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Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics

Overview of attention for article published in Journal of Translational Medicine, August 2017
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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1 news outlet
twitter
2 tweeters

Citations

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3 Dimensions

Readers on

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22 Mendeley
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Title
Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics
Published in
Journal of Translational Medicine, August 2017
DOI 10.1186/s12967-017-1276-7
Pubmed ID
Authors

Hui Wang, Christopher E. Barbieri, Jintang He, Yuqian Gao, Tujin Shi, Chaochao Wu, Athena A. Schepmoes, Thomas L. Fillmore, Sung-Suk Chae, Dennis Huang, Juan Miguel Mosquera, Wei-Jun Qian, Richard D. Smith, Sudhir Srivastava, Jacob Kagan, David G. Camp, Karin D. Rodland, Mark A. Rubin, Tao Liu

Abstract

Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ~10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for early detection, diagnosis, prognosis or targeted therapy of prostate cancer. Moreover, the SPOP mutation sites are distributed in a relatively short region with multiple lysine residues, posing significant challenges for bottom-up proteomics analysis of the SPOP mutations. To address this issue, PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry assays have been developed for quantifying wild-type SPOP protein and 11 prostate cancer-derived SPOP mutations. Despite inherent limitations due to amino acid sequence constraints, all the PRISM-SRM assays developed using Arg-C digestion showed a linear dynamic range of at least two orders of magnitude, with limits of quantification ranged from 0.1 to 1 fmol/μg of total protein in the cell lysate. Applying these SRM assays to analyze HEK293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations; however, at present, it is unknown if this also affects the biological activity of the SPOP protein. In summary, PRISM-SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, providing significant potential in biomarker development for prostate cancer.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 27%
Professor > Associate Professor 3 14%
Student > Master 3 14%
Researcher 2 9%
Professor 1 5%
Other 2 9%
Unknown 5 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 36%
Chemistry 3 14%
Medicine and Dentistry 3 14%
Computer Science 2 9%
Agricultural and Biological Sciences 1 5%
Other 1 5%
Unknown 4 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 20 August 2017.
All research outputs
#1,114,711
of 11,632,136 outputs
Outputs from Journal of Translational Medicine
#176
of 2,265 outputs
Outputs of similar age
#43,066
of 264,416 outputs
Outputs of similar age from Journal of Translational Medicine
#6
of 47 outputs
Altmetric has tracked 11,632,136 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,265 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done particularly well, scoring higher than 92% 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 264,416 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 83% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.