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

Wei Jiao, Gurnit Atwal, Paz Polak, Rosa Karlic, Edwin Cuppen, Alexandra Danyi, Jeroen Ridder, Carla Herpen, Martijn P. Lolkema, Neeltje Steeghs, Gad Getz, Quaid D. Morris and Lincoln D. Stein ()
Additional contact information
Wei Jiao: Ontario Institute for Cancer Research
Gurnit Atwal: Ontario Institute for Cancer Research
Paz Polak: Broad Institute of MIT and Harvard
Rosa Karlic: University of Zagreb
Edwin Cuppen: Hartwig Medical Foundation
Alexandra Danyi: University Medical Center Utrecht
Jeroen Ridder: University Medical Center Utrecht
Carla Herpen: Radboud University Medical Center
Martijn P. Lolkema: University Medical Center Rotterdam
Neeltje Steeghs: The Netherlands Cancer Institute
Gad Getz: Broad Institute of MIT and Harvard
Quaid D. Morris: Vector Institute
Lincoln D. Stein: Ontario Institute for Cancer Research

Nature Communications, 2020, vol. 11, issue 1, 1-12

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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-13825-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13825-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-13825-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().