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Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing

Overview of attention for article published in Genome Biology, December 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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10 news outlets
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47 X users
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1 Facebook page

Citations

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69 Dimensions

Readers on

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177 Mendeley
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2 CiteULike
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Title
Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing
Published in
Genome Biology, December 2016
DOI 10.1186/s13059-016-1109-7
Pubmed ID
Authors

Jonas Demeulemeester, Parveen Kumar, Elen K. Møller, Silje Nord, David C. Wedge, April Peterson, Randi R. Mathiesen, Renathe Fjelldal, Masoud Zamani Esteki, Koen Theunis, Elia Fernandez Gallardo, A. Jason Grundstad, Elin Borgen, Lars O. Baumbusch, Anne-Lise Børresen-Dale, Kevin P. White, Vessela N. Kristensen, Peter Van Loo, Thierry Voet, Bjørn Naume

Abstract

Single-cell micro-metastases of solid tumors often occur in the bone marrow. These disseminated tumor cells (DTCs) may resist therapy and lay dormant or progress to cause overt bone and visceral metastases. The molecular nature of DTCs remains elusive, as well as when and from where in the tumor they originate. Here, we apply single-cell sequencing to identify and trace the origin of DTCs in breast cancer. We sequence the genomes of 63 single cells isolated from six non-metastatic breast cancer patients. By comparing the cells' DNA copy number aberration (CNA) landscapes with those of the primary tumors and lymph node metastasis, we establish that 53% of the single cells morphologically classified as tumor cells are DTCs disseminating from the observed tumor. The remaining cells represent either non-aberrant "normal" cells or "aberrant cells of unknown origin" that have CNA landscapes discordant from the tumor. Further analyses suggest that the prevalence of aberrant cells of unknown origin is age-dependent and that at least a subset is hematopoietic in origin. Evolutionary reconstruction analysis of bulk tumor and DTC genomes enables ordering of CNA events in molecular pseudo-time and traced the origin of the DTCs to either the main tumor clone, primary tumor subclones, or subclones in an axillary lymph node metastasis. Single-cell sequencing of bone marrow epithelial-like cells, in parallel with intra-tumor genetic heterogeneity profiling from bulk DNA, is a powerful approach to identify and study DTCs, yielding insight into metastatic processes. A heterogeneous population of CNA-positive cells is present in the bone marrow of non-metastatic breast cancer patients, only part of which are derived from the observed tumor lineages.

X Demographics

X Demographics

The data shown below were collected from the profiles of 47 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Netherlands 1 <1%
Unknown 174 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 21%
Student > Ph. D. Student 36 20%
Student > Master 14 8%
Student > Doctoral Student 12 7%
Student > Bachelor 12 7%
Other 22 12%
Unknown 43 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 47 27%
Agricultural and Biological Sciences 33 19%
Medicine and Dentistry 29 16%
Immunology and Microbiology 3 2%
Neuroscience 2 1%
Other 14 8%
Unknown 49 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 105. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 September 2017.
All research outputs
#405,018
of 25,623,883 outputs
Outputs from Genome Biology
#211
of 4,496 outputs
Outputs of similar age
#8,253
of 421,713 outputs
Outputs of similar age from Genome Biology
#10
of 58 outputs
Altmetric has tracked 25,623,883 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,496 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,713 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.