Significance: New approaches for rapid identification and early preclinical validation of novel therapeutic targets are crucial to make important ?go/no-go? decisions and curb the cost of developing new cancer treatments. Genetically engineered mouse models (GEMMs) are a powerful platform to study disease initiation and maintenance, the tumor microenvironment and the responsiveness of cancers to known or novel therapeutics; however, the long lead times and high costs required to develop, intercross and maintain models with various cancer predisposing gene combinations have limited their practical utility in the drug discovery process. Recently, we have shown RNA interference (RNAi) in mice can serve as a fast alterative to gene deletion and be exploited experimentally to silence nearly any gene target, by the expression of synthetic short hairpin RNAs (shRNAs). Importantly, because it is reversible, gene silencing by RNAi better mimics the dynamics of small molecule inhibition than permanent genetic knockouts. Furthermore, with the advent of new genome editing techniques, such as CRISPR/Cas9 technology, we are able to introduce additional sensitizing lesions to induce disease pathogenesis. In synergy with RNAi technology, complex multi-allelic ESC based GEMMs can be generated without extensive intercrossing. Using this combination of CRISPR/Cas9 and RNAi technologies, we are able to not only model disease pathogenesis, but also mimic drug therapy in mice, giving us unprecedented capabilities to perform preclinical studies in vivo. Hypothesis: We hypothesize that CRISPR/Cas9-RNAi-GEMMs of cancer can be developed rapidly using new genome editing technologies (CRISPRs) to introduce additional sensitizing lesions and recombinase-mediated cassette exchange (RMCE) for precise integration of tetracycline inducible shRNAs to silence specific gene targets. Preliminary data: We have previously used CRISRP/Cas9 and RMCE to generate RNAi-GEMMs without any breeding.
Specific Aims : As a proof-of-concept, we will develop a model of lung adenocarcinoma by using the CRISPR/Cas9 system to introduce a conditional KrasG12D allele into the endogenous locus and in situ delivery of sgRNAs targeting Trp53 which will be activated by a conditionally expressed Cas9 allele. We will further modulate mutant Kras or Mek1/2 activity by introducing tetracycline inducible shRNAs to model therapeutic inhibition. Finally, we will expand our flexible platform by producing validated, ?off-the-shelf? viral vectors carrying combination sgRNAs targeting commonly altered genes in NSCLC. Together, these studies will define a new paradigm and accelerate drug discovery research by creating a flexible platform for the generation of RNAi- GEMMs that will serve as innovative research tools, guiding the development of novel and effective therapeutics.

Public Health Relevance

The goal of this project is to revolutionize drug discovery research by developing a pipeline for the rapid production of genetically engineered CRISPR/Cas9-RNAi mouse models of cancer ? powerful tools with combined features for both conditional gene-specific mutagenesis to drive development of genetically-defined cancers and inducible shRNA therapy to evaluate candidate targets for efficacy and safety, all within the same animal. This transformative platform technology will enable rapid and cost-effective creation of better model systems to identify and validate new targets, predict potential toxicities and ultimately lead to better therapeutic success in our fight against cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
2R44CA188154-02
Application #
9282298
Study Section
Special Emphasis Panel (ZRG1-OTC-H (10)R)
Program Officer
Canaria, Christie A
Project Start
2014-08-04
Project End
2019-03-31
Budget Start
2017-04-06
Budget End
2018-03-31
Support Year
2
Fiscal Year
2017
Total Cost
$656,563
Indirect Cost
Name
Mirimus, Inc.
Department
Type
Domestic for-Profits
DUNS #
963317479
City
Cold Spring Harbor
State
NY
Country
United States
Zip Code
11724
Pelossof, Raphael; Fairchild, Lauren; Huang, Chun-Hao et al. (2017) Prediction of potent shRNAs with a sequential classification algorithm. Nat Biotechnol 35:350-353