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Personalization of cancer treatment using predictive simulation

Overview of attention for article published in Journal of Translational Medicine, January 2015
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
17 tweeters
googleplus
1 Google+ user

Citations

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19 Dimensions

Readers on

mendeley
56 Mendeley
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Title
Personalization of cancer treatment using predictive simulation
Published in
Journal of Translational Medicine, January 2015
DOI 10.1186/s12967-015-0399-y
Pubmed ID
Authors

Nicole A Doudican, Ansu Kumar, Neeraj Singh, Prashant R Nair, Deepak A Lala, Kabya Basu, Anay A Talawdekar, Zeba Sultana, Krishna Tiwari, Anuj Tyagi, Taher Abbasi, Shireen Vali, Ravi Vij, Mark Fiala, Justin King, MaryAnn Perle, Amitabha Mazumder

Abstract

BackgroundThe personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug.MethodsWe used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells.ResultsHere, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells.ConclusionsThese multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Germany 1 2%
Canada 1 2%
Unknown 53 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 20%
Student > Ph. D. Student 8 14%
Student > Master 8 14%
Student > Bachelor 6 11%
Other 5 9%
Other 12 21%
Unknown 6 11%
Readers by discipline Count As %
Medicine and Dentistry 14 25%
Agricultural and Biological Sciences 9 16%
Biochemistry, Genetics and Molecular Biology 7 13%
Engineering 5 9%
Psychology 3 5%
Other 11 20%
Unknown 7 13%

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 30 October 2018.
All research outputs
#1,729,684
of 15,250,970 outputs
Outputs from Journal of Translational Medicine
#251
of 2,879 outputs
Outputs of similar age
#35,085
of 285,779 outputs
Outputs of similar age from Journal of Translational Medicine
#10
of 100 outputs
Altmetric has tracked 15,250,970 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,879 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done particularly well, scoring higher than 91% 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 285,779 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 87% of its contemporaries.
We're also able to compare this research output to 100 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 90% of its contemporaries.