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Patient-Derived Xenograft Models of Non-Small Cell Lung Cancer and Their Potential Utility in Personalized Medicine

Overview of attention for article published in Frontiers in oncology, January 2017
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Citations

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92 Mendeley
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Title
Patient-Derived Xenograft Models of Non-Small Cell Lung Cancer and Their Potential Utility in Personalized Medicine
Published in
Frontiers in oncology, January 2017
DOI 10.3389/fonc.2017.00002
Pubmed ID
Authors

Katherine M. Morgan, Gregory M. Riedlinger, Jeffrey Rosenfeld, Shridar Ganesan, Sharon R. Pine

Abstract

Traditional preclinical studies of cancer therapeutics have relied on the use of established human cell lines that have been adapted to grow in the laboratory and, therefore, may deviate from the cancer they were meant to represent. With the emphasis of cancer drug development shifting from non-specific cytotoxic agents to rationally designed molecularly targeted therapies or immunotherapy comes the need for better models with predictive value regarding therapeutic activity and response in clinical trials. Recently, the diversity and accessibility of immunodeficient mouse strains has greatly enhanced the production and utility of patient-derived xenograft (PDX) models for many tumor types, including non-small cell lung cancer (NSCLC). Combined with next-generation sequencing, NSCLC PDX mouse models offer an exciting tool for drug development and for studying targeted therapies while utilizing patient samples with the hope of eventually aiding in clinical decision-making. Here, we describe NSCLC PDX mouse models generated by us and others, their ability to reflect the parental tumors' histomorphological characteristics, as well as the effect of clonal selection and evolution on maintaining genomic integrity in low-passage PDXs compared to the donor tissue. We also raise vital questions regarding the practical utility of PDX and humanized PDX models in predicting patient response to therapy and make recommendations for addressing those questions. Once collaborations and standardized xenotransplantation and data management methods are established, NSCLC PDX mouse models have the potential to be universal and invaluable as a preclinical tool that guides clinical trials and standard therapeutic decisions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 92 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 14%
Researcher 12 13%
Student > Master 7 8%
Student > Doctoral Student 7 8%
Student > Bachelor 6 7%
Other 21 23%
Unknown 26 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 20%
Medicine and Dentistry 15 16%
Pharmacology, Toxicology and Pharmaceutical Science 7 8%
Agricultural and Biological Sciences 6 7%
Computer Science 4 4%
Other 13 14%
Unknown 29 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 January 2017.
All research outputs
#16,721,208
of 25,373,627 outputs
Outputs from Frontiers in oncology
#6,608
of 22,416 outputs
Outputs of similar age
#254,033
of 420,478 outputs
Outputs of similar age from Frontiers in oncology
#17
of 44 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,416 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 64% 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 420,478 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.