: 149 months). In a multivariable Cox proportional hazards regression analysis, advanced age, male sex and unmutated immunoglobulin heavy chain variable region genes were associated with inferior survival, but not the MDM2 genotype. The MDM2SNP309 is unlikely to influence disease characteristics and prognosis in chronic lymphocytic leukemia. Studies investigating the impact of individual single nucleotide polymorphisms on prognosis are often controversial. This may be due to selection bias and small sample size. A meta-analysis based on individual patient data provides a reasonable strategy for prognostic factor analyses in the case of small individual studies. Individual patient data-based meta-analysis can, therefore, be a powerful tool to assess genetic risk factors in the absence of large studies.
536 _ _ |a 317 - Translational cancer research (POF3-317)
|0 G:(DE-HGF)POF3-317
|c POF3-317
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed,
650 _ 7 |a MDM2 protein, human
|0 EC 2.3.2.27
|2 NLM Chemicals
650 _ 7 |a Proto-Oncogene Proteins c-mdm2
|0 EC 2.3.2.27
|2 NLM Chemicals
700 1 _ |a Mansouri, Larry
|b 1
700 1 _ |a Rossi, Davide
|b 2
700 1 _ |a Majid, Aneela
|b 3
700 1 _ |a Willander, Kerstin
|b 4
700 1 _ |a Parker, Anton
|b 5
700 1 _ |a Bond, Gareth
|b 6
700 1 _ |a Pavlova, Sarka
|b 7
700 1 _ |a Nückel, Holger
|b 8
700 1 _ |a Merkel, Olaf
|b 9
700 1 _ |a Ghia, Paolo
|b 10
700 1 _ |a Montserrat, Emili
|b 11
700 1 _ |a Kaderi, Mohd Arifin
|b 12
700 1 _ |a Rosenquist, Richard
|b 13
700 1 _ |a Gaidano, Gianluca
|b 14
700 1 _ |a Dyer, Martin J S
|b 15
700 1 _ |a Söderkvist, Peter
|b 16
700 1 _ |a Linderholm, Mats
|b 17
700 1 _ |a Oscier, David
|b 18
700 1 _ |a Tvaruzkova, Zuzana
|b 19
700 1 _ |a Pospisilova, Sarka
|b 20
700 1 _ |a Dührsen, Ulrich
|b 21
700 1 _ |a Greil, Richard
|b 22
700 1 _ |a Döhner, Hartmut
|b 23
700 1 _ |a Stilgenbauer, Stephan
|b 24
700 1 _ |a Zenz, Thorsten
|0 P:(DE-He78)f3d5f16b49eb47520def635be98d5576
|b 25
|u dkfz
700 1 _ |a CLL, European Research Initiative on
|b 26
|e Collaboration Author
773 _ _ |a 10.3324/haematol.2013.101170
|g Vol. 99, no. 8, p. 1285 - 1291
|0 PERI:(DE-600)2805244-4
|n 8
|p 1285 - 1291
|t Haematologica
|v 99
|y 2014
|x 1592-8721
909 C O |p VDB
|o oai:inrepo02.dkfz.de:119640
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 P:(DE-He78)e15dfa1260625c69d6690a197392a994
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 25
|6 P:(DE-He78)f3d5f16b49eb47520def635be98d5576
913 1 _ |a DE-HGF
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-317
|2 G:(DE-HGF)POF3-300
|v Translational cancer research
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Gesundheit
914 1 _ |y 2014
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l Biostatistik
|x 0
920 1 _ |0 I:(DE-He78)G100-20160331
|k G100
|l Translationale Onkologie
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a I:(DE-He78)G100-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21