Impact of concurrent genomic alterations in epidermal growth factor receptor (EGFR)-mutated lung cancer

Beatrice Gini1,2, Nicholas Thomas1,2, Collin M Blakely1,2

  • 1Department of Medicine, University of California, San Francisco, California, USA.

Insights

Secondary mutations in EGFR-mutated lung cancer, affecting pathways like p53 and cell cycle, often emerge during tyrosine kinase inhibitor (TKI) resistance. Understanding these genomic alterations is key to overcoming treatment resistance.

Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • Epidermal growth factor receptor (EGFR)-mutated lung cancers are often treated with tyrosine kinase inhibitors (TKIs).
  • Secondary genetic alterations frequently co-occur with the primary oncogenic EGFR mutation.
  • These co-occurring alterations are implicated in the development of resistance to EGFR-targeted therapies.

Purpose of the Study:

  • To review frequently identified tumor genomic alterations co-occurring with mutated EGFR.
  • To examine the evidence linking these alterations to EGFR TKI treatment resistance.
  • To discuss the role of clonal evolution in EGFR TKI resistance.

Main Methods:

  • Comprehensive genomic characterization of EGFR-mutated lung cancers.
  • Analysis of co-occurring mutations in pathways such as p53, RTKs, PIK3CA/KRAS, Wnt, and cell cycle.
  • Review of pre-clinical models investigating computational tools for tracking cancer clonal populations.

Main Results:

  • Common co-occurring alterations include mutations in p53 (60-65%), RTKs (5-10%), PIK3CA/KRAS (3-23%), Wnt (5-10%), cell cycle pathways (7-25%), and transcription factors MYC and NKX2-1 (10-15%).
  • The majority of these alterations are enriched in patients resistant to TKI treatment.
  • Multiple co-existing cancer cell populations (clonal and sub-clonal) can contribute to EGFR TKI resistance.

Conclusions:

  • Co-occurring genomic alterations play a significant role in driving resistance to EGFR TKI therapy.
  • Understanding the genomic landscape and clonal evolution is crucial for developing strategies to overcome treatment resistance.
  • Further research into computational tools and combination therapies is needed to delay or prevent resistance.