001     132560
005     20240229105023.0
024 7 _ |a pmid:29467179
|2 pmid
024 7 _ |a pmc:PMC5820685
|2 pmc
024 7 _ |a altmetric:33600209
|2 altmetric
024 7 _ |a DOI: 10.15252/msb.20177656
|2 doi
037 _ _ |a DKFZ-2018-00238
041 _ _ |a eng
082 _ _ |a 570
100 1 _ |a Rauscher, Benedikt Georg
|0 P:(DE-He78)7cff4a7ba55287548cc72604f7b19bb4
|b 0
|e First author
245 _ _ |a Toward an integrated map of genetic interactions in cancer cells.
260 _ _ |a Heidelberg
|c 2018
|b EMBO Press
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 1680685912_11715
|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 Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.
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 PubMed,
700 1 _ |a Heigwer, Florian
|0 0000-0002-8230-1485
|b 1
700 1 _ |a Henkel, Luisa
|0 P:(DE-He78)23e53a555a8fcc7b5f66d9201d3cb12c
|b 2
700 1 _ |a Hielscher, Thomas
|0 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f
|b 3
700 1 _ |a Voloshanenko, Oksana
|0 P:(DE-He78)ad64f12d9ccfe830ecddc2fe9635c569
|b 4
700 1 _ |a Boutros, Michael
|0 0000-0002-9458-817X
|b 5
|e Last author
773 _ _ |a DOI: 10.15252/msb.20177656
|g Vol. 14, no. 2
|0 PERI:(DE-600)2193510-5
|n 2
|p e7656
|t Molecular systems biology
|v 14
|y 2018
|x 1744-4292
909 C O |p VDB
|o oai:inrepo02.dkfz.de:132560
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 P:(DE-He78)7cff4a7ba55287548cc72604f7b19bb4
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 1
|6 0000-0002-8230-1485
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 2
|6 P:(DE-He78)23e53a555a8fcc7b5f66d9201d3cb12c
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 3
|6 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 4
|6 P:(DE-He78)ad64f12d9ccfe830ecddc2fe9635c569
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 0000-0002-9458-817X
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-312
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-300
|4 G:(DE-HGF)POF
|v Functional and structural genomics
|x 0
914 1 _ |y 2018
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MOL SYST BIOL : 2015
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)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 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)1030
|2 StatID
|b Current Contents - Life Sciences
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a IF >= 10
|0 StatID:(DE-HGF)9910
|2 StatID
|b MOL SYST BIOL : 2015
920 1 _ |0 I:(DE-He78)B110-20160331
|k B110
|l B110 Signalwege funktionelle Genomik
|x 0
920 1 _ |0 I:(DE-He78)C060-20160331
|k C060
|l C060 Biostatistik
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)B110-20160331
980 _ _ |a I:(DE-He78)C060-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21