The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Åke Borg

Åke Borg

Principal investigator

Åke Borg

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

Author

  • Wei Jiao
  • Gurnit Atwal
  • Paz Polak
  • Rosa Karlic
  • Edwin Cuppen
  • Alexandra Danyi
  • Jeroen de Ridder
  • Carla van Herpen
  • Martijn P Lolkema
  • Neeltje Steeghs
  • Gad Getz
  • Quaid D Morris
  • Lincoln D Stein
  • Fatima Al-Shahrour
  • Junjun Zhang
Show all

Other contributions

  • Åke Borg
  • Markus Ringnér
  • Johan Staaf

Summary, in English

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.

Department/s

  • LUCC: Lund University Cancer Centre
  • Familial Breast Cancer
  • Breastcancer-genetics
  • Molecular Cell Biology
  • Breast/lungcancer
  • Research Group Lung Cancer

Publishing year

2020-02-05

Language

English

Publication/Series

Nature Communications

Volume

11

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Medical Genetics

Keywords

  • Computational Biology/methods
  • Deep Learning
  • Female
  • Genome, Human
  • Humans
  • Male
  • Mutation
  • Neoplasm Metastasis
  • Neoplasms/genetics
  • Reproducibility of Results
  • Whole Genome Sequencing

Status

Published

Research group

  • Familial Breast Cancer
  • Research Group Lung Cancer

ISBN/ISSN/Other

  • ISSN: 2041-1723