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Utility of Glioblastoma Patient-Derived Orthotopic Xenografts in Drug Discovery and Personalized Therapy

Overview of attention for article published in Frontiers in oncology, February 2018
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Title
Utility of Glioblastoma Patient-Derived Orthotopic Xenografts in Drug Discovery and Personalized Therapy
Published in
Frontiers in oncology, February 2018
DOI 10.3389/fonc.2018.00023
Pubmed ID
Authors

Michele Patrizii, Monica Bartucci, Sharon R. Pine, Hatem E. Sabaawy

Abstract

Despite substantial effort and resources dedicated to drug discovery and development, new anticancer agents often fail in clinical trials. Among many reasons, the lack of reliable predictive preclinical cancer models is a fundamental one. For decades, immortalized cancer cell cultures have been used to lay the groundwork for cancer biology and the quest for therapeutic responses. However, cell lines do not usually recapitulate cancer heterogeneity or reveal therapeutic resistance cues. With the rapidly evolving exploration of cancer "omics," the scientific community is increasingly investigating whether the employment of short-term patient-derived tumor cell cultures (two- and three-dimensional) and/or patient-derived xenograft models might provide a more representative delineation of the cancer core and its therapeutic response. Patient-derived cancer models allow the integration of genomic with drug sensitivity data on a personalized basis and currently represent the ultimate approach for preclinical drug development and biomarker discovery. The proper use of these patient-derived cancer models might soon influence clinical outcomes and allow the implementation of tailored personalized therapy. When assessing drug efficacy for the treatment of glioblastoma multiforme (GBM), currently, the most reliable models are generated through direct injection of patient-derived cells or more frequently the isolation of glioblastoma cells endowed with stem-like features and orthotopically injecting these cells into the cerebrum of immunodeficient mice. Herein, we present the key strengths, weaknesses, and potential applications of cell- and animal-based models of GBM, highlighting our experience with the glioblastoma stem-like patient cell-derived xenograft model and its utility in drug discovery.

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The data shown below were collected from the profiles of 3 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 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 117 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 24%
Researcher 16 14%
Student > Bachelor 13 11%
Student > Master 13 11%
Other 5 4%
Other 12 10%
Unknown 30 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 21%
Neuroscience 13 11%
Medicine and Dentistry 13 11%
Agricultural and Biological Sciences 10 9%
Chemistry 5 4%
Other 16 14%
Unknown 36 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 February 2018.
All research outputs
#17,292,294
of 25,382,440 outputs
Outputs from Frontiers in oncology
#8,031
of 22,428 outputs
Outputs of similar age
#288,019
of 454,408 outputs
Outputs of similar age from Frontiers in oncology
#53
of 94 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,428 research outputs from this source. They receive a mean Attention Score of 3.0. This one has gotten more attention than average, scoring higher than 58% 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 454,408 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.