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

Jiao, W. et al. (2020) A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nature Communications, 11, 728. (doi: 10.1038/s41467-019-13825-8) (PMID:32024849) (PMCID:PMC7002586)

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.

• Item Type: Articles
• Additional Information: University of Glasgow authors are members of the PCAWG Tumor Subtypes and Clinical Translation Working Group.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wright, Mr Derek and Cooke, Dr Susie and Biankin, Professor Andrew and Martin, Ms Sancha and Bailey, Dr Peter and Chang, Professor David
• Authors: Jiao, W., Atwal, G., Polak, P., Karlic, R., Cuppen, E., PCAWG Tumor Subtypes and Clinical Translation Working Group, , Danyi, A., de Ridder, J., van Herpen, C., Lolkema, M. P., Steeghs, N., Getz, G., Morris, Q., Stein, L. D., PCAWG Consortium, , Bailey, P. J., Biankin, A. V., Chang, D. K., Cooke, S. L., Martin, S., and Wright, D. W.
• College/School: College of Medical Veterinary and Life Sciences > School of Cancer Sciences; College of Medical Veterinary and Life Sciences > School of Infection & Immunity; College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
• Journal Name: Nature Communications
• Publisher: Nature Research
• ISSN: 2041-1723
• ISSN (Online): 2041-1723
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in Nature Communications 11: 728
• Publisher Policy: Reproduced under a Creative Commons License