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Comprehensive analyses of tumor immunity: implications for cancer immunotherapy

Overview of attention for article published in Genome Biology, August 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 (95th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

news
1 news outlet
twitter
58 X users
patent
4 patents

Citations

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1698 Dimensions

Readers on

mendeley
940 Mendeley
citeulike
5 CiteULike
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Title
Comprehensive analyses of tumor immunity: implications for cancer immunotherapy
Published in
Genome Biology, August 2016
DOI 10.1186/s13059-016-1028-7
Pubmed ID
Authors

Bo Li, Eric Severson, Jean-Christophe Pignon, Haoquan Zhao, Taiwen Li, Jesse Novak, Peng Jiang, Hui Shen, Jon C. Aster, Scott Rodig, Sabina Signoretti, Jun S. Liu, X. Shirley Liu

Abstract

Understanding the interactions between tumor and the host immune system is critical to finding prognostic biomarkers, reducing drug resistance, and developing new therapies. Novel computational methods are needed to estimate tumor-infiltrating immune cells and understand tumor-immune interactions in cancers. We analyze tumor-infiltrating immune cells in over 10,000 RNA-seq samples across 23 cancer types from The Cancer Genome Atlas (TCGA). Our computationally inferred immune infiltrates associate much more strongly with patient clinical features, viral infection status, and cancer genetic alterations than other computational approaches. Analysis of cancer/testis antigen expression and CD8 T-cell abundance suggests that MAGEA3 is a potential immune target in melanoma, but not in non-small cell lung cancer, and implicates SPAG5 as an alternative cancer vaccine target in multiple cancers. We find that melanomas expressing high levels of CTLA4 separate into two distinct groups with respect to CD8 T-cell infiltration, which might influence clinical responses to anti-CTLA4 agents. We observe similar dichotomy of TIM3 expression with respect to CD8 T cells in kidney cancer and validate it experimentally. The abundance of immune infiltration, together with our downstream analyses and findings, are accessible through TIMER, a public resource at http://cistrome.org/TIMER . We develop a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Our resource of immune-infiltrate levels, clinical associations, as well as predicted therapeutic markers may inform effective cancer vaccine and checkpoint blockade therapies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 58 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 940 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Korea, Republic of 1 <1%
Australia 1 <1%
Israel 1 <1%
Sweden 1 <1%
Belgium 1 <1%
United Kingdom 1 <1%
Unknown 932 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 193 21%
Researcher 182 19%
Student > Master 101 11%
Student > Doctoral Student 59 6%
Student > Bachelor 59 6%
Other 145 15%
Unknown 201 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 221 24%
Agricultural and Biological Sciences 155 16%
Medicine and Dentistry 115 12%
Immunology and Microbiology 56 6%
Computer Science 45 5%
Other 106 11%
Unknown 242 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 51. 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 29 September 2022.
All research outputs
#831,376
of 25,374,647 outputs
Outputs from Genome Biology
#551
of 4,467 outputs
Outputs of similar age
#15,841
of 355,242 outputs
Outputs of similar age from Genome Biology
#12
of 56 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 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 well, scoring higher than 87% 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 355,242 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 95% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.