Immunotherapy for patients with EGFR-TKI-resistant non-small-cell lung cancer: Potential mechanisms, efficacy

Xinwei Wu1,2, Yangbin Shi1,2, Danni Wang1,2

  • 1Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.

Insights

Immunotherapy shows promise for advanced non-small-cell lung cancer (NSCLC) patients. This review explores immunotherapy effectiveness after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) failure in EGFR-mutant NSCLC.

Area of Science:

  • Oncology
  • Immunology
  • Genetics

Background:

  • Immunotherapy has transformed advanced non-small-cell lung cancer (NSCLC) treatment.
  • Its role in epidermal growth factor receptor (EGFR)-mutant NSCLC post-tyrosine kinase inhibitor (TKI) therapy is complex and varies significantly.
  • Tumor microenvironment (TME) remodeling under EGFR-TKI selective pressure influences treatment response.

Purpose of the Study:

  • To review the TME's evolution during and after EGFR-TKI treatment in NSCLC.
  • To summarize clinical data on immunotherapy efficacy following EGFR-TKI resistance.
  • To discuss biomarkers and novel strategies for optimizing immunotherapy in EGFR-mutant NSCLC.

Main Methods:

  • Literature review of TME dynamics during EGFR-TKI treatment and resistance.
  • Synthesis of current clinical evidence on immunotherapy post-TKI failure.
  • Analysis of predictive biomarkers and emerging therapeutic approaches.

Main Results:

  • EGFR-TKI treatment dynamically alters the TME, impacting subsequent immunotherapy response.
  • Heterogeneity in treatment outcomes is observed in EGFR-mutant NSCLC patients after TKI failure.
  • Specific biomarkers and combination therapies show potential for improved outcomes.

Conclusions:

  • Understanding TME evolution is crucial for predicting immunotherapy response in EGFR-mutant NSCLC.
  • Identifying patient subgroups likely to benefit from immunotherapy post-TKI failure is key.
  • Optimizing treatment strategies requires integrating mechanistic insights with clinical data.

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