Nicola McCarthy Oncology Program Manager Horizon Discovery

Using gene engineering in target identification and validation.

Researchers have had to come to terms with the fact that there is unlikely to be a single wonder drug that will effectively treat all patients with a specific type of cancer. The race is therefore now on to find ways of understanding drug sensitivities in specific patient populations.

Many drug discovery projects have focused on finding inhibitors of dominantly acting oncogenes, such as tyrosine kinases. Isogenic cell line pairs, which differ only by a single precisely engineered modification, can aid patient stratification for such targeted agents and also help to determine the mechanisms of resistance that might arise in patients. Generation of isogenic cell lines is a specialized process, but in the past 18 months a new technique has been added to the toolbox of cell line editing technologies—the RNA-guided nuclease Cas9, which can be targeted to specific regions of DNA by single guide RNAs (sgRNAs). The generation of knockout cells for reagent testing is much simpler using Cas9-sgRNA than with ZFNs or TALENs, and also combines well with homologous recombination-based methods such as rAAV. The melding of these technologies has substantially increased our capacity to induce specific mutations in cell lines1–3 and animal models.4–6

Tumors are complex entities, and several of the cellular phenotypes of tumor cells are likely to be emergent properties (meaning that they rely on disrupted genetic and epigenetic networks and signals from surrounding cells rather than on a single mutation), making identification of potential targets difficult. Genome-wide screens aid the identification of targets that are important in these networks and do not require a priori knowledge of the interactions between the networks. Many screens have been carried out using shRNA or siRNA, but off-target effects have proved to be a substantial limitation. Moreover, if the intention of a screen is to identify true synthetic lethal targets, then a robust loss of the target is warranted rather than its transient or partial knockdown. So, can cell-line gene editing technologies help in determining a distinct signal within the noise of a genetically heterogeneous population, such as one might find in a population of tumor cells?

Several recent high-profile publications have demonstrated the feasibility of whole-genome knockout screens using Cas9–sgRNA.7–9 The effectiveness of this approach for target identification and drug discovery is promising, but is yet to be fully tested and may well depend on what one is hoping to find. For example, it is fairly straightforward to use a pooled sgRNA approach (Figure 1) in positive selection screens, where one is looking for genes that when lost enable survival in the presence of a drug or a specific mutation. In these cases, the sgRNAs targeting relevant genes will increase in abundance as the screen progresses, making them easy to identify using next-generation sequencing (NGS) at the end of the screen. Conversely, negative, drop-out or synthetic lethal screens where loss of a gene might contribute to increased sensitivity to a drug or genetic mutation rely on finding sgRNAs that have a substantially reduced abundance at the end of a pooled screen. For this readout to work effectively using NGS, we must assume that a majority of the sgRNAs targeted to a particular essential gene will cut effectively in the cells in which they are expressed, leading to genetic disruption. However, if only two out of five sgRNAs effectively result in gene disruption in only 30% of the cells in which they are expressed, and the other three guides result in minimal genetic disruption, loss of this 30% of cells and their sgRNAs could be easily missed. At Horizon, we are investigating whether building smaller sgRNA libraries against specific subsets of the genome will enable synthetic lethal targets to be more readily identified.

Figure 1. Pooled approaches aim to express one lentivirus and therefore one sgRNA per cell.

The identification of new potential therapeutic targets from an initial Cas9–sgRNA screen is arguably the easy part. Target validation comes next, with new challenges. Many approaches can be taken here, and if several hundred potential hits have been identified then re-screening with new sgRNAs to potential hits using a pooled approach should help to narrow down the field. For tens rather than hundreds of hits, standard shRNA or siRNA approaches could be used. However, the correlation between targets identified using RNAi techniques and those identified using Cas9-sgRNAs seems to be poor.8 The recently described use of CRISPRi to induce target repression, or CRISPRa to activate gene expression, both requiring modified versions of Cas9, appear on paper to be another useful method that could be readily applied to target validation.10 Rapid validation of a small number of hits could also be achieved using vectors that express fluorescent proteins, by looking for changes in the ratio of red to green fluorescence (Figure 2). 11

The use of sgRNA–Cas9 genome engineering is evolving rapidly12–15 and although technically challenging, these techniques should broaden the range of available approaches and reduce the time taken to identify new drug targets and to validate them.

Figure 2. Using fluorescence to help validate new drug targets.

Nicola McCarthy ([email protected]) is the Oncology Program Manager at Horizon Discovery.

1. Zhou Y., et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487-491 (2014)
2. Smith C., et al. Whole-genome sequencing analysis reveals high specificity of CRISPR/Cas9 and TALEN-based genome editing in human iPSCs. Cell Stem Cell. 15, 12-13 (2014)
3. Yang L., et al. CRISPR/Cas9-Directed Genome Editing of Cultured Cells. Curr. Protoc. Mol. Biol. 107, 1.1-31.1.17 (2014)
4. Platt R.J., et al. CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling. Cell 159, 440-55 (2014)
5. Xue W., et al. CRISPR-mediated direct mutation of cancer genes in the mouse liver. Nature 514, 380-4 (2014)
6. Yang H., Wang H., & Jaenisch R. Generating genetically modified mice using CRISPR/Cas-mediated genome engineering. Nat. Protoc. 9, 1956-1968 (2014)
7. Koike-Yusa H, et al. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat Biotechnol., 32, 267-273 (2014)
8. Shalem O., et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science  343, 84-87 (2014)
9. Wang T., Wei J.J., Sabatini D.M., & Lander E.S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80-84 (2014)
10. Qi LS., Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173-1183 (2013)
11. Sanjana N.E., Shalem O., & Zhang F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783-784 (2014)
12. Hsu P.D., Lander E.S., & Zhang F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262-1278 (2014)
13. Kim H., & Kim J.S. A guide to genome engineering with programmable nucleases. Nat Rev Genet. 15, 321-334 (2014)
14. Sander J.D., & Joung J.K. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat Biotechnol. 32, 347-355 (2014)
15. Doench J.G., et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat Biotechnol. Sep 3 2014. doi: 10.1038/nbt.3026. 

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