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024 7 _ |a 10.1200/JCO.2013.50.9539
|2 doi
024 7 _ |a pmid:24493713
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024 7 _ |a pmc:PMC3948094
|2 pmc
024 7 _ |a 0732-183X
|2 ISSN
024 7 _ |a 1527-7755
|2 ISSN
024 7 _ |a altmetric:2119027
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037 _ _ |a DKFZ-2018-00257
041 _ _ |a eng
082 _ _ |a 050
100 1 _ |a Shih, David J H
|b 0
245 _ _ |a Cytogenetic prognostication within medulloblastoma subgroups.g
260 _ _ |a Alexandria, Va.
|c 2014
|b American Society of Clinical Oncology
336 7 _ |a article
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336 7 _ |a Output Types/Journal article
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336 7 _ |a Journal Article
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336 7 _ |a ARTICLE
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336 7 _ |a JOURNAL_ARTICLE
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336 7 _ |a Journal Article
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520 _ _ |a Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication.Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models.Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas.Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.
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650 _ 7 |a Biomarkers, Tumor
|2 NLM Chemicals
650 _ 7 |a GLI2 protein, human
|2 NLM Chemicals
650 _ 7 |a Hedgehog Proteins
|2 NLM Chemicals
650 _ 7 |a Kruppel-Like Transcription Factors
|2 NLM Chemicals
650 _ 7 |a MYC protein, human
|2 NLM Chemicals
650 _ 7 |a Nuclear Proteins
|2 NLM Chemicals
650 _ 7 |a Proto-Oncogene Proteins c-myc
|2 NLM Chemicals
650 _ 7 |a SHH protein, human
|2 NLM Chemicals
650 _ 7 |a Wnt Proteins
|2 NLM Chemicals
650 _ 7 |a Zinc Finger Protein Gli2
|2 NLM Chemicals
700 1 _ |a Northcott, Paul A
|0 P:(DE-HGF)0
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700 1 _ |a Remke, Marc
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700 1 _ |a Korshunov, Andrey
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700 1 _ |a Ramaswamy, Vijay
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700 1 _ |a Kool, Marcel
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700 1 _ |a Luu, Betty
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700 1 _ |a Yao, Yuan
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700 1 _ |a Wang, Xin
|b 8
700 1 _ |a Dubuc, Adrian M
|b 9
700 1 _ |a Garzia, Livia
|b 10
700 1 _ |a Peacock, John
|b 11
700 1 _ |a Mack, Stephen C
|b 12
700 1 _ |a Wu, Xiaochong
|b 13
700 1 _ |a Rolider, Adi
|b 14
700 1 _ |a Morrissy, A Sorana
|b 15
700 1 _ |a Cavalli, Florence M G
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700 1 _ |a Jones, David
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700 1 _ |a Zitterbart, Karel
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700 1 _ |a Faria, Claudia C
|b 19
700 1 _ |a Schüller, Ulrich
|b 20
700 1 _ |a Kren, Leos
|b 21
700 1 _ |a Kumabe, Toshihiro
|b 22
700 1 _ |a Tominaga, Teiji
|b 23
700 1 _ |a Shin Ra, Young
|b 24
700 1 _ |a Garami, Miklós
|b 25
700 1 _ |a Hauser, Peter
|b 26
700 1 _ |a Chan, Jennifer A
|b 27
700 1 _ |a Robinson, Shenandoah
|b 28
700 1 _ |a Bognár, László
|b 29
700 1 _ |a Klekner, Almos
|b 30
700 1 _ |a Saad, Ali G
|b 31
700 1 _ |a Liau, Linda M
|b 32
700 1 _ |a Albrecht, Steffen
|b 33
700 1 _ |a Fontebasso, Adam
|b 34
700 1 _ |a Cinalli, Giuseppe
|b 35
700 1 _ |a De Antonellis, Pasqualino
|b 36
700 1 _ |a Zollo, Massimo
|b 37
700 1 _ |a Cooper, Michael K
|b 38
700 1 _ |a Thompson, Reid C
|b 39
700 1 _ |a Bailey, Simon
|b 40
700 1 _ |a Lindsey, Janet C
|b 41
700 1 _ |a Di Rocco, Concezio
|b 42
700 1 _ |a Massimi, Luca
|b 43
700 1 _ |a Michiels, Erna M C
|b 44
700 1 _ |a Scherer, Stephen W
|b 45
700 1 _ |a Phillips, Joanna J
|b 46
700 1 _ |a Gupta, Nalin
|b 47
700 1 _ |a Fan, Xing
|b 48
700 1 _ |a Muraszko, Karin M
|b 49
700 1 _ |a Vibhakar, Rajeev
|b 50
700 1 _ |a Eberhart, Charles G
|b 51
700 1 _ |a Fouladi, Maryam
|b 52
700 1 _ |a Lach, Boleslaw
|b 53
700 1 _ |a Jung, Shin
|b 54
700 1 _ |a Wechsler-Reya, Robert J
|b 55
700 1 _ |a Fèvre-Montange, Michelle
|b 56
700 1 _ |a Jouvet, Anne
|b 57
700 1 _ |a Jabado, Nada
|b 58
700 1 _ |a Pollack, Ian F
|b 59
700 1 _ |a Weiss, William A
|b 60
700 1 _ |a Lee, Ji-Yeoun
|b 61
700 1 _ |a Cho, Byung-Kyu
|b 62
700 1 _ |a Kim, Seung-Ki
|b 63
700 1 _ |a Wang, Kyu-Chang
|b 64
700 1 _ |a Leonard, Jeffrey R
|b 65
700 1 _ |a Rubin, Joshua B
|b 66
700 1 _ |a de Torres, Carmen
|b 67
700 1 _ |a Lavarino, Cinzia
|b 68
700 1 _ |a Mora, Jaume
|b 69
700 1 _ |a Cho, Yoon-Jae
|b 70
700 1 _ |a Tabori, Uri
|b 71
700 1 _ |a Olson, James M
|b 72
700 1 _ |a Gajjar, Amar
|b 73
700 1 _ |a Packer, Roger J
|b 74
700 1 _ |a Rutkowski, Stefan
|b 75
700 1 _ |a Pomeroy, Scott L
|b 76
700 1 _ |a French, Pim J
|b 77
700 1 _ |a Kloosterhof, Nanne K
|b 78
700 1 _ |a Kros, Johan M
|b 79
700 1 _ |a Van Meir, Erwin G
|b 80
700 1 _ |a Clifford, Steven C
|b 81
700 1 _ |a Bourdeaut, Franck
|b 82
700 1 _ |a Delattre, Olivier
|b 83
700 1 _ |a Doz, François F
|b 84
700 1 _ |a Hawkins, Cynthia E
|b 85
700 1 _ |a Malkin, David
|b 86
700 1 _ |a Grajkowska, Wieslawa A
|b 87
700 1 _ |a Perek-Polnik, Marta
|b 88
700 1 _ |a Bouffet, Eric
|b 89
700 1 _ |a Rutka, James T
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700 1 _ |a Pfister, Stefan
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700 1 _ |a Taylor, Michael D
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773 _ _ |a 10.1200/JCO.2013.50.9539
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