Translating RNA sequencing into clinical diagnostics: opportunities and challenges

Abstract

With the emergence of RNA sequencing (RNA-seq) technologies, RNA-based biomolecules hold expanded promise for their diagnostic, prognostic and therapeutic applicability in various diseases, including cancers and infectious diseases. Detection of gene fusions and differential expression of known disease-causing transcripts by RNA-seq represent some of the most immediate opportunities. However, it is the diversity of RNA species detected through RNA-seq that holds new promise for the multi-faceted clinical applicability of RNA-based measures, including the potential of extracellular RNAs as non-invasive diagnostic indicators of disease. Ongoing efforts towards the establishment of benchmark standards, assay optimization for clinical conditions and demonstration of assay reproducibility are required to expand the clinical utility of RNA-seq.

Figure 1. Diversity of RNA species detection enabled by RNA sequencing applications.

Various RNA sequencing (RNA-seq) methodologies can be used to measure diverse, clinically relevant RNA species. Small RNA deep sequencing uses size selection to sequence various small non-coding RNAs, including microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs). Precursor RNAs can be measured using random primer amplification and oligo(dT) primers can be used to select polyadenylated transcripts. RNA-seq also allows for detection and measurement of alternative transcripts, chimeric gene fusion transcripts and viral RNA transcripts, as well as evaluation for allele-specific expression. HPV, human papillomavirus; lncRNA, long non-coding RNA; poly(A), polyadenylation; qRT-PCR, quantitative reverse transcription PCR; rRNA, ribosomal RNA; snoRNA, small nucleolar RNA; VUSs, variants of undetermined significance. PowerPoint slide

Figure 2. Criteria for clinical test development and adoption.

Before initial clinical introduction, a clinical test must demonstrate analytical validity, showing sufficient assay performance to produce accurate and reproducible technical results. Demonstration of analytical validity involves several measures, including sensitivity (true technical positives), specificity (true technical negatives), robustness and limits of detection. Clinical validity follows analytical validity and, depending on the approval path, demonstration of clinical validity can come before (US Food and Drug Administration (FDA) in vitro diagnostic device) or after (Clinical Laboratory Improvement Amendments (CLIA) laboratory-developed test) test clearance or approval. Clinical validity refers to the concordance between the test result and the clinical diagnosis or outcome and involves measures of sensitivity (true clinical positives) and specificity (true clinical negatives), as well as determination of positive and negative predictive values. Demonstration of both analytical validity and clinical validity occurs before that of clinical utility. Clinical utility requires clinical evidence that use of the test has an impact on patient care and includes evaluation of patient outcomes and the economic benefits associated with the test. ELSI, National Human Genome Research Institute's Ethical, Legal and Social Implications Research Program. PowerPoint slide