Noise and Bias in Microarray Analysis of Tumor Specimens

Microarray-based investigations can provide fundamental insights into cancer biology and have the potential to predict patient outcome and response to therapy. For microarray analysis to be informative, measured gene expression must accurately reflect tumor biology. Given the increasing use of microarray analysis in clinical oncology, it is imperative that investigators understand how to minimize expression noise and bias through effective trial design. Expression noise is inherent to microarray-based measurement of gene expression. Noise can be defined as gene expression variation that does not correlate with the biology or behavior being studied and is introduced both by unrelated biologic phenomena and during tissue processing for analysis. Expression noise can obscure informative patterns of gene expression (resulting in a false-negative finding, a beta error), and most computational approaches to microarray analysis are focused on finding true associations despite profound noise. Bias is not inherent to microarray-based analysis but is easily introduced by faulty experimental design. For this discussion, expression bias can be defined as expression variation that correlates with the hypothesis being tested but caused by experimental factors that are independent of the hypothesis. Bias confounds analysis and begets false associations (false-positive associations, alpha errors). Bias is not accounted for by most analytic methodologies, and investigators therefore need to minimize the potential for bias by implementing appropriate experimental design. In this issue of the Journal of Clinical Oncology, Lin et al detail gene expression changes in prostate tissue that occur during tissue processing by comparing prostate biopsies performed in situ during surgery with those performed ex vivo during gross pathologic assessment. The authors performed microarray analysis on paired samples from 12 patients and determined that 1.5% of the genes studied (n 62) have increased expression in the ex vivo biopsies. For this analysis, the authors used a false-discovery rate (FDR) threshold of 0.10 as an arbitrary statistical cutoff, which estimates that 10% of the genes identified are falsely associated with ischemia. No genes had significantly decreased expression using the same statistical cutoff. Quantitative polymerase chain reaction confirmed a representative subset of the genes identified. Pathway analysis using GenMAPP (http://www.genmapp.org/) determined that the observed gene expression was similar to that seen with stress responses caused by cytotoxic drugs, heat shock, and radiation. The trial design of Lin et al demonstrates how successful microarray investigations minimize noise and avoid bias through thoughtful experimental design and meticulous execution. Although their detailed methodology provides a solid roadmap for successful microarray analysis, there are several aspects of their methods that warrant specific comment. First, the authors minimized potential bias by taking biopsies from both the in situ and ex vivo prostates rather than comparing the in vivo biopsies to a section of tissue collected from the prostate during gross dissection. Second, the authors minimized noise by performing lasercapture microdissection (LCM). LCM allows the selective analysis of a specific cell population (eg, epithelial cells) and will minimize noise introduced by variation in tissue composition. Although LCM may not be appropriate for all tumor-based expression studies, its use by Lin et al minimized the effect of tissue heterogeneity on subsequent analysis. Finally, the use of FDR sufficiently accounts for multiple hypothesis testing and minimizes the chance of false positives caused solely by expression noise and FDR is becoming an accepted standard in microarray analysis. The genes with differential gene expression between in situ and ex vivo biopsies are characterized by the authors as being associated with cellular stress. This is supported by the presence of DUSP1 and JUN, which have previously been shown to have differential expression in response to cellular insults. Their ischemia-related genes also partially overlaps with a previous set of genes induced during warm ischemia (for example, JUNB and JUND). These genes likely contribute to noise in most tumorbased microarray studies as, even in the best of settings, the time between tissue devascularization during surgery and specimen freezing can vary significantly. Thus, this work further underscores the importance of standardized sample handling to minimize noise. However, the association between the genes induced by hypoxia with cellular stress also suggests that these changes can potentially act as confounding variables and introduce bias. As a quick survey to determine whether the 62 ischemic genes identified by Lin et al can introduce bias, we used gene set enrichment analysis (GSEA) to assess coordinate differential expression JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 24 NUMBER 23 AUGUST 1