A deep learning system can accurately classify primary and metastatic cancers based on patterns of passenger mutations
Jeroen de Ridder,
Carla van Herpen,
Martijn P. Lolkema,
Gad A. Getz,
Quaid D. Morris,
Lincoln D Stein,
PCAWG Pathology & Clinical Correlates Working Grp,
ICGC/TCGA Pan-cancer Analysis of Whole Genomes Net
Posted 05 Nov 2017
bioRxiv DOI: 10.1101/214494
Posted 05 Nov 2017
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of the time a cancer patient presents with metastatic tumour and no obvious primary. Challenges also arise when distinguishing a metastatic recurrence of a previously treated cancer from the emergence of a new one. Here 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. Our classifier achieves an accuracy of 91% on held-out tumor samples and 82% and 85% 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 classifier accuracy. Our results have immediate clinical applicability, underscoring how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of cell-free circulating tumour DNA.
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