Determining the Optimal Numbers of Cores Based on Tissue Microarray Antibody Assessment in Non-Small Cell Lung Cancer

Background: Tissue microarrays (TMAs) have been commonly utilized in translational research to rapidly screen numerous biomarkers in large samples. One major concern has been the adequate assessment of biomarkers affected by within-tumor heterogeneity and by molecular targets. Methods: Our study was designed to answer a fundamental question: How do researchers define the optimal cores to sample when designing a TMA study to minimize sampling bias and core artifact? We compared the staining results from a full-section tissue slide to the virtual TMA and from the actual TMA to the virtual TMA. Results: Three cores were demonstrated as optimal for markers such as TTF-1 and p53, but no optimal core number could be determined for markers such as Ki-67 due to the poor TMA representation of the entire tissue. Conclusion: We propose that before using TMAs to analyze large samples, particularly with significant withinsample heterogeneity, a preliminary investigation using a virtual TMA could help decide target markers to be tested for valid and valued results.