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

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

Authors:	Jiao, W.; Atwal, G.; Polak, P.; Karlic, R.; Cuppen, E.; Danyi, A.; de Ridder, J.; van Herpen, C.; Lolkema, M. P.; Steeghs, N.; Getz, G.; Morris, Q.; PCAWG Tumor Subtypes and Clinical Translation Working Group; & PCAWG Consortium
Contributors:	Abeshouse, A.; Al-Ahmadie, H.; Armenia, J.; Chen, H. W.; Davidson, N. R.; Gao, J.; Ghossein, R.; Giri, D. D.; Gundem, G.; Heins, Z.; Huse, J.; Iacobuzio-Donahue, C. A.; Kahles, A.; King, T. A.; Kundra, R.; Lehmann, K. V.; Levine, D. A.; Liu, E. M.; Ochoa, A.; Pastore, A.; Rätsch, G.; Reis-Filho, J.; Reuter, V.; Roehrl, M. H. A.; Sanchez-Vega, F.; Sander, C.; Schultz, N.; Senbabaoglu, Y.; Singer, S.; Socci, N. D.; Stark, S. G.; Vázquez-García, I.; Yellapantula, V. D.; Zhang, H.
Article Title:	A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
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. © 2020, The Author(s).
Keywords:	controlled study; human tissue; primary tumor; somatic mutation; genetics; mutation; histopathology; diagnostic accuracy; neoplasm; neoplasms; reproducibility; reproducibility of results; metastasis; biology; computational biology; pathology; validation study; mutational analysis; human genome; neoplasm metastasis; intermethod comparison; genome; cancer classification; tumor; genome, human; procedures; high throughput sequencing; cell component; dna sequencing; cancer; humans; human; male; female; article; whole genome sequencing; circulating tumor dna; deep learning; accuracy assessment; passenger mutation pattern
Journal Title:	Nature Communications
Volume:	11
ISSN:	2041-1723
Publisher:	Nature Publishing Group
Date Published:	2020-02-05
Start Page:	728
Language:	English
DOI:	10.1038/s41467-019-13825-8
PUBMED:	32024849
PROVIDER:	scopus
PMCID:	PMC7002586
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079062177&doi=10.1038%2fs41467-019-13825-8&partnerID=40&md5=d7b9aa6d50bd50ee79e42e691700c7c0
Notes:	Article -- Erratum issued, see DOI: 10.1038/s41467-022-32329-6 -- Export Date: 13 January 2023 -- Source: Scopus