From G12C To pan-RAS: The expanding therapeutic landscape of KRAS-mutant NSCLC

Kübra Canaslan1, Yasemin Başbınar2, İlhan Öztop3

  • 1Department of Translational Oncology, Dokuz Eylül University Oncology Institute, Izmir 35330, Turkiye; Department of Medical Oncology, Dokuz Eylül University, Izmir 35330, Türkiye.

Insights

Targeting KRAS mutations in non-small cell lung cancer (NSCLC) has evolved beyond KRAS G12C inhibitors. Next-generation therapies, including pan-RAS inhibitors, offer new hope for durable responses in KRAS-mutant NSCLC.

Area of Science:

  • Oncology
  • Molecular Biology
  • Drug Discovery

Background:

  • KRAS mutations are key drivers in non-small cell lung cancer (NSCLC), historically challenging to target.
  • KRAS G12C inhibitors (sotorasib, adagrasib) improved treatment but face resistance and limited durability.
  • Co-mutations (STK11, KEAP1, TP53) influence tumor metabolism and the immune microenvironment in KRAS-mutant NSCLC.

Purpose of the Study:

  • To review the evolving therapeutic landscape for KRAS-mutant NSCLC.
  • To discuss next-generation KRAS inhibitors and combination strategies.
  • To highlight the role of resistance mechanisms and biomarkers in guiding treatment.

Main Methods:

  • Comprehensive literature review of preclinical and clinical studies.
  • Analysis of molecular mechanisms underlying KRAS-mutant NSCLC pathogenesis and resistance.
  • Synthesis of data on emerging KRAS inhibitors and combination therapies.

Main Results:

  • Development of allele-specific inhibitors (e.g., G12D) and pan-RAS/RAS(ON) inhibitors (e.g., RMC-6236).
  • Identification of resistance mechanisms including on-target mutations and off-target pathway reactivation.
  • Emerging combination strategies involving KRAS inhibitors with immunotherapy, chemotherapy, and pathway inhibitors (SHP2/MEK).

Conclusions:

  • The treatment paradigm for KRAS-mutant NSCLC is shifting towards more comprehensive RAS pathway inhibition.
  • Understanding resistance mechanisms and co-mutations is crucial for developing effective combination therapies.
  • Personalized, biomarker-guided treatment algorithms are essential for achieving durable clinical benefit.

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