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Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden

Overview of attention for article published in Genome Medicine, April 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#35 of 1,038)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Citations

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672 Dimensions

Readers on

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920 Mendeley
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5 CiteULike
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Title
Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
Published in
Genome Medicine, April 2017
DOI 10.1186/s13073-017-0424-2
Pubmed ID
Authors

Zachary R. Chalmers, Caitlin F. Connelly, David Fabrizio, Laurie Gay, Siraj M. Ali, Riley Ennis, Alexa Schrock, Brittany Campbell, Adam Shlien, Juliann Chmielecki, Franklin Huang, Yuting He, James Sun, Uri Tabori, Mark Kennedy, Daniel S. Lieber, Steven Roels, Jared White, Geoffrey A. Otto, Jeffrey S. Ross, Levi Garraway, Vincent A. Miller, Phillip J. Stephens, Garrett M. Frampton

Abstract

High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors and has been shown to be more significantly associated with response to PD-1 and PD-L1 blockade immunotherapy than PD-1 or PD-L1 expression, as measured by immunohistochemistry (IHC). The distribution of TMB and the subset of patients with high TMB has not been well characterized in the majority of cancer types. In this study, we compare TMB measured by a targeted comprehensive genomic profiling (CGP) assay to TMB measured by exome sequencing and simulate the expected variance in TMB when sequencing less than the whole exome. We then describe the distribution of TMB across a diverse cohort of 100,000 cancer cases and test for association between somatic alterations and TMB in over 100 tumor types. We demonstrate that measurements of TMB from comprehensive genomic profiling are strongly reflective of measurements from whole exome sequencing and model that below 0.5 Mb the variance in measurement increases significantly. We find that a subset of patients exhibits high TMB across almost all types of cancer, including many rare tumor types, and characterize the relationship between high TMB and microsatellite instability status. We find that TMB increases significantly with age, showing a 2.4-fold difference between age 10 and age 90 years. Finally, we investigate the molecular basis of TMB and identify genes and mutations associated with TMB level. We identify a cluster of somatic mutations in the promoter of the gene PMS2, which occur in 10% of skin cancers and are highly associated with increased TMB. These results show that a CGP assay targeting ~1.1 Mb of coding genome can accurately assess TMB compared with sequencing the whole exome. Using this method, we find that many disease types have a substantial portion of patients with high TMB who might benefit from immunotherapy. Finally, we identify novel, recurrent promoter mutations in PMS2, which may be another example of regulatory mutations contributing to tumorigenesis.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 920 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 <1%
United Kingdom 1 <1%
Taiwan 1 <1%
Unknown 915 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 251 27%
Student > Ph. D. Student 154 17%
Other 97 11%
Student > Master 83 9%
Student > Doctoral Student 58 6%
Other 147 16%
Unknown 130 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 233 25%
Medicine and Dentistry 206 22%
Agricultural and Biological Sciences 168 18%
Immunology and Microbiology 39 4%
Computer Science 28 3%
Other 83 9%
Unknown 163 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 103. 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 30 January 2020.
All research outputs
#187,885
of 15,120,749 outputs
Outputs from Genome Medicine
#35
of 1,038 outputs
Outputs of similar age
#6,926
of 266,431 outputs
Outputs of similar age from Genome Medicine
#1
of 11 outputs
Altmetric has tracked 15,120,749 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 1,038 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.8. This one has done particularly well, scoring higher than 96% 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 266,431 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 97% of its contemporaries.
We're also able to compare this research output to 11 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.