A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

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  • dc.contributor.author Jiao, Wei
  • dc.contributor.author Atwal, Gurnit
  • dc.contributor.author Polak, Paz
  • dc.contributor.author Karlic, Rosa
  • dc.contributor.author Cuppen, Edwin
  • dc.contributor.author PCAWG Tumor Subtypes and Clinical Translation Working Group
  • dc.contributor.author Danyi, Alexandra
  • dc.contributor.author Ridder, Jeroen de
  • dc.contributor.author van Herpen, Carla
  • dc.contributor.author Lolkema, Martijn P.
  • dc.contributor.author Steeghs, Neeltje
  • dc.contributor.author Getz, Gad
  • dc.contributor.author Morris, Quaid
  • dc.contributor.author Stein, Lincoln D.
  • dc.contributor.author PCAWG Consortium
  • dc.contributor.author Stobbe, Miranda D.
  • dc.date.accessioned 2020-04-21T10:03:09Z
  • dc.date.available 2020-04-21T10:03:09Z
  • dc.date.issued 2020
  • dc.description.abstract In 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.
  • dc.description.sponsorship This research was supported by W.J., L.S. and Q.M. with funding from the Province of Ontario, Canada. QM’s research was supported by a gift from NVIDIA foundation, an advised fund of the Silicon ValleyCommunity Foundation. RK was supported by the European Structural and Investment Funds grant for the Croatian National Centre of Research Excellence in Personalized Healthcare (contract #KK.01.1.1.01.0010), Croatian National Centre of Research Excellence for Data Science and Advanced Cooperative Systems (contract KK.01.1.1.01.0009), the European Commission Seventh Framework Program (Integra-Life; grant 315997) and Croatian Science Foundation (grant IP-2014-09-6400). J.d.R. is supported by a NWO-Vidi grant (016.Vidi.178.023)
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Jiao W, Atwal G, Polak P, Karlic R, Cuppen E, PCAWG Tumor Subtypes and Clinical Translation Working Group et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat Commun. 2020;11(1):728. DOI: 10.1038/s41467-019-13825-8
  • dc.identifier.doi http://dx.doi.org/10.1038/s41467-019-13825-8
  • dc.identifier.issn 2041-1723
  • dc.identifier.uri http://hdl.handle.net/10230/44292
  • dc.language.iso eng
  • dc.publisher Nature Research
  • dc.relation.ispartof Nature Communications. 2020;11(1):728
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/315997
  • dc.rights © 2020 Wei Jiao et al. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.other Càncer
  • dc.subject.other Metàstasi
  • dc.subject.other Genomes
  • dc.title A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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