Literature DB >> 30876750

Efficient ten-gene analysis of NSCLC tissue samples by next-generation sequencing.

Xiuhuan Ji1, Nanying Che2, Rixu Lin1, Jianou Chen3, Xiuling Wu4.   

Abstract

In the era of personalized medicine, lung cancer is a typical disease which can be treated strategically based on the patient's histological and molecular diagnosis. Immunohistochemistry (IHC), fluorescence in-situ hybridization (FISH), Sanger sequencing and real-time PCR are techniques commonly used in clinical laboratories. Many patients are required to use several of the above technologies to get a complete diagnosis, which is expensive and timeconsuming. Next generation of sequencing (NGS) has the advantage to simultaneously analyze multigene mutations. The average cost for each patient is affordable if each run contains a certain number of samples. In this study, we tested a 10-gene, 32-mutation detection NGS method, which was used to test 195 samples from non-small cell lung cancer (NSCLC). Sanger sequencing and Amplification-refractory Mutation System (AMRS) PCR were employed to verify Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoma Kinase (ALK) results. This NGS method was partially proved to have a higher sensitivity to detect mutations with low abundance than Sanger sequencing and even ARMS PCR. Using genomic DNA to detect gene fusions may have some disadvantages to miss low abundance or large fragment fusions. As compared to using a few different technologies to analyze multigene mutations, small NGS analysis panel is a clinically applicable, efficient and affordable choice for NSCLC patients.
Copyright © 2019 Elsevier GmbH. All rights reserved.

Entities:  

Keywords:  ARMS PCR; Gene mutation; Next-generation sequencing; Non-small cell lung cancer; Sanger sequencing

Mesh:

Year:  2019        PMID: 30876750     DOI: 10.1016/j.prp.2019.02.017

Source DB:  PubMed          Journal:  Pathol Res Pract        ISSN: 0344-0338            Impact factor:   3.250


  1 in total

Review 1.  Next Generation Sequencing for Gene Fusion Analysis in Lung Cancer: A Literature Review.

Authors:  Rossella Bruno; Gabriella Fontanini
Journal:  Diagnostics (Basel)       Date:  2020-07-27
  1 in total

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