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Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model

Overview of attention for article published in Journal for Immunotherapy of Cancer, February 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

twitter
31 tweeters
patent
2 patents

Citations

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52 Dimensions

Readers on

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101 Mendeley
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Title
Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model
Published in
Journal for Immunotherapy of Cancer, February 2018
DOI 10.1186/s40425-018-0327-9
Pubmed ID
Authors

Yuri Kosinsky, Simon J. Dovedi, Kirill Peskov, Veronika Voronova, Lulu Chu, Helen Tomkinson, Nidal Al-Huniti, Donald R. Stanski, Gabriel Helmlinger

Abstract

Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

Twitter Demographics

The data shown below were collected from the profiles of 31 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 25%
Student > Ph. D. Student 18 18%
Student > Bachelor 10 10%
Student > Master 9 9%
Student > Doctoral Student 5 5%
Other 15 15%
Unknown 19 19%
Readers by discipline Count As %
Medicine and Dentistry 16 16%
Pharmacology, Toxicology and Pharmaceutical Science 9 9%
Biochemistry, Genetics and Molecular Biology 9 9%
Agricultural and Biological Sciences 9 9%
Mathematics 6 6%
Other 24 24%
Unknown 28 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 13 August 2020.
All research outputs
#1,151,675
of 18,652,463 outputs
Outputs from Journal for Immunotherapy of Cancer
#224
of 1,980 outputs
Outputs of similar age
#30,384
of 286,400 outputs
Outputs of similar age from Journal for Immunotherapy of Cancer
#1
of 1 outputs
Altmetric has tracked 18,652,463 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,980 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.7. 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 286,400 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 89% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them