Implications for Powering Biomarker Discovery Studies

This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in nonsmall-cell lung cancer. © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology.