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Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

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 (92nd percentile)

Mentioned by

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7 news outlets
blogs
1 blog
twitter
40 X users

Citations

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28 Dimensions

Readers on

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77 Mendeley
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Title
Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance
Published in
Genome Medicine, May 2018
DOI 10.1186/s13073-018-0545-2
Pubmed ID
Authors

Genevieve Stein-O’Brien, Luciane T. Kagohara, Sijia Li, Manjusha Thakar, Ruchira Ranaweera, Hiroyuki Ozawa, Haixia Cheng, Michael Considine, Sandra Schmitz, Alexander V. Favorov, Ludmila V. Danilova, Joseph A. Califano, Evgeny Izumchenko, Daria A. Gaykalova, Christine H. Chung, Elana J. Fertig

Abstract

Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients' treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data (RNA-sequencing and DNA methylation) weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. The CoGAPS unsupervised algorithm was used to determine the dynamics of the molecular changes associated with resistance during the time course of resistance development. CoGAPS was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize the resistant phenotype. These genes include FGFR1, which was associated with EGFR inhibitors resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. This understanding of the time course progression of molecular changes in acquired resistance is important for the development of alternative treatment strategies that would introduce appropriate selection of new drugs to treat cancer before the resistant phenotype develops.

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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 27%
Student > Ph. D. Student 12 16%
Student > Bachelor 5 6%
Student > Doctoral Student 5 6%
Student > Master 4 5%
Other 12 16%
Unknown 18 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 31%
Medicine and Dentistry 10 13%
Agricultural and Biological Sciences 8 10%
Computer Science 5 6%
Immunology and Microbiology 3 4%
Other 10 13%
Unknown 17 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 73. 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 04 May 2020.
All research outputs
#566,705
of 24,938,276 outputs
Outputs from Genome Medicine
#106
of 1,537 outputs
Outputs of similar age
#12,801
of 336,347 outputs
Outputs of similar age from Genome Medicine
#3
of 25 outputs
Altmetric has tracked 24,938,276 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 1,537 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.1. This one has done particularly well, scoring higher than 93% 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 336,347 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 25 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.