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Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure
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A pathway-dependency framework for acquired resistance to targeted therapy in EGFR-mutant, KRAS G12C-mutant, and BRAF

Xiaoxiao Li1, Yadong Guo2, Shize Yang1

  • 1Department of Thoracic Surgery, The First Hospital of China Medical University, China Medical University, Shenyang 110000, China.

Pharmacological Research
|August 6, 2026
PubMed

Insights

Acquired resistance in non-small cell lung cancer (NSCLC) is complex. A new framework helps understand pathway activity and guide treatment decisions for better patient outcomes.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genomics

Background:

  • Targeted therapies have improved outcomes for specific non-small cell lung cancer (NSCLC) subtypes (EGFR-, KRAS G12C-, BRAF V600E-driven).
  • Acquired resistance to these therapies is often molecularly and spatially heterogeneous, complicating post-progression care.
  • Current management relies on driver-specific algorithms and biomarkers, but determining causal dependencies and therapeutic vulnerabilities requires deeper insights.

Purpose of the Study:

  • To propose a novel framework for understanding and classifying resistance mechanisms in NSCLC.
  • To integrate molecular pathway activity (MAPK, PI3K-AKT-mTOR) with clinical progression patterns for improved treatment guidance.
  • To differentiate biological states of resistance and inform clinical management strategies.

Main Methods:

  • Development of a lesion- and time-specific pathway-dependency framework focusing on MAPK and PI3K-AKT-mTOR pathways.
  • Classification of resistance into three provisional biological states: MAPK-dominant, shared-input MAPK-PI3K, and PI3K-enriched/MAPK-low.
  • Integration of clinical progression patterns (histologic transformation, CNS-limited, oligoprogression, diffuse polyclonal progression) with biological assignments.

Main Results:

  • The framework distinguishes three provisional biological states of resistance.
  • It incorporates clinical progression patterns into a management branch, allowing for mixed or indeterminate assignments.
  • Biological assignment integrates lesion-level findings, clonality, histology, and pathway readouts, while progression patterns guide the clinical branch.

Conclusions:

  • The proposed framework complements genotype-based classification and aids in selecting appropriate treatments (systemic, histology-directed, local).
  • While some driver-specific interventions have evidence, many downstream MAPK strategies and PI3K-enriched approaches remain investigational.
  • Implementation challenges include limited tissue, heterogeneity, assay standardization, and toxicity; prospective studies are needed for validation, but the framework is valuable for mechanistic interpretation and trial design.
Keywords:
Acquired resistanceMAPK signalingNon-small cell lung cancerPI3K-AKT-mTOR signalingPathway dependencyTargeted therapy

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