A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
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Authors
Jiao, Wei
Cuppen, Edwin
Danyi, Alexandra
van Herpen, Carla
Steeghs, Neeltje
Al-Shahrour, Fatima
Bailey, Peter J.
Biankin, Andrew V.
Boutros, Paul C.
Campbell, Peter J.
Chang, David K.
Cooke, Susanna L.
Deshpande, Vikram
Faltas, Bishoy M.
Faquin, William C.
Garraway, Levi
Grimmond, Sean M.
Haider, Syed
Hoadley, Katherine A.
Kaiser, Vera B.
Karlić, Rosa
Kato, Mamoru
Kübler, Kirsten
Lazar, Alexander J.
Li, Constance H.
Louis, David N.
Margolin, Adam
Martin, Sancha
Nahal-Bose, Hardeep K.
Nielsen, G. Petur
Nik-Zainal, Serena
Omberg, Larsson
P’ng, Christine
Perry, Marc D.
Rheinbay, Esther
Rubin, Mark A.
Semple, Colin A.
Sgroi, Dennis C.
Shibata, Tatsuhiro
Siebert, Reiner
Smith, Jaclyn
Stein, Lincoln D.
Stobbe, Miranda D.
Sun, Ren X.
Thai, Kevin
Wright, Derek W.
Wu, Chin-Lee
Yuan, Ke
Zhang, Junjun
Publication Date
2020-02-05Journal Title
Nature Communications
Publisher
Nature Publishing Group UK
Volume
11
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Jiao, W., Karlic, R., Cuppen, E., Danyi, A., de Ridder, J., van Herpen, C., Lolkema, M. P., et al. (2020). A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nature Communications, 11 (1) https://doi.org/10.1038/s41467-019-13825-8
Abstract
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.
Keywords
Article, /631/208/69, /692/699/67/1680, /45, /45/23, /139, /119, article
Identifiers
s41467-019-13825-8, 13825
External DOI: https://doi.org/10.1038/s41467-019-13825-8
This record's URL: https://www.repository.cam.ac.uk/handle/1810/317145
Rights
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/
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