A Workflow for In Vivo Evaluation of Candidate Inputs and Outputs for Cell Classifier Gene Circuits

• Published in: ACS synthetic biology 2018-02, Vol.7 (2), p.474-489
• Main Authors: Dastor, Margaux, Schreiber, Joerg, Prochazka, Laura, Angelici, Bartolomeo, Kleinert, Jonathan, Klebba, Ina, Doshi, Jiten, Shen, Linling, Benenson, Yaakov
• Format: Article
• Language: English
• ISSN: 2161-5063; 2161-5063
• DOI: 10.1021/acssynbio.7b00303

Cell classifier gene circuits that integrate multiple molecular inputs to restrict the expression of therapeutic outputs to cancer cells have the potential to result in efficacious and safe cancer therapies. Preclinical translation of the hitherto developments requires creating the conditions where the animal model, the delivery platform, in vivo expression levels of the inputs, and the efficacy of the output, all come together to enable detailed evaluation of the fully assembled circuits. Here we show an integrated workflow that addresses these issues and builds the framework for preclinical classifier studies using the design framework of microRNA (miRNA, miR)-based classifier gene circuits. Specifically, we employ HCT-116 colorectal cancer cell xenograft in an experimental mouse metastatic liver tumor model together with Adeno-associated virus (AAV) vector delivery platform. Novel engineered AAV-based constructs are used to validate in vivo the candidate inputs miR-122 and miR-7 and, separately, the cytotoxic output HSV-TK/ganciclovir. We show that while the data are largely consistent with expectations, crucial insights are gained that could not have been obtained in vitro. The results highlight the importance of detailed stepwise interrogation of the experimental parameters as a necessary step toward clinical translation of synthetic gene circuits.