ABSTRACT
RNA is emerging as a valuable target for the development of novel therapeutic agents. The rational design of RNA-targeting small molecules, however, has been hampered by the relative lack of methods for the analysis of small molecule-RNA interactions. Here, we present our efforts to develop such a platform using photoaffinity labeling. This technique, termed Photoaffinity Evaluation of RNA Ligation-Sequencing (PEARL-seq), enables the rapid identification of small molecule binding locations within their RNA targets and can provide information on ligand selectivity across multiple different RNAs. These data, when supplemented with small molecule SAR data and RNA probing data enable the construction of a computational model of the RNA-ligand structure, thereby enabling the rational design of novel RNA-targeted ligands.
Subject(s)
Azides/chemistry , Diazomethane/analogs & derivatives , Photoaffinity Labels/chemistry , RNA/metabolism , Aptamers, Nucleotide/chemistry , Aptamers, Nucleotide/metabolism , Azides/metabolism , Azides/radiation effects , Binding Sites , Diazomethane/metabolism , Diazomethane/radiation effects , Ligands , Molecular Docking Simulation , Photoaffinity Labels/metabolism , Photoaffinity Labels/radiation effects , Proof of Concept Study , RNA/chemistry , Reverse Transcription , Sequence Analysis, DNAABSTRACT
As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95-99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.
