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Cited by 271

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

The first liquid biopsy test approved. Is it a new era of mutation testing for non-small cell lung cancer?

Open access
Kwapisz, Dorota·2017·Annals of Translational Medicine

Abstract

Specific mutations in epidermal growth factor receptor (EGFR) gene are predictive for response to the EGFR tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer patients (NSCLC). According to international guidelines, the molecular testing in patients with advanced NSCLC of a non-squamous subtype is recommended. However, obtain a tissue sample could be challenging. Liquid biopsy allows to determine patients suitable for EGFR-targeted therapy by analysis of circulating-free tumor DNA (cfDNA) in peripheral blood samples and might replace tissue biopsy. It allows to acquire a material in convenient minimally invasive manner, is easily repeatable, could be used for molecular identification and molecular changes monitoring. Many studies show a high concordance rate between tissue and plasma samples testing. When U.S. Food and Drug Administration (FDA) approved the first liquid biopsy test, analysis of driver gene mutation from cfDNA becomes a reality in clinical practice for patients with NSCLC.

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Metadata

Authors
Kwapisz, D.
Venue
Annals of Translational Medicine
Published
2017-02
Volume
5
Issue
3
Pages
46-46
ISSN
  • 2305-5839 (ISSN-L)
  • 2305-5847
OpenAlex
W2975734135
Cited by
271
References
7
FWCI
25.72
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: The first liquid biopsy test approved. Is it a new era of mutation testing for non-small cell lung cancer? https://app.cochat.ai/papers/10.21037/atm.2017.01.32
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Kwapisz, D. (2017). The first liquid biopsy test approved. Is it a new era of mutation testing for non-small cell lung cancer? Annals of Translational Medicine. https://doi.org/10.21037/atm.2017.01.32

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.