000177485 001__ 177485
000177485 005__ 20240320153338.0
000177485 0247_ $$2doi$$a10.1038/s41467-019-13825-8
000177485 0247_ $$2pmid$$apmid:32024849
000177485 0247_ $$2pmc$$apmc:PMC7002586
000177485 0247_ $$2altmetric$$aaltmetric:75084789
000177485 037__ $$aDKFZ-2021-02572
000177485 041__ $$aEnglish
000177485 082__ $$a500
000177485 1001_ $$aJiao, Wei$$b0
000177485 245__ $$aA deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.
000177485 260__ $$a[London]$$bNature Publishing Group UK$$c2020
000177485 3367_ $$2DRIVER$$aarticle
000177485 3367_ $$2DataCite$$aOutput Types/Journal article
000177485 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1710945077_4145
000177485 3367_ $$2BibTeX$$aARTICLE
000177485 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000177485 3367_ $$00$$2EndNote$$aJournal Article
000177485 500__ $$asiehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212437   /  https://doi.org/10.1038/s41467-022-32329-6
000177485 520__ $$aIn cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.
000177485 536__ $$0G:(DE-HGF)POF3-312$$a312 - Functional and structural genomics (POF3-312)$$cPOF3-312$$fPOF III$$x0
000177485 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
000177485 650_2 $$2MeSH$$aComputational Biology: methods
000177485 650_2 $$2MeSH$$aDeep Learning
000177485 650_2 $$2MeSH$$aFemale
000177485 650_2 $$2MeSH$$aGenome, Human
000177485 650_2 $$2MeSH$$aHumans
000177485 650_2 $$2MeSH$$aMale
000177485 650_2 $$2MeSH$$aMutation
000177485 650_2 $$2MeSH$$aNeoplasm Metastasis
000177485 650_2 $$2MeSH$$aNeoplasms: genetics
000177485 650_2 $$2MeSH$$aNeoplasms: pathology
000177485 650_2 $$2MeSH$$aReproducibility of Results
000177485 650_2 $$2MeSH$$aWhole Genome Sequencing
000177485 7001_ $$aAtwal, Gurnit$$b1
000177485 7001_ $$aPolak, Paz$$b2
000177485 7001_ $$aKarlic, Rosa$$b3
000177485 7001_ $$aCuppen, Edwin$$b4
000177485 7001_ $$0P:(DE-HGF)0$$aPCAWGTumorSubtypesClinicalTranslationWorkingGroup$$b5
000177485 7001_ $$aDanyi, Alexandra$$b6
000177485 7001_ $$ade Ridder, Jeroen$$b7
000177485 7001_ $$avan Herpen, Carla$$b8
000177485 7001_ $$aLolkema, Martijn P$$b9
000177485 7001_ $$aSteeghs, Neeltje$$b10
000177485 7001_ $$aGetz, Gad$$b11
000177485 7001_ $$aMorris, Quaid$$b12
000177485 7001_ $$aStein, Lincoln D$$b13
000177485 7001_ $$0P:(DE-HGF)0$$aPCAWGConsortium$$b14
000177485 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-019-13825-8$$gVol. 11, no. 1, p. 728$$n1$$p728$$tNature Communications$$v11$$x2041-1723$$y2020
000177485 909CO $$ooai:inrepo02.dkfz.de:177485$$pVDB
000177485 9131_ $$0G:(DE-HGF)POF3-312$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vFunctional and structural genomics$$x0
000177485 9141_ $$y2020
000177485 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNAT COMMUN : 2019$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2021-02-02
000177485 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)1040$$2StatID$$aDBCoverage$$bZoological Record$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)9910$$2StatID$$aIF >= 10$$bNAT COMMUN : 2019$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-02-02
000177485 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-02-02
000177485 9201_ $$0I:(DE-He78)B080-20160331$$kB080$$lTheoretische Bioinformatik$$x0
000177485 9201_ $$0I:(DE-He78)B240-20160331$$kB240$$lBioinformatik und Omics Data Analytics$$x1
000177485 9201_ $$0I:(DE-He78)B370-20160331$$kB370$$lEpigenomik$$x2
000177485 9201_ $$0I:(DE-He78)B330-20160331$$kB330$$lAngewandte Bioinformatik$$x3
000177485 9201_ $$0I:(DE-He78)HD01-20160331$$kHD01$$lDKTK HD zentral$$x4
000177485 9201_ $$0I:(DE-He78)B060-20160331$$kB060$$lB060 Molekulare Genetik$$x5
000177485 9201_ $$0I:(DE-He78)B360-20160331$$kB360$$lPädiatrische Gliomforschung$$x6
000177485 9201_ $$0I:(DE-He78)BE01-20160331$$kBE01$$lDKTK Koordinierungsstelle Berlin$$x7
000177485 9201_ $$0I:(DE-He78)B062-20160331$$kB062$$lB062 Pädiatrische Neuroonkologie$$x8
000177485 9201_ $$0I:(DE-He78)B066-20160331$$kB066$$lB066 Chromatin-Netzwerke$$x9
000177485 9201_ $$0I:(DE-He78)B063-20160331$$kB063$$lB063 Krebsgenomforschung$$x10
000177485 9201_ $$0I:(DE-He78)W190-20160331$$kW190$$lHochdurchsatz-Sequenzierung$$x11
000177485 9201_ $$0I:(DE-He78)B260-20160331$$kB260$$lB260 Bioinformatik der Genomik und Systemgenetik$$x12
000177485 9201_ $$0I:(DE-He78)W610-20160331$$kW610$$lCore Facility Omics IT$$x13
000177485 9201_ $$0I:(DE-He78)B087-20160331$$kB087$$lB087 Neuroblastom Genomik$$x14
000177485 980__ $$ajournal
000177485 980__ $$aVDB
000177485 980__ $$aI:(DE-He78)B080-20160331
000177485 980__ $$aI:(DE-He78)B240-20160331
000177485 980__ $$aI:(DE-He78)B370-20160331
000177485 980__ $$aI:(DE-He78)B330-20160331
000177485 980__ $$aI:(DE-He78)HD01-20160331
000177485 980__ $$aI:(DE-He78)B060-20160331
000177485 980__ $$aI:(DE-He78)B360-20160331
000177485 980__ $$aI:(DE-He78)BE01-20160331
000177485 980__ $$aI:(DE-He78)B062-20160331
000177485 980__ $$aI:(DE-He78)B066-20160331
000177485 980__ $$aI:(DE-He78)B063-20160331
000177485 980__ $$aI:(DE-He78)W190-20160331
000177485 980__ $$aI:(DE-He78)B260-20160331
000177485 980__ $$aI:(DE-He78)W610-20160331
000177485 980__ $$aI:(DE-He78)B087-20160331
000177485 980__ $$aUNRESTRICTED