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Reading between the lines; understanding drug response in the post genomic era

Overview of attention for article published in Molecular Oncology, June 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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6 X users

Citations

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12 Dimensions

Readers on

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51 Mendeley
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2 CiteULike
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Title
Reading between the lines; understanding drug response in the post genomic era
Published in
Molecular Oncology, June 2014
DOI 10.1016/j.molonc.2014.05.014
Pubmed ID
Authors

Constantine C. Alifrangis, Ultan McDermott

Abstract

Following the fanfare of initial, often dramatic, success with small molecule inhibitors in the treatment of defined genomic subgroups, it can be argued that the extension of targeted therapeutics to the majority of patients with solid cancers has stalled. Despite encouraging FDA approval rates, the attrition rates of these compounds remains high in early stage clinical studies, with single agent studies repeatedly showing poor efficacy In striking contrast, our understanding of the complexity of solid neoplasms has increased in huge increments, following the publication of large-scale genomic and transcriptomic datasets from large collaborations such as the International Cancer Genome Consortium (ICGC http://www.icgc.org/) and The Cancer Genome Atlas (TCGA http://cancergenome.nih.gov/). However, there remains a clear disconnect between these rich datasets describing the genomic complexity of cancer, including both intra- and inter-tumour heterogeneity, and what a treating oncologist can consider to be a clinically "actionable" mutation profile. Our understanding of these data is in its infancy and we still find difficulties ascribing characteristics to tumours that consistently predict therapeutic response for the majority of small molecule inhibitors. This article will seek to explore the recent studies of the patterns and impact of mutations in drug resistance, and demonstrate how we may use this data to reshape our thinking about biological pathways, critical dependencies and their therapeutic interruption.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 2%
United States 1 2%
Netherlands 1 2%
Unknown 48 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 27%
Student > Ph. D. Student 9 18%
Other 7 14%
Student > Bachelor 5 10%
Student > Postgraduate 3 6%
Other 7 14%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 41%
Medicine and Dentistry 12 24%
Biochemistry, Genetics and Molecular Biology 5 10%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Mathematics 1 2%
Other 4 8%
Unknown 6 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 27 June 2014.
All research outputs
#13,723,305
of 24,477,448 outputs
Outputs from Molecular Oncology
#810
of 1,635 outputs
Outputs of similar age
#107,907
of 233,968 outputs
Outputs of similar age from Molecular Oncology
#19
of 34 outputs
Altmetric has tracked 24,477,448 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,635 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 49th percentile – i.e., 49% 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 233,968 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 53% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.