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A deep learning sys...
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
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- Jiao, Wei (författare)
- Ontario Institute for Cancer Research
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- Atwal, Gurnit (författare)
- Ontario Institute for Cancer Research
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- Polak, Paz (författare)
- Broad Institute,Erasmus University Medical Center
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- Karlic, Rosa (författare)
- University of Zagreb
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- Cuppen, Edwin (författare)
- Hartwig Medical Foundation
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- Danyi, Alexandra (författare)
- University Medical Center Utrecht
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- de Ridder, Jeroen (författare)
- University Medical Center Utrecht
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- van Herpen, Carla (författare)
- Radboud University Medical Center
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- Lolkema, Martijn P (författare)
- Erasmus University Medical Center
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- Steeghs, Neeltje (författare)
- Netherlands Cancer Institute
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- Getz, Gad (författare)
- Broad Institute
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- Morris, Quaid D (författare)
- Vector Institute for Artificial Intelligence
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- Stein, Lincoln D (författare)
- Ontario Institute for Cancer Research
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- Al-Shahrour, Fatima (författare)
- Spanish National Cancer Research Center (CNIO)
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- Zhang, Junjun (författare)
- University of Glasgow
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- Borg, Åke (creator_code:cre_t)
- Lund University,Lunds universitet,Bröstcancer-genetik,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Familjär bröstcancer,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Breastcancer-genetics,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine,Familial Breast Cancer,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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- Ringnér, Markus (creator_code:cre_t)
- Lund University,Lunds universitet,Molekylär cellbiologi,Biologiska institutionen,Naturvetenskapliga fakulteten,Molecular Cell Biology,Department of Biology,Faculty of Science
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- Staaf, Johan (creator_code:cre_t)
- Lund University,Lunds universitet,Forskningsgrupp Lungcancer,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröst/lungcancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Research Group Lung Cancer,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breast/lungcancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
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- 2020-02-05
- 2020
- Engelska 12 s.
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11
- Relaterad länk:
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http://dx.doi.org/10... (free)
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Medical Genetics (hsv//eng)
Nyckelord
- Computational Biology/methods
- Deep Learning
- Female
- Genome, Human
- Humans
- Male
- Mutation
- Neoplasm Metastasis
- Neoplasms/genetics
- Reproducibility of Results
- Whole Genome Sequencing
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas
- Av författaren/redakt...
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Jiao, Wei
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Atwal, Gurnit
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Polak, Paz
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Karlic, Rosa
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Cuppen, Edwin
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Danyi, Alexandra
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visa fler...
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de Ridder, Jeroe ...
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van Herpen, Carl ...
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Lolkema, Martijn ...
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Steeghs, Neeltje
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Getz, Gad
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Morris, Quaid D
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Stein, Lincoln D
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Al-Shahrour, Fat ...
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Zhang, Junjun
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Borg, Åke
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Ringnér, Markus
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Staaf, Johan
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Medicinska och f ...
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och Medicinsk geneti ...
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Nature Communica ...
- Av lärosätet
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Lunds universitet
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Karolinska Institutet