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An unbiased in vivo functional genomics screening approach in mice identifies novel tumor cell-based regulators of immune rejection

Overview of attention for article published in Cancer Immunology, Immunotherapy, August 2017
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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Citations

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35 Mendeley
Title
An unbiased in vivo functional genomics screening approach in mice identifies novel tumor cell-based regulators of immune rejection
Published in
Cancer Immunology, Immunotherapy, August 2017
DOI 10.1007/s00262-017-2047-2
Pubmed ID
Authors

Casey W. Shuptrine, Reham Ajina, Elana J. Fertig, Sandra A. Jablonski, H. Kim Lyerly, Zachary C. Hartman, Louis M. Weiner

Abstract

The clinical successes of immune checkpoint therapies for cancer make it important to identify mechanisms of resistance to anti-tumor immune responses. Numerous resistance mechanisms have been identified employing studies of single genes or pathways, thereby parsing the tumor microenvironment complexity into tractable pieces. However, this limits the potential for novel gene discovery to in vivo immune attack. To address this challenge, we developed an unbiased in vivo genome-wide RNAi screening platform that leverages host immune selection in strains of immune-competent and immunodeficient mice to select for tumor cell-based genes that regulate in vivo sensitivity to immune attack. Utilizing this approach in a syngeneic triple-negative breast cancer (TNBC) model, we identified 709 genes that selectively regulated adaptive anti-tumor immunity and focused on five genes (CD47, TGFβ1, Sgpl1, Tex9 and Pex14) with the greatest impact. We validated the mechanisms that underlie the immune-related effects of expression of these genes in different TNBC lines, as well as tandem synergistic interactions. Furthermore, we demonstrate the impact of different genes with previously unknown immune functions (Tex9 and Pex14) on anti-tumor immunity. Thus, this innovative approach has utility in identifying unknown tumor-specific regulators of immune recognition in multiple settings to reveal novel targets for future immunotherapies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 23%
Student > Ph. D. Student 7 20%
Other 3 9%
Student > Doctoral Student 2 6%
Student > Bachelor 2 6%
Other 5 14%
Unknown 8 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 20%
Agricultural and Biological Sciences 7 20%
Medicine and Dentistry 4 11%
Immunology and Microbiology 3 9%
Computer Science 1 3%
Other 2 6%
Unknown 11 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 December 2017.
All research outputs
#7,778,943
of 24,938,276 outputs
Outputs from Cancer Immunology, Immunotherapy
#1,036
of 2,951 outputs
Outputs of similar age
#114,674
of 322,624 outputs
Outputs of similar age from Cancer Immunology, Immunotherapy
#12
of 22 outputs
Altmetric has tracked 24,938,276 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 2,951 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 63% 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 322,624 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.