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Predictive biomarkers in precision medicine and drug development against lung cancer

Overview of attention for article published in Cancer Communications, July 2015
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Title
Predictive biomarkers in precision medicine and drug development against lung cancer
Published in
Cancer Communications, 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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Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 14 22%
Other 11 17%
Student > Master 9 14%
Student > Ph. D. Student 7 11%
Researcher 6 9%
Other 7 11%
Unknown 10 16%
Readers by discipline Count As %
Medicine and Dentistry 17 27%
Biochemistry, Genetics and Molecular Biology 15 23%
Agricultural and Biological Sciences 9 14%
Nursing and Health Professions 4 6%
Computer Science 2 3%
Other 6 9%
Unknown 11 17%