Quality assurance of RNA expression profiling in clinical laboratories

Abstract

RNA expression profiles are increasingly used to diagnose and classify disease, based on expression patterns of as many as several thousand RNAs. To ensure quality of expression profiling services in clinical settings, a standard operating procedure incorporates multiple quality indicators and controls, beginning with preanalytic specimen preparation and proceeding thorough analysis, interpretation, and reporting. Before testing, histopathological examination of each cellular specimen, along with optional cell enrichment procedures, ensures adequacy of the input tissue. Other tactics include endogenous controls to evaluate adequacy of RNA and exogenous or spiked controls to evaluate run- and patient-specific performance of the test system, respectively. Unique aspects of quality assurance for array-based tests include controls for the pertinent outcome signatures that often supersede controls for each individual analyte, built-in redundancy for critical analytes or biochemical pathways, and software-supported scrutiny of abundant data by a laboratory physician who interprets the findings in a manner facilitating appropriate medical intervention. Access to high-quality reagents, instruments, and software from commercial sources promotes standardization and adoption in clinical settings, once an assay is vetted in validation studies as being analytically sound and clinically useful. Careful attention to the well-honed principles of laboratory medicine, along with guidance from government and professional groups on strategies to preserve RNA and manage large data sets, promotes clinical-grade assay performance.

Figure 1

Agilent Bioanalyzer electropherograms reflect RNA size spectrum. A: Intact RNA from frozen tissue has prominent 18S and 28S rRNA peaks surrounded by other relatively large RNA molecules. B: Degraded RNA from matched paraffin-embedded tissue has much smaller RNA fragments and a lower RNA integrity (RIN) score.

Figure 2

Quality metrics are displayed by Agilent feature extraction software (version 10.5.1.1) from a two-color gene expression experiment on an Agilent microarray system. Acceptability limits might include uniform spatial distribution, with local background of red and green signal <2% and numbers of features nonuniform <5% (A); the dynamic range of expression exceeding five orders of magnitude (B); even distribution of significantly up- or down-regulated genes across signal intensities (C); and spike-in RNA measurements being linear, with a slope >0.9, R² > 0.85, and replicate reproducibility signified by BGSubSignal <13 and processed signal <6 (D).

Figure 3

Raw data are interpreted after evaluation of virtually all approximately 22,000 human protein-coding genes using Agilent microarrays. A: A heat map shows gene expression profiles of 96 specimens tested for 50 listed genes, and unsupervised hierarchical clustering reveals distinct patterns of expression. B: To classify intrinsic subtypes of breast cancer, a single-sample predictor algorithm compares each patient's expression pattern with the consensus pattern for each of the five intrinsic subtypes, and Spearman's correlation coefficients help estimate the certainty of the classification. In the example shown, the patient's profile matches most closely with luminal (Lum) A, although Lum B subtype cannot be excluded, whereas three other subtypes (normal, basal, and HER2) are excluded based on low correlation coefficients.