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Genetic risk analysis of a patient with fulminant autoimmune type 1 diabetes mellitus secondary to combination ipilimumab and nivolumab immunotherapy

Overview of attention for article published in Journal for Immunotherapy of Cancer, December 2016
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Average Attention Score compared to outputs of the same age and source

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2 X users
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1 patent

Citations

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87 Dimensions

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78 Mendeley
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Title
Genetic risk analysis of a patient with fulminant autoimmune type 1 diabetes mellitus secondary to combination ipilimumab and nivolumab immunotherapy
Published in
Journal for Immunotherapy of Cancer, December 2016
DOI 10.1186/s40425-016-0196-z
Pubmed ID
Authors

Jared R. Lowe, Daniel J. Perry, April K. S. Salama, Clayton E. Mathews, Larry G. Moss, Brent A. Hanks

Abstract

Checkpoint inhibitor immunotherapy is becoming an effective treatment modality for an increasing number of malignancies. As a result, autoinflammatory side-effects are also being observed more commonly in the clinic. We are currently unable to predict which patients will develop more severe toxicities associated with these treatment regimens. We present a patient with stage IV melanoma that developed rapid onset autoimmune type 1 diabetes (T1D) in response to combination ipilimumab and nivolumab immunotherapy. At the time of the patient's presentation with diabetes ketoacidosis, a confirmed anti-GAD antibody seroconversion was noted. Longer-term follow-up of this patient has demonstrated a durable complete response based on PET CT imaging along with a persistently undetectable C-peptide level. Single nucleotide polymorphism gene sequencing and HLA risk allele analysis has revealed the patient to lack any established genetic predisposition to the development of autoimmune T1D. While larger studies are necessary to better understand the role of genetic risk factors for the development of autoimmune toxicities in those patients undergoing checkpoint inhibitor immunotherapy, these results suggest that pre-screening patients for known T1D risk alleles may not be indicated. Additional investigation is needed to determine whether an approach such as T cell receptor clonotypic analysis to identify the presence of autoreactive T cell clones may be an effective approach for predicting which patients are at risk for the development of autoinflammatory toxicities while undergoing checkpoint inhibitor immunotherapy.

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The data shown below were collected from the profiles of 2 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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 23%
Student > Master 10 13%
Student > Ph. D. Student 9 12%
Other 7 9%
Student > Bachelor 5 6%
Other 16 21%
Unknown 13 17%
Readers by discipline Count As %
Medicine and Dentistry 41 53%
Biochemistry, Genetics and Molecular Biology 4 5%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Agricultural and Biological Sciences 3 4%
Immunology and Microbiology 2 3%
Other 6 8%
Unknown 19 24%
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 11 May 2018.
All research outputs
#7,436,279
of 25,604,262 outputs
Outputs from Journal for Immunotherapy of Cancer
#1,833
of 3,470 outputs
Outputs of similar age
#125,864
of 424,265 outputs
Outputs of similar age from Journal for Immunotherapy of Cancer
#18
of 31 outputs
Altmetric has tracked 25,604,262 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 3,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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 424,265 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 69% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.