Journal Article DKFZ-2021-02572

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

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2020
Nature Publishing Group UK [London]

Nature Communications 11(1), 728 () [10.1038/s41467-019-13825-8]
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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.

Keyword(s): Computational Biology: methods (MeSH) ; Deep Learning (MeSH) ; Female (MeSH) ; Genome, Human (MeSH) ; Humans (MeSH) ; Male (MeSH) ; Mutation (MeSH) ; Neoplasm Metastasis (MeSH) ; Neoplasms: genetics (MeSH) ; Neoplasms: pathology (MeSH) ; Reproducibility of Results (MeSH) ; Whole Genome Sequencing (MeSH)

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Note: siehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212437 / https://doi.org/10.1038/s41467-022-32329-6

Contributing Institute(s):
  1. Theoretische Bioinformatik (B080)
  2. Bioinformatik und Omics Data Analytics (B240)
  3. Epigenomik (B370)
  4. Angewandte Bioinformatik (B330)
  5. DKTK HD zentral (HD01)
  6. B060 Molekulare Genetik (B060)
  7. Pädiatrische Gliomforschung (B360)
  8. DKTK Koordinierungsstelle Berlin (BE01)
  9. B062 Pädiatrische Neuroonkologie (B062)
  10. B066 Chromatin-Netzwerke (B066)
  11. B063 Krebsgenomforschung (B063)
  12. Hochdurchsatz-Sequenzierung (W190)
  13. B260 Bioinformatik der Genomik und Systemgenetik (B260)
  14. Core Facility Omics IT (W610)
  15. B087 Neuroblastom Genomik (B087)
Research Program(s):
  1. 312 - Functional and structural genomics (POF3-312) (POF3-312)

Appears in the scientific report 2020
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 Record created 2021-11-17, last modified 2024-03-20


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