Kinome sequencing reveals RET G691S polymorphism in human neuroendocrine lung cancer cell lines

Nadiya Sosonkina1, Seung-Keun Hong1, Dmytro Starenki1

  • 1Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin, 53226, USA.

Genes & Genomics
|December 23, 2014
PubMed

Insights

A specific RET gene mutation (G691S) may be linked to neuroendocrine lung tumors. Further research is needed to confirm this association and explore potential therapeutic targets in lung cancer.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • Neuroendocrine (NE) lung tumors represent a significant portion of lung cancer cases.
  • Effective curative treatments for NE lung tumors are currently lacking.
  • Identifying molecular alterations specific to NE lung tumor cells is crucial for developing targeted therapies.

Purpose of the Study:

  • To identify kinase mutations associated with NE lung tumors.
  • To screen a comprehensive set of kinase-encoding genes in lung cancer cell lines.
  • To investigate the potential role of specific genetic alterations in NE lung tumorigenesis.

Main Methods:

  • Next-generation sequencing of 517 kinase-encoding genes in six NE and three non-NE lung tumor cell lines.
  • Capillary sequencing to evaluate RET polymorphism frequency in 15 lung cancer cell lines.
  • Analysis of RET expression and its correlation with neuron-specific enolase (NSE) levels.

Main Results:

  • A nonsynonymous mutation in the proto-oncogene RET (c.2071G>A; p.G691S) was identified.
  • The minor allele (A-allele) of the RET polymorphism showed a significantly higher frequency in NE lung tumor lines compared to a reference population (p = 0.0001).
  • No significant difference in RET polymorphism frequency was observed between non-NE lung tumor lines and the reference group (p = 1.0).

Conclusions:

  • The G691S RET polymorphism suggests a potential association with NE lung tumors.
  • Further comprehensive evaluation is warranted to confirm the role of this RET polymorphism.
  • The generated kinase mutation profile dataset can aid in selecting appropriate cell line models for lung cancer research.

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