Abstract 4081: Leveraging single-cell RNA sequencing data to design multi-targeting SeekRRNA therapeutics

Abstract SeekR™ chimeric molecules consist of dual flanking RNA aptamers designed to bind specific proteins (BINDERS), joined by a double stranded RNA bridge that encodes multiple siRNAs designed to silence select disease target genes (SILENCERS). Aptamers are folded nucleic acid chains that act much like chemical antibodies in that they can bind proteins in a sequence-specific manner. By identifying RNA aptamers capable of seeking and binding to specific cell surface protein receptors, SeekR™ RNA therapeutics can be engineered to self-deliver to a particular receptor defined tissue type. For example, a SeekR™ can be directed to prostate tumors that selectively express PSMA. The combination of two RNA aptamer binders with two siRNA silencers thus enables the selective delivery of the siRNAs to tumor cells expressing the binder target, wherein it directs sequence-specific silencing of cancer target genes. To identify and prioritize the right combination of SeekR targets, we have taken advantage of single-cell sequencing (scRNA-seq) data sets. By analyzing scRNA-seq data, we can identify specific cell populations within tumors to co-target with an aptamer and siRNA. We leveraged several publicly available colon and non-small cell lung cancer scRNA data sets for this analysis. A custom analysis pipeline was applied to these data to identify specific cell types within tumor samples. We then profiled specific cancer targets and determined percentages of target co-expression within a single tumor compared to normal cells. This provides information for cell-type-specific targeting to reduce potential toxicity within non-tumor tissue. In addition to targeting aptamers, we performed ligand-receptor analyses to prioritize specific siRNA targets in particular cell types within the tumor environment. This analysis revealed novel combinations of binders and silencers that will more precisely deliver the right multi-targeted gene silencing to the correct cell types. In summary, scRNA-seq data provide a rich resource of data to direct SeekR™ development and optimization. Citation Format: Susan Massey, Richard Hsiao, Shweta Yadav, David Azorsa, Spyro Mousses, Jeffrey A. Kiefer. Leveraging single-cell RNA sequencing data to design multi-targeting SeekRRNA therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4081.