Is the current diagnostic algorithm reliable for selecting cases for EGFR- and KRAS-mutation analysis in lung cancer?

Area of Science:

• Oncology
• Molecular Pathology
• Diagnostic Pathology

Background:

• Lung adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) have distinct molecular profiles influencing treatment.
• Epidermal growth factor receptor (EGFR) and KRAS mutations are key targets in lung cancer therapy.
• Accurate classification of lung cancer subtypes is crucial for selecting appropriate mutation analysis.

Purpose of the Study:

• To evaluate the diagnostic algorithm's effectiveness in identifying lung cancer cases with EGFR or KRAS mutations.
• To determine the percentage of EGFR- and KRAS-mutations missed by current guidelines.
• To assess the impact of the 2011 lung cancer classification update on mutation analysis selection.

Main Methods:

• Retrospective analysis of 816 lung cancer cases from an institutional database.
• Selection of 336 cases with EGFR or KRAS mutations, untreated with EGFR-TKI.
• Reassessment of histological diagnoses and immunohistochemistry (IHC) profiles (TTF-1, p63, PAS-D) based on 2011 criteria.

Main Results:

• Of 336 selected cases, 70% harbored KRAS mutations and 30% harbored EGFR mutations.
• Six specimens (1.8%) were identified as potentially missed for mutation analysis under current guidelines.
• These missed cases (2.0% EGFR, 1.7% KRAS) were non-small cell lung cancer (NSCLC) with IHC profiles favoring SqCC.

Conclusions:

• The current diagnostic algorithm effectively selects most NSCLC cases for EGFR and KRAS mutation analysis.
• A small but clinically relevant proportion of mutation-harboring cases may be missed.
• Refinements to the diagnostic algorithm are warranted to improve the detection rate of actionable mutations.