Cancer therapeutics highly selective towards tumor cells that minimize off target effects and preserve patient quality of life are in high demand. Advances in bioinformatics and systems biology reinforce that cancer is an extremely complex disease, and multiple biomarkers must be considered to exclusively identify cancer cells. Furthermore, unique multi-input cancer signatures can vary between cancer subtype, individual patients, and even over time within an individual. We have developed a flexible platform based on synthetic nucleic acid computation for a smart cancer therapeutic that is up to the task of matching the complex and dynamic nature of cancer. Harnessing the powerful properties of nucleic acid strand displacement, our technology has the ability to control protein assembly through input-triggered nucleic acid circuits. By conjugating DNA to fluorescent proteins or a split version of yeast cytosine deaminase, a cancer prodrug activating enzyme, we can use nucleic acid computation to control protein behavior for sensing or therapeutic applications, respectively. We first created multi-input logic-gated circuits controlled by various cancer-specific miRNA inputs. Realizing the limitations that low input concentrations inside cells could place on our protein-DNA devices, we utilized catalytic hairpin assembly (CHA) for signal amplification. We developed a strategy to integrate Boolean-logic and amplification architectures into a programmable and streamlined design. Moving beyond a cell-free in vitro demonstration, we delivered and executed our protein-DNA computation device in various cancer cells lines that endogenously express cancer-specific miRNA/mRNA. To our knowledge, this is the first time protein assembly/activity controlled by strand displacement computation has been executed inside live mammalian cells, a significant step in realizing the power of nucleic acid nanotechnology for future applications.

Prodrug activation controlled by Boolean logic-gated nucleic acid strand displacement inside live cancer cells

253rd National Meeting (2017)

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