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Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Overview of attention for article published in BMC Cancer, February 2018
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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3 X users
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1 patent
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1 Redditor

Citations

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73 Mendeley
Title
Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy
Published in
BMC Cancer, February 2018
DOI 10.1186/s12885-018-4134-y
Pubmed ID
Authors

Kim A. Brogden, Deepak Parashar, Andrea R. Hallier, Terry Braun, Fang Qian, Naiyer A. Rizvi, Aaron D. Bossler, Mohammed M. Milhem, Timothy A. Chan, Taher Abbasi, Shireen Vali

Abstract

Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses. We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses. Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses. Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.

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X Demographics

The data shown below were collected from the profiles of 3 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 11 15%
Researcher 10 14%
Other 9 12%
Student > Master 9 12%
Student > Ph. D. Student 8 11%
Other 9 12%
Unknown 17 23%
Readers by discipline Count As %
Medicine and Dentistry 21 29%
Biochemistry, Genetics and Molecular Biology 13 18%
Agricultural and Biological Sciences 5 7%
Chemistry 3 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 3 4%
Unknown 26 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 December 2021.
All research outputs
#5,987,360
of 22,780,165 outputs
Outputs from BMC Cancer
#1,461
of 8,288 outputs
Outputs of similar age
#105,865
of 329,300 outputs
Outputs of similar age from BMC Cancer
#46
of 214 outputs
Altmetric has tracked 22,780,165 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 8,288 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 82% 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 329,300 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 67% of its contemporaries.
We're also able to compare this research output to 214 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.