ORCID Profile
0000-0002-0828-3477
Current Organisations
University Medical Center Utrecht
,
Nederlands Kanker Instituut - Antoni van Leeuwenhoek Ziekenhuis
,
Technische Universiteit Delft
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Publisher: Springer Science and Business Media LLC
Date: 05-02-2020
DOI: 10.1038/S41467-019-13825-8
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 s les and 88% and 83% respectively on independent primary and metastatic s les, 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.
Publisher: PeerJ
Date: 23-08-2019
DOI: 10.7287/PEERJ.PREPRINTS.27885V3
Abstract: The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.
Publisher: PeerJ
Date: 06-08-2019
DOI: 10.7287/PEERJ.PREPRINTS.27885V1
Abstract: The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.
Publisher: PeerJ
Date: 07-08-2019
DOI: 10.7287/PEERJ.PREPRINTS.27885V2
Abstract: The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.
Publisher: Springer Science and Business Media LLC
Date: 08-12-2022
Publisher: Public Library of Science (PLoS)
Date: 07-11-2013
Location: Netherlands
No related grants have been discovered for Jeroen de Ridder.