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Targeted therapies for patients with advanced NSCLC harboring wild‐type EGFR: what's new and what's enough

Overview of attention for article published in Cancer Communications, July 2015
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Title
Targeted therapies for patients with advanced NSCLC harboring wild‐type EGFR: what's new and what's enough
Published in
Cancer Communications, July 2015
DOI 10.1186/s40880-015-0036-4
Pubmed ID
Authors

Fei Zhou, Cai-Cun Zhou

Abstract

Historically, non-small cell lung cancer (NSCLC) is divided into squamous and nonsquamous subtypes based on histologic features. With a growing number of oncogenic drivers being identified in squamous and nonsquamous NSCLC, this malignancy has been recently divided into several distinct subtypes according to the specific molecular alterations. This new paradigm has substantially highlighted the treatment of advanced NSCLC, shifting it from standard chemotherapy according to specific histologic subtypes to targeted therapy according to specific oncogenic drivers. The application of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) in NSCLC patients harboring activating EGFR mutations has been a representative model of precise medicine in the treatment of NSCLC. As the role of EGFR-TKIs in routine management of patients with advanced NSCLC has been well established, this review provides an overview of alternative targeted therapy in the treatment of NSCLC, including EGFR-TKIs for patients with wild-type EGFR NSCLC, as well as other targeted agents either clinical available or in early- to late-stage development.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 21%
Student > Bachelor 4 17%
Unspecified 2 8%
Student > Ph. D. Student 2 8%
Student > Postgraduate 2 8%
Other 4 17%
Unknown 5 21%
Readers by discipline Count As %
Medicine and Dentistry 8 33%
Biochemistry, Genetics and Molecular Biology 3 13%
Unspecified 2 8%
Agricultural and Biological Sciences 2 8%
Computer Science 1 4%
Other 3 13%
Unknown 5 21%