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Pregled bibliografske jedinice broj: 1104725

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


Jiao, Wei; Atwal, Gurnit; Polak, Paz; Karlic, Rosa; Cuppen, Edwin; PCAWG Tumor Subtypes and Clinical Translation Working Group; Danyi, Alexandra; de Ridder, Jeroen; van Herpen, Carla; Lolkema, Martijn P. et al.
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns // Nature communications, 11 (2020), 1; 1-12 doi:10.1038/s41467-019-13825-8 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1104725 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Autori
Jiao, Wei ; Atwal, Gurnit ; Polak, Paz ; Karlic, Rosa ; Cuppen, Edwin ; PCAWG Tumor Subtypes and Clinical Translation Working Group ; Danyi, Alexandra ; de Ridder, Jeroen ; van Herpen, Carla ; Lolkema, Martijn P. ; Steeghs, Neeltje ; Getz, Gad ; Morris, Quaid ; Stein, Lincoln D. ; PCAWG Consortium

Izvornik
Nature communications (2041-1723) 11 (2020), 1; 1-12

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Cancer genomics ; Cancer of unknown primary

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Biologija



POVEZANOST RADA


Projekti:
HRZZ-IP-2014-09-6400 - Istraživanje razvoja, diferencijacije i evolucije životinja kroz genomiku bazalnih metazoa (BAMGEN) (Vlahoviček, Kristian, HRZZ - 2014-09) ( POIROT)
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
EK-KF-KK.01.1.1.01.0010 - ZCI za personaliziranu brigu o zdravlju (ZCIPersonHealth) (Secenji, Aleksandar, EK ) ( POIROT)

Ustanove:
Prirodoslovno-matematički fakultet, Zagreb

Profili:

Avatar Url Rosa Karlić (autor)

Citiraj ovu publikaciju

Jiao, Wei; Atwal, Gurnit; Polak, Paz; Karlic, Rosa; Cuppen, Edwin; PCAWG Tumor Subtypes and Clinical Translation Working Group; Danyi, Alexandra; de Ridder, Jeroen; van Herpen, Carla; Lolkema, Martijn P. et al.
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns // Nature communications, 11 (2020), 1; 1-12 doi:10.1038/s41467-019-13825-8 (međunarodna recenzija, članak, znanstveni)
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. (2020) A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nature communications, 11 (1), 1-12 doi:10.1038/s41467-019-13825-8.
@article{article, year = {2020}, pages = {1-12}, DOI = {10.1038/s41467-019-13825-8}, keywords = {Cancer genomics, Cancer of unknown primary}, journal = {Nature communications}, doi = {10.1038/s41467-019-13825-8}, volume = {11}, number = {1}, issn = {2041-1723}, title = {A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns}, keyword = {Cancer genomics, Cancer of unknown primary} }
@article{article, year = {2020}, pages = {1-12}, DOI = {10.1038/s41467-019-13825-8}, keywords = {Cancer genomics, Cancer of unknown primary}, journal = {Nature communications}, doi = {10.1038/s41467-019-13825-8}, volume = {11}, number = {1}, issn = {2041-1723}, title = {A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns}, keyword = {Cancer genomics, Cancer of unknown primary} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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