↓ Skip to main content

Deciphering the temporal heterogeneity of cancer-associated fibroblast subpopulations in breast cancer

Overview of attention for article published in Journal of Experimental & Clinical Cancer Research, May 2021
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
12 X users

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
62 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Deciphering the temporal heterogeneity of cancer-associated fibroblast subpopulations in breast cancer
Published in
Journal of Experimental & Clinical Cancer Research, May 2021
DOI 10.1186/s13046-021-01944-4
Pubmed ID
Authors

Freja Albjerg Venning, Kamilla Westarp Zornhagen, Lena Wullkopf, Jonas Sjölund, Carmen Rodriguez-Cupello, Pontus Kjellman, Mikkel Morsing, Morteza Chalabi Hajkarim, Kyoung Jae Won, Janine Terra Erler, Chris Denis Madsen

Abstract

Cancer-associated fibroblasts (CAFs) comprise a heterogeneous population of stromal cells within the tumour microenvironment. CAFs exhibit both tumour-promoting and tumour-suppressing functions, making them exciting targets for improving cancer treatments. Careful isolation, identification, and characterisation of CAF heterogeneity is thus necessary for ex vivo validation and future implementation of CAF-targeted strategies in cancer. Murine 4T1 (metastatic) and 4T07 (poorly/non-metastatic) orthotopic triple negative breast cancer tumours were collected after 7, 14, or 21 days. The tumours were analysed via flow cytometry for the simultaneous expression of six CAF markers: alpha smooth muscle actin (αSMA), fibroblast activation protein alpha (FAPα), platelet derived growth factor receptor alpha and beta (PDGFRα and PDGFRβ), CD26/DPP4 and podoplanin (PDPN). All non-CAFs were excluded from the analysis using a lineage marker cocktail (CD24, CD31, CD45, CD49f, EpCAM, LYVE-1, and TER-119). In total 128 murine tumours and 12 healthy mammary fat pads were analysed. We have developed a multicolour flow cytometry strategy based on exclusion of non-CAFs and successfully employed this to explore the temporal heterogeneity of freshly isolated CAFs in the 4T1 and 4T07 mouse models of triple-negative breast cancer. Analysing 128 murine tumours, we identified 5-6 main CAF populations and numerous minor ones based on the analysis of αSMA, FAPα, PDGFRα, PDGFRβ, CD26, and PDPN. All markers showed temporal changes with a distinct switch from primarily PDGFRα+ fibroblasts in healthy mammary tissue to predominantly PDGFRβ+ CAFs in tumours. CD26+ CAFs emerged as a large novel subpopulation, only matched by FAPα+ CAFs in abundance. We demonstrate that multiple subpopulations of CAFs co-exist in murine triple negative breast cancer, and that the abundance and dynamics for each marker differ depending on tumour type and time. Our results form the foundation needed to isolate and characterise specific CAF populations, and ultimately provide an opportunity to therapeutically target specific CAF subpopulations.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 23%
Researcher 11 18%
Student > Bachelor 9 15%
Student > Master 4 6%
Student > Doctoral Student 3 5%
Other 5 8%
Unknown 16 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 37%
Immunology and Microbiology 7 11%
Agricultural and Biological Sciences 4 6%
Chemistry 3 5%
Medicine and Dentistry 2 3%
Other 3 5%
Unknown 20 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 02 July 2021.
All research outputs
#4,604,965
of 25,387,668 outputs
Outputs from Journal of Experimental & Clinical Cancer Research
#265
of 2,383 outputs
Outputs of similar age
#105,100
of 456,972 outputs
Outputs of similar age from Journal of Experimental & Clinical Cancer Research
#8
of 85 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,383 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 88% 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 456,972 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 85 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.