Expression profiling defines a recurrence signature in lung squamous cell carcinoma

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Lung cancer is a leading cause of cancer mortality globally.
• Current prognostic markers for completely resected lung squamous cell carcinoma (SCC) have limited ability to predict recurrence.
• Treatment failure in lung SCC is often due to disease recurrence post-surgery.

Purpose of the Study:

• To identify a gene expression signature associated with tumor recurrence in lung squamous cell carcinoma (SCC).
• To develop a prognostic tool for predicting recurrence and survival in resected lung SCC patients.

Main Methods:

• Genome-wide gene expression profiling of 51 primary lung SCC tumors (Stages I-III) using microarrays.
• Comparison of gene expression between patients who remained disease-free and those who experienced recurrence.
• Cox proportional hazards modeling and leave-one-out cross-validation to identify predictive gene signatures.

Main Results:

• A 71-gene signature for predicting tumor recurrence and a 79-gene signature for predicting cancer-related death were identified.
• A combined 111-gene signature achieved 72% predictive accuracy for disease recurrence in an independent cohort.
• The 111-gene signature significantly predicted survival differences and outperformed conventional markers like TNM and N stage.

Conclusions:

• A distinct gene expression profile for recurrent lung SCC has been identified.
• The 111-gene signature shows potential as a clinically useful prognostic tool for resected lung SCC.
• This genomic approach may improve patient outcome prediction beyond current staging systems.