001     181024
005     20240229145637.0
024 7 _ |a 10.1038/s41375-022-01650-w
|2 doi
024 7 _ |a pmid:35922444
|2 pmid
024 7 _ |a 0887-6924
|2 ISSN
024 7 _ |a 1476-5551
|2 ISSN
024 7 _ |a altmetric:133629387
|2 altmetric
037 _ _ |a DKFZ-2022-01731
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Jahn, Nikolaus
|b 0
245 _ _ |a Genomic landscape of patients with FLT3-mutated acute myeloid leukemia (AML) treated within the CALGB 10603/RATIFY trial.
260 _ _ |a London
|c 2022
|b Springer Nature
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 1661948459_13424
|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
500 _ _ |a 2022 Sep;36(9):2218-2227
520 _ _ |a The aim of this study was to characterize the mutational landscape of patients with FLT3-mutated acute myeloid leukemia (AML) treated within the randomized CALGB 10603/RATIFY trial evaluating intensive chemotherapy plus the multi-kinase inhibitor midostaurin versus placebo. We performed sequencing of 262 genes in 475 patients: mutations occurring concurrently with the FLT3-mutation were most frequent in NPM1 (61%), DNMT3A (39%), WT1 (21%), TET2 (12%), NRAS (11%), RUNX1 (11%), PTPN11 (10%), and ASXL1 (8%) genes. To assess effects of clinical and genetic features and their possible interactions, we fitted random survival forests and interpreted the resulting variable importance. Highest prognostic impact was found for WT1 and NPM1 mutations, followed by white blood cell count, FLT3 mutation type (internal tandem duplications vs. tyrosine kinase domain mutations), treatment (midostaurin vs. placebo), ASXL1 mutation, and ECOG performance status. When evaluating two-fold variable combinations the most striking effects were found for WT1:NPM1 (with NPM1 mutation abrogating the negative effect of WT1 mutation), and for WT1:treatment (with midostaurin exerting a beneficial effect in WT1-mutated AML). This targeted gene sequencing study provides important, novel insights into the genomic background of FLT3-mutated AML including the prognostic impact of co-mutations, specific gene-gene interactions, and possible treatment effects of midostaurin.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
700 1 _ |a Jahn, Ekaterina
|b 1
700 1 _ |a Saadati, Maral
|b 2
700 1 _ |a Bullinger, Lars
|b 3
700 1 _ |a Larson, Richard A
|0 0000-0001-9168-3203
|b 4
700 1 _ |a Ottone, Tiziana
|b 5
700 1 _ |a Amadori, Sergio
|b 6
700 1 _ |a Prior, Thomas W
|b 7
700 1 _ |a Brandwein, Joseph M
|b 8
700 1 _ |a Appelbaum, Frederick R
|b 9
700 1 _ |a Medeiros, Bruno C
|0 0000-0001-6972-8137
|b 10
700 1 _ |a Tallman, Martin S
|b 11
700 1 _ |a Ehninger, Gerhard
|b 12
700 1 _ |a Heuser, Michael
|0 0000-0001-5318-9044
|b 13
700 1 _ |a Ganser, Arnold
|b 14
700 1 _ |a Pallaud, Celine
|b 15
700 1 _ |a Gathmann, Insa
|b 16
700 1 _ |a Krzykalla, Julia
|0 P:(DE-He78)5a7a75d1b29b770f98f1bb2062fc3df9
|b 17
|u dkfz
700 1 _ |a Benner, Axel
|0 P:(DE-He78)e15dfa1260625c69d6690a197392a994
|b 18
|u dkfz
700 1 _ |a Bloomfield, Clara D
|0 0000-0001-5465-7591
|b 19
700 1 _ |a Thiede, Christian
|0 0000-0003-1241-2048
|b 20
700 1 _ |a Stone, Richard M
|0 0000-0002-7526-2633
|b 21
700 1 _ |a Döhner, Hartmut
|b 22
700 1 _ |a Döhner, Konstanze
|0 0000-0002-2261-9862
|b 23
773 _ _ |a 10.1038/s41375-022-01650-w
|0 PERI:(DE-600)2008023-2
|n 9
|p 2218-2227
|t Leukemia
|v 36
|y 2022
|x 0887-6924
909 C O |p VDB
|o oai:inrepo02.dkfz.de:181024
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 17
|6 P:(DE-He78)5a7a75d1b29b770f98f1bb2062fc3df9
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 18
|6 P:(DE-He78)e15dfa1260625c69d6690a197392a994
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-29
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2022-11-18
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-18
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21