001     143213
005     20240229112544.0
024 7 _ |a 10.1186/s40478-019-0681-y
|2 doi
024 7 _ |a pmid:30832734
|2 pmid
024 7 _ |a pmc:PMC6398239
|2 pmc
024 7 _ |a altmetric:56522468
|2 altmetric
037 _ _ |a DKFZ-2019-00812
041 _ _ |a eng
082 _ _ |a 610
100 1 _ |a Cruzeiro, Gustavo Alencastro Veiga
|0 0000-0002-0005-3984
|b 0
245 _ _ |a A simplified approach using Taqman low-density array for medulloblastoma subgrouping.
260 _ _ |a London
|c 2019
|b Biomed Central
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 1554125462_27789
|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 Next-generation sequencing platforms are routinely used for molecular assignment due to their high impact for risk stratification and prognosis in medulloblastomas. Yet, low and middle-income countries still lack an accurate cost-effective platform to perform this allocation. TaqMan Low Density array (TLDA) assay was performed using a set of 20 genes in 92 medulloblastoma samples. The same methodology was assessed in silico using microarray data for 763 medulloblastoma samples from the GSE85217 study, which performed MB classification by a robust integrative method (Transcriptional, Methylation and cytogenetic profile). Furthermore, we validated in 11 MBs samples our proposed method by Methylation Array 450 K to assess methylation profile along with 390 MB samples (GSE109381) and copy number variations. TLDA with only 20 genes accurately assigned MB samples into WNT, SHH, Group 3 and Group 4 using Pearson distance with the average-linkage algorithm and showed concordance with molecular assignment provided by Methylation Array 450 k. Similarly, we tested this simplified set of gene signatures in 763 MB samples and we were able to recapitulate molecular assignment with an accuracy of 99.1% (SHH), 94.29% (WNT), 92.36% (Group 3) and 95.40% (Group 4), against 97.31, 97.14, 88.89 and 97.24% (respectively) with the Ward.D2 algorithm. t-SNE analysis revealed a high level of concordance (k = 4) with minor overlapping features between Group 3 and Group 4. Finally, we condensed the number of genes to 6 without significantly losing accuracy in classifying samples into SHH, WNT and non-SHH/non-WNT subgroups. Additionally, we found a relatively high frequency of WNT subgroup in our cohort, which requires further epidemiological studies. TLDA is a rapid, simple and cost-effective assay for classifying MB in low/middle income countries. A simplified method using six genes and restricting the final stratification into SHH, WNT and non-SHH/non-WNT appears to be a very interesting approach for rapid clinical decision-making.
536 _ _ |a 312 - Functional and structural genomics (POF3-312)
|0 G:(DE-HGF)POF3-312
|c POF3-312
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed,
700 1 _ |a Salomão, Karina Bezerra
|b 1
700 1 _ |a de Biagi, Carlos Alberto Oliveira
|b 2
700 1 _ |a Baumgartner, Martin
|b 3
700 1 _ |a Sturm, Dominik
|0 P:(DE-He78)a46a5b2a871859c8e2d63d2f8c666807
|b 4
|u dkfz
700 1 _ |a Lira, Régia Caroline Peixoto
|b 5
700 1 _ |a de Almeida Magalhães, Taciani
|b 6
700 1 _ |a Baroni Milan, Mirella
|b 7
700 1 _ |a da Silva Silveira, Vanessa
|b 8
700 1 _ |a Saggioro, Fabiano Pinto
|b 9
700 1 _ |a de Oliveira, Ricardo Santos
|b 10
700 1 _ |a Dos Santos Klinger, Paulo Henrique
|b 11
700 1 _ |a Seidinger, Ana Luiza
|b 12
700 1 _ |a Yunes, José Andrés
|b 13
700 1 _ |a de Paula Queiroz, Rosane Gomes
|b 14
700 1 _ |a Oba-Shinjo, Sueli Mieko
|b 15
700 1 _ |a Scrideli, Carlos Alberto
|b 16
700 1 _ |a Nagahashi, Suely Marie Kazue
|b 17
700 1 _ |a Tone, Luiz Gonzaga
|b 18
700 1 _ |a Valera, Elvis Terci
|b 19
773 _ _ |a 10.1186/s40478-019-0681-y
|g Vol. 7, no. 1, p. 33
|0 PERI:(DE-600)2715589-4
|n 1
|p 33
|t Acta Neuropathologica Communications
|v 7
|y 2019
|x 2051-5960
909 C O |o oai:inrepo02.dkfz.de:143213
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 4
|6 P:(DE-He78)a46a5b2a871859c8e2d63d2f8c666807
913 1 _ |a DE-HGF
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-312
|2 G:(DE-HGF)POF3-300
|v Functional and structural genomics
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Gesundheit
914 1 _ |y 2019
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b ACTA NEUROPATHOL COM : 2017
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)0320
|2 StatID
|b PubMed Central
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 Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Peer review
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)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b ACTA NEUROPATHOL COM : 2017
920 1 _ |0 I:(DE-He78)B360-20160331
|k B360
|l Nachwuchsgruppe Pädiatrische Gliomforschung
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)B360-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21