Clinical implementation of RNA signatures for pharmacogenomic decision-making

Abstract

RNA profiling is increasingly used to predict drug response, dose, or toxicity based on analysis of drug pharmacokinetic or pharmacodynamic pathways. Before implementing multiplexed RNA arrays in clinical practice, validation studies are carried out to demonstrate sufficient evidence of analytic and clinical performance, and to establish an assay protocol with quality assurance measures. Pathologists assure quality by selecting input tissue and by interpreting results in the context of the input tissue as well as the technologies that were used and the clinical setting in which the test was ordered. A strength of RNA profiling is the array-based measurement of tens to thousands of RNAs at once, including redundant tests for critical analytes or pathways to promote confidence in test results. Instrument and reagent manufacturers are crucial for supplying reliable components of the test system. Strategies for quality assurance include careful attention to RNA preservation and quality checks at pertinent steps in the assay protocol, beginning with specimen collection and proceeding through the various phases of transport, processing, storage, analysis, interpretation, and reporting. Specimen quality is checked by probing housekeeping transcripts, while spiked and exogenous controls serve as a check on analytic performance of the test system. Software is required to manipulate abundant array data and present it for interpretation by a laboratory physician who reports results in a manner facilitating therapeutic decision-making. Maintenance of the assay requires periodic documentation of personnel competency and laboratory proficiency. These strategies are shepherding genomic arrays into clinical settings to provide added value to patients and to the larger health care system.

Keywords: RNA; clinical laboratory; microarray; preanalytic; quality assurance; translational.

Figure 1

Steps in validating a laboratory assay.

Figure 2

Data interpretation is facilitated by software manipulation of abundant data generated by profiling virtually all approximately 22,000 human genes. (A) An unsupervised clustering algorithm was applied to the full dataset and then to a subset of 50 RNAs listed on the right to generate a heatmap showing patterns of expression in 96 breast cancer tissues. (B) A single sample predictor algorithm helps assign a subtype to a new patient specimen using Spearman’s correlation coefficients to estimate certainty of the classification. The expression pattern of a new sample matches the basal subtype of breast cancer most closely, and this result is likely to influence clinical management by virtue of a poor prognosis and lack of response to traditional antineoplastic agents. Typically basal subtype tumors are termed “triple-negative” because they lack immunohistochemical expression of ESR1, PGR, and ERBB2 proteins, so parallel testing of such proteins might serve as a quality assurance measure for the analytic process and also for the clinical categorization of this patient’s disease.

Figure 3

RNA profiling by high throughput quantitative reverse transcription polymerase chain reaction is a complex process requiring multiple quality checks. In this example, cell enrichment procedures are applied to blood or tissue on which a pathologist has confirmed lesional cells, spiked control RNA is added during cell lysis to measure downstream assay performance in the patient specimen, spectrophotometry assures adequate recovery of purified RNA, robotics standardize loading of cDNA and master mix into 384-well Roche RealTime Ready plates preloaded at the factory with lyophilized primers and probes, an internal probe enhances specificity of amplicon measurement beyond that achieved with dye alone, housekeeping RNA measurements reflect the overall adequacy of the specimen and the test system, and decision-support software assists with laboratory physician interpretation of the findings.

Figure 4

Example quality checks on frozen tissue profiled using an Agilent microarray two-color strategy. This is an example; acceptance limits must be established for each application.