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Cancer network activity associated with therapeutic response and synergism

Overview of attention for article published in Genome Medicine, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
53 Mendeley
citeulike
1 CiteULike
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Title
Cancer network activity associated with therapeutic response and synergism
Published in
Genome Medicine, August 2016
DOI 10.1186/s13073-016-0340-x
Pubmed ID
Authors

Jordi Serra-Musach, Francesca Mateo, Eva Capdevila-Busquets, Gorka Ruiz de Garibay, Xiaohu Zhang, Raj Guha, Craig J. Thomas, Judit Grueso, Alberto Villanueva, Samira Jaeger, Holger Heyn, Miguel Vizoso, Hector Pérez, Alex Cordero, Eva Gonzalez-Suarez, Manel Esteller, Gema Moreno-Bueno, Andreas Tjärnberg, Conxi Lázaro, Violeta Serra, Joaquín Arribas, Mikael Benson, Mika Gustafsson, Marc Ferrer, Patrick Aloy, Miquel Àngel Pujana

Abstract

Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Spain 1 2%
Lithuania 1 2%
United States 1 2%
Unknown 49 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 21%
Student > Ph. D. Student 10 19%
Student > Doctoral Student 6 11%
Professor > Associate Professor 4 8%
Student > Master 4 8%
Other 13 25%
Unknown 5 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 34%
Agricultural and Biological Sciences 16 30%
Medicine and Dentistry 5 9%
Psychology 3 6%
Computer Science 3 6%
Other 3 6%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 23 July 2017.
All research outputs
#2,566,385
of 11,505,507 outputs
Outputs from Genome Medicine
#563
of 848 outputs
Outputs of similar age
#68,323
of 259,419 outputs
Outputs of similar age from Genome Medicine
#16
of 28 outputs
Altmetric has tracked 11,505,507 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 848 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.2. This one is in the 33rd percentile – i.e., 33% 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 259,419 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 73% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.