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Predictive biomarkers in precision medicine and drug development against lung cancer

Overview of attention for article published in Ai zheng Aizheng Chinese journal of cancer, July 2015
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3 X users

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65 Mendeley
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Title
Predictive biomarkers in precision medicine and drug development against lung cancer
Published in
Ai zheng Aizheng Chinese journal of cancer, July 2015
DOI 10.1186/s40880-015-0028-4
Pubmed ID
Authors

Bingliang Fang, Reza J Mehran, John V Heymach, Stephen G Swisher

Abstract

The molecular characterization of various cancers has shown that cancers with the same origins, histopathologic diagnoses, and clinical stages can be highly heterogeneous in their genetic and epigenetic alterations that cause tumorigenesis. A number of cancer driver genes with functional abnormalities that trigger malignant transformation and that are required for the survival of cancer cells have been identified. Therapeutic agents targeting some of these cancer drivers have been successfully developed, resulting in substantial improvements in clinical symptom amelioration and outcomes in a subset of cancer patients. However, because such therapeutic drugs often benefit only a limited number of patients, the successes of clinical development and applications rely on the ability to identify those patients who are sensitive to the targeted therapies. Thus, biomarkers that can predict treatment responses are critical for the success of precision therapy for cancer patients and of anticancer drug development. This review discusses the molecular heterogeneity of lung cancer pathogenesis; predictive biomarkers for precision medicine in lung cancer therapy with drugs targeting epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), c-ros oncogene 1 receptor tyrosine kinase (ROS1), and immune checkpoints; biomarkers associated with resistance to these therapeutics; and approaches to identify predictive biomarkers in anticancer drug development. The identification of predictive biomarkers during anticancer drug development is expected to greatly facilitate such development because it will increase the chance of success or reduce the attrition rate. Additionally, such identification will accelerate the drug approval process by providing effective patient stratification strategies in clinical trials to reduce the sample size required to demonstrate clinical benefits.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 14 22%
Other 10 15%
Student > Ph. D. Student 8 12%
Student > Master 8 12%
Researcher 6 9%
Other 8 12%
Unknown 11 17%
Readers by discipline Count As %
Medicine and Dentistry 17 26%
Biochemistry, Genetics and Molecular Biology 14 22%
Agricultural and Biological Sciences 9 14%
Nursing and Health Professions 4 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 5%
Other 7 11%
Unknown 11 17%
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 05 July 2015.
All research outputs
#17,154,245
of 25,988,468 outputs
Outputs from Ai zheng Aizheng Chinese journal of cancer
#1
of 1 outputs
Outputs of similar age
#157,863
of 278,866 outputs
Outputs of similar age from Ai zheng Aizheng Chinese journal of cancer
#5
of 8 outputs
Altmetric has tracked 25,988,468 research outputs across all sources so far. This one is in the 31st percentile – i.e., 31% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 1.2. This one scored the same or higher as 0 of them.
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 278,866 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.