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PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data

Overview of attention for article published in Genome Medicine, May 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

news
5 news outlets
blogs
2 blogs
twitter
40 X users
facebook
1 Facebook page

Citations

dimensions_citation
70 Dimensions

Readers on

mendeley
156 Mendeley
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Title
PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data
Published in
Genome Medicine, May 2018
DOI 10.1186/s13073-018-0546-1
Pubmed ID
Authors

Elena Piñeiro-Yáñez, Miguel Reboiro-Jato, Gonzalo Gómez-López, Javier Perales-Patón, Kevin Troulé, José Manuel Rodríguez, Héctor Tejero, Takeshi Shimamura, Pedro Pablo López-Casas, Julián Carretero, Alfonso Valencia, Manuel Hidalgo, Daniel Glez-Peña, Fátima Al-Shahrour

Abstract

Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy. We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility. PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

X Demographics

X Demographics

The data shown below were collected from the profiles of 40 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 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 156 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 19%
Student > Ph. D. Student 22 14%
Student > Master 22 14%
Student > Bachelor 15 10%
Student > Postgraduate 10 6%
Other 17 11%
Unknown 40 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 45 29%
Computer Science 15 10%
Agricultural and Biological Sciences 15 10%
Medicine and Dentistry 14 9%
Engineering 6 4%
Other 18 12%
Unknown 43 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 70. 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 22 December 2019.
All research outputs
#571,491
of 24,293,076 outputs
Outputs from Genome Medicine
#107
of 1,500 outputs
Outputs of similar age
#13,297
of 335,280 outputs
Outputs of similar age from Genome Medicine
#5
of 24 outputs
Altmetric has tracked 24,293,076 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,500 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. 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 335,280 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.