Naslov
A deep learning system can accurately classify primary and metastatic cancers based on patterns of passenger mutations

Autori
Jiao, Wei ; Atwal, Gurnit ; Polak, Paz ; Karlic, Rosa ; Cuppen, Edwin ; Danyi, Alexandra ; de Ridder, Jeoren ; van Herpen, Carla ; Lolkema, Martijm ; Steeghs, Neeltje ; Getz, Gad ; Morris, Quaid ; Stein, Lincoln

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni

Skup
ISMB/ECCB 2019

Mjesto i datum
Basel, Švicarska, 21.07.2019. - 25.07.2019

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Cancer genomics ; Machine learning

Sažetak
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of new cancer diagnoses, a cancer patient presents with a 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 from this set.On primary and metastatic samples from an independent cohort, it achieves accuracies of 87% and 85%, respectively. This is double the accuracy of pathologists who were presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information about 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.

Izvorni jezik
Engleski

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