Cancer Classifiers Based on RNA Sensors in Living Cells

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Abstract There is a critical need for RNA sensors in living mammalian cells. With the advent of single-cell RNA sequencing, the transcriptome of any cell type is readily obtainable if not already available. In contrast, we are still in urgent need for a universal method to act on such transcriptomic information. If we can genetically express arbitrary effector proteins in specific cell types according to their transcriptional markers, we would transform large swaths of basic research and biomedical applications, such as immunology, neuroscience, and cancer therapy. In addition, we would like such sensors to be programmable and operate at the post-transcription level. One promising use case that would benefit from such sensors is cancer ablation, using an approach dubbed “circuits as medicine”, where a genetic vector encoding an entire “circuit” (metaphor for a collection of biomolecules engineered to regulate each other and implement specific functions) is delivered intracellularly. The circuit will sense the cellular states based on hallmarks of cancer (i.e., the overexpression of specific RNAs or the presence of specific mutations), process the signals, and deliver specific therapeutic payloads accordingly in cancer cells, directly killing them while educating the immune system to search and destroy other cancer cells. Previous efforts largely relied on strand displacement, a successful strategy for nucleic acid-based signal processing outside cells. However, their functionality has remained inadequate inside living mammalian cells, most likely because the double-stranded RNA (dsRNA) formed during strand displacement signals viral infection and are actively engaged by mammalian proteins in the immune pathways. We hypothesize that, because it is impossible to evade the omnipresent dsRNA-interacting proteins, it is wiser to embrace them. In this proposal, we will leverage endogenous human enzymes that recognize and specifically edit dsRNA, to create sensors that can be programmed to respond to arbitrary RNA transcripts (“triggers”). First, we will use fast design-build-test cycles in vitro to optimize sensor performance. We will focus on increasing sensor output in response to triggers by engineering the sensor configuration and its sequence choice, and we will characterize how the sensor affects and is affected by the cellular context. Second, to enable the quantitative distinction of different trigger levels and the integration of multiple triggers, we will engineer threshold-setting modifications and AND logic gates. Third, leveraging the unique post-transcriptional nature of such sensors and gates, we will combine them with mRNA or an oncolytic RNA virus as delivery vectors, which has traditionally been difficult to control. Last by not least, we will validate the performance and the therapeutic potential of the sensors, gates, and the RNA vectors in cancer cell lines. The future directions of the proposed project include continual optimization of the sensors, logic gates, and vectors, testing them in more realistic cancer models including mouse models and patient-derived organoids, and applying the tools to other fields.